diff --git a/.dev/md2yml.py b/.dev/md2yml.py
deleted file mode 100755
index fc9c67e470..0000000000
--- a/.dev/md2yml.py
+++ /dev/null
@@ -1,317 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright (c) OpenMMLab. All rights reserved.
-# This tool is used to update model-index.yml which is required by MIM, and
-# will be automatically called as a pre-commit hook. The updating will be
-# triggered if any change of model information (.md files in configs/) has been
-# detected before a commit.
-
-import glob
-import os
-import os.path as osp
-import re
-import sys
-
-from lxml import etree
-from mmengine.fileio import dump
-
-MMSEG_ROOT = osp.dirname(osp.dirname(osp.dirname(__file__)))
-
-COLLECTIONS = [
-    'ANN', 'APCNet', 'BiSeNetV1', 'BiSeNetV2', 'CCNet', 'CGNet', 'DANet',
-    'DeepLabV3', 'DeepLabV3+', 'DMNet', 'DNLNet', 'DPT', 'EMANet', 'EncNet',
-    'ERFNet', 'FastFCN', 'FastSCNN', 'FCN', 'GCNet', 'ICNet', 'ISANet', 'KNet',
-    'NonLocalNet', 'OCRNet', 'PointRend', 'PSANet', 'PSPNet', 'Segformer',
-    'Segmenter', 'FPN', 'SETR', 'STDC', 'UNet', 'UPerNet'
-]
-COLLECTIONS_TEMP = []
-
-
-def dump_yaml_and_check_difference(obj, filename, sort_keys=False):
-    """Dump object to a yaml file, and check if the file content is different
-    from the original.
-
-    Args:
-        obj (any): The python object to be dumped.
-        filename (str): YAML filename to dump the object to.
-        sort_keys (str); Sort key by dictionary order.
-    Returns:
-        Bool: If the target YAML file is different from the original.
-    """
-
-    str_dump = dump(obj, None, file_format='yaml', sort_keys=sort_keys)
-    if osp.isfile(filename):
-        file_exists = True
-        with open(filename, encoding='utf-8') as f:
-            str_orig = f.read()
-    else:
-        file_exists = False
-        str_orig = None
-
-    if file_exists and str_orig == str_dump:
-        is_different = False
-    else:
-        is_different = True
-        with open(filename, 'w', encoding='utf-8') as f:
-            f.write(str_dump)
-
-    return is_different
-
-
-def parse_md(md_file):
-    """Parse .md file and convert it to a .yml file which can be used for MIM.
-
-    Args:
-        md_file (str): Path to .md file.
-    Returns:
-        Bool: If the target YAML file is different from the original.
-    """
-    collection_name = osp.split(osp.dirname(md_file))[1]
-    configs = os.listdir(osp.dirname(md_file))
-
-    collection = dict(
-        Name=collection_name,
-        Metadata={'Training Data': []},
-        Paper={
-            'URL': '',
-            'Title': ''
-        },
-        README=md_file,
-        Code={
-            'URL': '',
-            'Version': ''
-        })
-    collection.update({'Converted From': {'Weights': '', 'Code': ''}})
-    models = []
-    datasets = []
-    paper_url = None
-    paper_title = None
-    code_url = None
-    code_version = None
-    repo_url = None
-
-    # To avoid re-counting number of backbone model in OpenMMLab,
-    # if certain model in configs folder is backbone whose name is already
-    # recorded in MMClassification, then the `COLLECTION` dict of this model
-    # in MMSegmentation should be deleted, and `In Collection` in `Models`
-    # should be set with head or neck of this config file.
-    is_backbone = None
-
-    with open(md_file, encoding='UTF-8') as md:
-        lines = md.readlines()
-        i = 0
-        current_dataset = ''
-        while i < len(lines):
-            line = lines[i].strip()
-            # In latest README.md the title and url are in the third line.
-            if i == 2:
-                paper_url = lines[i].split('](')[1].split(')')[0]
-                paper_title = lines[i].split('](')[0].split('[')[1]
-            if len(line) == 0:
-                i += 1
-                continue
-            elif line[:3] == '<a ':
-                content = etree.HTML(line)
-                node = content.xpath('//a')[0]
-                if node.text == 'Code Snippet':
-                    code_url = node.get('href', None)
-                    assert code_url is not None, (
-                        f'{collection_name} hasn\'t code snippet url.')
-                    # version extraction
-                    filter_str = r'blob/(.*)/mm'
-                    pattern = re.compile(filter_str)
-                    code_version = pattern.findall(code_url)
-                    assert len(code_version) == 1, (
-                        f'false regular expression ({filter_str}) use.')
-                    code_version = code_version[0]
-                elif node.text == 'Official Repo':
-                    repo_url = node.get('href', None)
-                    assert repo_url is not None, (
-                        f'{collection_name} hasn\'t official repo url.')
-                i += 1
-            elif line[:4] == '### ':
-                datasets.append(line[4:])
-                current_dataset = line[4:]
-                i += 2
-            elif line[:15] == '<!-- [BACKBONE]':
-                is_backbone = True
-                i += 1
-            elif (line[0] == '|' and (i + 1) < len(lines)
-                  and lines[i + 1][:3] == '| -' and 'Method' in line
-                  and 'Crop Size' in line and 'Mem (GB)' in line):
-                cols = [col.strip() for col in line.split('|')]
-                method_id = cols.index('Method')
-                backbone_id = cols.index('Backbone')
-                crop_size_id = cols.index('Crop Size')
-                lr_schd_id = cols.index('Lr schd')
-                mem_id = cols.index('Mem (GB)')
-                fps_id = cols.index('Inf time (fps)')
-                try:
-                    ss_id = cols.index('mIoU')
-                except ValueError:
-                    ss_id = cols.index('Dice')
-                try:
-                    ms_id = cols.index('mIoU(ms+flip)')
-                except ValueError:
-                    ms_id = False
-                config_id = cols.index('config')
-                download_id = cols.index('download')
-                j = i + 2
-                while j < len(lines) and lines[j][0] == '|':
-                    els = [el.strip() for el in lines[j].split('|')]
-                    config = ''
-                    model_name = ''
-                    weight = ''
-                    for fn in configs:
-                        if fn in els[config_id]:
-                            left = els[download_id].index(
-                                'https://download.openmmlab.com')
-                            right = els[download_id].index('.pth') + 4
-                            weight = els[download_id][left:right]
-                            config = f'configs/{collection_name}/{fn}'
-                            model_name = fn[:-3]
-                    fps = els[fps_id] if els[fps_id] != '-' and els[
-                        fps_id] != '' else -1
-                    mem = els[mem_id].split(
-                        '\\'
-                    )[0] if els[mem_id] != '-' and els[mem_id] != '' else -1
-                    crop_size = els[crop_size_id].split('x')
-                    assert len(crop_size) == 2
-                    method = els[method_id].split()[0].split('-')[-1]
-                    model = {
-                        'Name':
-                        model_name,
-                        'In Collection':
-                        method,
-                        'Metadata': {
-                            'backbone': els[backbone_id],
-                            'crop size': f'({crop_size[0]},{crop_size[1]})',
-                            'lr schd': int(els[lr_schd_id]),
-                        },
-                        'Results': [
-                            {
-                                'Task': 'Semantic Segmentation',
-                                'Dataset': current_dataset,
-                                'Metrics': {
-                                    cols[ss_id]: float(els[ss_id]),
-                                },
-                            },
-                        ],
-                        'Config':
-                        config,
-                        'Weights':
-                        weight,
-                    }
-                    if fps != -1:
-                        try:
-                            fps = float(fps)
-                        except Exception:
-                            j += 1
-                            continue
-                        model['Metadata']['inference time (ms/im)'] = [{
-                            'value':
-                            round(1000 / float(fps), 2),
-                            'hardware':
-                            'V100',
-                            'backend':
-                            'PyTorch',
-                            'batch size':
-                            1,
-                            'mode':
-                            'FP32' if 'amp' not in config else 'AMP',
-                            'resolution':
-                            f'({crop_size[0]},{crop_size[1]})'
-                        }]
-                    if mem != -1:
-                        model['Metadata']['Training Memory (GB)'] = float(mem)
-                    # Only have semantic segmentation now
-                    if ms_id and els[ms_id] != '-' and els[ms_id] != '':
-                        model['Results'][0]['Metrics'][
-                            'mIoU(ms+flip)'] = float(els[ms_id])
-                    models.append(model)
-                    j += 1
-                i = j
-            else:
-                i += 1
-    flag = (code_url is not None) and (paper_url is not None) and (repo_url
-                                                                   is not None)
-    assert flag, f'{collection_name} readme error'
-    collection['Name'] = method
-    collection['Metadata']['Training Data'] = datasets
-    collection['Code']['URL'] = code_url
-    collection['Code']['Version'] = code_version
-    collection['Paper']['URL'] = paper_url
-    collection['Paper']['Title'] = paper_title
-    collection['Converted From']['Code'] = repo_url
-    # ['Converted From']['Weights] miss
-    # remove empty attribute
-    check_key_list = ['Code', 'Paper', 'Converted From']
-    for check_key in check_key_list:
-        key_list = list(collection[check_key].keys())
-        for key in key_list:
-            if check_key not in collection:
-                break
-            if collection[check_key][key] == '':
-                if len(collection[check_key].keys()) == 1:
-                    collection.pop(check_key)
-                else:
-                    collection[check_key].pop(key)
-    yml_file = f'{md_file[:-9]}{collection_name}.yml'
-    if is_backbone:
-        if collection['Name'] not in COLLECTIONS:
-            result = {
-                'Collections': [collection],
-                'Models': models,
-                'Yml': yml_file
-            }
-            COLLECTIONS_TEMP.append(result)
-            return False
-        else:
-            result = {'Models': models}
-    else:
-        COLLECTIONS.append(collection['Name'])
-        result = {'Collections': [collection], 'Models': models}
-    return dump_yaml_and_check_difference(result, yml_file)
-
-
-def update_model_index():
-    """Update model-index.yml according to model .md files.
-
-    Returns:
-        Bool: If the updated model-index.yml is different from the original.
-    """
-    configs_dir = osp.join(MMSEG_ROOT, 'configs')
-    yml_files = glob.glob(osp.join(configs_dir, '**', '*.yml'), recursive=True)
-    yml_files.sort()
-
-    # add .replace('\\', '/') to avoid Windows Style path
-    model_index = {
-        'Import': [
-            osp.relpath(yml_file, MMSEG_ROOT).replace('\\', '/')
-            for yml_file in yml_files
-        ]
-    }
-    model_index_file = osp.join(MMSEG_ROOT, 'model-index.yml')
-    is_different = dump_yaml_and_check_difference(model_index,
-                                                  model_index_file)
-
-    return is_different
-
-
-if __name__ == '__main__':
-    file_list = [fn for fn in sys.argv[1:] if osp.basename(fn) == 'README.md']
-    if not file_list:
-        sys.exit(0)
-    file_modified = False
-    for fn in file_list:
-        file_modified |= parse_md(fn)
-
-    for result in COLLECTIONS_TEMP:
-        collection = result['Collections'][0]
-        yml_file = result.pop('Yml', None)
-        if collection['Name'] in COLLECTIONS:
-            result.pop('Collections')
-        file_modified |= dump_yaml_and_check_difference(result, yml_file)
-
-    file_modified |= update_model_index()
-    sys.exit(1 if file_modified else 0)
diff --git a/.dev/update_model_index.py b/.dev/update_model_index.py
new file mode 100755
index 0000000000..ae30fc3bff
--- /dev/null
+++ b/.dev/update_model_index.py
@@ -0,0 +1,292 @@
+#!/usr/bin/env python
+
+# Copyright (c) OpenMMLab. All rights reserved.
+# This tool is used to update model-index.yml which is required by MIM, and
+# will be automatically called as a pre-commit hook. The updating will be
+# triggered if any change of model information (.md files in configs/) has been
+# detected before a commit.
+
+import os
+import os.path as osp
+import re
+import sys
+from typing import List, Tuple
+
+import yaml
+
+MMSEG_ROOT = osp.abspath(osp.join(osp.dirname(__file__), '..'))
+
+
+def get_collection_name_list(md_file_list: List[str]) -> List[str]:
+    """Get the list of collection names."""
+    collection_name_list: List[str] = []
+    for md_file in md_file_list:
+        with open(md_file) as f:
+            lines = f.readlines()
+            collection_name = lines[0].split('#')[1].strip()
+            collection_name_list.append(collection_name)
+    return collection_name_list
+
+
+def get_md_file_list() -> Tuple[List[str], List[str]]:
+    """Get the list of md files."""
+    md_file_list: List[str] = []
+    md_dir_list: List[str] = []
+    for root, _, files in os.walk(osp.join(MMSEG_ROOT, 'configs')):
+        for file in files:
+            if file.endswith('.md'):
+                md_file_list.append(osp.join(root, file))
+                md_dir_list.append(root)
+                break
+    return md_file_list, md_dir_list
+
+
+def get_model_info(md_file: str, config_dir: str,
+                   collection_name_list: List[str]) -> Tuple[dict, str]:
+    """Get model information from md file."""
+    datasets: List[str] = []
+    models: List[dict] = []
+    current_dataset: str = ''
+    paper_name: str = ''
+    paper_url: str = ''
+    code_url: str = ''
+    is_backbone: bool = False
+    is_dataset: bool = False
+    collection_name: str = ''
+    with open(md_file) as f:
+        lines: List[str] = f.readlines()
+        i: int = 0
+
+        while i < len(lines):
+            line: str = lines[i].strip()
+            if len(line) == 0:
+                i += 1
+                continue
+            # get paper name and url
+            if re.match(r'> \[.*\]+\([a-zA-Z]+://[^\s]*\)', line):
+                paper_info = line.split('](')
+                paper_name = paper_info[0][paper_info[0].index('[') + 1:]
+                paper_url = paper_info[1][:len(paper_info[1]) - 1]
+
+            # get code info
+            if 'Code Snippet' in line:
+                code_url = line.split('"')[1].split('"')[0]
+
+            if line.startswith('<!-- [BACKBONE]'):
+                is_backbone = True
+
+            if line.startswith('<!-- [DATASET]'):
+                is_dataset = True
+
+            # get dataset names
+            if line.startswith('###'):
+                current_dataset = line.split('###')[1].strip()
+                datasets.append(current_dataset)
+
+            # get model info key id
+            if (line[0] == '|' and (i + 1) < len(lines)
+                    and lines[i + 1][:3] == '| -' and 'Method' in line
+                    and 'Crop Size' in line and 'Mem (GB)' in line):
+                keys: List[str] = [key.strip() for key in line.split('|')]
+                crop_size_idx: int = keys.index('Crop Size')
+                mem_idx: int = keys.index('Mem (GB)')
+                assert 'Device' in keys, f'No Device in {md_file}'
+                device_idx: int = keys.index('Device')
+
+                if 'mIoU' in keys:
+                    ss_idx = keys.index('mIoU')
+                elif 'mDice' in keys:
+                    ss_idx = keys.index('mDice')
+                else:
+                    raise ValueError(f'No mIoU or mDice in {md_file}')
+                if 'mIoU(ms+flip)' in keys:
+                    ms_idx = keys.index('mIoU(ms+flip)')
+                elif 'Dice' in keys:
+                    ms_idx = keys.index('Dice')
+                else:
+                    ms_idx = -1
+                config_idx = keys.index('config')
+                download_idx = keys.index('download')
+                j: int = i + 2
+                while j < len(lines) and lines[j][0] == '|':
+                    values = [value.strip() for value in lines[j].split('|')]
+                    # get config name
+                    try:
+                        config_url = re.findall(r'[a-zA-Z]+://[^\s]*py',
+                                                values[config_idx])[0]
+                        config_name = config_url.split('/')[-1]
+                        model_name = config_name.replace('.py', '')
+                    except IndexError:
+                        raise ValueError(
+                            f'config url is not found in {md_file}')
+
+                    # get model name
+                    try:
+                        weight_url = re.findall(r'[a-zA-Z]+://[^\s]*pth',
+                                                values[download_idx])[0]
+                        log_url = re.findall(r'[a-zA-Z]+://[^\s]*.json',
+                                             values[download_idx + 1])[0]
+                    except IndexError:
+                        raise ValueError(
+                            f'url is not found in {values[download_idx]}')
+
+                    # get batch size
+                    bs = re.findall(r'[0-9]*xb[0-9]*',
+                                    config_name)[0].split('xb')
+                    batch_size = int(bs[0]) * int(bs[1])
+
+                    # get crop size
+                    crop_size = values[crop_size_idx].split('x')
+                    crop_size = [int(crop_size[0]), int(crop_size[1])]
+
+                    mem = values[mem_idx].split('\\')[0] if values[
+                        mem_idx] != '-' and values[mem_idx] != '' else -1
+
+                    method = values[keys.index('Method')].strip()
+                    # method = [method.strip()] if '+' not in method else [
+                    #     m.strip() for m in method.split('+')
+                    # ]
+                    # split method name:
+                    if ' + ' in method:
+                        method = [m.strip() for m in method.split(' + ')]
+                    elif ' ' in method:
+                        method = [m for m in method.split(' ')]
+                    else:
+                        method = [method]
+                    backone: str = re.findall(
+                        r'[^\s]*', values[keys.index('Backbone')].strip())[0]
+                    archs = [backone] + method
+                    collection_name = method[0]
+                    config_path = osp.join('configs',
+                                           config_dir.split('/')[-1],
+                                           config_name)
+                    model = {
+                        'Name': model_name,
+                        'In Collection': collection_name,
+                        'Results': {
+                            'Task': 'Semantic Segmentation',
+                            'Dataset': current_dataset,
+                            'Metrics': {
+                                keys[ss_idx]: float(values[ss_idx])
+                            }
+                        },
+                        'Config': config_path,
+                        'Metadata': {
+                            'Training Data':
+                            current_dataset,
+                            'Batch Size':
+                            batch_size,
+                            'Architecture':
+                            archs,
+                            'Training Resources':
+                            f'{bs[0]}x {values[device_idx]} GPUS',
+                        },
+                        'Weights': weight_url,
+                        'Training log': log_url,
+                        'Paper': {
+                            'Title': paper_name,
+                            'URL': paper_url
+                        },
+                        'Code': code_url,
+                        'Framework': 'PyTorch'
+                    }
+                    if ms_idx != -1 and values[ms_idx] != '-' and values[
+                            ms_idx] != '':
+                        model['Results']['Metrics'].update(
+                            {keys[ms_idx]: float(values[ms_idx])})
+                    if mem != -1:
+                        model['Metadata']['Memory (GB)'] = float(mem)
+                    models.append(model)
+                    j += 1
+                i = j
+            i += 1
+
+    if not (is_dataset
+            or is_backbone) or collection_name not in collection_name_list:
+        collection = {
+            'Name': collection_name,
+            'License': 'Apache License 2.0',
+            'Metadata': {
+                'Training Data': datasets
+            },
+            'Paper': {
+                'Title': paper_name,
+                'URL': paper_url,
+            },
+            'README': osp.join('configs',
+                               config_dir.split('/')[-1], 'README.md'),
+            'Frameworks': ['PyTorch'],
+        }
+        results = {
+            'Collections': [collection],
+            'Models': models
+        }, collection_name
+    else:
+        results = {'Models': models}, ''
+
+    return results
+
+
+def dump_yaml_and_check_difference(model_info: dict, filename: str) -> bool:
+    """dump yaml file and check difference with the original file.
+
+    Args:
+        model_info (dict): model info dict.
+        filename (str): filename to save.
+    """
+    str_dump = yaml.dump(model_info, sort_keys=False)
+    if osp.isfile(filename):
+        file_exist = True
+        with open(filename, encoding='utf-8') as f:
+            str_orig = f.read()
+    else:
+        str_orig = None
+        file_exist = False
+
+    if file_exist and str_orig == str_dump:
+        is_different = False
+    else:
+        is_different = True
+        with open(filename, 'w', encoding='utf-8') as f:
+            f.write(str_dump)
+
+    return is_different
+
+
+def update_model_index(config_dir_list: List[str]) -> bool:
+    """update model index."""
+    yml_files = [
+        osp.join('configs',
+                 dir_name.split('/')[-1], 'metafile.yaml')
+        for dir_name in config_dir_list
+    ]
+    yml_files.sort()
+
+    model_index = {
+        'Import': [
+            osp.relpath(yml_file, MMSEG_ROOT).replace('\\', '/')
+            for yml_file in yml_files
+        ]
+    }
+    model_index_file = osp.join(MMSEG_ROOT, 'model-index.yml')
+    return dump_yaml_and_check_difference(model_index, model_index_file)
+
+
+if __name__ == '__main__':
+    # get md file list
+    md_file_list, config_dir_list = get_md_file_list()
+    file_modified = False
+    collection_name_list: List[str] = get_collection_name_list(md_file_list)
+    # hard code to add 'FPN'
+    collection_name_list.append('FPN')
+    # parse md file
+    for md_file, config_dir in zip(md_file_list, config_dir_list):
+        results, collection_name = get_model_info(md_file, config_dir,
+                                                  collection_name_list)
+        filename = osp.join(config_dir, 'metafile.yaml')
+        file_modified |= dump_yaml_and_check_difference(results, filename)
+        if collection_name != '':
+            collection_name_list.append(collection_name)
+
+    file_modified |= update_model_index(config_dir_list)
+    sys.exit(1 if file_modified else 0)
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 70952b7c9e..3bc3fd09a8 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -47,10 +47,9 @@ repos:
       - id: update-model-index
         name: update-model-index
         description: Collect model information and update model-index.yml
-        entry: .dev/md2yml.py
-        additional_dependencies: [mmengine, lxml, opencv-python]
+        entry: .dev/update_model_index.py
+        additional_dependencies: [pyyaml]
         language: python
-        files: ^configs/.*\.md$
         require_serial: true
   - repo: https://github.com/asottile/pyupgrade
     rev: v3.0.0
diff --git a/configs/ann/README.md b/configs/ann/README.md
index 6cb8bc70b0..cbc1be2dc1 100644
--- a/configs/ann/README.md
+++ b/configs/ann/README.md
@@ -1,6 +1,6 @@
 # ANN
 
-[Asymmetric Non-local Neural Networks for Semantic Segmentation](https://arxiv.org/abs/1908.07678)
+> [Asymmetric Non-local Neural Networks for Semantic Segmentation](https://arxiv.org/abs/1908.07678)
 
 ## Introduction
 
@@ -22,6 +22,39 @@ The non-local module works as a particularly useful technique for semantic segme
 <img src="https://user-images.githubusercontent.com/24582831/142898322-3bbd578c-e488-4bae-9c14-7598adac5cbd.png" width="70%"/>
 </div>
 
+## Results and models
+
+### Cityscapes
+
+| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                      | download                                                                                                                                                                                                                                                                                                                               |
+| ------ | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | --------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| ANN    | R-50-D8  | 512x1024  |   40000 | 6        | 3.71           | V100   | 77.40 |         78.57 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ann/ann_r50-d8_4xb2-40k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x1024_40k_cityscapes/ann_r50-d8_512x1024_40k_cityscapes_20200605_095211-049fc292.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x1024_40k_cityscapes/ann_r50-d8_512x1024_40k_cityscapes_20200605_095211.log.json)     |
+| ANN    | R-101-D8 | 512x1024  |   40000 | 9.5      | 2.55           | V100   | 76.55 |         78.85 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ann/ann_r101-d8_4xb2-40k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x1024_40k_cityscapes/ann_r101-d8_512x1024_40k_cityscapes_20200605_095243-adf6eece.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x1024_40k_cityscapes/ann_r101-d8_512x1024_40k_cityscapes_20200605_095243.log.json) |
+| ANN    | R-50-D8  | 769x769   |   40000 | 6.8      | 1.70           | V100   | 78.89 |         80.46 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ann/ann_r50-d8_4xb2-40k_cityscapes-769x769.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_769x769_40k_cityscapes/ann_r50-d8_769x769_40k_cityscapes_20200530_025712-2b46b04d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_769x769_40k_cityscapes/ann_r50-d8_769x769_40k_cityscapes_20200530_025712.log.json)         |
+| ANN    | R-101-D8 | 769x769   |   40000 | 10.7     | 1.15           | V100   | 79.32 |         80.94 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ann/ann_r101-d8_4xb2-40k_cityscapes-769x769.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_769x769_40k_cityscapes/ann_r101-d8_769x769_40k_cityscapes_20200530_025720-059bff28.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_769x769_40k_cityscapes/ann_r101-d8_769x769_40k_cityscapes_20200530_025720.log.json)     |
+| ANN    | R-50-D8  | 512x1024  |   80000 | -        | -              | V100   | 77.34 |         78.65 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ann/ann_r50-d8_4xb2-80k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x1024_80k_cityscapes/ann_r50-d8_512x1024_80k_cityscapes_20200607_101911-5a9ad545.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x1024_80k_cityscapes/ann_r50-d8_512x1024_80k_cityscapes_20200607_101911.log.json)     |
+| ANN    | R-101-D8 | 512x1024  |   80000 | -        | -              | V100   | 77.14 |         78.81 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ann/ann_r101-d8_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x1024_80k_cityscapes/ann_r101-d8_512x1024_80k_cityscapes_20200607_013728-aceccc6e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x1024_80k_cityscapes/ann_r101-d8_512x1024_80k_cityscapes_20200607_013728.log.json) |
+| ANN    | R-50-D8  | 769x769   |   80000 | -        | -              | V100   | 78.88 |         80.57 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ann/ann_r50-d8_4xb2-80k_cityscapes-769x769.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_769x769_80k_cityscapes/ann_r50-d8_769x769_80k_cityscapes_20200607_044426-cc7ff323.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_769x769_80k_cityscapes/ann_r50-d8_769x769_80k_cityscapes_20200607_044426.log.json)         |
+| ANN    | R-101-D8 | 769x769   |   80000 | -        | -              | V100   | 78.80 |         80.34 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ann/ann_r101-d8_4xb2-80k_cityscapes-769x769.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_769x769_80k_cityscapes/ann_r101-d8_769x769_80k_cityscapes_20200607_013713-a9d4be8d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_769x769_80k_cityscapes/ann_r101-d8_769x769_80k_cityscapes_20200607_013713.log.json)     |
+
+### ADE20K
+
+| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                  | download                                                                                                                                                                                                                                                                                                               |
+| ------ | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| ANN    | R-50-D8  | 512x512   |   80000 | 9.1      | 21.01          | V100   | 41.01 |         42.30 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ann/ann_r50-d8_4xb4-80k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_80k_ade20k/ann_r50-d8_512x512_80k_ade20k_20200615_014818-26f75e11.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_80k_ade20k/ann_r50-d8_512x512_80k_ade20k_20200615_014818.log.json)         |
+| ANN    | R-101-D8 | 512x512   |   80000 | 12.5     | 14.12          | V100   | 42.94 |         44.18 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ann/ann_r101-d8_4xb4-80k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_80k_ade20k/ann_r101-d8_512x512_80k_ade20k_20200615_014818-c0153543.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_80k_ade20k/ann_r101-d8_512x512_80k_ade20k_20200615_014818.log.json)     |
+| ANN    | R-50-D8  | 512x512   |  160000 | -        | -              | V100   | 41.74 |         42.62 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ann/ann_r50-d8_4xb4-160k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_160k_ade20k/ann_r50-d8_512x512_160k_ade20k_20200615_231733-892247bc.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_160k_ade20k/ann_r50-d8_512x512_160k_ade20k_20200615_231733.log.json)     |
+| ANN    | R-101-D8 | 512x512   |  160000 | -        | -              | V100   | 42.94 |         44.06 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ann/ann_r101-d8_4xb4-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_160k_ade20k/ann_r101-d8_512x512_160k_ade20k_20200615_231733-955eb1ec.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_160k_ade20k/ann_r101-d8_512x512_160k_ade20k_20200615_231733.log.json) |
+
+### Pascal VOC 2012 + Aug
+
+| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                   | download                                                                                                                                                                                                                                                                                                                   |
+| ------ | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| ANN    | R-50-D8  | 512x512   |   20000 | 6        | 20.92          | V100   | 74.86 |         76.13 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ann/ann_r50-d8_4xb4-20k_voc12aug-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_20k_voc12aug/ann_r50-d8_512x512_20k_voc12aug_20200617_222246-dfcb1c62.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_20k_voc12aug/ann_r50-d8_512x512_20k_voc12aug_20200617_222246.log.json)     |
+| ANN    | R-101-D8 | 512x512   |   20000 | 9.5      | 13.94          | V100   | 77.47 |         78.70 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ann/ann_r101-d8_4xb4-20k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_20k_voc12aug/ann_r101-d8_512x512_20k_voc12aug_20200617_222246-2fad0042.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_20k_voc12aug/ann_r101-d8_512x512_20k_voc12aug_20200617_222246.log.json) |
+| ANN    | R-50-D8  | 512x512   |   40000 | -        | -              | V100   | 76.56 |         77.51 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ann/ann_r50-d8_4xb4-40k_voc12aug-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_40k_voc12aug/ann_r50-d8_512x512_40k_voc12aug_20200613_231314-b5dac322.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_40k_voc12aug/ann_r50-d8_512x512_40k_voc12aug_20200613_231314.log.json)     |
+| ANN    | R-101-D8 | 512x512   |   40000 | -        | -              | V100   | 76.70 |         78.06 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ann/ann_r101-d8_4xb4-40k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_40k_voc12aug/ann_r101-d8_512x512_40k_voc12aug_20200613_231314-bd205bbe.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_40k_voc12aug/ann_r101-d8_512x512_40k_voc12aug_20200613_231314.log.json) |
+
 ## Citation
 
 ```bibtex
@@ -33,36 +66,3 @@ The non-local module works as a particularly useful technique for semantic segme
   year={2019}
 }
 ```
-
-## Results and models
-
-### Cityscapes
-
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                      | download                                                                                                                                                                                                                                                                                                                               |
-| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | --------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| ANN    | R-50-D8  | 512x1024  |   40000 | 6        | 3.71           | 77.40 |         78.57 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ann/ann_r50-d8_4xb2-40k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x1024_40k_cityscapes/ann_r50-d8_512x1024_40k_cityscapes_20200605_095211-049fc292.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x1024_40k_cityscapes/ann_r50-d8_512x1024_40k_cityscapes_20200605_095211.log.json)     |
-| ANN    | R-101-D8 | 512x1024  |   40000 | 9.5      | 2.55           | 76.55 |         78.85 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ann/ann_r101-d8_4xb2-40k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x1024_40k_cityscapes/ann_r101-d8_512x1024_40k_cityscapes_20200605_095243-adf6eece.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x1024_40k_cityscapes/ann_r101-d8_512x1024_40k_cityscapes_20200605_095243.log.json) |
-| ANN    | R-50-D8  | 769x769   |   40000 | 6.8      | 1.70           | 78.89 |         80.46 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ann/ann_r50-d8_4xb2-40k_cityscapes-769x769.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_769x769_40k_cityscapes/ann_r50-d8_769x769_40k_cityscapes_20200530_025712-2b46b04d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_769x769_40k_cityscapes/ann_r50-d8_769x769_40k_cityscapes_20200530_025712.log.json)         |
-| ANN    | R-101-D8 | 769x769   |   40000 | 10.7     | 1.15           | 79.32 |         80.94 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ann/ann_r101-d8_4xb2-40k_cityscapes-769x769.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_769x769_40k_cityscapes/ann_r101-d8_769x769_40k_cityscapes_20200530_025720-059bff28.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_769x769_40k_cityscapes/ann_r101-d8_769x769_40k_cityscapes_20200530_025720.log.json)     |
-| ANN    | R-50-D8  | 512x1024  |   80000 | -        | -              | 77.34 |         78.65 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ann/ann_r50-d8_4xb2-80k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x1024_80k_cityscapes/ann_r50-d8_512x1024_80k_cityscapes_20200607_101911-5a9ad545.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x1024_80k_cityscapes/ann_r50-d8_512x1024_80k_cityscapes_20200607_101911.log.json)     |
-| ANN    | R-101-D8 | 512x1024  |   80000 | -        | -              | 77.14 |         78.81 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ann/ann_r101-d8_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x1024_80k_cityscapes/ann_r101-d8_512x1024_80k_cityscapes_20200607_013728-aceccc6e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x1024_80k_cityscapes/ann_r101-d8_512x1024_80k_cityscapes_20200607_013728.log.json) |
-| ANN    | R-50-D8  | 769x769   |   80000 | -        | -              | 78.88 |         80.57 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ann/ann_r50-d8_4xb2-80k_cityscapes-769x769.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_769x769_80k_cityscapes/ann_r50-d8_769x769_80k_cityscapes_20200607_044426-cc7ff323.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_769x769_80k_cityscapes/ann_r50-d8_769x769_80k_cityscapes_20200607_044426.log.json)         |
-| ANN    | R-101-D8 | 769x769   |   80000 | -        | -              | 78.80 |         80.34 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ann/ann_r101-d8_4xb2-80k_cityscapes-769x769.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_769x769_80k_cityscapes/ann_r101-d8_769x769_80k_cityscapes_20200607_013713-a9d4be8d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_769x769_80k_cityscapes/ann_r101-d8_769x769_80k_cityscapes_20200607_013713.log.json)     |
-
-### ADE20K
-
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                  | download                                                                                                                                                                                                                                                                                                               |
-| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| ANN    | R-50-D8  | 512x512   |   80000 | 9.1      | 21.01          | 41.01 |         42.30 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ann/ann_r50-d8_4xb4-80k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_80k_ade20k/ann_r50-d8_512x512_80k_ade20k_20200615_014818-26f75e11.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_80k_ade20k/ann_r50-d8_512x512_80k_ade20k_20200615_014818.log.json)         |
-| ANN    | R-101-D8 | 512x512   |   80000 | 12.5     | 14.12          | 42.94 |         44.18 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ann/ann_r101-d8_4xb4-80k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_80k_ade20k/ann_r101-d8_512x512_80k_ade20k_20200615_014818-c0153543.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_80k_ade20k/ann_r101-d8_512x512_80k_ade20k_20200615_014818.log.json)     |
-| ANN    | R-50-D8  | 512x512   |  160000 | -        | -              | 41.74 |         42.62 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ann/ann_r50-d8_4xb4-160k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_160k_ade20k/ann_r50-d8_512x512_160k_ade20k_20200615_231733-892247bc.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_160k_ade20k/ann_r50-d8_512x512_160k_ade20k_20200615_231733.log.json)     |
-| ANN    | R-101-D8 | 512x512   |  160000 | -        | -              | 42.94 |         44.06 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ann/ann_r101-d8_4xb4-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_160k_ade20k/ann_r101-d8_512x512_160k_ade20k_20200615_231733-955eb1ec.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_160k_ade20k/ann_r101-d8_512x512_160k_ade20k_20200615_231733.log.json) |
-
-### Pascal VOC 2012 + Aug
-
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                   | download                                                                                                                                                                                                                                                                                                                   |
-| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| ANN    | R-50-D8  | 512x512   |   20000 | 6        | 20.92          | 74.86 |         76.13 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ann/ann_r50-d8_4xb4-20k_voc12aug-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_20k_voc12aug/ann_r50-d8_512x512_20k_voc12aug_20200617_222246-dfcb1c62.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_20k_voc12aug/ann_r50-d8_512x512_20k_voc12aug_20200617_222246.log.json)     |
-| ANN    | R-101-D8 | 512x512   |   20000 | 9.5      | 13.94          | 77.47 |         78.70 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ann/ann_r101-d8_4xb4-20k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_20k_voc12aug/ann_r101-d8_512x512_20k_voc12aug_20200617_222246-2fad0042.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_20k_voc12aug/ann_r101-d8_512x512_20k_voc12aug_20200617_222246.log.json) |
-| ANN    | R-50-D8  | 512x512   |   40000 | -        | -              | 76.56 |         77.51 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ann/ann_r50-d8_4xb4-40k_voc12aug-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_40k_voc12aug/ann_r50-d8_512x512_40k_voc12aug_20200613_231314-b5dac322.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_40k_voc12aug/ann_r50-d8_512x512_40k_voc12aug_20200613_231314.log.json)     |
-| ANN    | R-101-D8 | 512x512   |   40000 | -        | -              | 76.70 |         78.06 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ann/ann_r101-d8_4xb4-40k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_40k_voc12aug/ann_r101-d8_512x512_40k_voc12aug_20200613_231314-bd205bbe.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_40k_voc12aug/ann_r101-d8_512x512_40k_voc12aug_20200613_231314.log.json) |
diff --git a/configs/ann/ann.yml b/configs/ann/ann.yml
deleted file mode 100644
index 36721992f9..0000000000
--- a/configs/ann/ann.yml
+++ /dev/null
@@ -1,305 +0,0 @@
-Collections:
-- Name: ANN
-  Metadata:
-    Training Data:
-    - Cityscapes
-    - ADE20K
-    - Pascal VOC 2012 + Aug
-  Paper:
-    URL: https://arxiv.org/abs/1908.07678
-    Title: Asymmetric Non-local Neural Networks for Semantic Segmentation
-  README: configs/ann/README.md
-  Code:
-    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ann_head.py#L185
-    Version: v0.17.0
-  Converted From:
-    Code: https://github.com/MendelXu/ANN
-Models:
-- Name: ann_r50-d8_4xb2-40k_cityscapes-512x1024
-  In Collection: ANN
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,1024)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 269.54
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 6.0
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 77.4
-      mIoU(ms+flip): 78.57
-  Config: configs/ann/ann_r50-d8_4xb2-40k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x1024_40k_cityscapes/ann_r50-d8_512x1024_40k_cityscapes_20200605_095211-049fc292.pth
-- Name: ann_r101-d8_4xb2-40k_cityscapes-512x1024
-  In Collection: ANN
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,1024)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 392.16
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 9.5
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 76.55
-      mIoU(ms+flip): 78.85
-  Config: configs/ann/ann_r101-d8_4xb2-40k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x1024_40k_cityscapes/ann_r101-d8_512x1024_40k_cityscapes_20200605_095243-adf6eece.pth
-- Name: ann_r50-d8_4xb2-40k_cityscapes-769x769
-  In Collection: ANN
-  Metadata:
-    backbone: R-50-D8
-    crop size: (769,769)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 588.24
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (769,769)
-    Training Memory (GB): 6.8
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 78.89
-      mIoU(ms+flip): 80.46
-  Config: configs/ann/ann_r50-d8_4xb2-40k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_769x769_40k_cityscapes/ann_r50-d8_769x769_40k_cityscapes_20200530_025712-2b46b04d.pth
-- Name: ann_r101-d8_4xb2-40k_cityscapes-769x769
-  In Collection: ANN
-  Metadata:
-    backbone: R-101-D8
-    crop size: (769,769)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 869.57
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (769,769)
-    Training Memory (GB): 10.7
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 79.32
-      mIoU(ms+flip): 80.94
-  Config: configs/ann/ann_r101-d8_4xb2-40k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_769x769_40k_cityscapes/ann_r101-d8_769x769_40k_cityscapes_20200530_025720-059bff28.pth
-- Name: ann_r50-d8_4xb2-80k_cityscapes-512x1024
-  In Collection: ANN
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,1024)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 77.34
-      mIoU(ms+flip): 78.65
-  Config: configs/ann/ann_r50-d8_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x1024_80k_cityscapes/ann_r50-d8_512x1024_80k_cityscapes_20200607_101911-5a9ad545.pth
-- Name: ann_r101-d8_4xb2-80k_cityscapes-512x1024
-  In Collection: ANN
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,1024)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 77.14
-      mIoU(ms+flip): 78.81
-  Config: configs/ann/ann_r101-d8_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x1024_80k_cityscapes/ann_r101-d8_512x1024_80k_cityscapes_20200607_013728-aceccc6e.pth
-- Name: ann_r50-d8_4xb2-80k_cityscapes-769x769
-  In Collection: ANN
-  Metadata:
-    backbone: R-50-D8
-    crop size: (769,769)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 78.88
-      mIoU(ms+flip): 80.57
-  Config: configs/ann/ann_r50-d8_4xb2-80k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_769x769_80k_cityscapes/ann_r50-d8_769x769_80k_cityscapes_20200607_044426-cc7ff323.pth
-- Name: ann_r101-d8_4xb2-80k_cityscapes-769x769
-  In Collection: ANN
-  Metadata:
-    backbone: R-101-D8
-    crop size: (769,769)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 78.8
-      mIoU(ms+flip): 80.34
-  Config: configs/ann/ann_r101-d8_4xb2-80k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_769x769_80k_cityscapes/ann_r101-d8_769x769_80k_cityscapes_20200607_013713-a9d4be8d.pth
-- Name: ann_r50-d8_4xb4-80k_ade20k-512x512
-  In Collection: ANN
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 47.6
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 9.1
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 41.01
-      mIoU(ms+flip): 42.3
-  Config: configs/ann/ann_r50-d8_4xb4-80k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_80k_ade20k/ann_r50-d8_512x512_80k_ade20k_20200615_014818-26f75e11.pth
-- Name: ann_r101-d8_4xb4-80k_ade20k-512x512
-  In Collection: ANN
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 70.82
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 12.5
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 42.94
-      mIoU(ms+flip): 44.18
-  Config: configs/ann/ann_r101-d8_4xb4-80k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_80k_ade20k/ann_r101-d8_512x512_80k_ade20k_20200615_014818-c0153543.pth
-- Name: ann_r50-d8_4xb4-160k_ade20k-512x512
-  In Collection: ANN
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 160000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 41.74
-      mIoU(ms+flip): 42.62
-  Config: configs/ann/ann_r50-d8_4xb4-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_160k_ade20k/ann_r50-d8_512x512_160k_ade20k_20200615_231733-892247bc.pth
-- Name: ann_r101-d8_4xb4-160k_ade20k-512x512
-  In Collection: ANN
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 160000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 42.94
-      mIoU(ms+flip): 44.06
-  Config: configs/ann/ann_r101-d8_4xb4-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_160k_ade20k/ann_r101-d8_512x512_160k_ade20k_20200615_231733-955eb1ec.pth
-- Name: ann_r50-d8_4xb4-20k_voc12aug-512x512
-  In Collection: ANN
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 20000
-    inference time (ms/im):
-    - value: 47.8
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 6.0
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal VOC 2012 + Aug
-    Metrics:
-      mIoU: 74.86
-      mIoU(ms+flip): 76.13
-  Config: configs/ann/ann_r50-d8_4xb4-20k_voc12aug-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_20k_voc12aug/ann_r50-d8_512x512_20k_voc12aug_20200617_222246-dfcb1c62.pth
-- Name: ann_r101-d8_4xb4-20k_voc12aug-512x512
-  In Collection: ANN
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 20000
-    inference time (ms/im):
-    - value: 71.74
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 9.5
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal VOC 2012 + Aug
-    Metrics:
-      mIoU: 77.47
-      mIoU(ms+flip): 78.7
-  Config: configs/ann/ann_r101-d8_4xb4-20k_voc12aug-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_20k_voc12aug/ann_r101-d8_512x512_20k_voc12aug_20200617_222246-2fad0042.pth
-- Name: ann_r50-d8_4xb4-40k_voc12aug-512x512
-  In Collection: ANN
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 40000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal VOC 2012 + Aug
-    Metrics:
-      mIoU: 76.56
-      mIoU(ms+flip): 77.51
-  Config: configs/ann/ann_r50-d8_4xb4-40k_voc12aug-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_40k_voc12aug/ann_r50-d8_512x512_40k_voc12aug_20200613_231314-b5dac322.pth
-- Name: ann_r101-d8_4xb4-40k_voc12aug-512x512
-  In Collection: ANN
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 40000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal VOC 2012 + Aug
-    Metrics:
-      mIoU: 76.7
-      mIoU(ms+flip): 78.06
-  Config: configs/ann/ann_r101-d8_4xb4-40k_voc12aug-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_40k_voc12aug/ann_r101-d8_512x512_40k_voc12aug_20200613_231314-bd205bbe.pth
diff --git a/configs/ann/metafile.yaml b/configs/ann/metafile.yaml
new file mode 100644
index 0000000000..0d118681fd
--- /dev/null
+++ b/configs/ann/metafile.yaml
@@ -0,0 +1,391 @@
+Collections:
+- Name: ANN
+  License: Apache License 2.0
+  Metadata:
+    Training Data:
+    - Cityscapes
+    - ADE20K
+    - Pascal VOC 2012 + Aug
+  Paper:
+    Title: Asymmetric Non-local Neural Networks for Semantic Segmentation
+    URL: https://arxiv.org/abs/1908.07678
+  README: configs/ann/README.md
+  Frameworks:
+  - PyTorch
+Models:
+- Name: ann_r50-d8_4xb2-40k_cityscapes-512x1024
+  In Collection: ANN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.4
+      mIoU(ms+flip): 78.57
+  Config: configs/ann/ann_r50-d8_4xb2-40k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - ANN
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 6.0
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x1024_40k_cityscapes/ann_r50-d8_512x1024_40k_cityscapes_20200605_095211-049fc292.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x1024_40k_cityscapes/ann_r50-d8_512x1024_40k_cityscapes_20200605_095211.log.json
+  Paper:
+    Title: Asymmetric Non-local Neural Networks for Semantic Segmentation
+    URL: https://arxiv.org/abs/1908.07678
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ann_head.py#L185
+  Framework: PyTorch
+- Name: ann_r101-d8_4xb2-40k_cityscapes-512x1024
+  In Collection: ANN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 76.55
+      mIoU(ms+flip): 78.85
+  Config: configs/ann/ann_r101-d8_4xb2-40k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - ANN
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 9.5
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x1024_40k_cityscapes/ann_r101-d8_512x1024_40k_cityscapes_20200605_095243-adf6eece.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x1024_40k_cityscapes/ann_r101-d8_512x1024_40k_cityscapes_20200605_095243.log.json
+  Paper:
+    Title: Asymmetric Non-local Neural Networks for Semantic Segmentation
+    URL: https://arxiv.org/abs/1908.07678
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ann_head.py#L185
+  Framework: PyTorch
+- Name: ann_r50-d8_4xb2-40k_cityscapes-769x769
+  In Collection: ANN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.89
+      mIoU(ms+flip): 80.46
+  Config: configs/ann/ann_r50-d8_4xb2-40k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - ANN
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 6.8
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_769x769_40k_cityscapes/ann_r50-d8_769x769_40k_cityscapes_20200530_025712-2b46b04d.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_769x769_40k_cityscapes/ann_r50-d8_769x769_40k_cityscapes_20200530_025712.log.json
+  Paper:
+    Title: Asymmetric Non-local Neural Networks for Semantic Segmentation
+    URL: https://arxiv.org/abs/1908.07678
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ann_head.py#L185
+  Framework: PyTorch
+- Name: ann_r101-d8_4xb2-40k_cityscapes-769x769
+  In Collection: ANN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.32
+      mIoU(ms+flip): 80.94
+  Config: configs/ann/ann_r101-d8_4xb2-40k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - ANN
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 10.7
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_769x769_40k_cityscapes/ann_r101-d8_769x769_40k_cityscapes_20200530_025720-059bff28.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_769x769_40k_cityscapes/ann_r101-d8_769x769_40k_cityscapes_20200530_025720.log.json
+  Paper:
+    Title: Asymmetric Non-local Neural Networks for Semantic Segmentation
+    URL: https://arxiv.org/abs/1908.07678
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ann_head.py#L185
+  Framework: PyTorch
+- Name: ann_r50-d8_4xb2-80k_cityscapes-512x1024
+  In Collection: ANN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.34
+      mIoU(ms+flip): 78.65
+  Config: configs/ann/ann_r50-d8_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - ANN
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x1024_80k_cityscapes/ann_r50-d8_512x1024_80k_cityscapes_20200607_101911-5a9ad545.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x1024_80k_cityscapes/ann_r50-d8_512x1024_80k_cityscapes_20200607_101911.log.json
+  Paper:
+    Title: Asymmetric Non-local Neural Networks for Semantic Segmentation
+    URL: https://arxiv.org/abs/1908.07678
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ann_head.py#L185
+  Framework: PyTorch
+- Name: ann_r101-d8_4xb2-80k_cityscapes-512x1024
+  In Collection: ANN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.14
+      mIoU(ms+flip): 78.81
+  Config: configs/ann/ann_r101-d8_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - ANN
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x1024_80k_cityscapes/ann_r101-d8_512x1024_80k_cityscapes_20200607_013728-aceccc6e.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x1024_80k_cityscapes/ann_r101-d8_512x1024_80k_cityscapes_20200607_013728.log.json
+  Paper:
+    Title: Asymmetric Non-local Neural Networks for Semantic Segmentation
+    URL: https://arxiv.org/abs/1908.07678
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ann_head.py#L185
+  Framework: PyTorch
+- Name: ann_r50-d8_4xb2-80k_cityscapes-769x769
+  In Collection: ANN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.88
+      mIoU(ms+flip): 80.57
+  Config: configs/ann/ann_r50-d8_4xb2-80k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - ANN
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_769x769_80k_cityscapes/ann_r50-d8_769x769_80k_cityscapes_20200607_044426-cc7ff323.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_769x769_80k_cityscapes/ann_r50-d8_769x769_80k_cityscapes_20200607_044426.log.json
+  Paper:
+    Title: Asymmetric Non-local Neural Networks for Semantic Segmentation
+    URL: https://arxiv.org/abs/1908.07678
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ann_head.py#L185
+  Framework: PyTorch
+- Name: ann_r101-d8_4xb2-80k_cityscapes-769x769
+  In Collection: ANN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.8
+      mIoU(ms+flip): 80.34
+  Config: configs/ann/ann_r101-d8_4xb2-80k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - ANN
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_769x769_80k_cityscapes/ann_r101-d8_769x769_80k_cityscapes_20200607_013713-a9d4be8d.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_769x769_80k_cityscapes/ann_r101-d8_769x769_80k_cityscapes_20200607_013713.log.json
+  Paper:
+    Title: Asymmetric Non-local Neural Networks for Semantic Segmentation
+    URL: https://arxiv.org/abs/1908.07678
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ann_head.py#L185
+  Framework: PyTorch
+- Name: ann_r50-d8_4xb4-80k_ade20k-512x512
+  In Collection: ANN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 41.01
+      mIoU(ms+flip): 42.3
+  Config: configs/ann/ann_r50-d8_4xb4-80k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - ANN
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 9.1
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_80k_ade20k/ann_r50-d8_512x512_80k_ade20k_20200615_014818-26f75e11.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_80k_ade20k/ann_r50-d8_512x512_80k_ade20k_20200615_014818.log.json
+  Paper:
+    Title: Asymmetric Non-local Neural Networks for Semantic Segmentation
+    URL: https://arxiv.org/abs/1908.07678
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ann_head.py#L185
+  Framework: PyTorch
+- Name: ann_r101-d8_4xb4-80k_ade20k-512x512
+  In Collection: ANN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 42.94
+      mIoU(ms+flip): 44.18
+  Config: configs/ann/ann_r101-d8_4xb4-80k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - ANN
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 12.5
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_80k_ade20k/ann_r101-d8_512x512_80k_ade20k_20200615_014818-c0153543.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_80k_ade20k/ann_r101-d8_512x512_80k_ade20k_20200615_014818.log.json
+  Paper:
+    Title: Asymmetric Non-local Neural Networks for Semantic Segmentation
+    URL: https://arxiv.org/abs/1908.07678
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ann_head.py#L185
+  Framework: PyTorch
+- Name: ann_r50-d8_4xb4-160k_ade20k-512x512
+  In Collection: ANN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 41.74
+      mIoU(ms+flip): 42.62
+  Config: configs/ann/ann_r50-d8_4xb4-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - ANN
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_160k_ade20k/ann_r50-d8_512x512_160k_ade20k_20200615_231733-892247bc.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_160k_ade20k/ann_r50-d8_512x512_160k_ade20k_20200615_231733.log.json
+  Paper:
+    Title: Asymmetric Non-local Neural Networks for Semantic Segmentation
+    URL: https://arxiv.org/abs/1908.07678
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ann_head.py#L185
+  Framework: PyTorch
+- Name: ann_r101-d8_4xb4-160k_ade20k-512x512
+  In Collection: ANN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 42.94
+      mIoU(ms+flip): 44.06
+  Config: configs/ann/ann_r101-d8_4xb4-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - ANN
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_160k_ade20k/ann_r101-d8_512x512_160k_ade20k_20200615_231733-955eb1ec.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_160k_ade20k/ann_r101-d8_512x512_160k_ade20k_20200615_231733.log.json
+  Paper:
+    Title: Asymmetric Non-local Neural Networks for Semantic Segmentation
+    URL: https://arxiv.org/abs/1908.07678
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ann_head.py#L185
+  Framework: PyTorch
+- Name: ann_r50-d8_4xb4-20k_voc12aug-512x512
+  In Collection: ANN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 74.86
+      mIoU(ms+flip): 76.13
+  Config: configs/ann/ann_r50-d8_4xb4-20k_voc12aug-512x512.py
+  Metadata:
+    Training Data: Pascal VOC 2012 + Aug
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - ANN
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 6.0
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_20k_voc12aug/ann_r50-d8_512x512_20k_voc12aug_20200617_222246-dfcb1c62.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_20k_voc12aug/ann_r50-d8_512x512_20k_voc12aug_20200617_222246.log.json
+  Paper:
+    Title: Asymmetric Non-local Neural Networks for Semantic Segmentation
+    URL: https://arxiv.org/abs/1908.07678
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ann_head.py#L185
+  Framework: PyTorch
+- Name: ann_r101-d8_4xb4-20k_voc12aug-512x512
+  In Collection: ANN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 77.47
+      mIoU(ms+flip): 78.7
+  Config: configs/ann/ann_r101-d8_4xb4-20k_voc12aug-512x512.py
+  Metadata:
+    Training Data: Pascal VOC 2012 + Aug
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - ANN
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 9.5
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_20k_voc12aug/ann_r101-d8_512x512_20k_voc12aug_20200617_222246-2fad0042.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_20k_voc12aug/ann_r101-d8_512x512_20k_voc12aug_20200617_222246.log.json
+  Paper:
+    Title: Asymmetric Non-local Neural Networks for Semantic Segmentation
+    URL: https://arxiv.org/abs/1908.07678
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ann_head.py#L185
+  Framework: PyTorch
+- Name: ann_r50-d8_4xb4-40k_voc12aug-512x512
+  In Collection: ANN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 76.56
+      mIoU(ms+flip): 77.51
+  Config: configs/ann/ann_r50-d8_4xb4-40k_voc12aug-512x512.py
+  Metadata:
+    Training Data: Pascal VOC 2012 + Aug
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - ANN
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_40k_voc12aug/ann_r50-d8_512x512_40k_voc12aug_20200613_231314-b5dac322.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_40k_voc12aug/ann_r50-d8_512x512_40k_voc12aug_20200613_231314.log.json
+  Paper:
+    Title: Asymmetric Non-local Neural Networks for Semantic Segmentation
+    URL: https://arxiv.org/abs/1908.07678
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ann_head.py#L185
+  Framework: PyTorch
+- Name: ann_r101-d8_4xb4-40k_voc12aug-512x512
+  In Collection: ANN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 76.7
+      mIoU(ms+flip): 78.06
+  Config: configs/ann/ann_r101-d8_4xb4-40k_voc12aug-512x512.py
+  Metadata:
+    Training Data: Pascal VOC 2012 + Aug
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - ANN
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_40k_voc12aug/ann_r101-d8_512x512_40k_voc12aug_20200613_231314-bd205bbe.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_40k_voc12aug/ann_r101-d8_512x512_40k_voc12aug_20200613_231314.log.json
+  Paper:
+    Title: Asymmetric Non-local Neural Networks for Semantic Segmentation
+    URL: https://arxiv.org/abs/1908.07678
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ann_head.py#L185
+  Framework: PyTorch
diff --git a/configs/apcnet/README.md b/configs/apcnet/README.md
index 11fb1a1454..0d7f1d6614 100644
--- a/configs/apcnet/README.md
+++ b/configs/apcnet/README.md
@@ -1,6 +1,6 @@
 # APCNet
 
-[Adaptive Pyramid Context Network for Semantic Segmentation](https://openaccess.thecvf.com/content_CVPR_2019/html/He_Adaptive_Pyramid_Context_Network_for_Semantic_Segmentation_CVPR_2019_paper.html)
+> [Adaptive Pyramid Context Network for Semantic Segmentation](https://openaccess.thecvf.com/content_CVPR_2019/html/He_Adaptive_Pyramid_Context_Network_for_Semantic_Segmentation_CVPR_2019_paper.html)
 
 ## Introduction
 
@@ -22,6 +22,30 @@ Recent studies witnessed that context features can significantly improve the per
 <img src="https://user-images.githubusercontent.com/24582831/142898638-e1c0c6ae-9270-448e-aa01-bbac3a236db5.png" width="70%"/>
 </div>
 
+## Results and models
+
+### Cityscapes
+
+| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                            | download                                                                                                                                                                                                                                                                                                                                                 |
+| ------ | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | --------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| APCNet | R-50-D8  | 512x1024  |   40000 | 7.7      | 3.57           | V100   | 78.02 |         79.26 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/apcnet/apcnet_r50-d8_4xb2-40k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x1024_40k_cityscapes/apcnet_r50-d8_512x1024_40k_cityscapes_20201214_115717-5e88fa33.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x1024_40k_cityscapes/apcnet_r50-d8_512x1024_40k_cityscapes-20201214_115717.log.json)     |
+| APCNet | R-101-D8 | 512x1024  |   40000 | 11.2     | 2.15           | V100   | 79.08 |         80.34 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/apcnet/apcnet_r101-d8_4xb2-40k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x1024_40k_cityscapes/apcnet_r101-d8_512x1024_40k_cityscapes_20201214_115716-abc9d111.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x1024_40k_cityscapes/apcnet_r101-d8_512x1024_40k_cityscapes-20201214_115716.log.json) |
+| APCNet | R-50-D8  | 769x769   |   40000 | 8.7      | 1.52           | V100   | 77.89 |         79.75 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/apcnet/apcnet_r50-d8_4xb2-40k_cityscapes-769x769.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_769x769_40k_cityscapes/apcnet_r50-d8_769x769_40k_cityscapes_20201214_115717-2a2628d7.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_769x769_40k_cityscapes/apcnet_r50-d8_769x769_40k_cityscapes-20201214_115717.log.json)         |
+| APCNet | R-101-D8 | 769x769   |   40000 | 12.7     | 1.03           | V100   | 77.96 |         79.24 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/apcnet/apcnet_r101-d8_4xb2-40k_cityscapes-769x769.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_769x769_40k_cityscapes/apcnet_r101-d8_769x769_40k_cityscapes_20201214_115718-b650de90.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_769x769_40k_cityscapes/apcnet_r101-d8_769x769_40k_cityscapes-20201214_115718.log.json)     |
+| APCNet | R-50-D8  | 512x1024  |   80000 | -        | -              | V100   | 78.96 |         79.94 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/apcnet/apcnet_r50-d8_4xb2-80k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x1024_80k_cityscapes/apcnet_r50-d8_512x1024_80k_cityscapes_20201214_115716-987f51e3.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x1024_80k_cityscapes/apcnet_r50-d8_512x1024_80k_cityscapes-20201214_115716.log.json)     |
+| APCNet | R-101-D8 | 512x1024  |   80000 | -        | -              | V100   | 79.64 |         80.61 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/apcnet/apcnet_r101-d8_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x1024_80k_cityscapes/apcnet_r101-d8_512x1024_80k_cityscapes_20201214_115705-b1ff208a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x1024_80k_cityscapes/apcnet_r101-d8_512x1024_80k_cityscapes-20201214_115705.log.json) |
+| APCNet | R-50-D8  | 769x769   |   80000 | -        | -              | V100   | 78.79 |         80.35 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/apcnet/apcnet_r50-d8_4xb2-80k_cityscapes-769x769.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_769x769_80k_cityscapes/apcnet_r50-d8_769x769_80k_cityscapes_20201214_115718-7ea9fa12.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_769x769_80k_cityscapes/apcnet_r50-d8_769x769_80k_cityscapes-20201214_115718.log.json)         |
+| APCNet | R-101-D8 | 769x769   |   80000 | -        | -              | V100   | 78.45 |         79.91 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/apcnet/apcnet_r101-d8_4xb2-80k_cityscapes-769x769.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_769x769_80k_cityscapes/apcnet_r101-d8_769x769_80k_cityscapes_20201214_115716-a7fbc2ab.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_769x769_80k_cityscapes/apcnet_r101-d8_769x769_80k_cityscapes-20201214_115716.log.json)     |
+
+### ADE20K
+
+| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                        | download                                                                                                                                                                                                                                                                                                                                 |
+| ------ | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| APCNet | R-50-D8  | 512x512   |   80000 | 10.1     | 19.61          | V100   | 42.20 |         43.30 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/apcnet/apcnet_r50-d8_4xb4-80k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x512_80k_ade20k/apcnet_r50-d8_512x512_80k_ade20k_20201214_115705-a8626293.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x512_80k_ade20k/apcnet_r50-d8_512x512_80k_ade20k-20201214_115705.log.json)         |
+| APCNet | R-101-D8 | 512x512   |   80000 | 13.6     | 13.10          | V100   | 45.54 |         46.65 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/apcnet/apcnet_r101-d8_4xb4-80k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x512_80k_ade20k/apcnet_r101-d8_512x512_80k_ade20k_20201214_115704-c656c3fb.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x512_80k_ade20k/apcnet_r101-d8_512x512_80k_ade20k-20201214_115704.log.json)     |
+| APCNet | R-50-D8  | 512x512   |  160000 | -        | -              | V100   | 43.40 |         43.94 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/apcnet/apcnet_r50-d8_4xb4-160k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x512_160k_ade20k/apcnet_r50-d8_512x512_160k_ade20k_20201214_115706-25fb92c2.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x512_160k_ade20k/apcnet_r50-d8_512x512_160k_ade20k-20201214_115706.log.json)     |
+| APCNet | R-101-D8 | 512x512   |  160000 | -        | -              | V100   | 45.41 |         46.63 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/apcnet/apcnet_r101-d8_4xb4-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x512_160k_ade20k/apcnet_r101-d8_512x512_160k_ade20k_20201214_115705-73f9a8d7.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x512_160k_ade20k/apcnet_r101-d8_512x512_160k_ade20k-20201214_115705.log.json) |
+
 ## Citation
 
 ```bibtex
@@ -33,27 +57,3 @@ month = {June},
 year = {2019}
 }
 ```
-
-## Results and models
-
-### Cityscapes
-
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                            | download                                                                                                                                                                                                                                                                                                                                                 |
-| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | --------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| APCNet | R-50-D8  | 512x1024  |   40000 | 7.7      | 3.57           | 78.02 |         79.26 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/apcnet/apcnet_r50-d8_4xb2-40k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x1024_40k_cityscapes/apcnet_r50-d8_512x1024_40k_cityscapes_20201214_115717-5e88fa33.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x1024_40k_cityscapes/apcnet_r50-d8_512x1024_40k_cityscapes-20201214_115717.log.json)     |
-| APCNet | R-101-D8 | 512x1024  |   40000 | 11.2     | 2.15           | 79.08 |         80.34 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/apcnet/apcnet_r101-d8_4xb2-40k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x1024_40k_cityscapes/apcnet_r101-d8_512x1024_40k_cityscapes_20201214_115716-abc9d111.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x1024_40k_cityscapes/apcnet_r101-d8_512x1024_40k_cityscapes-20201214_115716.log.json) |
-| APCNet | R-50-D8  | 769x769   |   40000 | 8.7      | 1.52           | 77.89 |         79.75 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/apcnet/apcnet_r50-d8_4xb2-40k_cityscapes-769x769.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_769x769_40k_cityscapes/apcnet_r50-d8_769x769_40k_cityscapes_20201214_115717-2a2628d7.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_769x769_40k_cityscapes/apcnet_r50-d8_769x769_40k_cityscapes-20201214_115717.log.json)         |
-| APCNet | R-101-D8 | 769x769   |   40000 | 12.7     | 1.03           | 77.96 |         79.24 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/apcnet/apcnet_r101-d8_4xb2-40k_cityscapes-769x769.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_769x769_40k_cityscapes/apcnet_r101-d8_769x769_40k_cityscapes_20201214_115718-b650de90.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_769x769_40k_cityscapes/apcnet_r101-d8_769x769_40k_cityscapes-20201214_115718.log.json)     |
-| APCNet | R-50-D8  | 512x1024  |   80000 | -        | -              | 78.96 |         79.94 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/apcnet/apcnet_r50-d8_4xb2-80k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x1024_80k_cityscapes/apcnet_r50-d8_512x1024_80k_cityscapes_20201214_115716-987f51e3.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x1024_80k_cityscapes/apcnet_r50-d8_512x1024_80k_cityscapes-20201214_115716.log.json)     |
-| APCNet | R-101-D8 | 512x1024  |   80000 | -        | -              | 79.64 |         80.61 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/apcnet/apcnet_r101-d8_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x1024_80k_cityscapes/apcnet_r101-d8_512x1024_80k_cityscapes_20201214_115705-b1ff208a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x1024_80k_cityscapes/apcnet_r101-d8_512x1024_80k_cityscapes-20201214_115705.log.json) |
-| APCNet | R-50-D8  | 769x769   |   80000 | -        | -              | 78.79 |         80.35 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/apcnet/apcnet_r50-d8_4xb2-80k_cityscapes-769x769.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_769x769_80k_cityscapes/apcnet_r50-d8_769x769_80k_cityscapes_20201214_115718-7ea9fa12.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_769x769_80k_cityscapes/apcnet_r50-d8_769x769_80k_cityscapes-20201214_115718.log.json)         |
-| APCNet | R-101-D8 | 769x769   |   80000 | -        | -              | 78.45 |         79.91 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/apcnet/apcnet_r101-d8_4xb2-80k_cityscapes-769x769.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_769x769_80k_cityscapes/apcnet_r101-d8_769x769_80k_cityscapes_20201214_115716-a7fbc2ab.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_769x769_80k_cityscapes/apcnet_r101-d8_769x769_80k_cityscapes-20201214_115716.log.json)     |
-
-### ADE20K
-
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                        | download                                                                                                                                                                                                                                                                                                                                 |
-| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| APCNet | R-50-D8  | 512x512   |   80000 | 10.1     | 19.61          | 42.20 |         43.30 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/apcnet/apcnet_r50-d8_4xb4-80k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x512_80k_ade20k/apcnet_r50-d8_512x512_80k_ade20k_20201214_115705-a8626293.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x512_80k_ade20k/apcnet_r50-d8_512x512_80k_ade20k-20201214_115705.log.json)         |
-| APCNet | R-101-D8 | 512x512   |   80000 | 13.6     | 13.10          | 45.54 |         46.65 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/apcnet/apcnet_r101-d8_4xb4-80k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x512_80k_ade20k/apcnet_r101-d8_512x512_80k_ade20k_20201214_115704-c656c3fb.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x512_80k_ade20k/apcnet_r101-d8_512x512_80k_ade20k-20201214_115704.log.json)     |
-| APCNet | R-50-D8  | 512x512   |  160000 | -        | -              | 43.40 |         43.94 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/apcnet/apcnet_r50-d8_4xb4-160k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x512_160k_ade20k/apcnet_r50-d8_512x512_160k_ade20k_20201214_115706-25fb92c2.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x512_160k_ade20k/apcnet_r50-d8_512x512_160k_ade20k-20201214_115706.log.json)     |
-| APCNet | R-101-D8 | 512x512   |  160000 | -        | -              | 45.41 |         46.63 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/apcnet/apcnet_r101-d8_4xb4-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x512_160k_ade20k/apcnet_r101-d8_512x512_160k_ade20k_20201214_115705-73f9a8d7.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x512_160k_ade20k/apcnet_r101-d8_512x512_160k_ade20k-20201214_115705.log.json) |
diff --git a/configs/apcnet/apcnet.yml b/configs/apcnet/apcnet.yml
deleted file mode 100644
index 737da973d4..0000000000
--- a/configs/apcnet/apcnet.yml
+++ /dev/null
@@ -1,232 +0,0 @@
-Collections:
-- Name: APCNet
-  Metadata:
-    Training Data:
-    - Cityscapes
-    - ADE20K
-  Paper:
-    URL: https://openaccess.thecvf.com/content_CVPR_2019/html/He_Adaptive_Pyramid_Context_Network_for_Semantic_Segmentation_CVPR_2019_paper.html
-    Title: Adaptive Pyramid Context Network for Semantic Segmentation
-  README: configs/apcnet/README.md
-  Code:
-    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111
-    Version: v0.17.0
-  Converted From:
-    Code: https://github.com/Junjun2016/APCNet
-Models:
-- Name: apcnet_r50-d8_4xb2-40k_cityscapes-512x1024
-  In Collection: APCNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,1024)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 280.11
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 7.7
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 78.02
-      mIoU(ms+flip): 79.26
-  Config: configs/apcnet/apcnet_r50-d8_4xb2-40k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x1024_40k_cityscapes/apcnet_r50-d8_512x1024_40k_cityscapes_20201214_115717-5e88fa33.pth
-- Name: apcnet_r101-d8_4xb2-40k_cityscapes-512x1024
-  In Collection: APCNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,1024)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 465.12
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 11.2
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 79.08
-      mIoU(ms+flip): 80.34
-  Config: configs/apcnet/apcnet_r101-d8_4xb2-40k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x1024_40k_cityscapes/apcnet_r101-d8_512x1024_40k_cityscapes_20201214_115716-abc9d111.pth
-- Name: apcnet_r50-d8_4xb2-40k_cityscapes-769x769
-  In Collection: APCNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (769,769)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 657.89
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (769,769)
-    Training Memory (GB): 8.7
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 77.89
-      mIoU(ms+flip): 79.75
-  Config: configs/apcnet/apcnet_r50-d8_4xb2-40k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_769x769_40k_cityscapes/apcnet_r50-d8_769x769_40k_cityscapes_20201214_115717-2a2628d7.pth
-- Name: apcnet_r101-d8_4xb2-40k_cityscapes-769x769
-  In Collection: APCNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (769,769)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 970.87
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (769,769)
-    Training Memory (GB): 12.7
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 77.96
-      mIoU(ms+flip): 79.24
-  Config: configs/apcnet/apcnet_r101-d8_4xb2-40k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_769x769_40k_cityscapes/apcnet_r101-d8_769x769_40k_cityscapes_20201214_115718-b650de90.pth
-- Name: apcnet_r50-d8_4xb2-80k_cityscapes-512x1024
-  In Collection: APCNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,1024)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 78.96
-      mIoU(ms+flip): 79.94
-  Config: configs/apcnet/apcnet_r50-d8_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x1024_80k_cityscapes/apcnet_r50-d8_512x1024_80k_cityscapes_20201214_115716-987f51e3.pth
-- Name: apcnet_r101-d8_4xb2-80k_cityscapes-512x1024
-  In Collection: APCNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,1024)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 79.64
-      mIoU(ms+flip): 80.61
-  Config: configs/apcnet/apcnet_r101-d8_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x1024_80k_cityscapes/apcnet_r101-d8_512x1024_80k_cityscapes_20201214_115705-b1ff208a.pth
-- Name: apcnet_r50-d8_4xb2-80k_cityscapes-769x769
-  In Collection: APCNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (769,769)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 78.79
-      mIoU(ms+flip): 80.35
-  Config: configs/apcnet/apcnet_r50-d8_4xb2-80k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_769x769_80k_cityscapes/apcnet_r50-d8_769x769_80k_cityscapes_20201214_115718-7ea9fa12.pth
-- Name: apcnet_r101-d8_4xb2-80k_cityscapes-769x769
-  In Collection: APCNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (769,769)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 78.45
-      mIoU(ms+flip): 79.91
-  Config: configs/apcnet/apcnet_r101-d8_4xb2-80k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_769x769_80k_cityscapes/apcnet_r101-d8_769x769_80k_cityscapes_20201214_115716-a7fbc2ab.pth
-- Name: apcnet_r50-d8_4xb4-80k_ade20k-512x512
-  In Collection: APCNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 50.99
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 10.1
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 42.2
-      mIoU(ms+flip): 43.3
-  Config: configs/apcnet/apcnet_r50-d8_4xb4-80k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x512_80k_ade20k/apcnet_r50-d8_512x512_80k_ade20k_20201214_115705-a8626293.pth
-- Name: apcnet_r101-d8_4xb4-80k_ade20k-512x512
-  In Collection: APCNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 76.34
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 13.6
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 45.54
-      mIoU(ms+flip): 46.65
-  Config: configs/apcnet/apcnet_r101-d8_4xb4-80k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x512_80k_ade20k/apcnet_r101-d8_512x512_80k_ade20k_20201214_115704-c656c3fb.pth
-- Name: apcnet_r50-d8_4xb4-160k_ade20k-512x512
-  In Collection: APCNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 160000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 43.4
-      mIoU(ms+flip): 43.94
-  Config: configs/apcnet/apcnet_r50-d8_4xb4-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x512_160k_ade20k/apcnet_r50-d8_512x512_160k_ade20k_20201214_115706-25fb92c2.pth
-- Name: apcnet_r101-d8_4xb4-160k_ade20k-512x512
-  In Collection: APCNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 160000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 45.41
-      mIoU(ms+flip): 46.63
-  Config: configs/apcnet/apcnet_r101-d8_4xb4-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x512_160k_ade20k/apcnet_r101-d8_512x512_160k_ade20k_20201214_115705-73f9a8d7.pth
diff --git a/configs/apcnet/metafile.yaml b/configs/apcnet/metafile.yaml
new file mode 100644
index 0000000000..3f4072c8fd
--- /dev/null
+++ b/configs/apcnet/metafile.yaml
@@ -0,0 +1,296 @@
+Collections:
+- Name: APCNet
+  License: Apache License 2.0
+  Metadata:
+    Training Data:
+    - Cityscapes
+    - ADE20K
+  Paper:
+    Title: Adaptive Pyramid Context Network for Semantic Segmentation
+    URL: https://openaccess.thecvf.com/content_CVPR_2019/html/He_Adaptive_Pyramid_Context_Network_for_Semantic_Segmentation_CVPR_2019_paper.html
+  README: configs/apcnet/README.md
+  Frameworks:
+  - PyTorch
+Models:
+- Name: apcnet_r50-d8_4xb2-40k_cityscapes-512x1024
+  In Collection: APCNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.02
+      mIoU(ms+flip): 79.26
+  Config: configs/apcnet/apcnet_r50-d8_4xb2-40k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - APCNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 7.7
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x1024_40k_cityscapes/apcnet_r50-d8_512x1024_40k_cityscapes_20201214_115717-5e88fa33.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x1024_40k_cityscapes/apcnet_r50-d8_512x1024_40k_cityscapes-20201214_115717.log.json
+  Paper:
+    Title: Adaptive Pyramid Context Network for Semantic Segmentation
+    URL: https://openaccess.thecvf.com/content_CVPR_2019/html/He_Adaptive_Pyramid_Context_Network_for_Semantic_Segmentation_CVPR_2019_paper.html
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111
+  Framework: PyTorch
+- Name: apcnet_r101-d8_4xb2-40k_cityscapes-512x1024
+  In Collection: APCNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.08
+      mIoU(ms+flip): 80.34
+  Config: configs/apcnet/apcnet_r101-d8_4xb2-40k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - APCNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 11.2
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x1024_40k_cityscapes/apcnet_r101-d8_512x1024_40k_cityscapes_20201214_115716-abc9d111.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x1024_40k_cityscapes/apcnet_r101-d8_512x1024_40k_cityscapes-20201214_115716.log.json
+  Paper:
+    Title: Adaptive Pyramid Context Network for Semantic Segmentation
+    URL: https://openaccess.thecvf.com/content_CVPR_2019/html/He_Adaptive_Pyramid_Context_Network_for_Semantic_Segmentation_CVPR_2019_paper.html
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111
+  Framework: PyTorch
+- Name: apcnet_r50-d8_4xb2-40k_cityscapes-769x769
+  In Collection: APCNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.89
+      mIoU(ms+flip): 79.75
+  Config: configs/apcnet/apcnet_r50-d8_4xb2-40k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - APCNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 8.7
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_769x769_40k_cityscapes/apcnet_r50-d8_769x769_40k_cityscapes_20201214_115717-2a2628d7.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_769x769_40k_cityscapes/apcnet_r50-d8_769x769_40k_cityscapes-20201214_115717.log.json
+  Paper:
+    Title: Adaptive Pyramid Context Network for Semantic Segmentation
+    URL: https://openaccess.thecvf.com/content_CVPR_2019/html/He_Adaptive_Pyramid_Context_Network_for_Semantic_Segmentation_CVPR_2019_paper.html
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111
+  Framework: PyTorch
+- Name: apcnet_r101-d8_4xb2-40k_cityscapes-769x769
+  In Collection: APCNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.96
+      mIoU(ms+flip): 79.24
+  Config: configs/apcnet/apcnet_r101-d8_4xb2-40k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - APCNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 12.7
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_769x769_40k_cityscapes/apcnet_r101-d8_769x769_40k_cityscapes_20201214_115718-b650de90.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_769x769_40k_cityscapes/apcnet_r101-d8_769x769_40k_cityscapes-20201214_115718.log.json
+  Paper:
+    Title: Adaptive Pyramid Context Network for Semantic Segmentation
+    URL: https://openaccess.thecvf.com/content_CVPR_2019/html/He_Adaptive_Pyramid_Context_Network_for_Semantic_Segmentation_CVPR_2019_paper.html
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111
+  Framework: PyTorch
+- Name: apcnet_r50-d8_4xb2-80k_cityscapes-512x1024
+  In Collection: APCNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.96
+      mIoU(ms+flip): 79.94
+  Config: configs/apcnet/apcnet_r50-d8_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - APCNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x1024_80k_cityscapes/apcnet_r50-d8_512x1024_80k_cityscapes_20201214_115716-987f51e3.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x1024_80k_cityscapes/apcnet_r50-d8_512x1024_80k_cityscapes-20201214_115716.log.json
+  Paper:
+    Title: Adaptive Pyramid Context Network for Semantic Segmentation
+    URL: https://openaccess.thecvf.com/content_CVPR_2019/html/He_Adaptive_Pyramid_Context_Network_for_Semantic_Segmentation_CVPR_2019_paper.html
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111
+  Framework: PyTorch
+- Name: apcnet_r101-d8_4xb2-80k_cityscapes-512x1024
+  In Collection: APCNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.64
+      mIoU(ms+flip): 80.61
+  Config: configs/apcnet/apcnet_r101-d8_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - APCNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x1024_80k_cityscapes/apcnet_r101-d8_512x1024_80k_cityscapes_20201214_115705-b1ff208a.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x1024_80k_cityscapes/apcnet_r101-d8_512x1024_80k_cityscapes-20201214_115705.log.json
+  Paper:
+    Title: Adaptive Pyramid Context Network for Semantic Segmentation
+    URL: https://openaccess.thecvf.com/content_CVPR_2019/html/He_Adaptive_Pyramid_Context_Network_for_Semantic_Segmentation_CVPR_2019_paper.html
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111
+  Framework: PyTorch
+- Name: apcnet_r50-d8_4xb2-80k_cityscapes-769x769
+  In Collection: APCNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.79
+      mIoU(ms+flip): 80.35
+  Config: configs/apcnet/apcnet_r50-d8_4xb2-80k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - APCNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_769x769_80k_cityscapes/apcnet_r50-d8_769x769_80k_cityscapes_20201214_115718-7ea9fa12.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_769x769_80k_cityscapes/apcnet_r50-d8_769x769_80k_cityscapes-20201214_115718.log.json
+  Paper:
+    Title: Adaptive Pyramid Context Network for Semantic Segmentation
+    URL: https://openaccess.thecvf.com/content_CVPR_2019/html/He_Adaptive_Pyramid_Context_Network_for_Semantic_Segmentation_CVPR_2019_paper.html
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111
+  Framework: PyTorch
+- Name: apcnet_r101-d8_4xb2-80k_cityscapes-769x769
+  In Collection: APCNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.45
+      mIoU(ms+flip): 79.91
+  Config: configs/apcnet/apcnet_r101-d8_4xb2-80k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - APCNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_769x769_80k_cityscapes/apcnet_r101-d8_769x769_80k_cityscapes_20201214_115716-a7fbc2ab.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_769x769_80k_cityscapes/apcnet_r101-d8_769x769_80k_cityscapes-20201214_115716.log.json
+  Paper:
+    Title: Adaptive Pyramid Context Network for Semantic Segmentation
+    URL: https://openaccess.thecvf.com/content_CVPR_2019/html/He_Adaptive_Pyramid_Context_Network_for_Semantic_Segmentation_CVPR_2019_paper.html
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111
+  Framework: PyTorch
+- Name: apcnet_r50-d8_4xb4-80k_ade20k-512x512
+  In Collection: APCNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 42.2
+      mIoU(ms+flip): 43.3
+  Config: configs/apcnet/apcnet_r50-d8_4xb4-80k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - APCNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 10.1
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x512_80k_ade20k/apcnet_r50-d8_512x512_80k_ade20k_20201214_115705-a8626293.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x512_80k_ade20k/apcnet_r50-d8_512x512_80k_ade20k-20201214_115705.log.json
+  Paper:
+    Title: Adaptive Pyramid Context Network for Semantic Segmentation
+    URL: https://openaccess.thecvf.com/content_CVPR_2019/html/He_Adaptive_Pyramid_Context_Network_for_Semantic_Segmentation_CVPR_2019_paper.html
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111
+  Framework: PyTorch
+- Name: apcnet_r101-d8_4xb4-80k_ade20k-512x512
+  In Collection: APCNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 45.54
+      mIoU(ms+flip): 46.65
+  Config: configs/apcnet/apcnet_r101-d8_4xb4-80k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - APCNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 13.6
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x512_80k_ade20k/apcnet_r101-d8_512x512_80k_ade20k_20201214_115704-c656c3fb.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x512_80k_ade20k/apcnet_r101-d8_512x512_80k_ade20k-20201214_115704.log.json
+  Paper:
+    Title: Adaptive Pyramid Context Network for Semantic Segmentation
+    URL: https://openaccess.thecvf.com/content_CVPR_2019/html/He_Adaptive_Pyramid_Context_Network_for_Semantic_Segmentation_CVPR_2019_paper.html
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111
+  Framework: PyTorch
+- Name: apcnet_r50-d8_4xb4-160k_ade20k-512x512
+  In Collection: APCNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 43.4
+      mIoU(ms+flip): 43.94
+  Config: configs/apcnet/apcnet_r50-d8_4xb4-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - APCNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x512_160k_ade20k/apcnet_r50-d8_512x512_160k_ade20k_20201214_115706-25fb92c2.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x512_160k_ade20k/apcnet_r50-d8_512x512_160k_ade20k-20201214_115706.log.json
+  Paper:
+    Title: Adaptive Pyramid Context Network for Semantic Segmentation
+    URL: https://openaccess.thecvf.com/content_CVPR_2019/html/He_Adaptive_Pyramid_Context_Network_for_Semantic_Segmentation_CVPR_2019_paper.html
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111
+  Framework: PyTorch
+- Name: apcnet_r101-d8_4xb4-160k_ade20k-512x512
+  In Collection: APCNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 45.41
+      mIoU(ms+flip): 46.63
+  Config: configs/apcnet/apcnet_r101-d8_4xb4-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - APCNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x512_160k_ade20k/apcnet_r101-d8_512x512_160k_ade20k_20201214_115705-73f9a8d7.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x512_160k_ade20k/apcnet_r101-d8_512x512_160k_ade20k-20201214_115705.log.json
+  Paper:
+    Title: Adaptive Pyramid Context Network for Semantic Segmentation
+    URL: https://openaccess.thecvf.com/content_CVPR_2019/html/He_Adaptive_Pyramid_Context_Network_for_Semantic_Segmentation_CVPR_2019_paper.html
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111
+  Framework: PyTorch
diff --git a/configs/beit/README.md b/configs/beit/README.md
index 380d788741..8e34e29410 100644
--- a/configs/beit/README.md
+++ b/configs/beit/README.md
@@ -1,6 +1,6 @@
 # BEiT
 
-[BEiT: BERT Pre-Training of Image Transformers](https://arxiv.org/abs/2106.08254)
+> [BEiT: BERT Pre-Training of Image Transformers](https://arxiv.org/abs/2106.08254)
 
 ## Introduction
 
@@ -22,18 +22,6 @@ We introduce a self-supervised vision representation model BEiT, which stands fo
 <img src="https://user-images.githubusercontent.com/93248678/160155758-781c9a45-b1d7-4530-9015-88eca6645006.png" width="70%"/>
 </div>
 
-## Citation
-
-```bibtex
-@inproceedings{beit,
-      title={{BEiT}: {BERT} Pre-Training of Image Transformers},
-      author={Hangbo Bao and Li Dong and Songhao Piao and Furu Wei},
-      booktitle={International Conference on Learning Representations},
-      year={2022},
-      url={https://openreview.net/forum?id=p-BhZSz59o4}
-}
-```
-
 ## Usage
 
 To use other repositories' pre-trained models, it is necessary to convert keys.
@@ -79,7 +67,19 @@ upernet_beit-large_fp16_8x1_640x640_160k_ade20k-8fc0dd5d.pth $GPUS --eval mIoU
 
 ### ADE20K
 
-| Method  | Backbone | Crop Size | pretrain     | pretrain img size | Batch Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU  | mIoU(ms+flip) | config                                                                                                                              | download                                                                                                                                                                                                                                                                                                                                                 |
-| ------- | -------- | --------- | ------------ | ----------------- | ---------- | ------- | -------- | -------------- | ----- | ------------: | ----------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| UPerNet | BEiT-B   | 640x640   | ImageNet-22K | 224x224           | 16         | 160000  | 15.88    | 2.00           | 53.08 |         53.84 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/beit/beit-base_upernet_8xb2-160k_ade20k-640x640.py)      | [model](https://download.openmmlab.com/mmsegmentation/v0.5/beit/upernet_beit-base_8x2_640x640_160k_ade20k/upernet_beit-base_8x2_640x640_160k_ade20k-eead221d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/beit/upernet_beit-base_8x2_640x640_160k_ade20k/upernet_beit-base_8x2_640x640_160k_ade20k.log.json)                         |
-| UPerNet | BEiT-L   | 640x640   | ImageNet-22K | 224x224           | 8          | 320000  | 22.64    | 0.96           | 56.33 |         56.84 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/beit/beit-large_upernet_8xb1-amp-160k_ade20k-640x640.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/beit/upernet_beit-large_fp16_8x1_640x640_160k_ade20k/upernet_beit-large_fp16_8x1_640x640_160k_ade20k-8fc0dd5d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/beit/upernet_beit-large_fp16_8x1_640x640_160k_ade20k/upernet_beit-large_fp16_8x1_640x640_160k_ade20k.log.json) |
+| Method  | Backbone | Crop Size | pretrain     | pretrain img size | Batch Size | Lr schd | Mem (GB) | Inf time (fps) | Device | mIoU  | mIoU(ms+flip) | config                                                                                                                              | download                                                                                                                                                                                                                                                                                                                                                 |
+| ------- | -------- | --------- | ------------ | ----------------- | ---------- | ------- | -------- | -------------- | ------ | ----- | ------------: | ----------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| UPerNet | BEiT-B   | 640x640   | ImageNet-22K | 224x224           | 16         | 160000  | 15.88    | 2.00           | V100   | 53.08 |         53.84 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/beit/beit-base_upernet_8xb2-160k_ade20k-640x640.py)      | [model](https://download.openmmlab.com/mmsegmentation/v0.5/beit/upernet_beit-base_8x2_640x640_160k_ade20k/upernet_beit-base_8x2_640x640_160k_ade20k-eead221d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/beit/upernet_beit-base_8x2_640x640_160k_ade20k/upernet_beit-base_8x2_640x640_160k_ade20k.log.json)                         |
+| UPerNet | BEiT-L   | 640x640   | ImageNet-22K | 224x224           | 8          | 320000  | 22.64    | 0.96           | V100   | 56.33 |         56.84 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/beit/beit-large_upernet_8xb1-amp-160k_ade20k-640x640.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/beit/upernet_beit-large_fp16_8x1_640x640_160k_ade20k/upernet_beit-large_fp16_8x1_640x640_160k_ade20k-8fc0dd5d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/beit/upernet_beit-large_fp16_8x1_640x640_160k_ade20k/upernet_beit-large_fp16_8x1_640x640_160k_ade20k.log.json) |
+
+## Citation
+
+```bibtex
+@inproceedings{beit,
+      title={{BEiT}: {BERT} Pre-Training of Image Transformers},
+      author={Hangbo Bao and Li Dong and Songhao Piao and Furu Wei},
+      booktitle={International Conference on Learning Representations},
+      year={2022},
+      url={https://openreview.net/forum?id=p-BhZSz59o4}
+}
+```
diff --git a/configs/beit/beit.yml b/configs/beit/beit.yml
deleted file mode 100644
index f6cc0160bf..0000000000
--- a/configs/beit/beit.yml
+++ /dev/null
@@ -1,45 +0,0 @@
-Models:
-- Name: beit-base_upernet_8xb2-160k_ade20k-640x640
-  In Collection: UPerNet
-  Metadata:
-    backbone: BEiT-B
-    crop size: (640,640)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 500.0
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (640,640)
-    Training Memory (GB): 15.88
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 53.08
-      mIoU(ms+flip): 53.84
-  Config: configs/beit/beit-base_upernet_8xb2-160k_ade20k-640x640.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/beit/upernet_beit-base_8x2_640x640_160k_ade20k/upernet_beit-base_8x2_640x640_160k_ade20k-eead221d.pth
-- Name: beit-large_upernet_8xb1-amp-160k_ade20k-640x640
-  In Collection: UPerNet
-  Metadata:
-    backbone: BEiT-L
-    crop size: (640,640)
-    lr schd: 320000
-    inference time (ms/im):
-    - value: 1041.67
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: AMP
-      resolution: (640,640)
-    Training Memory (GB): 22.64
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 56.33
-      mIoU(ms+flip): 56.84
-  Config: configs/beit/beit-large_upernet_8xb1-amp-160k_ade20k-640x640.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/beit/upernet_beit-large_fp16_8x1_640x640_160k_ade20k/upernet_beit-large_fp16_8x1_640x640_160k_ade20k-8fc0dd5d.pth
diff --git a/configs/beit/metafile.yaml b/configs/beit/metafile.yaml
new file mode 100644
index 0000000000..ef6124e8dc
--- /dev/null
+++ b/configs/beit/metafile.yaml
@@ -0,0 +1,49 @@
+Models:
+- Name: beit-base_upernet_8xb2-160k_ade20k-640x640
+  In Collection: UPerNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 53.08
+      mIoU(ms+flip): 53.84
+  Config: configs/beit/beit-base_upernet_8xb2-160k_ade20k-640x640.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - BEiT-B
+    - UPerNet
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 15.88
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/beit/upernet_beit-base_8x2_640x640_160k_ade20k/upernet_beit-base_8x2_640x640_160k_ade20k-eead221d.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/beit/upernet_beit-base_8x2_640x640_160k_ade20k/upernet_beit-base_8x2_640x640_160k_ade20k.log.json
+  Paper:
+    Title: 'BEiT: BERT Pre-Training of Image Transformers'
+    URL: https://arxiv.org/abs/2106.08254
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.23.0/mmseg/models/backbones/beit.py#1404
+  Framework: PyTorch
+- Name: beit-large_upernet_8xb1-amp-160k_ade20k-640x640
+  In Collection: UPerNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 56.33
+      mIoU(ms+flip): 56.84
+  Config: configs/beit/beit-large_upernet_8xb1-amp-160k_ade20k-640x640.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 8
+    Architecture:
+    - BEiT-L
+    - UPerNet
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 22.64
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/beit/upernet_beit-large_fp16_8x1_640x640_160k_ade20k/upernet_beit-large_fp16_8x1_640x640_160k_ade20k-8fc0dd5d.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/beit/upernet_beit-large_fp16_8x1_640x640_160k_ade20k/upernet_beit-large_fp16_8x1_640x640_160k_ade20k.log.json
+  Paper:
+    Title: 'BEiT: BERT Pre-Training of Image Transformers'
+    URL: https://arxiv.org/abs/2106.08254
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.23.0/mmseg/models/backbones/beit.py#1404
+  Framework: PyTorch
diff --git a/configs/bisenetv1/README.md b/configs/bisenetv1/README.md
index 72fdd474cb..c895fdfe63 100644
--- a/configs/bisenetv1/README.md
+++ b/configs/bisenetv1/README.md
@@ -1,6 +1,6 @@
 # BiSeNetV1
 
-[BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation](https://arxiv.org/abs/1808.00897)
+> [BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation](https://arxiv.org/abs/1808.00897)
 
 ## Introduction
 
@@ -22,43 +22,43 @@ Semantic segmentation requires both rich spatial information and sizeable recept
 <img src="https://user-images.githubusercontent.com/24582831/142898839-a0a78148-848a-41b2-8682-b1f61ac004ba.png" width="70%"/>
 </div>
 
-## Citation
-
-```bibtex
-@inproceedings{yu2018bisenet,
-  title={Bisenet: Bilateral segmentation network for real-time semantic segmentation},
-  author={Yu, Changqian and Wang, Jingbo and Peng, Chao and Gao, Changxin and Yu, Gang and Sang, Nong},
-  booktitle={Proceedings of the European conference on computer vision (ECCV)},
-  pages={325--341},
-  year={2018}
-}
-```
-
 ## Results and models
 
 ### Cityscapes
 
-| Method                  | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                                             | download                                                                                                                                                                                                                                                                                                                                                                                                                               |
-| ----------------------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------- | -------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| BiSeNetV1 (No Pretrain) | R-18-D32 | 1024x1024 |  160000 | 5.69     | 31.77          | 74.44 | 77.05         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/bisenetv1/bisenetv1_r18-d32_4xb4-160k_cityscapes-1024x1024.py)          | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes/bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes_20210922_172239-c55e78e2.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes/bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes_20210922_172239.log.json)                                     |
-| BiSeNetV1               | R-18-D32 | 1024x1024 |  160000 | 5.69     | 31.77          | 74.37 | 76.91         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/bisenetv1/bisenetv1_r18-d32-in1k-pre_4xb4-160k_cityscapes-1024x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes/bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes_20210905_220251-8ba80eff.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes/bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes_20210905_220251.log.json) |
-| BiSeNetV1 (4x8)         | R-18-D32 | 1024x1024 |  160000 | 11.17    | 31.77          | 75.16 | 77.24         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/bisenetv1/bisenetv1_r18-d32-in1k-pre_4xb8-160k_cityscapes-1024x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x8_1024x1024_160k_cityscapes/bisenetv1_r18-d32_in1k-pre_4x8_1024x1024_160k_cityscapes_20210905_220322-bb8db75f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x8_1024x1024_160k_cityscapes/bisenetv1_r18-d32_in1k-pre_4x8_1024x1024_160k_cityscapes_20210905_220322.log.json) |
-| BiSeNetV1 (No Pretrain) | R-50-D32 | 1024x1024 |  160000 | 15.39    | 7.71           | 76.92 | 78.87         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/bisenetv1/bisenetv1_r50-d32_4xb4-160k_cityscapes-1024x1024.py)          | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes/bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes_20210923_222639-7b28a2a6.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes/bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes_20210923_222639.log.json)                                     |
-| BiSeNetV1               | R-50-D32 | 1024x1024 |  160000 | 15.39    | 7.71           | 77.68 | 79.57         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/bisenetv1/bisenetv1_r50-d32-in1k-pre_4xb4-160k_cityscapes-1024x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes_20210917_234628-8b304447.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes_20210917_234628.log.json) |
+| Method    | Backbone               | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                                             | download                                                                                                                                                                                                                                                                                                                                                                                                                               |
+| --------- | ---------------------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------- | -------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| BiSeNetV1 | R-18-D32 (No Pretrain) | 1024x1024 |  160000 | 5.69     | 31.77          | V100   | 74.44 | 77.05         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/bisenetv1/bisenetv1_r18-d32_4xb4-160k_cityscapes-1024x1024.py)          | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes/bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes_20210922_172239-c55e78e2.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes/bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes_20210922_172239.log.json)                                     |
+| BiSeNetV1 | R-18-D32               | 1024x1024 |  160000 | 5.69     | 31.77          | V100   | 74.37 | 76.91         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/bisenetv1/bisenetv1_r18-d32-in1k-pre_4xb4-160k_cityscapes-1024x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes/bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes_20210905_220251-8ba80eff.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes/bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes_20210905_220251.log.json) |
+| BiSeNetV1 | R-18-D32 (4x8)         | 1024x1024 |  160000 | 11.17    | 31.77          | V100   | 75.16 | 77.24         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/bisenetv1/bisenetv1_r18-d32-in1k-pre_4xb8-160k_cityscapes-1024x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x8_1024x1024_160k_cityscapes/bisenetv1_r18-d32_in1k-pre_4x8_1024x1024_160k_cityscapes_20210905_220322-bb8db75f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x8_1024x1024_160k_cityscapes/bisenetv1_r18-d32_in1k-pre_4x8_1024x1024_160k_cityscapes_20210905_220322.log.json) |
+| BiSeNetV1 | R-50-D32 (No Pretrain) | 1024x1024 |  160000 | 15.39    | 7.71           | V100   | 76.92 | 78.87         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/bisenetv1/bisenetv1_r50-d32_4xb4-160k_cityscapes-1024x1024.py)          | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes/bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes_20210923_222639-7b28a2a6.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes/bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes_20210923_222639.log.json)                                     |
+| BiSeNetV1 | R-50-D32               | 1024x1024 |  160000 | 15.39    | 7.71           | V100   | 77.68 | 79.57         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/bisenetv1/bisenetv1_r50-d32-in1k-pre_4xb4-160k_cityscapes-1024x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes_20210917_234628-8b304447.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes_20210917_234628.log.json) |
 
 ### COCO-Stuff 164k
 
-| Method                  | Backbone  | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                                                | download                                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
-| ----------------------- | --------- | --------- | ------: | -------- | -------------- | ----: | ------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| BiSeNetV1 (No Pretrain) | R-18-D32  | 512x512   |  160000 | -        | -              | 25.45 | 26.15         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/bisenetv1/bisenetv1_r18-d32_4xb4-160k_coco-stuff164k-512x512.py)           | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211022_054328-046aa2f2.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211022_054328.log.json)                                         |
-| BiSeNetV1               | R-18-D32  | 512x512   |  160000 | 6.33     | 74.24          | 28.55 | 29.26         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/bisenetv1/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100.log.json)     |
-| BiSeNetV1 (No Pretrain) | R-50-D32  | 512x512   |  160000 | -        | -              | 29.82 | 30.33         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/bisenetv1/bisenetv1_r50-d32_4xb4-160k_coco-stuff164k-512x512.py)           | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_040616-d2bb0df4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_040616.log.json)                                         |
-| BiSeNetV1               | R-50-D32  | 512x512   |  160000 | 9.28     | 32.60          | 34.88 | 35.37         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/bisenetv1/bisenetv1_r50-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_181932-66747911.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_181932.log.json)     |
-| BiSeNetV1 (No Pretrain) | R-101-D32 | 512x512   |  160000 | -        | -              | 31.14 | 31.76         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/bisenetv1/bisenetv1_r50-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211102_164147-c6b32c3b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211102_164147.log.json)                                     |
-| BiSeNetV1               | R-101-D32 | 512x512   |  160000 | 10.36    | 25.25          | 37.38 | 37.99         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/bisenetv1/bisenetv1_r101-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_225220-28c8f092.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_225220.log.json) |
+| Method    | Backbone                | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                                                | download                                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
+| --------- | ----------------------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| BiSeNetV1 | R-18-D32 (No Pretrain)  | 512x512   |  160000 | -        | -              | V100   | 25.45 | 26.15         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/bisenetv1/bisenetv1_r18-d32_4xb4-160k_coco-stuff164k-512x512.py)           | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211022_054328-046aa2f2.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211022_054328.log.json)                                         |
+| BiSeNetV1 | R-18-D32                | 512x512   |  160000 | 6.33     | 74.24          | V100   | 28.55 | 29.26         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/bisenetv1/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100.log.json)     |
+| BiSeNetV1 | R-50-D32 (No Pretrain)  | 512x512   |  160000 | -        | -              | V100   | 29.82 | 30.33         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/bisenetv1/bisenetv1_r50-d32_4xb4-160k_coco-stuff164k-512x512.py)           | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_040616-d2bb0df4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_040616.log.json)                                         |
+| BiSeNetV1 | R-50-D32                | 512x512   |  160000 | 9.28     | 32.60          | V100   | 34.88 | 35.37         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/bisenetv1/bisenetv1_r50-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_181932-66747911.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_181932.log.json)     |
+| BiSeNetV1 | R-101-D32 (No Pretrain) | 512x512   |  160000 | -        | -              | V100   | 31.14 | 31.76         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/bisenetv1/bisenetv1_r50-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211102_164147-c6b32c3b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211102_164147.log.json)                                     |
+| BiSeNetV1 | R-101-D32               | 512x512   |  160000 | 10.36    | 25.25          | V100   | 37.38 | 37.99         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/bisenetv1/bisenetv1_r101-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_225220-28c8f092.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_225220.log.json) |
 
 Note:
 
 - `4x8`: Using 4 GPUs with 8 samples per GPU in training.
 - For BiSeNetV1 on Cityscapes dataset, default setting is 4 GPUs with 4 samples per GPU in training.
 - `No Pretrain` means the model is trained from scratch.
+
+## Citation
+
+```bibtex
+@inproceedings{yu2018bisenet,
+  title={Bisenet: Bilateral segmentation network for real-time semantic segmentation},
+  author={Yu, Changqian and Wang, Jingbo and Peng, Chao and Gao, Changxin and Yu, Gang and Sang, Nong},
+  booktitle={Proceedings of the European conference on computer vision (ECCV)},
+  pages={325--341},
+  year={2018}
+}
+```
diff --git a/configs/bisenetv1/bisenetv1.yml b/configs/bisenetv1/bisenetv1.yml
deleted file mode 100644
index f5aade4de4..0000000000
--- a/configs/bisenetv1/bisenetv1.yml
+++ /dev/null
@@ -1,234 +0,0 @@
-Collections:
-- Name: BiSeNetV1
-  Metadata:
-    Training Data:
-    - Cityscapes
-    - COCO-Stuff 164k
-  Paper:
-    URL: https://arxiv.org/abs/1808.00897
-    Title: 'BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation'
-  README: configs/bisenetv1/README.md
-  Code:
-    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/backbones/bisenetv1.py#L266
-    Version: v0.18.0
-  Converted From:
-    Code: https://github.com/ycszen/TorchSeg/tree/master/model/bisenet
-Models:
-- Name: bisenetv1_r18-d32_4xb4-160k_cityscapes-1024x1024
-  In Collection: BiSeNetV1
-  Metadata:
-    backbone: R-18-D32
-    crop size: (1024,1024)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 31.48
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (1024,1024)
-    Training Memory (GB): 5.69
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 74.44
-      mIoU(ms+flip): 77.05
-  Config: configs/bisenetv1/bisenetv1_r18-d32_4xb4-160k_cityscapes-1024x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes/bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes_20210922_172239-c55e78e2.pth
-- Name: bisenetv1_r18-d32-in1k-pre_4xb4-160k_cityscapes-1024x1024
-  In Collection: BiSeNetV1
-  Metadata:
-    backbone: R-18-D32
-    crop size: (1024,1024)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 31.48
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (1024,1024)
-    Training Memory (GB): 5.69
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 74.37
-      mIoU(ms+flip): 76.91
-  Config: configs/bisenetv1/bisenetv1_r18-d32-in1k-pre_4xb4-160k_cityscapes-1024x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes/bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes_20210905_220251-8ba80eff.pth
-- Name: bisenetv1_r18-d32-in1k-pre_4xb8-160k_cityscapes-1024x1024
-  In Collection: BiSeNetV1
-  Metadata:
-    backbone: R-18-D32
-    crop size: (1024,1024)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 31.48
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (1024,1024)
-    Training Memory (GB): 11.17
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 75.16
-      mIoU(ms+flip): 77.24
-  Config: configs/bisenetv1/bisenetv1_r18-d32-in1k-pre_4xb8-160k_cityscapes-1024x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x8_1024x1024_160k_cityscapes/bisenetv1_r18-d32_in1k-pre_4x8_1024x1024_160k_cityscapes_20210905_220322-bb8db75f.pth
-- Name: bisenetv1_r50-d32_4xb4-160k_cityscapes-1024x1024
-  In Collection: BiSeNetV1
-  Metadata:
-    backbone: R-50-D32
-    crop size: (1024,1024)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 129.7
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (1024,1024)
-    Training Memory (GB): 15.39
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 76.92
-      mIoU(ms+flip): 78.87
-  Config: configs/bisenetv1/bisenetv1_r50-d32_4xb4-160k_cityscapes-1024x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes/bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes_20210923_222639-7b28a2a6.pth
-- Name: bisenetv1_r50-d32-in1k-pre_4xb4-160k_cityscapes-1024x1024
-  In Collection: BiSeNetV1
-  Metadata:
-    backbone: R-50-D32
-    crop size: (1024,1024)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 129.7
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (1024,1024)
-    Training Memory (GB): 15.39
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 77.68
-      mIoU(ms+flip): 79.57
-  Config: configs/bisenetv1/bisenetv1_r50-d32-in1k-pre_4xb4-160k_cityscapes-1024x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes_20210917_234628-8b304447.pth
-- Name: bisenetv1_r18-d32_4xb4-160k_coco-stuff164k-512x512
-  In Collection: BiSeNetV1
-  Metadata:
-    backbone: R-18-D32
-    crop size: (512,512)
-    lr schd: 160000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: COCO-Stuff 164k
-    Metrics:
-      mIoU: 25.45
-      mIoU(ms+flip): 26.15
-  Config: configs/bisenetv1/bisenetv1_r18-d32_4xb4-160k_coco-stuff164k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211022_054328-046aa2f2.pth
-- Name: bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512
-  In Collection: BiSeNetV1
-  Metadata:
-    backbone: R-18-D32
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 13.47
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 6.33
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: COCO-Stuff 164k
-    Metrics:
-      mIoU: 28.55
-      mIoU(ms+flip): 29.26
-  Config: configs/bisenetv1/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth
-- Name: bisenetv1_r50-d32_4xb4-160k_coco-stuff164k-512x512
-  In Collection: BiSeNetV1
-  Metadata:
-    backbone: R-50-D32
-    crop size: (512,512)
-    lr schd: 160000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: COCO-Stuff 164k
-    Metrics:
-      mIoU: 29.82
-      mIoU(ms+flip): 30.33
-  Config: configs/bisenetv1/bisenetv1_r50-d32_4xb4-160k_coco-stuff164k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_040616-d2bb0df4.pth
-- Name: bisenetv1_r50-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512
-  In Collection: BiSeNetV1
-  Metadata:
-    backbone: R-50-D32
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 30.67
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 9.28
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: COCO-Stuff 164k
-    Metrics:
-      mIoU: 34.88
-      mIoU(ms+flip): 35.37
-  Config: configs/bisenetv1/bisenetv1_r50-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_181932-66747911.pth
-- Name: bisenetv1_r50-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512
-  In Collection: BiSeNetV1
-  Metadata:
-    backbone: R-101-D32
-    crop size: (512,512)
-    lr schd: 160000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: COCO-Stuff 164k
-    Metrics:
-      mIoU: 31.14
-      mIoU(ms+flip): 31.76
-  Config: configs/bisenetv1/bisenetv1_r50-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211102_164147-c6b32c3b.pth
-- Name: bisenetv1_r101-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512
-  In Collection: BiSeNetV1
-  Metadata:
-    backbone: R-101-D32
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 39.6
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 10.36
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: COCO-Stuff 164k
-    Metrics:
-      mIoU: 37.38
-      mIoU(ms+flip): 37.99
-  Config: configs/bisenetv1/bisenetv1_r101-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_225220-28c8f092.pth
diff --git a/configs/bisenetv1/metafile.yaml b/configs/bisenetv1/metafile.yaml
new file mode 100644
index 0000000000..e37f632b2f
--- /dev/null
+++ b/configs/bisenetv1/metafile.yaml
@@ -0,0 +1,275 @@
+Collections:
+- Name: BiSeNetV1
+  License: Apache License 2.0
+  Metadata:
+    Training Data:
+    - Cityscapes
+    - COCO-Stuff 164k
+  Paper:
+    Title: 'BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation'
+    URL: https://arxiv.org/abs/1808.00897
+  README: configs/bisenetv1/README.md
+  Frameworks:
+  - PyTorch
+Models:
+- Name: bisenetv1_r18-d32_4xb4-160k_cityscapes-1024x1024
+  In Collection: BiSeNetV1
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 74.44
+      mIoU(ms+flip): 77.05
+  Config: configs/bisenetv1/bisenetv1_r18-d32_4xb4-160k_cityscapes-1024x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 16
+    Architecture:
+    - R-18-D32
+    - BiSeNetV1
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 5.69
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes/bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes_20210922_172239-c55e78e2.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes/bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes_20210922_172239.log.json
+  Paper:
+    Title: 'BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation'
+    URL: https://arxiv.org/abs/1808.00897
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/backbones/bisenetv1.py#L266
+  Framework: PyTorch
+- Name: bisenetv1_r18-d32-in1k-pre_4xb4-160k_cityscapes-1024x1024
+  In Collection: BiSeNetV1
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 74.37
+      mIoU(ms+flip): 76.91
+  Config: configs/bisenetv1/bisenetv1_r18-d32-in1k-pre_4xb4-160k_cityscapes-1024x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 16
+    Architecture:
+    - R-18-D32
+    - BiSeNetV1
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 5.69
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes/bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes_20210905_220251-8ba80eff.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes/bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes_20210905_220251.log.json
+  Paper:
+    Title: 'BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation'
+    URL: https://arxiv.org/abs/1808.00897
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/backbones/bisenetv1.py#L266
+  Framework: PyTorch
+- Name: bisenetv1_r18-d32-in1k-pre_4xb8-160k_cityscapes-1024x1024
+  In Collection: BiSeNetV1
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 75.16
+      mIoU(ms+flip): 77.24
+  Config: configs/bisenetv1/bisenetv1_r18-d32-in1k-pre_4xb8-160k_cityscapes-1024x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 32
+    Architecture:
+    - R-18-D32
+    - BiSeNetV1
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 11.17
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x8_1024x1024_160k_cityscapes/bisenetv1_r18-d32_in1k-pre_4x8_1024x1024_160k_cityscapes_20210905_220322-bb8db75f.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x8_1024x1024_160k_cityscapes/bisenetv1_r18-d32_in1k-pre_4x8_1024x1024_160k_cityscapes_20210905_220322.log.json
+  Paper:
+    Title: 'BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation'
+    URL: https://arxiv.org/abs/1808.00897
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/backbones/bisenetv1.py#L266
+  Framework: PyTorch
+- Name: bisenetv1_r50-d32_4xb4-160k_cityscapes-1024x1024
+  In Collection: BiSeNetV1
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 76.92
+      mIoU(ms+flip): 78.87
+  Config: configs/bisenetv1/bisenetv1_r50-d32_4xb4-160k_cityscapes-1024x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 16
+    Architecture:
+    - R-50-D32
+    - BiSeNetV1
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 15.39
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes/bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes_20210923_222639-7b28a2a6.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes/bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes_20210923_222639.log.json
+  Paper:
+    Title: 'BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation'
+    URL: https://arxiv.org/abs/1808.00897
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/backbones/bisenetv1.py#L266
+  Framework: PyTorch
+- Name: bisenetv1_r50-d32-in1k-pre_4xb4-160k_cityscapes-1024x1024
+  In Collection: BiSeNetV1
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.68
+      mIoU(ms+flip): 79.57
+  Config: configs/bisenetv1/bisenetv1_r50-d32-in1k-pre_4xb4-160k_cityscapes-1024x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 16
+    Architecture:
+    - R-50-D32
+    - BiSeNetV1
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 15.39
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes_20210917_234628-8b304447.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes_20210917_234628.log.json
+  Paper:
+    Title: 'BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation'
+    URL: https://arxiv.org/abs/1808.00897
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/backbones/bisenetv1.py#L266
+  Framework: PyTorch
+- Name: bisenetv1_r18-d32_4xb4-160k_coco-stuff164k-512x512
+  In Collection: BiSeNetV1
+  Results:
+    Task: Semantic Segmentation
+    Dataset: COCO-Stuff 164k
+    Metrics:
+      mIoU: 25.45
+      mIoU(ms+flip): 26.15
+  Config: configs/bisenetv1/bisenetv1_r18-d32_4xb4-160k_coco-stuff164k-512x512.py
+  Metadata:
+    Training Data: COCO-Stuff 164k
+    Batch Size: 16
+    Architecture:
+    - R-18-D32
+    - BiSeNetV1
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211022_054328-046aa2f2.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211022_054328.log.json
+  Paper:
+    Title: 'BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation'
+    URL: https://arxiv.org/abs/1808.00897
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/backbones/bisenetv1.py#L266
+  Framework: PyTorch
+- Name: bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512
+  In Collection: BiSeNetV1
+  Results:
+    Task: Semantic Segmentation
+    Dataset: COCO-Stuff 164k
+    Metrics:
+      mIoU: 28.55
+      mIoU(ms+flip): 29.26
+  Config: configs/bisenetv1/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py
+  Metadata:
+    Training Data: COCO-Stuff 164k
+    Batch Size: 16
+    Architecture:
+    - R-18-D32
+    - BiSeNetV1
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 6.33
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100.log.json
+  Paper:
+    Title: 'BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation'
+    URL: https://arxiv.org/abs/1808.00897
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/backbones/bisenetv1.py#L266
+  Framework: PyTorch
+- Name: bisenetv1_r50-d32_4xb4-160k_coco-stuff164k-512x512
+  In Collection: BiSeNetV1
+  Results:
+    Task: Semantic Segmentation
+    Dataset: COCO-Stuff 164k
+    Metrics:
+      mIoU: 29.82
+      mIoU(ms+flip): 30.33
+  Config: configs/bisenetv1/bisenetv1_r50-d32_4xb4-160k_coco-stuff164k-512x512.py
+  Metadata:
+    Training Data: COCO-Stuff 164k
+    Batch Size: 16
+    Architecture:
+    - R-50-D32
+    - BiSeNetV1
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_040616-d2bb0df4.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_040616.log.json
+  Paper:
+    Title: 'BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation'
+    URL: https://arxiv.org/abs/1808.00897
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/backbones/bisenetv1.py#L266
+  Framework: PyTorch
+- Name: bisenetv1_r50-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512
+  In Collection: BiSeNetV1
+  Results:
+    Task: Semantic Segmentation
+    Dataset: COCO-Stuff 164k
+    Metrics:
+      mIoU: 34.88
+      mIoU(ms+flip): 35.37
+  Config: configs/bisenetv1/bisenetv1_r50-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py
+  Metadata:
+    Training Data: COCO-Stuff 164k
+    Batch Size: 16
+    Architecture:
+    - R-50-D32
+    - BiSeNetV1
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 9.28
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_181932-66747911.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_181932.log.json
+  Paper:
+    Title: 'BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation'
+    URL: https://arxiv.org/abs/1808.00897
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/backbones/bisenetv1.py#L266
+  Framework: PyTorch
+- Name: bisenetv1_r50-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512
+  In Collection: BiSeNetV1
+  Results:
+    Task: Semantic Segmentation
+    Dataset: COCO-Stuff 164k
+    Metrics:
+      mIoU: 31.14
+      mIoU(ms+flip): 31.76
+  Config: configs/bisenetv1/bisenetv1_r50-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py
+  Metadata:
+    Training Data: COCO-Stuff 164k
+    Batch Size: 16
+    Architecture:
+    - R-101-D32
+    - BiSeNetV1
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211102_164147-c6b32c3b.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211102_164147.log.json
+  Paper:
+    Title: 'BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation'
+    URL: https://arxiv.org/abs/1808.00897
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/backbones/bisenetv1.py#L266
+  Framework: PyTorch
+- Name: bisenetv1_r101-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512
+  In Collection: BiSeNetV1
+  Results:
+    Task: Semantic Segmentation
+    Dataset: COCO-Stuff 164k
+    Metrics:
+      mIoU: 37.38
+      mIoU(ms+flip): 37.99
+  Config: configs/bisenetv1/bisenetv1_r101-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py
+  Metadata:
+    Training Data: COCO-Stuff 164k
+    Batch Size: 16
+    Architecture:
+    - R-101-D32
+    - BiSeNetV1
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 10.36
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_225220-28c8f092.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_225220.log.json
+  Paper:
+    Title: 'BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation'
+    URL: https://arxiv.org/abs/1808.00897
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/backbones/bisenetv1.py#L266
+  Framework: PyTorch
diff --git a/configs/bisenetv2/README.md b/configs/bisenetv2/README.md
index 7cde5c02c1..1f44c01f36 100644
--- a/configs/bisenetv2/README.md
+++ b/configs/bisenetv2/README.md
@@ -1,6 +1,6 @@
 # BiSeNetV2
 
-[Bisenet v2: Bilateral Network with Guided Aggregation for Real-time Semantic Segmentation](https://arxiv.org/abs/2004.02147)
+> [Bisenet v2: Bilateral Network with Guided Aggregation for Real-time Semantic Segmentation](https://arxiv.org/abs/2004.02147)
 
 ## Introduction
 
@@ -22,6 +22,23 @@ The low-level details and high-level semantics are both essential to the semanti
 <img src="https://user-images.githubusercontent.com/24582831/142898966-ec4a81da-b4b0-41ee-b083-1d964582c18a.png" width="70%"/>
 </div>
 
+## Results and models
+
+### Cityscapes
+
+| Method    | Backbone         | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                                     | download                                                                                                                                                                                                                                                                                                                                                                                               |
+| --------- | ---------------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| BiSeNetV2 | BiSeNetV2        | 1024x1024 |  160000 | 7.64     | 31.77          | V100   | 73.21 |         75.74 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/bisenetv2/bisenetv2_fcn_4xb4-160k_cityscapes-1024x1024.py)      | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_4x4_1024x1024_160k_cityscapes_20210902_015551-bcf10f09.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_4x4_1024x1024_160k_cityscapes_20210902_015551.log.json)                     |
+| BiSeNetV2 | BiSeNetV2 (OHEM) | 1024x1024 |  160000 | 7.64     | -              | V100   | 73.57 |         75.80 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/bisenetv2/bisenetv2_fcn_4xb4-ohem-160k_cityscapes-1024x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes_20210902_112947-5f8103b4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes_20210902_112947.log.json) |
+| BiSeNetV2 | BiSeNetV2 (4x8)  | 1024x1024 |  160000 | 15.05    | -              | V100   | 75.76 |         77.79 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/bisenetv2/bisenetv2_fcn_4xb8-160k_cityscapes-1024x1024.py)      | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_4x8_1024x1024_160k_cityscapes/bisenetv2_fcn_4x8_1024x1024_160k_cityscapes_20210903_000032-e1a2eed6.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_4x8_1024x1024_160k_cityscapes/bisenetv2_fcn_4x8_1024x1024_160k_cityscapes_20210903_000032.log.json)                     |
+| BiSeNetV2 | BiSeNetV2 (FP16) | 1024x1024 |  160000 | 5.77     | 36.65          | V100   | 73.07 |         75.13 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/bisenetv2/bisenetv2_fcn_4xb4-amp-160k_cityscapes-1024x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes_20210902_045942-b979777b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes_20210902_045942.log.json) |
+
+Note:
+
+- `OHEM` means Online Hard Example Mining (OHEM) is adopted in training.
+- `FP16` means Mixed Precision (FP16) is adopted in training.
+- `4x8` means 4 GPUs with 8 samples per GPU in training.
+
 ## Citation
 
 ```bibtex
@@ -34,20 +51,3 @@ The low-level details and high-level semantics are both essential to the semanti
   publisher={Springer}
 }
 ```
-
-## Results and models
-
-### Cityscapes
-
-| Method           | Backbone  | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                                     | download                                                                                                                                                                                                                                                                                                                                                                                               |
-| ---------------- | --------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| BiSeNetV2        | BiSeNetV2 | 1024x1024 |  160000 | 7.64     | 31.77          | 73.21 |         75.74 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/bisenetv2/bisenetv2_fcn_4xb4-160k_cityscapes-1024x1024.py)      | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_4x4_1024x1024_160k_cityscapes_20210902_015551-bcf10f09.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_4x4_1024x1024_160k_cityscapes_20210902_015551.log.json)                     |
-| BiSeNetV2 (OHEM) | BiSeNetV2 | 1024x1024 |  160000 | 7.64     | -              | 73.57 |         75.80 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/bisenetv2/bisenetv2_fcn_4xb4-ohem-160k_cityscapes-1024x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes_20210902_112947-5f8103b4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes_20210902_112947.log.json) |
-| BiSeNetV2 (4x8)  | BiSeNetV2 | 1024x1024 |  160000 | 15.05    | -              | 75.76 |         77.79 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/bisenetv2/bisenetv2_fcn_4xb8-160k_cityscapes-1024x1024.py)      | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_4x8_1024x1024_160k_cityscapes/bisenetv2_fcn_4x8_1024x1024_160k_cityscapes_20210903_000032-e1a2eed6.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_4x8_1024x1024_160k_cityscapes/bisenetv2_fcn_4x8_1024x1024_160k_cityscapes_20210903_000032.log.json)                     |
-| BiSeNetV2 (FP16) | BiSeNetV2 | 1024x1024 |  160000 | 5.77     | 36.65          | 73.07 |         75.13 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/bisenetv2/bisenetv2_fcn_4xb4-amp-160k_cityscapes-1024x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes_20210902_045942-b979777b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes_20210902_045942.log.json) |
-
-Note:
-
-- `OHEM` means Online Hard Example Mining (OHEM) is adopted in training.
-- `FP16` means Mixed Precision (FP16) is adopted in training.
-- `4x8` means 4 GPUs with 8 samples per GPU in training.
diff --git a/configs/bisenetv2/bisenetv2.yml b/configs/bisenetv2/bisenetv2.yml
deleted file mode 100644
index 70c4326a55..0000000000
--- a/configs/bisenetv2/bisenetv2.yml
+++ /dev/null
@@ -1,88 +0,0 @@
-Collections:
-- Name: BiSeNetV2
-  Metadata:
-    Training Data:
-    - Cityscapes
-  Paper:
-    URL: https://arxiv.org/abs/2004.02147
-    Title: 'Bisenet v2: Bilateral Network with Guided Aggregation for Real-time Semantic
-      Segmentation'
-  README: configs/bisenetv2/README.md
-  Code:
-    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/backbones/bisenetv2.py#L545
-    Version: v0.18.0
-Models:
-- Name: bisenetv2_fcn_4xb4-160k_cityscapes-1024x1024
-  In Collection: BiSeNetV2
-  Metadata:
-    backbone: BiSeNetV2
-    crop size: (1024,1024)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 31.48
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (1024,1024)
-    Training Memory (GB): 7.64
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 73.21
-      mIoU(ms+flip): 75.74
-  Config: configs/bisenetv2/bisenetv2_fcn_4xb4-160k_cityscapes-1024x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_4x4_1024x1024_160k_cityscapes_20210902_015551-bcf10f09.pth
-- Name: bisenetv2_fcn_4xb4-ohem-160k_cityscapes-1024x1024
-  In Collection: BiSeNetV2
-  Metadata:
-    backbone: BiSeNetV2
-    crop size: (1024,1024)
-    lr schd: 160000
-    Training Memory (GB): 7.64
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 73.57
-      mIoU(ms+flip): 75.8
-  Config: configs/bisenetv2/bisenetv2_fcn_4xb4-ohem-160k_cityscapes-1024x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes_20210902_112947-5f8103b4.pth
-- Name: bisenetv2_fcn_4xb8-160k_cityscapes-1024x1024
-  In Collection: BiSeNetV2
-  Metadata:
-    backbone: BiSeNetV2
-    crop size: (1024,1024)
-    lr schd: 160000
-    Training Memory (GB): 15.05
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 75.76
-      mIoU(ms+flip): 77.79
-  Config: configs/bisenetv2/bisenetv2_fcn_4xb8-160k_cityscapes-1024x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_4x8_1024x1024_160k_cityscapes/bisenetv2_fcn_4x8_1024x1024_160k_cityscapes_20210903_000032-e1a2eed6.pth
-- Name: bisenetv2_fcn_4xb4-amp-160k_cityscapes-1024x1024
-  In Collection: BiSeNetV2
-  Metadata:
-    backbone: BiSeNetV2
-    crop size: (1024,1024)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 27.29
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: AMP
-      resolution: (1024,1024)
-    Training Memory (GB): 5.77
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 73.07
-      mIoU(ms+flip): 75.13
-  Config: configs/bisenetv2/bisenetv2_fcn_4xb4-amp-160k_cityscapes-1024x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes_20210902_045942-b979777b.pth
diff --git a/configs/bisenetv2/metafile.yaml b/configs/bisenetv2/metafile.yaml
new file mode 100644
index 0000000000..5430ec3071
--- /dev/null
+++ b/configs/bisenetv2/metafile.yaml
@@ -0,0 +1,114 @@
+Collections:
+- Name: BiSeNetV2
+  License: Apache License 2.0
+  Metadata:
+    Training Data:
+    - Cityscapes
+  Paper:
+    Title: 'Bisenet v2: Bilateral Network with Guided Aggregation for Real-time Semantic
+      Segmentation'
+    URL: https://arxiv.org/abs/2004.02147
+  README: configs/bisenetv2/README.md
+  Frameworks:
+  - PyTorch
+Models:
+- Name: bisenetv2_fcn_4xb4-160k_cityscapes-1024x1024
+  In Collection: BiSeNetV2
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 73.21
+      mIoU(ms+flip): 75.74
+  Config: configs/bisenetv2/bisenetv2_fcn_4xb4-160k_cityscapes-1024x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 16
+    Architecture:
+    - BiSeNetV2
+    - BiSeNetV2
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 7.64
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_4x4_1024x1024_160k_cityscapes_20210902_015551-bcf10f09.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_4x4_1024x1024_160k_cityscapes_20210902_015551.log.json
+  Paper:
+    Title: 'Bisenet v2: Bilateral Network with Guided Aggregation for Real-time Semantic
+      Segmentation'
+    URL: https://arxiv.org/abs/2004.02147
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/backbones/bisenetv2.py#L545
+  Framework: PyTorch
+- Name: bisenetv2_fcn_4xb4-ohem-160k_cityscapes-1024x1024
+  In Collection: BiSeNetV2
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 73.57
+      mIoU(ms+flip): 75.8
+  Config: configs/bisenetv2/bisenetv2_fcn_4xb4-ohem-160k_cityscapes-1024x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 16
+    Architecture:
+    - BiSeNetV2
+    - BiSeNetV2
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 7.64
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes_20210902_112947-5f8103b4.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes_20210902_112947.log.json
+  Paper:
+    Title: 'Bisenet v2: Bilateral Network with Guided Aggregation for Real-time Semantic
+      Segmentation'
+    URL: https://arxiv.org/abs/2004.02147
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/backbones/bisenetv2.py#L545
+  Framework: PyTorch
+- Name: bisenetv2_fcn_4xb8-160k_cityscapes-1024x1024
+  In Collection: BiSeNetV2
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 75.76
+      mIoU(ms+flip): 77.79
+  Config: configs/bisenetv2/bisenetv2_fcn_4xb8-160k_cityscapes-1024x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 32
+    Architecture:
+    - BiSeNetV2
+    - BiSeNetV2
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 15.05
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_4x8_1024x1024_160k_cityscapes/bisenetv2_fcn_4x8_1024x1024_160k_cityscapes_20210903_000032-e1a2eed6.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_4x8_1024x1024_160k_cityscapes/bisenetv2_fcn_4x8_1024x1024_160k_cityscapes_20210903_000032.log.json
+  Paper:
+    Title: 'Bisenet v2: Bilateral Network with Guided Aggregation for Real-time Semantic
+      Segmentation'
+    URL: https://arxiv.org/abs/2004.02147
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/backbones/bisenetv2.py#L545
+  Framework: PyTorch
+- Name: bisenetv2_fcn_4xb4-amp-160k_cityscapes-1024x1024
+  In Collection: BiSeNetV2
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 73.07
+      mIoU(ms+flip): 75.13
+  Config: configs/bisenetv2/bisenetv2_fcn_4xb4-amp-160k_cityscapes-1024x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 16
+    Architecture:
+    - BiSeNetV2
+    - BiSeNetV2
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 5.77
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes_20210902_045942-b979777b.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes_20210902_045942.log.json
+  Paper:
+    Title: 'Bisenet v2: Bilateral Network with Guided Aggregation for Real-time Semantic
+      Segmentation'
+    URL: https://arxiv.org/abs/2004.02147
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/backbones/bisenetv2.py#L545
+  Framework: PyTorch
diff --git a/configs/ccnet/README.md b/configs/ccnet/README.md
index 9d110f4df9..c7a0163495 100644
--- a/configs/ccnet/README.md
+++ b/configs/ccnet/README.md
@@ -1,6 +1,6 @@
 # CCNet
 
-[CCNet: Criss-Cross Attention for Semantic Segmentation](https://arxiv.org/abs/1811.11721)
+> [CCNet: Criss-Cross Attention for Semantic Segmentation](https://arxiv.org/abs/1811.11721)
 
 ## Introduction
 
@@ -22,46 +22,46 @@ Contextual information is vital in visual understanding problems, such as semant
 <img src="https://user-images.githubusercontent.com/24582831/142899159-b329c12a-0fde-44df-8718-def6cfb004e4.png" width="70%"/>
 </div>
 
-## Citation
-
-```bibtex
-@article{huang2018ccnet,
-    title={CCNet: Criss-Cross Attention for Semantic Segmentation},
-    author={Huang, Zilong and Wang, Xinggang and Huang, Lichao and Huang, Chang and Wei, Yunchao and Liu, Wenyu},
-    booktitle={ICCV},
-    year={2019}
-}
-```
-
 ## Results and models
 
 ### Cityscapes
 
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                          | download                                                                                                                                                                                                                                                                                                                                           |
-| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| CCNet  | R-50-D8  | 512x1024  |   40000 | 6        | 3.32           | 77.76 |         78.87 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ccnet/ccnet_r50-d8_4xb2-40k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x1024_40k_cityscapes/ccnet_r50-d8_512x1024_40k_cityscapes_20200616_142517-4123f401.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x1024_40k_cityscapes/ccnet_r50-d8_512x1024_40k_cityscapes_20200616_142517.log.json)     |
-| CCNet  | R-101-D8 | 512x1024  |   40000 | 9.5      | 2.31           | 76.35 |         78.19 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ccnet/ccnet_r101-d8_4xb2-40k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x1024_40k_cityscapes/ccnet_r101-d8_512x1024_40k_cityscapes_20200616_142540-a3b84ba6.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x1024_40k_cityscapes/ccnet_r101-d8_512x1024_40k_cityscapes_20200616_142540.log.json) |
-| CCNet  | R-50-D8  | 769x769   |   40000 | 6.8      | 1.43           | 78.46 |         79.93 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ccnet/ccnet_r50-d8_4xb2-40k_cityscapes-769x769.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_769x769_40k_cityscapes/ccnet_r50-d8_769x769_40k_cityscapes_20200616_145125-76d11884.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_769x769_40k_cityscapes/ccnet_r50-d8_769x769_40k_cityscapes_20200616_145125.log.json)         |
-| CCNet  | R-101-D8 | 769x769   |   40000 | 10.7     | 1.01           | 76.94 |         78.62 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ccnet/ccnet_r101-d8_4xb2-40k_cityscapes-769x769.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_769x769_40k_cityscapes/ccnet_r101-d8_769x769_40k_cityscapes_20200617_101428-4f57c8d0.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_769x769_40k_cityscapes/ccnet_r101-d8_769x769_40k_cityscapes_20200617_101428.log.json)     |
-| CCNet  | R-50-D8  | 512x1024  |   80000 | -        | -              | 79.03 |         80.16 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ccnet/ccnet_r50-d8_4xb2-80k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x1024_80k_cityscapes/ccnet_r50-d8_512x1024_80k_cityscapes_20200617_010421-869a3423.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x1024_80k_cityscapes/ccnet_r50-d8_512x1024_80k_cityscapes_20200617_010421.log.json)     |
-| CCNet  | R-101-D8 | 512x1024  |   80000 | -        | -              | 78.87 |         79.90 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ccnet/ccnet_r101-d8_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x1024_80k_cityscapes/ccnet_r101-d8_512x1024_80k_cityscapes_20200617_203935-ffae8917.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x1024_80k_cityscapes/ccnet_r101-d8_512x1024_80k_cityscapes_20200617_203935.log.json) |
-| CCNet  | R-50-D8  | 769x769   |   80000 | -        | -              | 79.29 |         81.08 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ccnet/ccnet_r50-d8_4xb2-80k_cityscapes-769x769.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_769x769_80k_cityscapes/ccnet_r50-d8_769x769_80k_cityscapes_20200617_010421-73eed8ca.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_769x769_80k_cityscapes/ccnet_r50-d8_769x769_80k_cityscapes_20200617_010421.log.json)         |
-| CCNet  | R-101-D8 | 769x769   |   80000 | -        | -              | 79.45 |         80.66 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ccnet/ccnet_r101-d8_4xb2-80k_cityscapes-769x769.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_769x769_80k_cityscapes/ccnet_r101-d8_769x769_80k_cityscapes_20200618_011502-ad3cd481.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_769x769_80k_cityscapes/ccnet_r101-d8_769x769_80k_cityscapes_20200618_011502.log.json)     |
+| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                          | download                                                                                                                                                                                                                                                                                                                                           |
+| ------ | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| CCNet  | R-50-D8  | 512x1024  |   40000 | 6        | 3.32           | V100   | 77.76 |         78.87 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ccnet/ccnet_r50-d8_4xb2-40k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x1024_40k_cityscapes/ccnet_r50-d8_512x1024_40k_cityscapes_20200616_142517-4123f401.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x1024_40k_cityscapes/ccnet_r50-d8_512x1024_40k_cityscapes_20200616_142517.log.json)     |
+| CCNet  | R-101-D8 | 512x1024  |   40000 | 9.5      | 2.31           | V100   | 76.35 |         78.19 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ccnet/ccnet_r101-d8_4xb2-40k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x1024_40k_cityscapes/ccnet_r101-d8_512x1024_40k_cityscapes_20200616_142540-a3b84ba6.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x1024_40k_cityscapes/ccnet_r101-d8_512x1024_40k_cityscapes_20200616_142540.log.json) |
+| CCNet  | R-50-D8  | 769x769   |   40000 | 6.8      | 1.43           | V100   | 78.46 |         79.93 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ccnet/ccnet_r50-d8_4xb2-40k_cityscapes-769x769.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_769x769_40k_cityscapes/ccnet_r50-d8_769x769_40k_cityscapes_20200616_145125-76d11884.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_769x769_40k_cityscapes/ccnet_r50-d8_769x769_40k_cityscapes_20200616_145125.log.json)         |
+| CCNet  | R-101-D8 | 769x769   |   40000 | 10.7     | 1.01           | V100   | 76.94 |         78.62 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ccnet/ccnet_r101-d8_4xb2-40k_cityscapes-769x769.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_769x769_40k_cityscapes/ccnet_r101-d8_769x769_40k_cityscapes_20200617_101428-4f57c8d0.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_769x769_40k_cityscapes/ccnet_r101-d8_769x769_40k_cityscapes_20200617_101428.log.json)     |
+| CCNet  | R-50-D8  | 512x1024  |   80000 | -        | -              | V100   | 79.03 |         80.16 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ccnet/ccnet_r50-d8_4xb2-80k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x1024_80k_cityscapes/ccnet_r50-d8_512x1024_80k_cityscapes_20200617_010421-869a3423.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x1024_80k_cityscapes/ccnet_r50-d8_512x1024_80k_cityscapes_20200617_010421.log.json)     |
+| CCNet  | R-101-D8 | 512x1024  |   80000 | -        | -              | V100   | 78.87 |         79.90 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ccnet/ccnet_r101-d8_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x1024_80k_cityscapes/ccnet_r101-d8_512x1024_80k_cityscapes_20200617_203935-ffae8917.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x1024_80k_cityscapes/ccnet_r101-d8_512x1024_80k_cityscapes_20200617_203935.log.json) |
+| CCNet  | R-50-D8  | 769x769   |   80000 | -        | -              | V100   | 79.29 |         81.08 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ccnet/ccnet_r50-d8_4xb2-80k_cityscapes-769x769.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_769x769_80k_cityscapes/ccnet_r50-d8_769x769_80k_cityscapes_20200617_010421-73eed8ca.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_769x769_80k_cityscapes/ccnet_r50-d8_769x769_80k_cityscapes_20200617_010421.log.json)         |
+| CCNet  | R-101-D8 | 769x769   |   80000 | -        | -              | V100   | 79.45 |         80.66 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ccnet/ccnet_r101-d8_4xb2-80k_cityscapes-769x769.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_769x769_80k_cityscapes/ccnet_r101-d8_769x769_80k_cityscapes_20200618_011502-ad3cd481.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_769x769_80k_cityscapes/ccnet_r101-d8_769x769_80k_cityscapes_20200618_011502.log.json)     |
 
 ### ADE20K
 
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                      | download                                                                                                                                                                                                                                                                                                                           |
-| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | --------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| CCNet  | R-50-D8  | 512x512   |   80000 | 8.8      | 20.89          | 41.78 |         42.98 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ccnet/ccnet_r50-d8_4xb4-80k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_80k_ade20k/ccnet_r50-d8_512x512_80k_ade20k_20200615_014848-aa37f61e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_80k_ade20k/ccnet_r50-d8_512x512_80k_ade20k_20200615_014848.log.json)         |
-| CCNet  | R-101-D8 | 512x512   |   80000 | 12.2     | 14.11          | 43.97 |         45.13 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ccnet/ccnet_r101-d8_4xb4-80k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_80k_ade20k/ccnet_r101-d8_512x512_80k_ade20k_20200615_014848-1f4929a3.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_80k_ade20k/ccnet_r101-d8_512x512_80k_ade20k_20200615_014848.log.json)     |
-| CCNet  | R-50-D8  | 512x512   |  160000 | -        | -              | 42.08 |         43.13 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ccnet/ccnet_r50-d8_4xb4-160k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_160k_ade20k/ccnet_r50-d8_512x512_160k_ade20k_20200616_084435-7c97193b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_160k_ade20k/ccnet_r50-d8_512x512_160k_ade20k_20200616_084435.log.json)     |
-| CCNet  | R-101-D8 | 512x512   |  160000 | -        | -              | 43.71 |         45.04 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ccnet/ccnet_r101-d8_4xb4-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_160k_ade20k/ccnet_r101-d8_512x512_160k_ade20k_20200616_000644-e849e007.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_160k_ade20k/ccnet_r101-d8_512x512_160k_ade20k_20200616_000644.log.json) |
+| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                      | download                                                                                                                                                                                                                                                                                                                           |
+| ------ | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | --------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| CCNet  | R-50-D8  | 512x512   |   80000 | 8.8      | 20.89          | V100   | 41.78 |         42.98 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ccnet/ccnet_r50-d8_4xb4-80k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_80k_ade20k/ccnet_r50-d8_512x512_80k_ade20k_20200615_014848-aa37f61e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_80k_ade20k/ccnet_r50-d8_512x512_80k_ade20k_20200615_014848.log.json)         |
+| CCNet  | R-101-D8 | 512x512   |   80000 | 12.2     | 14.11          | V100   | 43.97 |         45.13 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ccnet/ccnet_r101-d8_4xb4-80k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_80k_ade20k/ccnet_r101-d8_512x512_80k_ade20k_20200615_014848-1f4929a3.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_80k_ade20k/ccnet_r101-d8_512x512_80k_ade20k_20200615_014848.log.json)     |
+| CCNet  | R-50-D8  | 512x512   |  160000 | -        | -              | V100   | 42.08 |         43.13 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ccnet/ccnet_r50-d8_4xb4-160k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_160k_ade20k/ccnet_r50-d8_512x512_160k_ade20k_20200616_084435-7c97193b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_160k_ade20k/ccnet_r50-d8_512x512_160k_ade20k_20200616_084435.log.json)     |
+| CCNet  | R-101-D8 | 512x512   |  160000 | -        | -              | V100   | 43.71 |         45.04 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ccnet/ccnet_r101-d8_4xb4-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_160k_ade20k/ccnet_r101-d8_512x512_160k_ade20k_20200616_000644-e849e007.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_160k_ade20k/ccnet_r101-d8_512x512_160k_ade20k_20200616_000644.log.json) |
 
 ### Pascal VOC 2012 + Aug
 
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                       | download                                                                                                                                                                                                                                                                                                                               |
-| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ---------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| CCNet  | R-50-D8  | 512x512   |   20000 | 6        | 20.45          | 76.17 |         77.51 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ccnet/ccnet_r50-d8_4xb4-20k_voc12aug-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_20k_voc12aug/ccnet_r50-d8_512x512_20k_voc12aug_20200617_193212-fad81784.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_20k_voc12aug/ccnet_r50-d8_512x512_20k_voc12aug_20200617_193212.log.json)     |
-| CCNet  | R-101-D8 | 512x512   |   20000 | 9.5      | 13.64          | 77.27 |         79.02 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ccnet/ccnet_r101-d8_4xb4-20k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_20k_voc12aug/ccnet_r101-d8_512x512_20k_voc12aug_20200617_193212-0007b61d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_20k_voc12aug/ccnet_r101-d8_512x512_20k_voc12aug_20200617_193212.log.json) |
-| CCNet  | R-50-D8  | 512x512   |   40000 | -        | -              | 75.96 |         77.04 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ccnet/ccnet_r50-d8_4xb4-40k_voc12aug-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_40k_voc12aug/ccnet_r50-d8_512x512_40k_voc12aug_20200613_232127-c2a15f02.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_40k_voc12aug/ccnet_r50-d8_512x512_40k_voc12aug_20200613_232127.log.json)     |
-| CCNet  | R-101-D8 | 512x512   |   40000 | -        | -              | 77.87 |         78.90 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ccnet/ccnet_r101-d8_4xb4-40k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_40k_voc12aug/ccnet_r101-d8_512x512_40k_voc12aug_20200613_232127-c30da577.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_40k_voc12aug/ccnet_r101-d8_512x512_40k_voc12aug_20200613_232127.log.json) |
+| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                       | download                                                                                                                                                                                                                                                                                                                               |
+| ------ | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ---------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| CCNet  | R-50-D8  | 512x512   |   20000 | 6        | 20.45          | V100   | 76.17 |         77.51 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ccnet/ccnet_r50-d8_4xb4-20k_voc12aug-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_20k_voc12aug/ccnet_r50-d8_512x512_20k_voc12aug_20200617_193212-fad81784.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_20k_voc12aug/ccnet_r50-d8_512x512_20k_voc12aug_20200617_193212.log.json)     |
+| CCNet  | R-101-D8 | 512x512   |   20000 | 9.5      | 13.64          | V100   | 77.27 |         79.02 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ccnet/ccnet_r101-d8_4xb4-20k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_20k_voc12aug/ccnet_r101-d8_512x512_20k_voc12aug_20200617_193212-0007b61d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_20k_voc12aug/ccnet_r101-d8_512x512_20k_voc12aug_20200617_193212.log.json) |
+| CCNet  | R-50-D8  | 512x512   |   40000 | -        | -              | V100   | 75.96 |         77.04 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ccnet/ccnet_r50-d8_4xb4-40k_voc12aug-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_40k_voc12aug/ccnet_r50-d8_512x512_40k_voc12aug_20200613_232127-c2a15f02.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_40k_voc12aug/ccnet_r50-d8_512x512_40k_voc12aug_20200613_232127.log.json)     |
+| CCNet  | R-101-D8 | 512x512   |   40000 | -        | -              | V100   | 77.87 |         78.90 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ccnet/ccnet_r101-d8_4xb4-40k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_40k_voc12aug/ccnet_r101-d8_512x512_40k_voc12aug_20200613_232127-c30da577.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_40k_voc12aug/ccnet_r101-d8_512x512_40k_voc12aug_20200613_232127.log.json) |
+
+## Citation
+
+```bibtex
+@article{huang2018ccnet,
+    title={CCNet: Criss-Cross Attention for Semantic Segmentation},
+    author={Huang, Zilong and Wang, Xinggang and Huang, Lichao and Huang, Chang and Wei, Yunchao and Liu, Wenyu},
+    booktitle={ICCV},
+    year={2019}
+}
+```
diff --git a/configs/ccnet/ccnet.yml b/configs/ccnet/ccnet.yml
deleted file mode 100644
index b05863dacb..0000000000
--- a/configs/ccnet/ccnet.yml
+++ /dev/null
@@ -1,305 +0,0 @@
-Collections:
-- Name: CCNet
-  Metadata:
-    Training Data:
-    - Cityscapes
-    - ADE20K
-    - Pascal VOC 2012 + Aug
-  Paper:
-    URL: https://arxiv.org/abs/1811.11721
-    Title: 'CCNet: Criss-Cross Attention for Semantic Segmentation'
-  README: configs/ccnet/README.md
-  Code:
-    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111
-    Version: v0.17.0
-  Converted From:
-    Code: https://github.com/speedinghzl/CCNet
-Models:
-- Name: ccnet_r50-d8_4xb2-40k_cityscapes-512x1024
-  In Collection: CCNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,1024)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 301.2
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 6.0
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 77.76
-      mIoU(ms+flip): 78.87
-  Config: configs/ccnet/ccnet_r50-d8_4xb2-40k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x1024_40k_cityscapes/ccnet_r50-d8_512x1024_40k_cityscapes_20200616_142517-4123f401.pth
-- Name: ccnet_r101-d8_4xb2-40k_cityscapes-512x1024
-  In Collection: CCNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,1024)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 432.9
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 9.5
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 76.35
-      mIoU(ms+flip): 78.19
-  Config: configs/ccnet/ccnet_r101-d8_4xb2-40k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x1024_40k_cityscapes/ccnet_r101-d8_512x1024_40k_cityscapes_20200616_142540-a3b84ba6.pth
-- Name: ccnet_r50-d8_4xb2-40k_cityscapes-769x769
-  In Collection: CCNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (769,769)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 699.3
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (769,769)
-    Training Memory (GB): 6.8
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 78.46
-      mIoU(ms+flip): 79.93
-  Config: configs/ccnet/ccnet_r50-d8_4xb2-40k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_769x769_40k_cityscapes/ccnet_r50-d8_769x769_40k_cityscapes_20200616_145125-76d11884.pth
-- Name: ccnet_r101-d8_4xb2-40k_cityscapes-769x769
-  In Collection: CCNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (769,769)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 990.1
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (769,769)
-    Training Memory (GB): 10.7
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 76.94
-      mIoU(ms+flip): 78.62
-  Config: configs/ccnet/ccnet_r101-d8_4xb2-40k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_769x769_40k_cityscapes/ccnet_r101-d8_769x769_40k_cityscapes_20200617_101428-4f57c8d0.pth
-- Name: ccnet_r50-d8_4xb2-80k_cityscapes-512x1024
-  In Collection: CCNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,1024)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 79.03
-      mIoU(ms+flip): 80.16
-  Config: configs/ccnet/ccnet_r50-d8_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x1024_80k_cityscapes/ccnet_r50-d8_512x1024_80k_cityscapes_20200617_010421-869a3423.pth
-- Name: ccnet_r101-d8_4xb2-80k_cityscapes-512x1024
-  In Collection: CCNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,1024)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 78.87
-      mIoU(ms+flip): 79.9
-  Config: configs/ccnet/ccnet_r101-d8_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x1024_80k_cityscapes/ccnet_r101-d8_512x1024_80k_cityscapes_20200617_203935-ffae8917.pth
-- Name: ccnet_r50-d8_4xb2-80k_cityscapes-769x769
-  In Collection: CCNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (769,769)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 79.29
-      mIoU(ms+flip): 81.08
-  Config: configs/ccnet/ccnet_r50-d8_4xb2-80k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_769x769_80k_cityscapes/ccnet_r50-d8_769x769_80k_cityscapes_20200617_010421-73eed8ca.pth
-- Name: ccnet_r101-d8_4xb2-80k_cityscapes-769x769
-  In Collection: CCNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (769,769)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 79.45
-      mIoU(ms+flip): 80.66
-  Config: configs/ccnet/ccnet_r101-d8_4xb2-80k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_769x769_80k_cityscapes/ccnet_r101-d8_769x769_80k_cityscapes_20200618_011502-ad3cd481.pth
-- Name: ccnet_r50-d8_4xb4-80k_ade20k-512x512
-  In Collection: CCNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 47.87
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 8.8
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 41.78
-      mIoU(ms+flip): 42.98
-  Config: configs/ccnet/ccnet_r50-d8_4xb4-80k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_80k_ade20k/ccnet_r50-d8_512x512_80k_ade20k_20200615_014848-aa37f61e.pth
-- Name: ccnet_r101-d8_4xb4-80k_ade20k-512x512
-  In Collection: CCNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 70.87
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 12.2
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 43.97
-      mIoU(ms+flip): 45.13
-  Config: configs/ccnet/ccnet_r101-d8_4xb4-80k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_80k_ade20k/ccnet_r101-d8_512x512_80k_ade20k_20200615_014848-1f4929a3.pth
-- Name: ccnet_r50-d8_4xb4-160k_ade20k-512x512
-  In Collection: CCNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 160000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 42.08
-      mIoU(ms+flip): 43.13
-  Config: configs/ccnet/ccnet_r50-d8_4xb4-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_160k_ade20k/ccnet_r50-d8_512x512_160k_ade20k_20200616_084435-7c97193b.pth
-- Name: ccnet_r101-d8_4xb4-160k_ade20k-512x512
-  In Collection: CCNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 160000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 43.71
-      mIoU(ms+flip): 45.04
-  Config: configs/ccnet/ccnet_r101-d8_4xb4-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_160k_ade20k/ccnet_r101-d8_512x512_160k_ade20k_20200616_000644-e849e007.pth
-- Name: ccnet_r50-d8_4xb4-20k_voc12aug-512x512
-  In Collection: CCNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 20000
-    inference time (ms/im):
-    - value: 48.9
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 6.0
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal VOC 2012 + Aug
-    Metrics:
-      mIoU: 76.17
-      mIoU(ms+flip): 77.51
-  Config: configs/ccnet/ccnet_r50-d8_4xb4-20k_voc12aug-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_20k_voc12aug/ccnet_r50-d8_512x512_20k_voc12aug_20200617_193212-fad81784.pth
-- Name: ccnet_r101-d8_4xb4-20k_voc12aug-512x512
-  In Collection: CCNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 20000
-    inference time (ms/im):
-    - value: 73.31
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 9.5
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal VOC 2012 + Aug
-    Metrics:
-      mIoU: 77.27
-      mIoU(ms+flip): 79.02
-  Config: configs/ccnet/ccnet_r101-d8_4xb4-20k_voc12aug-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_20k_voc12aug/ccnet_r101-d8_512x512_20k_voc12aug_20200617_193212-0007b61d.pth
-- Name: ccnet_r50-d8_4xb4-40k_voc12aug-512x512
-  In Collection: CCNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 40000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal VOC 2012 + Aug
-    Metrics:
-      mIoU: 75.96
-      mIoU(ms+flip): 77.04
-  Config: configs/ccnet/ccnet_r50-d8_4xb4-40k_voc12aug-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_40k_voc12aug/ccnet_r50-d8_512x512_40k_voc12aug_20200613_232127-c2a15f02.pth
-- Name: ccnet_r101-d8_4xb4-40k_voc12aug-512x512
-  In Collection: CCNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 40000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal VOC 2012 + Aug
-    Metrics:
-      mIoU: 77.87
-      mIoU(ms+flip): 78.9
-  Config: configs/ccnet/ccnet_r101-d8_4xb4-40k_voc12aug-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_40k_voc12aug/ccnet_r101-d8_512x512_40k_voc12aug_20200613_232127-c30da577.pth
diff --git a/configs/ccnet/metafile.yaml b/configs/ccnet/metafile.yaml
new file mode 100644
index 0000000000..62e5694e47
--- /dev/null
+++ b/configs/ccnet/metafile.yaml
@@ -0,0 +1,391 @@
+Collections:
+- Name: CCNet
+  License: Apache License 2.0
+  Metadata:
+    Training Data:
+    - Cityscapes
+    - ADE20K
+    - Pascal VOC 2012 + Aug
+  Paper:
+    Title: 'CCNet: Criss-Cross Attention for Semantic Segmentation'
+    URL: https://arxiv.org/abs/1811.11721
+  README: configs/ccnet/README.md
+  Frameworks:
+  - PyTorch
+Models:
+- Name: ccnet_r50-d8_4xb2-40k_cityscapes-512x1024
+  In Collection: CCNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.76
+      mIoU(ms+flip): 78.87
+  Config: configs/ccnet/ccnet_r50-d8_4xb2-40k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - CCNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 6.0
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x1024_40k_cityscapes/ccnet_r50-d8_512x1024_40k_cityscapes_20200616_142517-4123f401.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x1024_40k_cityscapes/ccnet_r50-d8_512x1024_40k_cityscapes_20200616_142517.log.json
+  Paper:
+    Title: 'CCNet: Criss-Cross Attention for Semantic Segmentation'
+    URL: https://arxiv.org/abs/1811.11721
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111
+  Framework: PyTorch
+- Name: ccnet_r101-d8_4xb2-40k_cityscapes-512x1024
+  In Collection: CCNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 76.35
+      mIoU(ms+flip): 78.19
+  Config: configs/ccnet/ccnet_r101-d8_4xb2-40k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - CCNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 9.5
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x1024_40k_cityscapes/ccnet_r101-d8_512x1024_40k_cityscapes_20200616_142540-a3b84ba6.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x1024_40k_cityscapes/ccnet_r101-d8_512x1024_40k_cityscapes_20200616_142540.log.json
+  Paper:
+    Title: 'CCNet: Criss-Cross Attention for Semantic Segmentation'
+    URL: https://arxiv.org/abs/1811.11721
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111
+  Framework: PyTorch
+- Name: ccnet_r50-d8_4xb2-40k_cityscapes-769x769
+  In Collection: CCNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.46
+      mIoU(ms+flip): 79.93
+  Config: configs/ccnet/ccnet_r50-d8_4xb2-40k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - CCNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 6.8
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_769x769_40k_cityscapes/ccnet_r50-d8_769x769_40k_cityscapes_20200616_145125-76d11884.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_769x769_40k_cityscapes/ccnet_r50-d8_769x769_40k_cityscapes_20200616_145125.log.json
+  Paper:
+    Title: 'CCNet: Criss-Cross Attention for Semantic Segmentation'
+    URL: https://arxiv.org/abs/1811.11721
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111
+  Framework: PyTorch
+- Name: ccnet_r101-d8_4xb2-40k_cityscapes-769x769
+  In Collection: CCNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 76.94
+      mIoU(ms+flip): 78.62
+  Config: configs/ccnet/ccnet_r101-d8_4xb2-40k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - CCNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 10.7
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_769x769_40k_cityscapes/ccnet_r101-d8_769x769_40k_cityscapes_20200617_101428-4f57c8d0.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_769x769_40k_cityscapes/ccnet_r101-d8_769x769_40k_cityscapes_20200617_101428.log.json
+  Paper:
+    Title: 'CCNet: Criss-Cross Attention for Semantic Segmentation'
+    URL: https://arxiv.org/abs/1811.11721
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111
+  Framework: PyTorch
+- Name: ccnet_r50-d8_4xb2-80k_cityscapes-512x1024
+  In Collection: CCNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.03
+      mIoU(ms+flip): 80.16
+  Config: configs/ccnet/ccnet_r50-d8_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - CCNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x1024_80k_cityscapes/ccnet_r50-d8_512x1024_80k_cityscapes_20200617_010421-869a3423.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x1024_80k_cityscapes/ccnet_r50-d8_512x1024_80k_cityscapes_20200617_010421.log.json
+  Paper:
+    Title: 'CCNet: Criss-Cross Attention for Semantic Segmentation'
+    URL: https://arxiv.org/abs/1811.11721
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111
+  Framework: PyTorch
+- Name: ccnet_r101-d8_4xb2-80k_cityscapes-512x1024
+  In Collection: CCNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.87
+      mIoU(ms+flip): 79.9
+  Config: configs/ccnet/ccnet_r101-d8_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - CCNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x1024_80k_cityscapes/ccnet_r101-d8_512x1024_80k_cityscapes_20200617_203935-ffae8917.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x1024_80k_cityscapes/ccnet_r101-d8_512x1024_80k_cityscapes_20200617_203935.log.json
+  Paper:
+    Title: 'CCNet: Criss-Cross Attention for Semantic Segmentation'
+    URL: https://arxiv.org/abs/1811.11721
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111
+  Framework: PyTorch
+- Name: ccnet_r50-d8_4xb2-80k_cityscapes-769x769
+  In Collection: CCNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.29
+      mIoU(ms+flip): 81.08
+  Config: configs/ccnet/ccnet_r50-d8_4xb2-80k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - CCNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_769x769_80k_cityscapes/ccnet_r50-d8_769x769_80k_cityscapes_20200617_010421-73eed8ca.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_769x769_80k_cityscapes/ccnet_r50-d8_769x769_80k_cityscapes_20200617_010421.log.json
+  Paper:
+    Title: 'CCNet: Criss-Cross Attention for Semantic Segmentation'
+    URL: https://arxiv.org/abs/1811.11721
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111
+  Framework: PyTorch
+- Name: ccnet_r101-d8_4xb2-80k_cityscapes-769x769
+  In Collection: CCNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.45
+      mIoU(ms+flip): 80.66
+  Config: configs/ccnet/ccnet_r101-d8_4xb2-80k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - CCNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_769x769_80k_cityscapes/ccnet_r101-d8_769x769_80k_cityscapes_20200618_011502-ad3cd481.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_769x769_80k_cityscapes/ccnet_r101-d8_769x769_80k_cityscapes_20200618_011502.log.json
+  Paper:
+    Title: 'CCNet: Criss-Cross Attention for Semantic Segmentation'
+    URL: https://arxiv.org/abs/1811.11721
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111
+  Framework: PyTorch
+- Name: ccnet_r50-d8_4xb4-80k_ade20k-512x512
+  In Collection: CCNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 41.78
+      mIoU(ms+flip): 42.98
+  Config: configs/ccnet/ccnet_r50-d8_4xb4-80k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - CCNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 8.8
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_80k_ade20k/ccnet_r50-d8_512x512_80k_ade20k_20200615_014848-aa37f61e.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_80k_ade20k/ccnet_r50-d8_512x512_80k_ade20k_20200615_014848.log.json
+  Paper:
+    Title: 'CCNet: Criss-Cross Attention for Semantic Segmentation'
+    URL: https://arxiv.org/abs/1811.11721
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111
+  Framework: PyTorch
+- Name: ccnet_r101-d8_4xb4-80k_ade20k-512x512
+  In Collection: CCNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 43.97
+      mIoU(ms+flip): 45.13
+  Config: configs/ccnet/ccnet_r101-d8_4xb4-80k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - CCNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 12.2
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_80k_ade20k/ccnet_r101-d8_512x512_80k_ade20k_20200615_014848-1f4929a3.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_80k_ade20k/ccnet_r101-d8_512x512_80k_ade20k_20200615_014848.log.json
+  Paper:
+    Title: 'CCNet: Criss-Cross Attention for Semantic Segmentation'
+    URL: https://arxiv.org/abs/1811.11721
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111
+  Framework: PyTorch
+- Name: ccnet_r50-d8_4xb4-160k_ade20k-512x512
+  In Collection: CCNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 42.08
+      mIoU(ms+flip): 43.13
+  Config: configs/ccnet/ccnet_r50-d8_4xb4-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - CCNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_160k_ade20k/ccnet_r50-d8_512x512_160k_ade20k_20200616_084435-7c97193b.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_160k_ade20k/ccnet_r50-d8_512x512_160k_ade20k_20200616_084435.log.json
+  Paper:
+    Title: 'CCNet: Criss-Cross Attention for Semantic Segmentation'
+    URL: https://arxiv.org/abs/1811.11721
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111
+  Framework: PyTorch
+- Name: ccnet_r101-d8_4xb4-160k_ade20k-512x512
+  In Collection: CCNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 43.71
+      mIoU(ms+flip): 45.04
+  Config: configs/ccnet/ccnet_r101-d8_4xb4-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - CCNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_160k_ade20k/ccnet_r101-d8_512x512_160k_ade20k_20200616_000644-e849e007.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_160k_ade20k/ccnet_r101-d8_512x512_160k_ade20k_20200616_000644.log.json
+  Paper:
+    Title: 'CCNet: Criss-Cross Attention for Semantic Segmentation'
+    URL: https://arxiv.org/abs/1811.11721
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111
+  Framework: PyTorch
+- Name: ccnet_r50-d8_4xb4-20k_voc12aug-512x512
+  In Collection: CCNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 76.17
+      mIoU(ms+flip): 77.51
+  Config: configs/ccnet/ccnet_r50-d8_4xb4-20k_voc12aug-512x512.py
+  Metadata:
+    Training Data: Pascal VOC 2012 + Aug
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - CCNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 6.0
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_20k_voc12aug/ccnet_r50-d8_512x512_20k_voc12aug_20200617_193212-fad81784.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_20k_voc12aug/ccnet_r50-d8_512x512_20k_voc12aug_20200617_193212.log.json
+  Paper:
+    Title: 'CCNet: Criss-Cross Attention for Semantic Segmentation'
+    URL: https://arxiv.org/abs/1811.11721
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111
+  Framework: PyTorch
+- Name: ccnet_r101-d8_4xb4-20k_voc12aug-512x512
+  In Collection: CCNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 77.27
+      mIoU(ms+flip): 79.02
+  Config: configs/ccnet/ccnet_r101-d8_4xb4-20k_voc12aug-512x512.py
+  Metadata:
+    Training Data: Pascal VOC 2012 + Aug
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - CCNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 9.5
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_20k_voc12aug/ccnet_r101-d8_512x512_20k_voc12aug_20200617_193212-0007b61d.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_20k_voc12aug/ccnet_r101-d8_512x512_20k_voc12aug_20200617_193212.log.json
+  Paper:
+    Title: 'CCNet: Criss-Cross Attention for Semantic Segmentation'
+    URL: https://arxiv.org/abs/1811.11721
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111
+  Framework: PyTorch
+- Name: ccnet_r50-d8_4xb4-40k_voc12aug-512x512
+  In Collection: CCNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 75.96
+      mIoU(ms+flip): 77.04
+  Config: configs/ccnet/ccnet_r50-d8_4xb4-40k_voc12aug-512x512.py
+  Metadata:
+    Training Data: Pascal VOC 2012 + Aug
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - CCNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_40k_voc12aug/ccnet_r50-d8_512x512_40k_voc12aug_20200613_232127-c2a15f02.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_40k_voc12aug/ccnet_r50-d8_512x512_40k_voc12aug_20200613_232127.log.json
+  Paper:
+    Title: 'CCNet: Criss-Cross Attention for Semantic Segmentation'
+    URL: https://arxiv.org/abs/1811.11721
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111
+  Framework: PyTorch
+- Name: ccnet_r101-d8_4xb4-40k_voc12aug-512x512
+  In Collection: CCNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 77.87
+      mIoU(ms+flip): 78.9
+  Config: configs/ccnet/ccnet_r101-d8_4xb4-40k_voc12aug-512x512.py
+  Metadata:
+    Training Data: Pascal VOC 2012 + Aug
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - CCNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_40k_voc12aug/ccnet_r101-d8_512x512_40k_voc12aug_20200613_232127-c30da577.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_40k_voc12aug/ccnet_r101-d8_512x512_40k_voc12aug_20200613_232127.log.json
+  Paper:
+    Title: 'CCNet: Criss-Cross Attention for Semantic Segmentation'
+    URL: https://arxiv.org/abs/1811.11721
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111
+  Framework: PyTorch
diff --git a/configs/cgnet/README.md b/configs/cgnet/README.md
index 709d0c0b8f..60cde58291 100644
--- a/configs/cgnet/README.md
+++ b/configs/cgnet/README.md
@@ -1,6 +1,6 @@
 # CGNet
 
-[CGNet: A Light-weight Context Guided Network for Semantic Segmentation](https://arxiv.org/abs/1811.08201)
+> [CGNet: A Light-weight Context Guided Network for Semantic Segmentation](https://arxiv.org/abs/1811.08201)
 
 ## Introduction
 
@@ -22,6 +22,15 @@ The demand of applying semantic segmentation model on mobile devices has been in
 <img src="https://user-images.githubusercontent.com/24582831/142900351-89559574-79cc-4f57-8f69-5d88765ec38d.png" width="80%"/>
 </div>
 
+## Results and models
+
+### Cityscapes
+
+| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                      | download                                                                                                                                                                                                                                                                                                           |
+| ------ | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | --------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| CGNet  | M3N21    | 680x680   |   60000 | 7.5      | 30.51          | V100   | 65.63 |         68.04 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/cgnet/cgnet_fcn_4xb4-60k_cityscapes-680x680.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/cgnet/cgnet_680x680_60k_cityscapes/cgnet_680x680_60k_cityscapes_20201101_110253-4c0b2f2d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/cgnet/cgnet_680x680_60k_cityscapes/cgnet_680x680_60k_cityscapes-20201101_110253.log.json)     |
+| CGNet  | M3N21    | 512x1024  |   60000 | 8.3      | 31.14          | V100   | 68.27 |         70.33 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/cgnet/cgnet_fcn_4xb8-60k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/cgnet/cgnet_512x1024_60k_cityscapes/cgnet_512x1024_60k_cityscapes_20201101_110254-124ea03b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/cgnet/cgnet_512x1024_60k_cityscapes/cgnet_512x1024_60k_cityscapes-20201101_110254.log.json) |
+
 ## Citation
 
 ```bibtext
@@ -35,12 +44,3 @@ The demand of applying semantic segmentation model on mobile devices has been in
   publisher={IEEE}
 }
 ```
-
-## Results and models
-
-### Cityscapes
-
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                      | download                                                                                                                                                                                                                                                                                                           |
-| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | --------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| CGNet  | M3N21    | 680x680   |   60000 | 7.5      | 30.51          | 65.63 |         68.04 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/cgnet/cgnet_fcn_4xb4-60k_cityscapes-680x680.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/cgnet/cgnet_680x680_60k_cityscapes/cgnet_680x680_60k_cityscapes_20201101_110253-4c0b2f2d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/cgnet/cgnet_680x680_60k_cityscapes/cgnet_680x680_60k_cityscapes-20201101_110253.log.json)     |
-| CGNet  | M3N21    | 512x1024  |   60000 | 8.3      | 31.14          | 68.27 |         70.33 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/cgnet/cgnet_fcn_4xb8-60k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/cgnet/cgnet_512x1024_60k_cityscapes/cgnet_512x1024_60k_cityscapes_20201101_110254-124ea03b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/cgnet/cgnet_512x1024_60k_cityscapes/cgnet_512x1024_60k_cityscapes-20201101_110254.log.json) |
diff --git a/configs/cgnet/cgnet.yml b/configs/cgnet/cgnet.yml
deleted file mode 100644
index be79b89355..0000000000
--- a/configs/cgnet/cgnet.yml
+++ /dev/null
@@ -1,59 +0,0 @@
-Collections:
-- Name: CGNet
-  Metadata:
-    Training Data:
-    - Cityscapes
-  Paper:
-    URL: https://arxiv.org/abs/1811.08201
-    Title: 'CGNet: A Light-weight Context Guided Network for Semantic Segmentation'
-  README: configs/cgnet/README.md
-  Code:
-    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/cgnet.py#L187
-    Version: v0.17.0
-  Converted From:
-    Code: https://github.com/wutianyiRosun/CGNet
-Models:
-- Name: cgnet_fcn_4xb4-60k_cityscapes-680x680
-  In Collection: CGNet
-  Metadata:
-    backbone: M3N21
-    crop size: (680,680)
-    lr schd: 60000
-    inference time (ms/im):
-    - value: 32.78
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (680,680)
-    Training Memory (GB): 7.5
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 65.63
-      mIoU(ms+flip): 68.04
-  Config: configs/cgnet/cgnet_fcn_4xb4-60k_cityscapes-680x680.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/cgnet/cgnet_680x680_60k_cityscapes/cgnet_680x680_60k_cityscapes_20201101_110253-4c0b2f2d.pth
-- Name: cgnet_fcn_4xb8-60k_cityscapes-512x1024
-  In Collection: CGNet
-  Metadata:
-    backbone: M3N21
-    crop size: (512,1024)
-    lr schd: 60000
-    inference time (ms/im):
-    - value: 32.11
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 8.3
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 68.27
-      mIoU(ms+flip): 70.33
-  Config: configs/cgnet/cgnet_fcn_4xb8-60k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/cgnet/cgnet_512x1024_60k_cityscapes/cgnet_512x1024_60k_cityscapes_20201101_110254-124ea03b.pth
diff --git a/configs/cgnet/metafile.yaml b/configs/cgnet/metafile.yaml
new file mode 100644
index 0000000000..063fc8b3c6
--- /dev/null
+++ b/configs/cgnet/metafile.yaml
@@ -0,0 +1,61 @@
+Collections:
+- Name: CGNet
+  License: Apache License 2.0
+  Metadata:
+    Training Data:
+    - Cityscapes
+  Paper:
+    Title: 'CGNet: A Light-weight Context Guided Network for Semantic Segmentation'
+    URL: https://arxiv.org/abs/1811.08201
+  README: configs/cgnet/README.md
+  Frameworks:
+  - PyTorch
+Models:
+- Name: cgnet_fcn_4xb4-60k_cityscapes-680x680
+  In Collection: CGNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 65.63
+      mIoU(ms+flip): 68.04
+  Config: configs/cgnet/cgnet_fcn_4xb4-60k_cityscapes-680x680.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 16
+    Architecture:
+    - M3N21
+    - CGNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 7.5
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/cgnet/cgnet_680x680_60k_cityscapes/cgnet_680x680_60k_cityscapes_20201101_110253-4c0b2f2d.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/cgnet/cgnet_680x680_60k_cityscapes/cgnet_680x680_60k_cityscapes-20201101_110253.log.json
+  Paper:
+    Title: 'CGNet: A Light-weight Context Guided Network for Semantic Segmentation'
+    URL: https://arxiv.org/abs/1811.08201
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/cgnet.py#L187
+  Framework: PyTorch
+- Name: cgnet_fcn_4xb8-60k_cityscapes-512x1024
+  In Collection: CGNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 68.27
+      mIoU(ms+flip): 70.33
+  Config: configs/cgnet/cgnet_fcn_4xb8-60k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 32
+    Architecture:
+    - M3N21
+    - CGNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 8.3
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/cgnet/cgnet_512x1024_60k_cityscapes/cgnet_512x1024_60k_cityscapes_20201101_110254-124ea03b.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/cgnet/cgnet_512x1024_60k_cityscapes/cgnet_512x1024_60k_cityscapes-20201101_110254.log.json
+  Paper:
+    Title: 'CGNet: A Light-weight Context Guided Network for Semantic Segmentation'
+    URL: https://arxiv.org/abs/1811.08201
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/cgnet.py#L187
+  Framework: PyTorch
diff --git a/configs/convnext/README.md b/configs/convnext/README.md
index 6a826b9d6a..91339377c0 100644
--- a/configs/convnext/README.md
+++ b/configs/convnext/README.md
@@ -1,6 +1,6 @@
 # ConvNeXt
 
-[A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545)
+> [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545)
 
 ## Introduction
 
@@ -22,15 +22,6 @@ The "Roaring 20s" of visual recognition began with the introduction of Vision Tr
 <img src="https://user-images.githubusercontent.com/8370623/148624004-e9581042-ea4d-4e10-b3bd-42c92b02053b.png" width="90%"/>
 </div>
 
-```bibtex
-@article{liu2022convnet,
-  title={A ConvNet for the 2020s},
-  author={Liu, Zhuang and Mao, Hanzi and Wu, Chao-Yuan and Feichtenhofer, Christoph and Darrell, Trevor and Xie, Saining},
-  journal={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
-  year={2022}
-}
-```
-
 ### Usage
 
 - ConvNeXt backbone needs to install [MMClassification](https://github.com/open-mmlab/mmclassification) first, which has abundant backbones for downstream tasks.
@@ -58,15 +49,26 @@ The pre-trained models on ImageNet-1k or ImageNet-21k are used to fine-tune on t
 
 ### ADE20K
 
-| Method  | Backbone    | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU  | mIoU(ms+flip) | config                                                                                                                                       | download                                                                                                                                                                                                                                                                                                                                                                                             |
-| ------- | ----------- | --------- | ------- | -------- | -------------- | ----- | ------------- | -------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| UPerNet | ConvNeXt-T  | 512x512   | 160000  | 4.23     | 19.90          | 46.11 | 46.62         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/convnext/convnext-tiny_upernet_8xb2-amp-160k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553.log.json)         |
-| UPerNet | ConvNeXt-S  | 512x512   | 160000  | 5.16     | 15.18          | 48.56 | 49.02         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/convnext/convnext-small_upernet_8xb2-amp-160k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208.log.json)     |
-| UPerNet | ConvNeXt-B  | 512x512   | 160000  | 6.33     | 14.41          | 48.71 | 49.54         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/convnext/convnext-base_upernet_8xb2-amp-160k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227.log.json)         |
-| UPerNet | ConvNeXt-B  | 640x640   | 160000  | 8.53     | 10.88          | 52.13 | 52.66         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/convnext/convnext-base_upernet_8xb2-amp-160k_ade20k-640x640.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_640x640_160k_ade20k/upernet_convnext_base_fp16_640x640_160k_ade20k_20220227_182859-9280e39b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_640x640_160k_ade20k/upernet_convnext_base_fp16_640x640_160k_ade20k_20220227_182859.log.json)         |
-| UPerNet | ConvNeXt-L  | 640x640   | 160000  | 12.08    | 7.69           | 53.16 | 53.38         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/convnext/convnext-large_upernet_8xb2-amp-160k_ade20k-640x640.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532.log.json)     |
-| UPerNet | ConvNeXt-XL | 640x640   | 160000  | 26.16\*  | 6.33           | 53.58 | 54.11         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/convnext/convnext-xlarge_upernet_8xb2-amp-160k_ade20k-640x640.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344.log.json) |
+| Method  | Backbone    | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device | mIoU  | mIoU(ms+flip) | config                                                                                                                                       | download                                                                                                                                                                                                                                                                                                                                                                                             |
+| ------- | ----------- | --------- | ------- | -------- | -------------- | ------ | ----- | ------------- | -------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| UPerNet | ConvNeXt-T  | 512x512   | 160000  | 4.23     | 19.90          | V100   | 46.11 | 46.62         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/convnext/convnext-tiny_upernet_8xb2-amp-160k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553.log.json)         |
+| UPerNet | ConvNeXt-S  | 512x512   | 160000  | 5.16     | 15.18          | V100   | 48.56 | 49.02         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/convnext/convnext-small_upernet_8xb2-amp-160k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208.log.json)     |
+| UPerNet | ConvNeXt-B  | 512x512   | 160000  | 6.33     | 14.41          | V100   | 48.71 | 49.54         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/convnext/convnext-base_upernet_8xb2-amp-160k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227.log.json)         |
+| UPerNet | ConvNeXt-B  | 640x640   | 160000  | 8.53     | 10.88          | V100   | 52.13 | 52.66         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/convnext/convnext-base_upernet_8xb2-amp-160k_ade20k-640x640.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_640x640_160k_ade20k/upernet_convnext_base_fp16_640x640_160k_ade20k_20220227_182859-9280e39b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_640x640_160k_ade20k/upernet_convnext_base_fp16_640x640_160k_ade20k_20220227_182859.log.json)         |
+| UPerNet | ConvNeXt-L  | 640x640   | 160000  | 12.08    | 7.69           | V100   | 53.16 | 53.38         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/convnext/convnext-large_upernet_8xb2-amp-160k_ade20k-640x640.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532.log.json)     |
+| UPerNet | ConvNeXt-XL | 640x640   | 160000  | 26.16\*  | 6.33           | V100   | 53.58 | 54.11         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/convnext/convnext-xlarge_upernet_8xb2-amp-160k_ade20k-640x640.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344.log.json) |
 
 Note:
 
 - `Mem (GB)` with * is collected when `cudnn_benchmark=True`, and hardware is V100.
+
+## Citation
+
+```bibtex
+@article{liu2022convnet,
+  title={A ConvNet for the 2020s},
+  author={Liu, Zhuang and Mao, Hanzi and Wu, Chao-Yuan and Feichtenhofer, Christoph and Darrell, Trevor and Xie, Saining},
+  journal={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
+  year={2022}
+}
+```
diff --git a/configs/convnext/convnext.yml b/configs/convnext/convnext.yml
deleted file mode 100644
index 2162e0c50c..0000000000
--- a/configs/convnext/convnext.yml
+++ /dev/null
@@ -1,133 +0,0 @@
-Models:
-- Name: convnext-tiny_upernet_8xb2-amp-160k_ade20k-512x512
-  In Collection: UPerNet
-  Metadata:
-    backbone: ConvNeXt-T
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 50.25
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: AMP
-      resolution: (512,512)
-    Training Memory (GB): 4.23
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 46.11
-      mIoU(ms+flip): 46.62
-  Config: configs/convnext/convnext-tiny_upernet_8xb2-amp-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth
-- Name: convnext-small_upernet_8xb2-amp-160k_ade20k-512x512
-  In Collection: UPerNet
-  Metadata:
-    backbone: ConvNeXt-S
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 65.88
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: AMP
-      resolution: (512,512)
-    Training Memory (GB): 5.16
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 48.56
-      mIoU(ms+flip): 49.02
-  Config: configs/convnext/convnext-small_upernet_8xb2-amp-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth
-- Name: convnext-base_upernet_8xb2-amp-160k_ade20k-512x512
-  In Collection: UPerNet
-  Metadata:
-    backbone: ConvNeXt-B
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 69.4
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: AMP
-      resolution: (512,512)
-    Training Memory (GB): 6.33
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 48.71
-      mIoU(ms+flip): 49.54
-  Config: configs/convnext/convnext-base_upernet_8xb2-amp-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth
-- Name: convnext-base_upernet_8xb2-amp-160k_ade20k-640x640
-  In Collection: UPerNet
-  Metadata:
-    backbone: ConvNeXt-B
-    crop size: (640,640)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 91.91
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: AMP
-      resolution: (640,640)
-    Training Memory (GB): 8.53
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 52.13
-      mIoU(ms+flip): 52.66
-  Config: configs/convnext/convnext-base_upernet_8xb2-amp-160k_ade20k-640x640.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_640x640_160k_ade20k/upernet_convnext_base_fp16_640x640_160k_ade20k_20220227_182859-9280e39b.pth
-- Name: convnext-large_upernet_8xb2-amp-160k_ade20k-640x640
-  In Collection: UPerNet
-  Metadata:
-    backbone: ConvNeXt-L
-    crop size: (640,640)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 130.04
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: AMP
-      resolution: (640,640)
-    Training Memory (GB): 12.08
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 53.16
-      mIoU(ms+flip): 53.38
-  Config: configs/convnext/convnext-large_upernet_8xb2-amp-160k_ade20k-640x640.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth
-- Name: convnext-xlarge_upernet_8xb2-amp-160k_ade20k-640x640
-  In Collection: UPerNet
-  Metadata:
-    backbone: ConvNeXt-XL
-    crop size: (640,640)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 157.98
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: AMP
-      resolution: (640,640)
-    Training Memory (GB): 26.16
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 53.58
-      mIoU(ms+flip): 54.11
-  Config: configs/convnext/convnext-xlarge_upernet_8xb2-amp-160k_ade20k-640x640.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth
diff --git a/configs/convnext/metafile.yaml b/configs/convnext/metafile.yaml
new file mode 100644
index 0000000000..8340a373c2
--- /dev/null
+++ b/configs/convnext/metafile.yaml
@@ -0,0 +1,145 @@
+Models:
+- Name: convnext-tiny_upernet_8xb2-amp-160k_ade20k-512x512
+  In Collection: UPerNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 46.11
+      mIoU(ms+flip): 46.62
+  Config: configs/convnext/convnext-tiny_upernet_8xb2-amp-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - ConvNeXt-T
+    - UPerNet
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 4.23
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553.log.json
+  Paper:
+    Title: A ConvNet for the 2020s
+    URL: https://arxiv.org/abs/2201.03545
+  Code: https://github.com/open-mmlab/mmclassification/blob/v0.20.1/mmcls/models/backbones/convnext.py#L133
+  Framework: PyTorch
+- Name: convnext-small_upernet_8xb2-amp-160k_ade20k-512x512
+  In Collection: UPerNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 48.56
+      mIoU(ms+flip): 49.02
+  Config: configs/convnext/convnext-small_upernet_8xb2-amp-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - ConvNeXt-S
+    - UPerNet
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 5.16
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208.log.json
+  Paper:
+    Title: A ConvNet for the 2020s
+    URL: https://arxiv.org/abs/2201.03545
+  Code: https://github.com/open-mmlab/mmclassification/blob/v0.20.1/mmcls/models/backbones/convnext.py#L133
+  Framework: PyTorch
+- Name: convnext-base_upernet_8xb2-amp-160k_ade20k-512x512
+  In Collection: UPerNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 48.71
+      mIoU(ms+flip): 49.54
+  Config: configs/convnext/convnext-base_upernet_8xb2-amp-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - ConvNeXt-B
+    - UPerNet
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 6.33
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227.log.json
+  Paper:
+    Title: A ConvNet for the 2020s
+    URL: https://arxiv.org/abs/2201.03545
+  Code: https://github.com/open-mmlab/mmclassification/blob/v0.20.1/mmcls/models/backbones/convnext.py#L133
+  Framework: PyTorch
+- Name: convnext-base_upernet_8xb2-amp-160k_ade20k-640x640
+  In Collection: UPerNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 52.13
+      mIoU(ms+flip): 52.66
+  Config: configs/convnext/convnext-base_upernet_8xb2-amp-160k_ade20k-640x640.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - ConvNeXt-B
+    - UPerNet
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 8.53
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_640x640_160k_ade20k/upernet_convnext_base_fp16_640x640_160k_ade20k_20220227_182859-9280e39b.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_640x640_160k_ade20k/upernet_convnext_base_fp16_640x640_160k_ade20k_20220227_182859.log.json
+  Paper:
+    Title: A ConvNet for the 2020s
+    URL: https://arxiv.org/abs/2201.03545
+  Code: https://github.com/open-mmlab/mmclassification/blob/v0.20.1/mmcls/models/backbones/convnext.py#L133
+  Framework: PyTorch
+- Name: convnext-large_upernet_8xb2-amp-160k_ade20k-640x640
+  In Collection: UPerNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 53.16
+      mIoU(ms+flip): 53.38
+  Config: configs/convnext/convnext-large_upernet_8xb2-amp-160k_ade20k-640x640.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - ConvNeXt-L
+    - UPerNet
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 12.08
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532.log.json
+  Paper:
+    Title: A ConvNet for the 2020s
+    URL: https://arxiv.org/abs/2201.03545
+  Code: https://github.com/open-mmlab/mmclassification/blob/v0.20.1/mmcls/models/backbones/convnext.py#L133
+  Framework: PyTorch
+- Name: convnext-xlarge_upernet_8xb2-amp-160k_ade20k-640x640
+  In Collection: UPerNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 53.58
+      mIoU(ms+flip): 54.11
+  Config: configs/convnext/convnext-xlarge_upernet_8xb2-amp-160k_ade20k-640x640.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - ConvNeXt-XL
+    - UPerNet
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 26.16
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344.log.json
+  Paper:
+    Title: A ConvNet for the 2020s
+    URL: https://arxiv.org/abs/2201.03545
+  Code: https://github.com/open-mmlab/mmclassification/blob/v0.20.1/mmcls/models/backbones/convnext.py#L133
+  Framework: PyTorch
diff --git a/configs/danet/README.md b/configs/danet/README.md
index 52059e93a5..b5841e23be 100644
--- a/configs/danet/README.md
+++ b/configs/danet/README.md
@@ -1,6 +1,6 @@
 # DANet
 
-[Dual Attention Network for Scene Segmentation](https://arxiv.org/abs/1809.02983)
+> [Dual Attention Network for Scene Segmentation](https://arxiv.org/abs/1809.02983)
 
 ## Introduction
 
@@ -22,46 +22,46 @@ In this paper, we address the scene segmentation task by capturing rich contextu
 <img src="https://user-images.githubusercontent.com/24582831/142900467-f832fdb9-3b7d-47d3-8e80-e6ee9303bdfb.png" width="70%"/>
 </div>
 
-## Citation
-
-```bibtex
-@article{fu2018dual,
-  title={Dual Attention Network for Scene Segmentation},
-  author={Jun Fu, Jing Liu, Haijie Tian, Yong Li, Yongjun Bao, Zhiwei Fang,and Hanqing Lu},
-  booktitle={The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
-  year={2019}
-}
-```
-
 ## Results and models
 
 ### Cityscapes
 
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                          | download                                                                                                                                                                                                                                                                                                                                           |
-| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------- | ------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| DANet  | R-50-D8  | 512x1024  |   40000 | 7.4      | 2.66           | 78.74 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/danet/danet_r50-d8_4xb2-40k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_40k_cityscapes/danet_r50-d8_512x1024_40k_cityscapes_20200605_191324-c0dbfa5f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_40k_cityscapes/danet_r50-d8_512x1024_40k_cityscapes_20200605_191324.log.json)     |
-| DANet  | R-101-D8 | 512x1024  |   40000 | 10.9     | 1.99           | 80.52 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/danet/danet_r101-d8_4xb2-40k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_40k_cityscapes/danet_r101-d8_512x1024_40k_cityscapes_20200605_200831-c57a7157.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_40k_cityscapes/danet_r101-d8_512x1024_40k_cityscapes_20200605_200831.log.json) |
-| DANet  | R-50-D8  | 769x769   |   40000 | 8.8      | 1.56           | 78.88 | 80.62         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/danet/danet_r50-d8_4xb2-40k_cityscapes-769x769.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_40k_cityscapes/danet_r50-d8_769x769_40k_cityscapes_20200530_025703-76681c60.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_40k_cityscapes/danet_r50-d8_769x769_40k_cityscapes_20200530_025703.log.json)         |
-| DANet  | R-101-D8 | 769x769   |   40000 | 12.8     | 1.07           | 79.88 | 81.47         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/danet/danet_r101-d8_4xb2-40k_cityscapes-769x769.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_40k_cityscapes/danet_r101-d8_769x769_40k_cityscapes_20200530_025717-dcb7fd4e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_40k_cityscapes/danet_r101-d8_769x769_40k_cityscapes_20200530_025717.log.json)     |
-| DANet  | R-50-D8  | 512x1024  |   80000 | -        | -              | 79.34 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/danet/danet_r50-d8_4xb2-80k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_80k_cityscapes/danet_r50-d8_512x1024_80k_cityscapes_20200607_133029-2bfa2293.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_80k_cityscapes/danet_r50-d8_512x1024_80k_cityscapes_20200607_133029.log.json)     |
-| DANet  | R-101-D8 | 512x1024  |   80000 | -        | -              | 80.41 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/danet/danet_r101-d8_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_80k_cityscapes/danet_r101-d8_512x1024_80k_cityscapes_20200607_132918-955e6350.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_80k_cityscapes/danet_r101-d8_512x1024_80k_cityscapes_20200607_132918.log.json) |
-| DANet  | R-50-D8  | 769x769   |   80000 | -        | -              | 79.27 | 80.96         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/danet/danet_r50-d8_4xb2-80k_cityscapes-769x769.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_80k_cityscapes/danet_r50-d8_769x769_80k_cityscapes_20200607_132954-495689b4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_80k_cityscapes/danet_r50-d8_769x769_80k_cityscapes_20200607_132954.log.json)         |
-| DANet  | R-101-D8 | 769x769   |   80000 | -        | -              | 80.47 | 82.02         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/danet/danet_r101-d8_4xb2-80k_cityscapes-769x769.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_80k_cityscapes/danet_r101-d8_769x769_80k_cityscapes_20200607_132918-f3a929e7.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_80k_cityscapes/danet_r101-d8_769x769_80k_cityscapes_20200607_132918.log.json)     |
+| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                          | download                                                                                                                                                                                                                                                                                                                                           |
+| ------ | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------- | ------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| DANet  | R-50-D8  | 512x1024  |   40000 | 7.4      | 2.66           | V100   | 78.74 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/danet/danet_r50-d8_4xb2-40k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_40k_cityscapes/danet_r50-d8_512x1024_40k_cityscapes_20200605_191324-c0dbfa5f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_40k_cityscapes/danet_r50-d8_512x1024_40k_cityscapes_20200605_191324.log.json)     |
+| DANet  | R-101-D8 | 512x1024  |   40000 | 10.9     | 1.99           | V100   | 80.52 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/danet/danet_r101-d8_4xb2-40k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_40k_cityscapes/danet_r101-d8_512x1024_40k_cityscapes_20200605_200831-c57a7157.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_40k_cityscapes/danet_r101-d8_512x1024_40k_cityscapes_20200605_200831.log.json) |
+| DANet  | R-50-D8  | 769x769   |   40000 | 8.8      | 1.56           | V100   | 78.88 | 80.62         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/danet/danet_r50-d8_4xb2-40k_cityscapes-769x769.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_40k_cityscapes/danet_r50-d8_769x769_40k_cityscapes_20200530_025703-76681c60.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_40k_cityscapes/danet_r50-d8_769x769_40k_cityscapes_20200530_025703.log.json)         |
+| DANet  | R-101-D8 | 769x769   |   40000 | 12.8     | 1.07           | V100   | 79.88 | 81.47         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/danet/danet_r101-d8_4xb2-40k_cityscapes-769x769.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_40k_cityscapes/danet_r101-d8_769x769_40k_cityscapes_20200530_025717-dcb7fd4e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_40k_cityscapes/danet_r101-d8_769x769_40k_cityscapes_20200530_025717.log.json)     |
+| DANet  | R-50-D8  | 512x1024  |   80000 | -        | -              | V100   | 79.34 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/danet/danet_r50-d8_4xb2-80k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_80k_cityscapes/danet_r50-d8_512x1024_80k_cityscapes_20200607_133029-2bfa2293.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_80k_cityscapes/danet_r50-d8_512x1024_80k_cityscapes_20200607_133029.log.json)     |
+| DANet  | R-101-D8 | 512x1024  |   80000 | -        | -              | V100   | 80.41 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/danet/danet_r101-d8_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_80k_cityscapes/danet_r101-d8_512x1024_80k_cityscapes_20200607_132918-955e6350.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_80k_cityscapes/danet_r101-d8_512x1024_80k_cityscapes_20200607_132918.log.json) |
+| DANet  | R-50-D8  | 769x769   |   80000 | -        | -              | V100   | 79.27 | 80.96         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/danet/danet_r50-d8_4xb2-80k_cityscapes-769x769.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_80k_cityscapes/danet_r50-d8_769x769_80k_cityscapes_20200607_132954-495689b4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_80k_cityscapes/danet_r50-d8_769x769_80k_cityscapes_20200607_132954.log.json)         |
+| DANet  | R-101-D8 | 769x769   |   80000 | -        | -              | V100   | 80.47 | 82.02         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/danet/danet_r101-d8_4xb2-80k_cityscapes-769x769.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_80k_cityscapes/danet_r101-d8_769x769_80k_cityscapes_20200607_132918-f3a929e7.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_80k_cityscapes/danet_r101-d8_769x769_80k_cityscapes_20200607_132918.log.json)     |
 
 ### ADE20K
 
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                      | download                                                                                                                                                                                                                                                                                                                           |
-| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | --------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| DANet  | R-50-D8  | 512x512   |   80000 | 11.5     | 21.20          | 41.66 |         42.90 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/danet/danet_r50-d8_4xb4-80k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_80k_ade20k/danet_r50-d8_512x512_80k_ade20k_20200615_015125-edb18e08.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_80k_ade20k/danet_r50-d8_512x512_80k_ade20k_20200615_015125.log.json)         |
-| DANet  | R-101-D8 | 512x512   |   80000 | 15       | 14.18          | 43.64 |         45.19 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/danet/danet_r101-d8_4xb4-80k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_80k_ade20k/danet_r101-d8_512x512_80k_ade20k_20200615_015126-d0357c73.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_80k_ade20k/danet_r101-d8_512x512_80k_ade20k_20200615_015126.log.json)     |
-| DANet  | R-50-D8  | 512x512   |  160000 | -        | -              | 42.45 |         43.25 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/danet/danet_r50-d8_4xb4-160k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_160k_ade20k/danet_r50-d8_512x512_160k_ade20k_20200616_082340-9cb35dcd.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_160k_ade20k/danet_r50-d8_512x512_160k_ade20k_20200616_082340.log.json)     |
-| DANet  | R-101-D8 | 512x512   |  160000 | -        | -              | 44.17 |         45.02 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/danet/danet_r101-d8_4xb4-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_160k_ade20k/danet_r101-d8_512x512_160k_ade20k_20200616_082348-23bf12f9.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_160k_ade20k/danet_r101-d8_512x512_160k_ade20k_20200616_082348.log.json) |
+| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                      | download                                                                                                                                                                                                                                                                                                                           |
+| ------ | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | --------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| DANet  | R-50-D8  | 512x512   |   80000 | 11.5     | 21.20          | V100   | 41.66 |         42.90 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/danet/danet_r50-d8_4xb4-80k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_80k_ade20k/danet_r50-d8_512x512_80k_ade20k_20200615_015125-edb18e08.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_80k_ade20k/danet_r50-d8_512x512_80k_ade20k_20200615_015125.log.json)         |
+| DANet  | R-101-D8 | 512x512   |   80000 | 15       | 14.18          | V100   | 43.64 |         45.19 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/danet/danet_r101-d8_4xb4-80k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_80k_ade20k/danet_r101-d8_512x512_80k_ade20k_20200615_015126-d0357c73.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_80k_ade20k/danet_r101-d8_512x512_80k_ade20k_20200615_015126.log.json)     |
+| DANet  | R-50-D8  | 512x512   |  160000 | -        | -              | V100   | 42.45 |         43.25 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/danet/danet_r50-d8_4xb4-160k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_160k_ade20k/danet_r50-d8_512x512_160k_ade20k_20200616_082340-9cb35dcd.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_160k_ade20k/danet_r50-d8_512x512_160k_ade20k_20200616_082340.log.json)     |
+| DANet  | R-101-D8 | 512x512   |  160000 | -        | -              | V100   | 44.17 |         45.02 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/danet/danet_r101-d8_4xb4-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_160k_ade20k/danet_r101-d8_512x512_160k_ade20k_20200616_082348-23bf12f9.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_160k_ade20k/danet_r101-d8_512x512_160k_ade20k_20200616_082348.log.json) |
 
 ### Pascal VOC 2012 + Aug
 
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                       | download                                                                                                                                                                                                                                                                                                                               |
-| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ---------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| DANet  | R-50-D8  | 512x512   |   20000 | 6.5      | 20.94          | 74.45 |         75.69 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/danet/danet_r50-d8_4xb4-20k_voc12aug-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_20k_voc12aug/danet_r50-d8_512x512_20k_voc12aug_20200618_070026-9e9e3ab3.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_20k_voc12aug/danet_r50-d8_512x512_20k_voc12aug_20200618_070026.log.json)     |
-| DANet  | R-101-D8 | 512x512   |   20000 | 9.9      | 13.76          | 76.02 |         77.23 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/danet/danet_r101-d8_4xb4-20k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_20k_voc12aug/danet_r101-d8_512x512_20k_voc12aug_20200618_070026-d48d23b2.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_20k_voc12aug/danet_r101-d8_512x512_20k_voc12aug_20200618_070026.log.json) |
-| DANet  | R-50-D8  | 512x512   |   40000 | -        | -              | 76.37 |         77.29 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/danet/danet_r50-d8_4xb4-40k_voc12aug-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_40k_voc12aug/danet_r50-d8_512x512_40k_voc12aug_20200613_235526-426e3a64.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_40k_voc12aug/danet_r50-d8_512x512_40k_voc12aug_20200613_235526.log.json)     |
-| DANet  | R-101-D8 | 512x512   |   40000 | -        | -              | 76.51 |         77.32 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/danet/danet_r101-d8_4xb4-40k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_40k_voc12aug/danet_r101-d8_512x512_40k_voc12aug_20200613_223031-788e232a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_40k_voc12aug/danet_r101-d8_512x512_40k_voc12aug_20200613_223031.log.json) |
+| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                       | download                                                                                                                                                                                                                                                                                                                               |
+| ------ | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ---------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| DANet  | R-50-D8  | 512x512   |   20000 | 6.5      | 20.94          | V100   | 74.45 |         75.69 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/danet/danet_r50-d8_4xb4-20k_voc12aug-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_20k_voc12aug/danet_r50-d8_512x512_20k_voc12aug_20200618_070026-9e9e3ab3.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_20k_voc12aug/danet_r50-d8_512x512_20k_voc12aug_20200618_070026.log.json)     |
+| DANet  | R-101-D8 | 512x512   |   20000 | 9.9      | 13.76          | V100   | 76.02 |         77.23 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/danet/danet_r101-d8_4xb4-20k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_20k_voc12aug/danet_r101-d8_512x512_20k_voc12aug_20200618_070026-d48d23b2.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_20k_voc12aug/danet_r101-d8_512x512_20k_voc12aug_20200618_070026.log.json) |
+| DANet  | R-50-D8  | 512x512   |   40000 | -        | -              | V100   | 76.37 |         77.29 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/danet/danet_r50-d8_4xb4-40k_voc12aug-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_40k_voc12aug/danet_r50-d8_512x512_40k_voc12aug_20200613_235526-426e3a64.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_40k_voc12aug/danet_r50-d8_512x512_40k_voc12aug_20200613_235526.log.json)     |
+| DANet  | R-101-D8 | 512x512   |   40000 | -        | -              | V100   | 76.51 |         77.32 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/danet/danet_r101-d8_4xb4-40k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_40k_voc12aug/danet_r101-d8_512x512_40k_voc12aug_20200613_223031-788e232a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_40k_voc12aug/danet_r101-d8_512x512_40k_voc12aug_20200613_223031.log.json) |
+
+## Citation
+
+```bibtex
+@article{fu2018dual,
+  title={Dual Attention Network for Scene Segmentation},
+  author={Jun Fu, Jing Liu, Haijie Tian, Yong Li, Yongjun Bao, Zhiwei Fang,and Hanqing Lu},
+  booktitle={The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
+  year={2019}
+}
+```
diff --git a/configs/danet/danet.yml b/configs/danet/danet.yml
deleted file mode 100644
index 2a6658e428..0000000000
--- a/configs/danet/danet.yml
+++ /dev/null
@@ -1,301 +0,0 @@
-Collections:
-- Name: DANet
-  Metadata:
-    Training Data:
-    - Cityscapes
-    - ADE20K
-    - Pascal VOC 2012 + Aug
-  Paper:
-    URL: https://arxiv.org/abs/1809.02983
-    Title: Dual Attention Network for Scene Segmentation
-  README: configs/danet/README.md
-  Code:
-    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/da_head.py#L76
-    Version: v0.17.0
-  Converted From:
-    Code: https://github.com/junfu1115/DANet/
-Models:
-- Name: danet_r50-d8_4xb2-40k_cityscapes-512x1024
-  In Collection: DANet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,1024)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 375.94
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 7.4
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 78.74
-  Config: configs/danet/danet_r50-d8_4xb2-40k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_40k_cityscapes/danet_r50-d8_512x1024_40k_cityscapes_20200605_191324-c0dbfa5f.pth
-- Name: danet_r101-d8_4xb2-40k_cityscapes-512x1024
-  In Collection: DANet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,1024)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 502.51
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 10.9
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 80.52
-  Config: configs/danet/danet_r101-d8_4xb2-40k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_40k_cityscapes/danet_r101-d8_512x1024_40k_cityscapes_20200605_200831-c57a7157.pth
-- Name: danet_r50-d8_4xb2-40k_cityscapes-769x769
-  In Collection: DANet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (769,769)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 641.03
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (769,769)
-    Training Memory (GB): 8.8
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 78.88
-      mIoU(ms+flip): 80.62
-  Config: configs/danet/danet_r50-d8_4xb2-40k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_40k_cityscapes/danet_r50-d8_769x769_40k_cityscapes_20200530_025703-76681c60.pth
-- Name: danet_r101-d8_4xb2-40k_cityscapes-769x769
-  In Collection: DANet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (769,769)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 934.58
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (769,769)
-    Training Memory (GB): 12.8
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 79.88
-      mIoU(ms+flip): 81.47
-  Config: configs/danet/danet_r101-d8_4xb2-40k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_40k_cityscapes/danet_r101-d8_769x769_40k_cityscapes_20200530_025717-dcb7fd4e.pth
-- Name: danet_r50-d8_4xb2-80k_cityscapes-512x1024
-  In Collection: DANet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,1024)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 79.34
-  Config: configs/danet/danet_r50-d8_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_80k_cityscapes/danet_r50-d8_512x1024_80k_cityscapes_20200607_133029-2bfa2293.pth
-- Name: danet_r101-d8_4xb2-80k_cityscapes-512x1024
-  In Collection: DANet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,1024)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 80.41
-  Config: configs/danet/danet_r101-d8_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_80k_cityscapes/danet_r101-d8_512x1024_80k_cityscapes_20200607_132918-955e6350.pth
-- Name: danet_r50-d8_4xb2-80k_cityscapes-769x769
-  In Collection: DANet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (769,769)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 79.27
-      mIoU(ms+flip): 80.96
-  Config: configs/danet/danet_r50-d8_4xb2-80k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_80k_cityscapes/danet_r50-d8_769x769_80k_cityscapes_20200607_132954-495689b4.pth
-- Name: danet_r101-d8_4xb2-80k_cityscapes-769x769
-  In Collection: DANet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (769,769)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 80.47
-      mIoU(ms+flip): 82.02
-  Config: configs/danet/danet_r101-d8_4xb2-80k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_80k_cityscapes/danet_r101-d8_769x769_80k_cityscapes_20200607_132918-f3a929e7.pth
-- Name: danet_r50-d8_4xb4-80k_ade20k-512x512
-  In Collection: DANet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 47.17
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 11.5
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 41.66
-      mIoU(ms+flip): 42.9
-  Config: configs/danet/danet_r50-d8_4xb4-80k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_80k_ade20k/danet_r50-d8_512x512_80k_ade20k_20200615_015125-edb18e08.pth
-- Name: danet_r101-d8_4xb4-80k_ade20k-512x512
-  In Collection: DANet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 70.52
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 15.0
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 43.64
-      mIoU(ms+flip): 45.19
-  Config: configs/danet/danet_r101-d8_4xb4-80k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_80k_ade20k/danet_r101-d8_512x512_80k_ade20k_20200615_015126-d0357c73.pth
-- Name: danet_r50-d8_4xb4-160k_ade20k-512x512
-  In Collection: DANet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 160000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 42.45
-      mIoU(ms+flip): 43.25
-  Config: configs/danet/danet_r50-d8_4xb4-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_160k_ade20k/danet_r50-d8_512x512_160k_ade20k_20200616_082340-9cb35dcd.pth
-- Name: danet_r101-d8_4xb4-160k_ade20k-512x512
-  In Collection: DANet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 160000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 44.17
-      mIoU(ms+flip): 45.02
-  Config: configs/danet/danet_r101-d8_4xb4-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_160k_ade20k/danet_r101-d8_512x512_160k_ade20k_20200616_082348-23bf12f9.pth
-- Name: danet_r50-d8_4xb4-20k_voc12aug-512x512
-  In Collection: DANet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 20000
-    inference time (ms/im):
-    - value: 47.76
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 6.5
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal VOC 2012 + Aug
-    Metrics:
-      mIoU: 74.45
-      mIoU(ms+flip): 75.69
-  Config: configs/danet/danet_r50-d8_4xb4-20k_voc12aug-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_20k_voc12aug/danet_r50-d8_512x512_20k_voc12aug_20200618_070026-9e9e3ab3.pth
-- Name: danet_r101-d8_4xb4-20k_voc12aug-512x512
-  In Collection: DANet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 20000
-    inference time (ms/im):
-    - value: 72.67
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 9.9
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal VOC 2012 + Aug
-    Metrics:
-      mIoU: 76.02
-      mIoU(ms+flip): 77.23
-  Config: configs/danet/danet_r101-d8_4xb4-20k_voc12aug-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_20k_voc12aug/danet_r101-d8_512x512_20k_voc12aug_20200618_070026-d48d23b2.pth
-- Name: danet_r50-d8_4xb4-40k_voc12aug-512x512
-  In Collection: DANet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 40000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal VOC 2012 + Aug
-    Metrics:
-      mIoU: 76.37
-      mIoU(ms+flip): 77.29
-  Config: configs/danet/danet_r50-d8_4xb4-40k_voc12aug-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_40k_voc12aug/danet_r50-d8_512x512_40k_voc12aug_20200613_235526-426e3a64.pth
-- Name: danet_r101-d8_4xb4-40k_voc12aug-512x512
-  In Collection: DANet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 40000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal VOC 2012 + Aug
-    Metrics:
-      mIoU: 76.51
-      mIoU(ms+flip): 77.32
-  Config: configs/danet/danet_r101-d8_4xb4-40k_voc12aug-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_40k_voc12aug/danet_r101-d8_512x512_40k_voc12aug_20200613_223031-788e232a.pth
diff --git a/configs/danet/metafile.yaml b/configs/danet/metafile.yaml
new file mode 100644
index 0000000000..daff925baf
--- /dev/null
+++ b/configs/danet/metafile.yaml
@@ -0,0 +1,387 @@
+Collections:
+- Name: DANet
+  License: Apache License 2.0
+  Metadata:
+    Training Data:
+    - Cityscapes
+    - ADE20K
+    - Pascal VOC 2012 + Aug
+  Paper:
+    Title: Dual Attention Network for Scene Segmentation
+    URL: https://arxiv.org/abs/1809.02983
+  README: configs/danet/README.md
+  Frameworks:
+  - PyTorch
+Models:
+- Name: danet_r50-d8_4xb2-40k_cityscapes-512x1024
+  In Collection: DANet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.74
+  Config: configs/danet/danet_r50-d8_4xb2-40k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - DANet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 7.4
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_40k_cityscapes/danet_r50-d8_512x1024_40k_cityscapes_20200605_191324-c0dbfa5f.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_40k_cityscapes/danet_r50-d8_512x1024_40k_cityscapes_20200605_191324.log.json
+  Paper:
+    Title: Dual Attention Network for Scene Segmentation
+    URL: https://arxiv.org/abs/1809.02983
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/da_head.py#L76
+  Framework: PyTorch
+- Name: danet_r101-d8_4xb2-40k_cityscapes-512x1024
+  In Collection: DANet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 80.52
+  Config: configs/danet/danet_r101-d8_4xb2-40k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - DANet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 10.9
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_40k_cityscapes/danet_r101-d8_512x1024_40k_cityscapes_20200605_200831-c57a7157.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_40k_cityscapes/danet_r101-d8_512x1024_40k_cityscapes_20200605_200831.log.json
+  Paper:
+    Title: Dual Attention Network for Scene Segmentation
+    URL: https://arxiv.org/abs/1809.02983
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/da_head.py#L76
+  Framework: PyTorch
+- Name: danet_r50-d8_4xb2-40k_cityscapes-769x769
+  In Collection: DANet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.88
+      mIoU(ms+flip): 80.62
+  Config: configs/danet/danet_r50-d8_4xb2-40k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - DANet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 8.8
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_40k_cityscapes/danet_r50-d8_769x769_40k_cityscapes_20200530_025703-76681c60.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_40k_cityscapes/danet_r50-d8_769x769_40k_cityscapes_20200530_025703.log.json
+  Paper:
+    Title: Dual Attention Network for Scene Segmentation
+    URL: https://arxiv.org/abs/1809.02983
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/da_head.py#L76
+  Framework: PyTorch
+- Name: danet_r101-d8_4xb2-40k_cityscapes-769x769
+  In Collection: DANet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.88
+      mIoU(ms+flip): 81.47
+  Config: configs/danet/danet_r101-d8_4xb2-40k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - DANet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 12.8
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_40k_cityscapes/danet_r101-d8_769x769_40k_cityscapes_20200530_025717-dcb7fd4e.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_40k_cityscapes/danet_r101-d8_769x769_40k_cityscapes_20200530_025717.log.json
+  Paper:
+    Title: Dual Attention Network for Scene Segmentation
+    URL: https://arxiv.org/abs/1809.02983
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/da_head.py#L76
+  Framework: PyTorch
+- Name: danet_r50-d8_4xb2-80k_cityscapes-512x1024
+  In Collection: DANet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.34
+  Config: configs/danet/danet_r50-d8_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - DANet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_80k_cityscapes/danet_r50-d8_512x1024_80k_cityscapes_20200607_133029-2bfa2293.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_80k_cityscapes/danet_r50-d8_512x1024_80k_cityscapes_20200607_133029.log.json
+  Paper:
+    Title: Dual Attention Network for Scene Segmentation
+    URL: https://arxiv.org/abs/1809.02983
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/da_head.py#L76
+  Framework: PyTorch
+- Name: danet_r101-d8_4xb2-80k_cityscapes-512x1024
+  In Collection: DANet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 80.41
+  Config: configs/danet/danet_r101-d8_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - DANet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_80k_cityscapes/danet_r101-d8_512x1024_80k_cityscapes_20200607_132918-955e6350.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_80k_cityscapes/danet_r101-d8_512x1024_80k_cityscapes_20200607_132918.log.json
+  Paper:
+    Title: Dual Attention Network for Scene Segmentation
+    URL: https://arxiv.org/abs/1809.02983
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/da_head.py#L76
+  Framework: PyTorch
+- Name: danet_r50-d8_4xb2-80k_cityscapes-769x769
+  In Collection: DANet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.27
+      mIoU(ms+flip): 80.96
+  Config: configs/danet/danet_r50-d8_4xb2-80k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - DANet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_80k_cityscapes/danet_r50-d8_769x769_80k_cityscapes_20200607_132954-495689b4.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_80k_cityscapes/danet_r50-d8_769x769_80k_cityscapes_20200607_132954.log.json
+  Paper:
+    Title: Dual Attention Network for Scene Segmentation
+    URL: https://arxiv.org/abs/1809.02983
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/da_head.py#L76
+  Framework: PyTorch
+- Name: danet_r101-d8_4xb2-80k_cityscapes-769x769
+  In Collection: DANet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 80.47
+      mIoU(ms+flip): 82.02
+  Config: configs/danet/danet_r101-d8_4xb2-80k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - DANet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_80k_cityscapes/danet_r101-d8_769x769_80k_cityscapes_20200607_132918-f3a929e7.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_80k_cityscapes/danet_r101-d8_769x769_80k_cityscapes_20200607_132918.log.json
+  Paper:
+    Title: Dual Attention Network for Scene Segmentation
+    URL: https://arxiv.org/abs/1809.02983
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/da_head.py#L76
+  Framework: PyTorch
+- Name: danet_r50-d8_4xb4-80k_ade20k-512x512
+  In Collection: DANet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 41.66
+      mIoU(ms+flip): 42.9
+  Config: configs/danet/danet_r50-d8_4xb4-80k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - DANet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 11.5
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_80k_ade20k/danet_r50-d8_512x512_80k_ade20k_20200615_015125-edb18e08.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_80k_ade20k/danet_r50-d8_512x512_80k_ade20k_20200615_015125.log.json
+  Paper:
+    Title: Dual Attention Network for Scene Segmentation
+    URL: https://arxiv.org/abs/1809.02983
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/da_head.py#L76
+  Framework: PyTorch
+- Name: danet_r101-d8_4xb4-80k_ade20k-512x512
+  In Collection: DANet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 43.64
+      mIoU(ms+flip): 45.19
+  Config: configs/danet/danet_r101-d8_4xb4-80k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - DANet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 15.0
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_80k_ade20k/danet_r101-d8_512x512_80k_ade20k_20200615_015126-d0357c73.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_80k_ade20k/danet_r101-d8_512x512_80k_ade20k_20200615_015126.log.json
+  Paper:
+    Title: Dual Attention Network for Scene Segmentation
+    URL: https://arxiv.org/abs/1809.02983
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/da_head.py#L76
+  Framework: PyTorch
+- Name: danet_r50-d8_4xb4-160k_ade20k-512x512
+  In Collection: DANet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 42.45
+      mIoU(ms+flip): 43.25
+  Config: configs/danet/danet_r50-d8_4xb4-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - DANet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_160k_ade20k/danet_r50-d8_512x512_160k_ade20k_20200616_082340-9cb35dcd.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_160k_ade20k/danet_r50-d8_512x512_160k_ade20k_20200616_082340.log.json
+  Paper:
+    Title: Dual Attention Network for Scene Segmentation
+    URL: https://arxiv.org/abs/1809.02983
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/da_head.py#L76
+  Framework: PyTorch
+- Name: danet_r101-d8_4xb4-160k_ade20k-512x512
+  In Collection: DANet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 44.17
+      mIoU(ms+flip): 45.02
+  Config: configs/danet/danet_r101-d8_4xb4-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - DANet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_160k_ade20k/danet_r101-d8_512x512_160k_ade20k_20200616_082348-23bf12f9.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_160k_ade20k/danet_r101-d8_512x512_160k_ade20k_20200616_082348.log.json
+  Paper:
+    Title: Dual Attention Network for Scene Segmentation
+    URL: https://arxiv.org/abs/1809.02983
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/da_head.py#L76
+  Framework: PyTorch
+- Name: danet_r50-d8_4xb4-20k_voc12aug-512x512
+  In Collection: DANet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 74.45
+      mIoU(ms+flip): 75.69
+  Config: configs/danet/danet_r50-d8_4xb4-20k_voc12aug-512x512.py
+  Metadata:
+    Training Data: Pascal VOC 2012 + Aug
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - DANet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 6.5
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_20k_voc12aug/danet_r50-d8_512x512_20k_voc12aug_20200618_070026-9e9e3ab3.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_20k_voc12aug/danet_r50-d8_512x512_20k_voc12aug_20200618_070026.log.json
+  Paper:
+    Title: Dual Attention Network for Scene Segmentation
+    URL: https://arxiv.org/abs/1809.02983
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/da_head.py#L76
+  Framework: PyTorch
+- Name: danet_r101-d8_4xb4-20k_voc12aug-512x512
+  In Collection: DANet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 76.02
+      mIoU(ms+flip): 77.23
+  Config: configs/danet/danet_r101-d8_4xb4-20k_voc12aug-512x512.py
+  Metadata:
+    Training Data: Pascal VOC 2012 + Aug
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - DANet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 9.9
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_20k_voc12aug/danet_r101-d8_512x512_20k_voc12aug_20200618_070026-d48d23b2.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_20k_voc12aug/danet_r101-d8_512x512_20k_voc12aug_20200618_070026.log.json
+  Paper:
+    Title: Dual Attention Network for Scene Segmentation
+    URL: https://arxiv.org/abs/1809.02983
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/da_head.py#L76
+  Framework: PyTorch
+- Name: danet_r50-d8_4xb4-40k_voc12aug-512x512
+  In Collection: DANet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 76.37
+      mIoU(ms+flip): 77.29
+  Config: configs/danet/danet_r50-d8_4xb4-40k_voc12aug-512x512.py
+  Metadata:
+    Training Data: Pascal VOC 2012 + Aug
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - DANet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_40k_voc12aug/danet_r50-d8_512x512_40k_voc12aug_20200613_235526-426e3a64.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_40k_voc12aug/danet_r50-d8_512x512_40k_voc12aug_20200613_235526.log.json
+  Paper:
+    Title: Dual Attention Network for Scene Segmentation
+    URL: https://arxiv.org/abs/1809.02983
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/da_head.py#L76
+  Framework: PyTorch
+- Name: danet_r101-d8_4xb4-40k_voc12aug-512x512
+  In Collection: DANet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 76.51
+      mIoU(ms+flip): 77.32
+  Config: configs/danet/danet_r101-d8_4xb4-40k_voc12aug-512x512.py
+  Metadata:
+    Training Data: Pascal VOC 2012 + Aug
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - DANet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_40k_voc12aug/danet_r101-d8_512x512_40k_voc12aug_20200613_223031-788e232a.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_40k_voc12aug/danet_r101-d8_512x512_40k_voc12aug_20200613_223031.log.json
+  Paper:
+    Title: Dual Attention Network for Scene Segmentation
+    URL: https://arxiv.org/abs/1809.02983
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/da_head.py#L76
+  Framework: PyTorch
diff --git a/configs/deeplabv3/README.md b/configs/deeplabv3/README.md
index b37836ab21..836db6a9a5 100644
--- a/configs/deeplabv3/README.md
+++ b/configs/deeplabv3/README.md
@@ -1,6 +1,6 @@
 # DeepLabV3
 
-[Rethinking atrous convolution for semantic image segmentation](https://arxiv.org/abs/1706.05587)
+> [Rethinking atrous convolution for semantic image segmentation](https://arxiv.org/abs/1706.05587)
 
 ## Introduction
 
@@ -18,105 +18,101 @@ In this work, we revisit atrous convolution, a powerful tool to explicitly adjus
 
 <!-- [IMAGE] -->
 
-<div align=center>
-<img src="https://user-images.githubusercontent.com/24582831/142900575-f30a7755-09aa-406a-bf78-45893a61ee9a.png" width="80%"/>
-</div>
-
 <div align=center >
 <img alt="DEEPLABv3_ResNet-D8" src="https://user-images.githubusercontent.com/61172629/209305311-87ff9e36-b7cd-46d7-8b4c-9e26e10c27d0.jpg"/>
 DEEPLABv3_ResNet-D8 model structure
 </div>
 
-## Citation
-
-```bibtext
-@article{chen2017rethinking,
-  title={Rethinking atrous convolution for semantic image segmentation},
-  author={Chen, Liang-Chieh and Papandreou, George and Schroff, Florian and Adam, Hartwig},
-  journal={arXiv preprint arXiv:1706.05587},
-  year={2017}
-}
-```
-
 ## Results and models
 
 ### Cityscapes
 
-| Method           | Backbone        | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                                         | download                                                                                                                                                                                                                                                                                                                                                                                               |
-| ---------------- | --------------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ---------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| DeepLabV3        | R-50-D8         | 512x1024  |   40000 | 6.1      | 2.57           | 79.09 |         80.45 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r50-d8_4xb2-40k_cityscapes-512x1024.py)         | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x1024_40k_cityscapes/deeplabv3_r50-d8_512x1024_40k_cityscapes_20200605_022449-acadc2f8.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x1024_40k_cityscapes/deeplabv3_r50-d8_512x1024_40k_cityscapes_20200605_022449.log.json)                                 |
-| DeepLabV3        | R-101-D8        | 512x1024  |   40000 | 9.6      | 1.92           | 77.12 |         79.61 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r101-d8_4xb2-40k_cityscapes-512x1024.py)        | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x1024_40k_cityscapes/deeplabv3_r101-d8_512x1024_40k_cityscapes_20200605_012241-7fd3f799.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x1024_40k_cityscapes/deeplabv3_r101-d8_512x1024_40k_cityscapes_20200605_012241.log.json)                             |
-| DeepLabV3        | R-50-D8         | 769x769   |   40000 | 6.9      | 1.11           | 78.58 |         79.89 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r50-d8_4xb2-40k_cityscapes-769x769.py)          | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_769x769_40k_cityscapes/deeplabv3_r50-d8_769x769_40k_cityscapes_20200606_113723-7eda553c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_769x769_40k_cityscapes/deeplabv3_r50-d8_769x769_40k_cityscapes_20200606_113723.log.json)                                     |
-| DeepLabV3        | R-101-D8        | 769x769   |   40000 | 10.9     | 0.83           | 79.27 |         80.11 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r101-d8_4xb2-40k_cityscapes-769x769.py)         | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_769x769_40k_cityscapes/deeplabv3_r101-d8_769x769_40k_cityscapes_20200606_113809-c64f889f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_769x769_40k_cityscapes/deeplabv3_r101-d8_769x769_40k_cityscapes_20200606_113809.log.json)                                 |
-| DeepLabV3        | R-18-D8         | 512x1024  |   80000 | 1.7      | 13.78          | 76.70 |         78.27 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r18-d8_4xb2-80k_cityscapes-512x1024.py)         | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18-d8_512x1024_80k_cityscapes/deeplabv3_r18-d8_512x1024_80k_cityscapes_20201225_021506-23dffbe2.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18-d8_512x1024_80k_cityscapes/deeplabv3_r18-d8_512x1024_80k_cityscapes-20201225_021506.log.json)                                 |
-| DeepLabV3        | R-50-D8         | 512x1024  |   80000 | -        | -              | 79.32 |         80.57 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r50-d8_4xb2-80k_cityscapes-512x1024.py)         | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x1024_80k_cityscapes/deeplabv3_r50-d8_512x1024_80k_cityscapes_20200606_113404-b92cfdd4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x1024_80k_cityscapes/deeplabv3_r50-d8_512x1024_80k_cityscapes_20200606_113404.log.json)                                 |
-| DeepLabV3        | R-101-D8        | 512x1024  |   80000 | -        | -              | 80.20 |         81.21 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r101-d8_4xb2-80k_cityscapes-512x1024.py)        | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes/deeplabv3_r101-d8_512x1024_80k_cityscapes_20200606_113503-9e428899.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes/deeplabv3_r101-d8_512x1024_80k_cityscapes_20200606_113503.log.json)                             |
-| DeepLabV3 (FP16) | R-101-D8        | 512x1024  |   80000 | 5.75     | 3.86           | 80.48 |             - | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r101-d8_4xb2-amp-80k_cityscapes-512x1024.py)    | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_fp16_512x1024_80k_cityscapes/deeplabv3_r101-d8_fp16_512x1024_80k_cityscapes_20200717_230920-774d9cec.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_fp16_512x1024_80k_cityscapes/deeplabv3_r101-d8_fp16_512x1024_80k_cityscapes_20200717_230920.log.json)         |
-| DeepLabV3        | R-18-D8         | 769x769   |   80000 | 1.9      | 5.55           | 76.60 |         78.26 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r18-d8_4xb2-80k_cityscapes-769x769.py)          | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18-d8_769x769_80k_cityscapes/deeplabv3_r18-d8_769x769_80k_cityscapes_20201225_021506-6452126a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18-d8_769x769_80k_cityscapes/deeplabv3_r18-d8_769x769_80k_cityscapes-20201225_021506.log.json)                                     |
-| DeepLabV3        | R-50-D8         | 769x769   |   80000 | -        | -              | 79.89 |         81.06 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r50-d8_4xb2-80k_cityscapes-769x769.py)          | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_769x769_80k_cityscapes/deeplabv3_r50-d8_769x769_80k_cityscapes_20200606_221338-788d6228.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_769x769_80k_cityscapes/deeplabv3_r50-d8_769x769_80k_cityscapes_20200606_221338.log.json)                                     |
-| DeepLabV3        | R-101-D8        | 769x769   |   80000 | -        | -              | 79.67 |         80.81 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r101-d8_4xb2-80k_cityscapes-769x769.py)         | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_769x769_80k_cityscapes/deeplabv3_r101-d8_769x769_80k_cityscapes_20200607_013353-60e95418.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_769x769_80k_cityscapes/deeplabv3_r101-d8_769x769_80k_cityscapes_20200607_013353.log.json)                                 |
-| DeepLabV3        | R-101-D16-MG124 | 512x1024  |   40000 | 4.7      | - 6.96         | 76.71 |         78.63 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r101-d16-mg124_4xb2-40k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes/deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes_20200908_005644-67b0c992.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes/deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes-20200908_005644.log.json) |
-| DeepLabV3        | R-101-D16-MG124 | 512x1024  |   80000 | -        | -              | 78.36 |         79.84 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r101-d16-mg124_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes_20200908_005644-57bb8425.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes-20200908_005644.log.json) |
-| DeepLabV3        | R-18b-D8        | 512x1024  |   80000 | 1.6      | 13.93          | 76.26 |         77.88 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r18b-d8_4xb2-80k_cityscapes-512x1024.py)        | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18b-d8_512x1024_80k_cityscapes/deeplabv3_r18b-d8_512x1024_80k_cityscapes_20201225_094144-46040cef.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18b-d8_512x1024_80k_cityscapes/deeplabv3_r18b-d8_512x1024_80k_cityscapes-20201225_094144.log.json)                             |
-| DeepLabV3        | R-50b-D8        | 512x1024  |   80000 | 6.0      | 2.74           | 79.63 |         80.98 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r50b-d8_4xb2-80k_cityscapes-512x1024.py)        | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50b-d8_512x1024_80k_cityscapes/deeplabv3_r50b-d8_512x1024_80k_cityscapes_20201225_155148-ec368954.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50b-d8_512x1024_80k_cityscapes/deeplabv3_r50b-d8_512x1024_80k_cityscapes-20201225_155148.log.json)                             |
-| DeepLabV3        | R-101b-D8       | 512x1024  |   80000 | 9.5      | 1.81           | 80.01 |         81.21 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r101b-d8_4xb2-80k_cityscapes-512x1024.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101b-d8_512x1024_80k_cityscapes/deeplabv3_r101b-d8_512x1024_80k_cityscapes_20201226_171821-8fd49503.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101b-d8_512x1024_80k_cityscapes/deeplabv3_r101b-d8_512x1024_80k_cityscapes-20201226_171821.log.json)                         |
-| DeepLabV3        | R-18b-D8        | 769x769   |   80000 | 1.8      | 5.79           | 75.63 |         77.51 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r18b-d8_4xb2-80k_cityscapes-769x769.py)         | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18b-d8_769x769_80k_cityscapes/deeplabv3_r18b-d8_769x769_80k_cityscapes_20201225_094144-fdc985d9.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18b-d8_769x769_80k_cityscapes/deeplabv3_r18b-d8_769x769_80k_cityscapes-20201225_094144.log.json)                                 |
-| DeepLabV3        | R-50b-D8        | 769x769   |   80000 | 6.8      | 1.16           | 78.80 |         80.27 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r50b-d8_4xb2-80k_cityscapes-769x769.py)         | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50b-d8_769x769_80k_cityscapes/deeplabv3_r50b-d8_769x769_80k_cityscapes_20201225_155404-87fb0cf4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50b-d8_769x769_80k_cityscapes/deeplabv3_r50b-d8_769x769_80k_cityscapes-20201225_155404.log.json)                                 |
-| DeepLabV3        | R-101b-D8       | 769x769   |   80000 | 10.7     | 0.82           | 79.41 |         80.73 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r101b-d8_4xb2-80k_cityscapes-769x769.py)        | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101b-d8_769x769_80k_cityscapes/deeplabv3_r101b-d8_769x769_80k_cityscapes_20201226_190843-9142ee57.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101b-d8_769x769_80k_cityscapes/deeplabv3_r101b-d8_769x769_80k_cityscapes-20201226_190843.log.json)                             |
+| Method           | Backbone        | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                                         | download                                                                                                                                                                                                                                                                                                                                                                                               |
+| ---------------- | --------------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ---------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| DeepLabV3        | R-50-D8         | 512x1024  |   40000 | 6.1      | 2.57           | V100   | 79.09 |         80.45 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r50-d8_4xb2-40k_cityscapes-512x1024.py)         | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x1024_40k_cityscapes/deeplabv3_r50-d8_512x1024_40k_cityscapes_20200605_022449-acadc2f8.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x1024_40k_cityscapes/deeplabv3_r50-d8_512x1024_40k_cityscapes_20200605_022449.log.json)                                 |
+| DeepLabV3        | R-101-D8        | 512x1024  |   40000 | 9.6      | 1.92           | V100   | 77.12 |         79.61 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r101-d8_4xb2-40k_cityscapes-512x1024.py)        | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x1024_40k_cityscapes/deeplabv3_r101-d8_512x1024_40k_cityscapes_20200605_012241-7fd3f799.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x1024_40k_cityscapes/deeplabv3_r101-d8_512x1024_40k_cityscapes_20200605_012241.log.json)                             |
+| DeepLabV3        | R-50-D8         | 769x769   |   40000 | 6.9      | 1.11           | V100   | 78.58 |         79.89 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r50-d8_4xb2-40k_cityscapes-769x769.py)          | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_769x769_40k_cityscapes/deeplabv3_r50-d8_769x769_40k_cityscapes_20200606_113723-7eda553c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_769x769_40k_cityscapes/deeplabv3_r50-d8_769x769_40k_cityscapes_20200606_113723.log.json)                                     |
+| DeepLabV3        | R-101-D8        | 769x769   |   40000 | 10.9     | 0.83           | V100   | 79.27 |         80.11 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r101-d8_4xb2-40k_cityscapes-769x769.py)         | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_769x769_40k_cityscapes/deeplabv3_r101-d8_769x769_40k_cityscapes_20200606_113809-c64f889f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_769x769_40k_cityscapes/deeplabv3_r101-d8_769x769_40k_cityscapes_20200606_113809.log.json)                                 |
+| DeepLabV3        | R-18-D8         | 512x1024  |   80000 | 1.7      | 13.78          | V100   | 76.70 |         78.27 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r18-d8_4xb2-80k_cityscapes-512x1024.py)         | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18-d8_512x1024_80k_cityscapes/deeplabv3_r18-d8_512x1024_80k_cityscapes_20201225_021506-23dffbe2.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18-d8_512x1024_80k_cityscapes/deeplabv3_r18-d8_512x1024_80k_cityscapes-20201225_021506.log.json)                                 |
+| DeepLabV3        | R-50-D8         | 512x1024  |   80000 | -        | -              | V100   | 79.32 |         80.57 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r50-d8_4xb2-80k_cityscapes-512x1024.py)         | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x1024_80k_cityscapes/deeplabv3_r50-d8_512x1024_80k_cityscapes_20200606_113404-b92cfdd4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x1024_80k_cityscapes/deeplabv3_r50-d8_512x1024_80k_cityscapes_20200606_113404.log.json)                                 |
+| DeepLabV3        | R-101-D8        | 512x1024  |   80000 | -        | -              | V100   | 80.20 |         81.21 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r101-d8_4xb2-80k_cityscapes-512x1024.py)        | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes/deeplabv3_r101-d8_512x1024_80k_cityscapes_20200606_113503-9e428899.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes/deeplabv3_r101-d8_512x1024_80k_cityscapes_20200606_113503.log.json)                             |
+| DeepLabV3 (FP16) | R-101-D8        | 512x1024  |   80000 | 5.75     | 3.86           | V100   | 80.48 |             - | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r101-d8_4xb2-amp-80k_cityscapes-512x1024.py)    | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_fp16_512x1024_80k_cityscapes/deeplabv3_r101-d8_fp16_512x1024_80k_cityscapes_20200717_230920-774d9cec.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_fp16_512x1024_80k_cityscapes/deeplabv3_r101-d8_fp16_512x1024_80k_cityscapes_20200717_230920.log.json)         |
+| DeepLabV3        | R-18-D8         | 769x769   |   80000 | 1.9      | 5.55           | V100   | 76.60 |         78.26 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r18-d8_4xb2-80k_cityscapes-769x769.py)          | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18-d8_769x769_80k_cityscapes/deeplabv3_r18-d8_769x769_80k_cityscapes_20201225_021506-6452126a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18-d8_769x769_80k_cityscapes/deeplabv3_r18-d8_769x769_80k_cityscapes-20201225_021506.log.json)                                     |
+| DeepLabV3        | R-50-D8         | 769x769   |   80000 | -        | -              | V100   | 79.89 |         81.06 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r50-d8_4xb2-80k_cityscapes-769x769.py)          | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_769x769_80k_cityscapes/deeplabv3_r50-d8_769x769_80k_cityscapes_20200606_221338-788d6228.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_769x769_80k_cityscapes/deeplabv3_r50-d8_769x769_80k_cityscapes_20200606_221338.log.json)                                     |
+| DeepLabV3        | R-101-D8        | 769x769   |   80000 | -        | -              | V100   | 79.67 |         80.81 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r101-d8_4xb2-80k_cityscapes-769x769.py)         | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_769x769_80k_cityscapes/deeplabv3_r101-d8_769x769_80k_cityscapes_20200607_013353-60e95418.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_769x769_80k_cityscapes/deeplabv3_r101-d8_769x769_80k_cityscapes_20200607_013353.log.json)                                 |
+| DeepLabV3        | R-101-D16-MG124 | 512x1024  |   40000 | 4.7      | 6.96           | V100   | 76.71 |         78.63 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r101-d16-mg124_4xb2-40k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes/deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes_20200908_005644-67b0c992.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes/deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes-20200908_005644.log.json) |
+| DeepLabV3        | R-101-D16-MG124 | 512x1024  |   80000 | -        | -              | V100   | 78.36 |         79.84 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r101-d16-mg124_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes_20200908_005644-57bb8425.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes-20200908_005644.log.json) |
+| DeepLabV3        | R-18b-D8        | 512x1024  |   80000 | 1.6      | 13.93          | V100   | 76.26 |         77.88 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r18b-d8_4xb2-80k_cityscapes-512x1024.py)        | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18b-d8_512x1024_80k_cityscapes/deeplabv3_r18b-d8_512x1024_80k_cityscapes_20201225_094144-46040cef.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18b-d8_512x1024_80k_cityscapes/deeplabv3_r18b-d8_512x1024_80k_cityscapes-20201225_094144.log.json)                             |
+| DeepLabV3        | R-50b-D8        | 512x1024  |   80000 | 6.0      | 2.74           | V100   | 79.63 |         80.98 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r50b-d8_4xb2-80k_cityscapes-512x1024.py)        | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50b-d8_512x1024_80k_cityscapes/deeplabv3_r50b-d8_512x1024_80k_cityscapes_20201225_155148-ec368954.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50b-d8_512x1024_80k_cityscapes/deeplabv3_r50b-d8_512x1024_80k_cityscapes-20201225_155148.log.json)                             |
+| DeepLabV3        | R-101b-D8       | 512x1024  |   80000 | 9.5      | 1.81           | V100   | 80.01 |         81.21 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r101b-d8_4xb2-80k_cityscapes-512x1024.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101b-d8_512x1024_80k_cityscapes/deeplabv3_r101b-d8_512x1024_80k_cityscapes_20201226_171821-8fd49503.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101b-d8_512x1024_80k_cityscapes/deeplabv3_r101b-d8_512x1024_80k_cityscapes-20201226_171821.log.json)                         |
+| DeepLabV3        | R-18b-D8        | 769x769   |   80000 | 1.8      | 5.79           | V100   | 75.63 |         77.51 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r18b-d8_4xb2-80k_cityscapes-769x769.py)         | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18b-d8_769x769_80k_cityscapes/deeplabv3_r18b-d8_769x769_80k_cityscapes_20201225_094144-fdc985d9.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18b-d8_769x769_80k_cityscapes/deeplabv3_r18b-d8_769x769_80k_cityscapes-20201225_094144.log.json)                                 |
+| DeepLabV3        | R-50b-D8        | 769x769   |   80000 | 6.8      | 1.16           | V100   | 78.80 |         80.27 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r50b-d8_4xb2-80k_cityscapes-769x769.py)         | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50b-d8_769x769_80k_cityscapes/deeplabv3_r50b-d8_769x769_80k_cityscapes_20201225_155404-87fb0cf4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50b-d8_769x769_80k_cityscapes/deeplabv3_r50b-d8_769x769_80k_cityscapes-20201225_155404.log.json)                                 |
+| DeepLabV3        | R-101b-D8       | 769x769   |   80000 | 10.7     | 0.82           | V100   | 79.41 |         80.73 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r101b-d8_4xb2-80k_cityscapes-769x769.py)        | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101b-d8_769x769_80k_cityscapes/deeplabv3_r101b-d8_769x769_80k_cityscapes_20201226_190843-9142ee57.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101b-d8_769x769_80k_cityscapes/deeplabv3_r101b-d8_769x769_80k_cityscapes-20201226_190843.log.json)                             |
 
 ### ADE20K
 
-| Method    | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                              | download                                                                                                                                                                                                                                                                                                                                                   |
-| --------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| DeepLabV3 | R-50-D8  | 512x512   |   80000 | 8.9      | 14.76          | 42.42 |         43.28 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r50-d8_4xb4-80k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_80k_ade20k/deeplabv3_r50-d8_512x512_80k_ade20k_20200614_185028-0bb3f844.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_80k_ade20k/deeplabv3_r50-d8_512x512_80k_ade20k_20200614_185028.log.json)         |
-| DeepLabV3 | R-101-D8 | 512x512   |   80000 | 12.4     | 10.14          | 44.08 |         45.19 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r101-d8_4xb4-80k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_80k_ade20k/deeplabv3_r101-d8_512x512_80k_ade20k_20200615_021256-d89c7fa4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_80k_ade20k/deeplabv3_r101-d8_512x512_80k_ade20k_20200615_021256.log.json)     |
-| DeepLabV3 | R-50-D8  | 512x512   |  160000 | -        | -              | 42.66 |         44.09 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r50-d8_4xb4-160k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_160k_ade20k/deeplabv3_r50-d8_512x512_160k_ade20k_20200615_123227-5d0ee427.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_160k_ade20k/deeplabv3_r50-d8_512x512_160k_ade20k_20200615_123227.log.json)     |
-| DeepLabV3 | R-101-D8 | 512x512   |  160000 | -        | -              | 45.00 |         46.66 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r101-d8_4xb4-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k/deeplabv3_r101-d8_512x512_160k_ade20k_20200615_105816-b1f72b3b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k/deeplabv3_r101-d8_512x512_160k_ade20k_20200615_105816.log.json) |
+| Method    | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                              | download                                                                                                                                                                                                                                                                                                                                                   |
+| --------- | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| DeepLabV3 | R-50-D8  | 512x512   |   80000 | 8.9      | 14.76          | V100   | 42.42 |         43.28 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r50-d8_4xb4-80k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_80k_ade20k/deeplabv3_r50-d8_512x512_80k_ade20k_20200614_185028-0bb3f844.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_80k_ade20k/deeplabv3_r50-d8_512x512_80k_ade20k_20200614_185028.log.json)         |
+| DeepLabV3 | R-101-D8 | 512x512   |   80000 | 12.4     | 10.14          | V100   | 44.08 |         45.19 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r101-d8_4xb4-80k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_80k_ade20k/deeplabv3_r101-d8_512x512_80k_ade20k_20200615_021256-d89c7fa4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_80k_ade20k/deeplabv3_r101-d8_512x512_80k_ade20k_20200615_021256.log.json)     |
+| DeepLabV3 | R-50-D8  | 512x512   |  160000 | -        | -              | V100   | 42.66 |         44.09 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r50-d8_4xb4-160k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_160k_ade20k/deeplabv3_r50-d8_512x512_160k_ade20k_20200615_123227-5d0ee427.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_160k_ade20k/deeplabv3_r50-d8_512x512_160k_ade20k_20200615_123227.log.json)     |
+| DeepLabV3 | R-101-D8 | 512x512   |  160000 | -        | -              | V100   | 45.00 |         46.66 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r101-d8_4xb4-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k/deeplabv3_r101-d8_512x512_160k_ade20k_20200615_105816-b1f72b3b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k/deeplabv3_r101-d8_512x512_160k_ade20k_20200615_105816.log.json) |
 
 ### Pascal VOC 2012 + Aug
 
-| Method    | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                               | download                                                                                                                                                                                                                                                                                                                                                       |
-| --------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| DeepLabV3 | R-50-D8  | 512x512   |   20000 | 6.1      | 13.88          | 76.17 |         77.42 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r50-d8_4xb4-20k_voc12aug-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_20k_voc12aug/deeplabv3_r50-d8_512x512_20k_voc12aug_20200617_010906-596905ef.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_20k_voc12aug/deeplabv3_r50-d8_512x512_20k_voc12aug_20200617_010906.log.json)     |
-| DeepLabV3 | R-101-D8 | 512x512   |   20000 | 9.6      | 9.81           | 78.70 |         79.95 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r101-d8_4xb4-20k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_20k_voc12aug/deeplabv3_r101-d8_512x512_20k_voc12aug_20200617_010932-8d13832f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_20k_voc12aug/deeplabv3_r101-d8_512x512_20k_voc12aug_20200617_010932.log.json) |
-| DeepLabV3 | R-50-D8  | 512x512   |   40000 | -        | -              | 77.68 |         78.78 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r50-d8_4xb4-40k_voc12aug-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_40k_voc12aug/deeplabv3_r50-d8_512x512_40k_voc12aug_20200613_161546-2ae96e7e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_40k_voc12aug/deeplabv3_r50-d8_512x512_40k_voc12aug_20200613_161546.log.json)     |
-| DeepLabV3 | R-101-D8 | 512x512   |   40000 | -        | -              | 77.92 |         79.18 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r101-d8_4xb4-40k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_40k_voc12aug/deeplabv3_r101-d8_512x512_40k_voc12aug_20200613_161432-0017d784.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_40k_voc12aug/deeplabv3_r101-d8_512x512_40k_voc12aug_20200613_161432.log.json) |
+| Method    | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                               | download                                                                                                                                                                                                                                                                                                                                                       |
+| --------- | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| DeepLabV3 | R-50-D8  | 512x512   |   20000 | 6.1      | 13.88          | V100   | 76.17 |         77.42 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r50-d8_4xb4-20k_voc12aug-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_20k_voc12aug/deeplabv3_r50-d8_512x512_20k_voc12aug_20200617_010906-596905ef.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_20k_voc12aug/deeplabv3_r50-d8_512x512_20k_voc12aug_20200617_010906.log.json)     |
+| DeepLabV3 | R-101-D8 | 512x512   |   20000 | 9.6      | 9.81           | V100   | 78.70 |         79.95 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r101-d8_4xb4-20k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_20k_voc12aug/deeplabv3_r101-d8_512x512_20k_voc12aug_20200617_010932-8d13832f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_20k_voc12aug/deeplabv3_r101-d8_512x512_20k_voc12aug_20200617_010932.log.json) |
+| DeepLabV3 | R-50-D8  | 512x512   |   40000 | -        | -              | V100   | 77.68 |         78.78 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r50-d8_4xb4-40k_voc12aug-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_40k_voc12aug/deeplabv3_r50-d8_512x512_40k_voc12aug_20200613_161546-2ae96e7e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_40k_voc12aug/deeplabv3_r50-d8_512x512_40k_voc12aug_20200613_161546.log.json)     |
+| DeepLabV3 | R-101-D8 | 512x512   |   40000 | -        | -              | V100   | 77.92 |         79.18 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r101-d8_4xb4-40k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_40k_voc12aug/deeplabv3_r101-d8_512x512_40k_voc12aug_20200613_161432-0017d784.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_40k_voc12aug/deeplabv3_r101-d8_512x512_40k_voc12aug_20200613_161432.log.json) |
 
 ### Pascal Context
 
-| Method    | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                                     | download                                                                                                                                                                                                                                                                                                                                                                               |
-| --------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| DeepLabV3 | R-101-D8 | 480x480   |   40000 | 9.2      | 7.09           | 46.55 |         47.81 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r101-d8_4xb4-40k_pascal-context-480x480.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context/deeplabv3_r101-d8_480x480_40k_pascal_context_20200911_204118-1aa27336.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context/deeplabv3_r101-d8_480x480_40k_pascal_context-20200911_204118.log.json) |
-| DeepLabV3 | R-101-D8 | 480x480   |   80000 | -        | -              | 46.42 |         47.53 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r101-d8_4xb4-80k_pascal-context-480x480.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context/deeplabv3_r101-d8_480x480_80k_pascal_context_20200911_170155-2a21fff3.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context/deeplabv3_r101-d8_480x480_80k_pascal_context-20200911_170155.log.json) |
+| Method    | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                                     | download                                                                                                                                                                                                                                                                                                                                                                               |
+| --------- | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| DeepLabV3 | R-101-D8 | 480x480   |   40000 | 9.2      | 7.09           | V100   | 46.55 |         47.81 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r101-d8_4xb4-40k_pascal-context-480x480.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context/deeplabv3_r101-d8_480x480_40k_pascal_context_20200911_204118-1aa27336.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context/deeplabv3_r101-d8_480x480_40k_pascal_context-20200911_204118.log.json) |
+| DeepLabV3 | R-101-D8 | 480x480   |   80000 | -        | -              | V100   | 46.42 |         47.53 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r101-d8_4xb4-80k_pascal-context-480x480.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context/deeplabv3_r101-d8_480x480_80k_pascal_context_20200911_170155-2a21fff3.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context/deeplabv3_r101-d8_480x480_80k_pascal_context-20200911_170155.log.json) |
 
 ### Pascal Context 59
 
-| Method    | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                                        | download                                                                                                                                                                                                                                                                                                                                                                                           |
-| --------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | --------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| DeepLabV3 | R-101-D8 | 480x480   |   40000 | -        | -              | 52.61 |         54.28 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r101-d8_4xb4-40k_pascal-context-59-480x480.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59/deeplabv3_r101-d8_480x480_40k_pascal_context_59_20210416_110332-cb08ea46.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59/deeplabv3_r101-d8_480x480_40k_pascal_context_59-20210416_110332.log.json) |
-| DeepLabV3 | R-101-D8 | 480x480   |   80000 | -        | -              | 52.46 |         54.09 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r101-d8_4xb4-80k_pascal-context-59-480x480.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context_59/deeplabv3_r101-d8_480x480_80k_pascal_context_59_20210416_113002-26303993.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context_59/deeplabv3_r101-d8_480x480_80k_pascal_context_59-20210416_113002.log.json) |
+| Method    | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                                        | download                                                                                                                                                                                                                                                                                                                                                                                           |
+| --------- | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | --------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| DeepLabV3 | R-101-D8 | 480x480   |   40000 | -        | -              | V100   | 52.61 |         54.28 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r101-d8_4xb4-40k_pascal-context-59-480x480.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59/deeplabv3_r101-d8_480x480_40k_pascal_context_59_20210416_110332-cb08ea46.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59/deeplabv3_r101-d8_480x480_40k_pascal_context_59-20210416_110332.log.json) |
+| DeepLabV3 | R-101-D8 | 480x480   |   80000 | -        | -              | V100   | 52.46 |         54.09 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r101-d8_4xb4-80k_pascal-context-59-480x480.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context_59/deeplabv3_r101-d8_480x480_80k_pascal_context_59_20210416_113002-26303993.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context_59/deeplabv3_r101-d8_480x480_80k_pascal_context_59-20210416_113002.log.json) |
 
 ### COCO-Stuff 10k
 
-| Method    | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                                    | download                                                                                                                                                                                                                                                                                                                                                                                           |
-| --------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| DeepLabV3 | R-50-D8  | 512x512   |   20000 | 9.6      | 10.8           | 34.66 |         36.08 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r50-d8_4xb4-20k_coco-stuff10k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_20k_coco-stuff10k/deeplabv3_r50-d8_512x512_4x4_20k_coco-stuff10k_20210821_043025-b35f789d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_20k_coco-stuff10k/deeplabv3_r50-d8_512x512_4x4_20k_coco-stuff10k_20210821_043025.log.json)     |
-| DeepLabV3 | R-101-D8 | 512x512   |   20000 | 13.2     | 8.7            | 37.30 |         38.42 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r101-d8_4xb4-20k_coco-stuff10k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_20k_coco-stuff10k/deeplabv3_r101-d8_512x512_4x4_20k_coco-stuff10k_20210821_043025-c49752cb.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_20k_coco-stuff10k/deeplabv3_r101-d8_512x512_4x4_20k_coco-stuff10k_20210821_043025.log.json) |
-| DeepLabV3 | R-50-D8  | 512x512   |   40000 | -        | -              | 35.73 |         37.09 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r50-d8_4xb4-40k_coco-stuff10k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_40k_coco-stuff10k/deeplabv3_r50-d8_512x512_4x4_40k_coco-stuff10k_20210821_043305-dc76f3ff.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_40k_coco-stuff10k/deeplabv3_r50-d8_512x512_4x4_40k_coco-stuff10k_20210821_043305.log.json)     |
-| DeepLabV3 | R-101-D8 | 512x512   |   40000 | -        | -              | 37.81 |         38.80 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r101-d8_4xb4-40k_coco-stuff10k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_40k_coco-stuff10k/deeplabv3_r101-d8_512x512_4x4_40k_coco-stuff10k_20210821_043305-636cb433.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_40k_coco-stuff10k/deeplabv3_r101-d8_512x512_4x4_40k_coco-stuff10k_20210821_043305.log.json) |
+| Method    | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                                    | download                                                                                                                                                                                                                                                                                                                                                                                           |
+| --------- | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| DeepLabV3 | R-50-D8  | 512x512   |   20000 | 9.6      | 10.8           | V100   | 34.66 |         36.08 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r50-d8_4xb4-20k_coco-stuff10k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_20k_coco-stuff10k/deeplabv3_r50-d8_512x512_4x4_20k_coco-stuff10k_20210821_043025-b35f789d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_20k_coco-stuff10k/deeplabv3_r50-d8_512x512_4x4_20k_coco-stuff10k_20210821_043025.log.json)     |
+| DeepLabV3 | R-101-D8 | 512x512   |   20000 | 13.2     | 8.7            | V100   | 37.30 |         38.42 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r101-d8_4xb4-20k_coco-stuff10k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_20k_coco-stuff10k/deeplabv3_r101-d8_512x512_4x4_20k_coco-stuff10k_20210821_043025-c49752cb.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_20k_coco-stuff10k/deeplabv3_r101-d8_512x512_4x4_20k_coco-stuff10k_20210821_043025.log.json) |
+| DeepLabV3 | R-50-D8  | 512x512   |   40000 | -        | -              | V100   | 35.73 |         37.09 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r50-d8_4xb4-40k_coco-stuff10k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_40k_coco-stuff10k/deeplabv3_r50-d8_512x512_4x4_40k_coco-stuff10k_20210821_043305-dc76f3ff.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_40k_coco-stuff10k/deeplabv3_r50-d8_512x512_4x4_40k_coco-stuff10k_20210821_043305.log.json)     |
+| DeepLabV3 | R-101-D8 | 512x512   |   40000 | -        | -              | V100   | 37.81 |         38.80 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r101-d8_4xb4-40k_coco-stuff10k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_40k_coco-stuff10k/deeplabv3_r101-d8_512x512_4x4_40k_coco-stuff10k_20210821_043305-636cb433.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_40k_coco-stuff10k/deeplabv3_r101-d8_512x512_4x4_40k_coco-stuff10k_20210821_043305.log.json) |
 
 ### COCO-Stuff 164k
 
-| Method    | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                                      | download                                                                                                                                                                                                                                                                                                                                                                                                   |
-| --------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| DeepLabV3 | R-50-D8  | 512x512   |   80000 | 9.6      | 10.8           | 39.38 |         40.03 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r50-d8_4xb4-80k_coco-stuff164k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_80k_coco-stuff164k/deeplabv3_r50-d8_512x512_4x4_80k_coco-stuff164k_20210709_163016-88675c24.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_80k_coco-stuff164k/deeplabv3_r50-d8_512x512_4x4_80k_coco-stuff164k_20210709_163016.log.json)         |
-| DeepLabV3 | R-101-D8 | 512x512   |   80000 | 13.2     | 8.7            | 40.87 |         41.50 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r101-d8_4xb4-80k_coco-stuff164k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_80k_coco-stuff164k/deeplabv3_r101-d8_512x512_4x4_80k_coco-stuff164k_20210709_201252-13600dc2.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_80k_coco-stuff164k/deeplabv3_r101-d8_512x512_4x4_80k_coco-stuff164k_20210709_201252.log.json)     |
-| DeepLabV3 | R-50-D8  | 512x512   |  160000 | -        | -              | 41.09 |         41.69 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r50-d8_4xb4-160k_coco-stuff164k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_160k_coco-stuff164k/deeplabv3_r50-d8_512x512_4x4_160k_coco-stuff164k_20210709_163016-49f2812b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_160k_coco-stuff164k/deeplabv3_r50-d8_512x512_4x4_160k_coco-stuff164k_20210709_163016.log.json)     |
-| DeepLabV3 | R-101-D8 | 512x512   |  160000 | -        | -              | 41.82 |         42.49 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r101-d8_4xb4-160k_coco-stuff164k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_160k_coco-stuff164k/deeplabv3_r101-d8_512x512_4x4_160k_coco-stuff164k_20210709_155402-f035acfd.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_160k_coco-stuff164k/deeplabv3_r101-d8_512x512_4x4_160k_coco-stuff164k_20210709_155402.log.json) |
-| DeepLabV3 | R-50-D8  | 512x512   |  320000 | -        | -              | 41.37 |         42.22 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r50-d8_4xb4-320k_coco-stuff164k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_320k_coco-stuff164k/deeplabv3_r50-d8_512x512_4x4_320k_coco-stuff164k_20210709_155403-51b21115.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_320k_coco-stuff164k/deeplabv3_r50-d8_512x512_4x4_320k_coco-stuff164k_20210709_155403.log.json)     |
-| DeepLabV3 | R-101-D8 | 512x512   |  320000 | -        | -              | 42.61 |         43.42 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r101-d8_4xb4-320k_coco-stuff164k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k/deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k_20210709_155402-3cbca14d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k/deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k_20210709_155402.log.json) |
+| Method    | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                                      | download                                                                                                                                                                                                                                                                                                                                                                                                   |
+| --------- | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| DeepLabV3 | R-50-D8  | 512x512   |   80000 | 9.6      | 10.8           | V100   | 39.38 |         40.03 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r50-d8_4xb4-80k_coco-stuff164k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_80k_coco-stuff164k/deeplabv3_r50-d8_512x512_4x4_80k_coco-stuff164k_20210709_163016-88675c24.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_80k_coco-stuff164k/deeplabv3_r50-d8_512x512_4x4_80k_coco-stuff164k_20210709_163016.log.json)         |
+| DeepLabV3 | R-101-D8 | 512x512   |   80000 | 13.2     | 8.7            | V100   | 40.87 |         41.50 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r101-d8_4xb4-80k_coco-stuff164k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_80k_coco-stuff164k/deeplabv3_r101-d8_512x512_4x4_80k_coco-stuff164k_20210709_201252-13600dc2.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_80k_coco-stuff164k/deeplabv3_r101-d8_512x512_4x4_80k_coco-stuff164k_20210709_201252.log.json)     |
+| DeepLabV3 | R-50-D8  | 512x512   |  160000 | -        | -              | V100   | 41.09 |         41.69 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r50-d8_4xb4-160k_coco-stuff164k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_160k_coco-stuff164k/deeplabv3_r50-d8_512x512_4x4_160k_coco-stuff164k_20210709_163016-49f2812b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_160k_coco-stuff164k/deeplabv3_r50-d8_512x512_4x4_160k_coco-stuff164k_20210709_163016.log.json)     |
+| DeepLabV3 | R-101-D8 | 512x512   |  160000 | -        | -              | V100   | 41.82 |         42.49 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r101-d8_4xb4-160k_coco-stuff164k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_160k_coco-stuff164k/deeplabv3_r101-d8_512x512_4x4_160k_coco-stuff164k_20210709_155402-f035acfd.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_160k_coco-stuff164k/deeplabv3_r101-d8_512x512_4x4_160k_coco-stuff164k_20210709_155402.log.json) |
+| DeepLabV3 | R-50-D8  | 512x512   |  320000 | -        | -              | V100   | 41.37 |         42.22 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r50-d8_4xb4-320k_coco-stuff164k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_320k_coco-stuff164k/deeplabv3_r50-d8_512x512_4x4_320k_coco-stuff164k_20210709_155403-51b21115.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_320k_coco-stuff164k/deeplabv3_r50-d8_512x512_4x4_320k_coco-stuff164k_20210709_155403.log.json)     |
+| DeepLabV3 | R-101-D8 | 512x512   |  320000 | -        | -              | V100   | 42.61 |         43.42 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3/deeplabv3_r101-d8_4xb4-320k_coco-stuff164k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k/deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k_20210709_155402-3cbca14d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k/deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k_20210709_155402.log.json) |
 
 Note:
 
 - `D-8` here corresponding to the output stride 8 setting for DeepLab series.
 - `FP16` means Mixed Precision (FP16) is adopted in training.
+
+## Citation
+
+```bibtext
+@article{chen2017rethinking,
+  title={Rethinking atrous convolution for semantic image segmentation},
+  author={Chen, Liang-Chieh and Papandreou, George and Schroff, Florian and Adam, Hartwig},
+  journal={arXiv preprint arXiv:1706.05587},
+  year={2017}
+}
+```
diff --git a/configs/deeplabv3/deeplabv3.yml b/configs/deeplabv3/deeplabv3.yml
deleted file mode 100644
index 6196212992..0000000000
--- a/configs/deeplabv3/deeplabv3.yml
+++ /dev/null
@@ -1,756 +0,0 @@
-Collections:
-- Name: DeepLabV3
-  Metadata:
-    Training Data:
-    - Cityscapes
-    - ADE20K
-    - Pascal VOC 2012 + Aug
-    - Pascal Context
-    - Pascal Context 59
-    - COCO-Stuff 10k
-    - COCO-Stuff 164k
-  Paper:
-    URL: https://arxiv.org/abs/1706.05587
-    Title: Rethinking atrous convolution for semantic image segmentation
-  README: configs/deeplabv3/README.md
-  Code:
-    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54
-    Version: v0.17.0
-  Converted From:
-    Code: https://github.com/tensorflow/models/tree/master/research/deeplab
-Models:
-- Name: deeplabv3_r50-d8_4xb2-40k_cityscapes-512x1024
-  In Collection: DeepLabV3
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,1024)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 389.11
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 6.1
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 79.09
-      mIoU(ms+flip): 80.45
-  Config: configs/deeplabv3/deeplabv3_r50-d8_4xb2-40k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x1024_40k_cityscapes/deeplabv3_r50-d8_512x1024_40k_cityscapes_20200605_022449-acadc2f8.pth
-- Name: deeplabv3_r101-d8_4xb2-40k_cityscapes-512x1024
-  In Collection: DeepLabV3
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,1024)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 520.83
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 9.6
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 77.12
-      mIoU(ms+flip): 79.61
-  Config: configs/deeplabv3/deeplabv3_r101-d8_4xb2-40k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x1024_40k_cityscapes/deeplabv3_r101-d8_512x1024_40k_cityscapes_20200605_012241-7fd3f799.pth
-- Name: deeplabv3_r50-d8_4xb2-40k_cityscapes-769x769
-  In Collection: DeepLabV3
-  Metadata:
-    backbone: R-50-D8
-    crop size: (769,769)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 900.9
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (769,769)
-    Training Memory (GB): 6.9
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 78.58
-      mIoU(ms+flip): 79.89
-  Config: configs/deeplabv3/deeplabv3_r50-d8_4xb2-40k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_769x769_40k_cityscapes/deeplabv3_r50-d8_769x769_40k_cityscapes_20200606_113723-7eda553c.pth
-- Name: deeplabv3_r101-d8_4xb2-40k_cityscapes-769x769
-  In Collection: DeepLabV3
-  Metadata:
-    backbone: R-101-D8
-    crop size: (769,769)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 1204.82
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (769,769)
-    Training Memory (GB): 10.9
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 79.27
-      mIoU(ms+flip): 80.11
-  Config: configs/deeplabv3/deeplabv3_r101-d8_4xb2-40k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_769x769_40k_cityscapes/deeplabv3_r101-d8_769x769_40k_cityscapes_20200606_113809-c64f889f.pth
-- Name: deeplabv3_r18-d8_4xb2-80k_cityscapes-512x1024
-  In Collection: DeepLabV3
-  Metadata:
-    backbone: R-18-D8
-    crop size: (512,1024)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 72.57
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 1.7
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 76.7
-      mIoU(ms+flip): 78.27
-  Config: configs/deeplabv3/deeplabv3_r18-d8_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18-d8_512x1024_80k_cityscapes/deeplabv3_r18-d8_512x1024_80k_cityscapes_20201225_021506-23dffbe2.pth
-- Name: deeplabv3_r50-d8_4xb2-80k_cityscapes-512x1024
-  In Collection: DeepLabV3
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,1024)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 79.32
-      mIoU(ms+flip): 80.57
-  Config: configs/deeplabv3/deeplabv3_r50-d8_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x1024_80k_cityscapes/deeplabv3_r50-d8_512x1024_80k_cityscapes_20200606_113404-b92cfdd4.pth
-- Name: deeplabv3_r101-d8_4xb2-80k_cityscapes-512x1024
-  In Collection: DeepLabV3
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,1024)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 80.2
-      mIoU(ms+flip): 81.21
-  Config: configs/deeplabv3/deeplabv3_r101-d8_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes/deeplabv3_r101-d8_512x1024_80k_cityscapes_20200606_113503-9e428899.pth
-- Name: deeplabv3_r101-d8_4xb2-amp-80k_cityscapes-512x1024
-  In Collection: DeepLabV3
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,1024)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 259.07
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: AMP
-      resolution: (512,1024)
-    Training Memory (GB): 5.75
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 80.48
-  Config: configs/deeplabv3/deeplabv3_r101-d8_4xb2-amp-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_fp16_512x1024_80k_cityscapes/deeplabv3_r101-d8_fp16_512x1024_80k_cityscapes_20200717_230920-774d9cec.pth
-- Name: deeplabv3_r18-d8_4xb2-80k_cityscapes-769x769
-  In Collection: DeepLabV3
-  Metadata:
-    backbone: R-18-D8
-    crop size: (769,769)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 180.18
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (769,769)
-    Training Memory (GB): 1.9
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 76.6
-      mIoU(ms+flip): 78.26
-  Config: configs/deeplabv3/deeplabv3_r18-d8_4xb2-80k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18-d8_769x769_80k_cityscapes/deeplabv3_r18-d8_769x769_80k_cityscapes_20201225_021506-6452126a.pth
-- Name: deeplabv3_r50-d8_4xb2-80k_cityscapes-769x769
-  In Collection: DeepLabV3
-  Metadata:
-    backbone: R-50-D8
-    crop size: (769,769)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 79.89
-      mIoU(ms+flip): 81.06
-  Config: configs/deeplabv3/deeplabv3_r50-d8_4xb2-80k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_769x769_80k_cityscapes/deeplabv3_r50-d8_769x769_80k_cityscapes_20200606_221338-788d6228.pth
-- Name: deeplabv3_r101-d8_4xb2-80k_cityscapes-769x769
-  In Collection: DeepLabV3
-  Metadata:
-    backbone: R-101-D8
-    crop size: (769,769)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 79.67
-      mIoU(ms+flip): 80.81
-  Config: configs/deeplabv3/deeplabv3_r101-d8_4xb2-80k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_769x769_80k_cityscapes/deeplabv3_r101-d8_769x769_80k_cityscapes_20200607_013353-60e95418.pth
-- Name: deeplabv3_r101-d16-mg124_4xb2-80k_cityscapes-512x1024
-  In Collection: DeepLabV3
-  Metadata:
-    backbone: R-101-D16-MG124
-    crop size: (512,1024)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 78.36
-      mIoU(ms+flip): 79.84
-  Config: configs/deeplabv3/deeplabv3_r101-d16-mg124_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes_20200908_005644-57bb8425.pth
-- Name: deeplabv3_r18b-d8_4xb2-80k_cityscapes-512x1024
-  In Collection: DeepLabV3
-  Metadata:
-    backbone: R-18b-D8
-    crop size: (512,1024)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 71.79
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 1.6
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 76.26
-      mIoU(ms+flip): 77.88
-  Config: configs/deeplabv3/deeplabv3_r18b-d8_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18b-d8_512x1024_80k_cityscapes/deeplabv3_r18b-d8_512x1024_80k_cityscapes_20201225_094144-46040cef.pth
-- Name: deeplabv3_r50b-d8_4xb2-80k_cityscapes-512x1024
-  In Collection: DeepLabV3
-  Metadata:
-    backbone: R-50b-D8
-    crop size: (512,1024)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 364.96
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 6.0
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 79.63
-      mIoU(ms+flip): 80.98
-  Config: configs/deeplabv3/deeplabv3_r50b-d8_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50b-d8_512x1024_80k_cityscapes/deeplabv3_r50b-d8_512x1024_80k_cityscapes_20201225_155148-ec368954.pth
-- Name: deeplabv3_r101b-d8_4xb2-80k_cityscapes-512x1024
-  In Collection: DeepLabV3
-  Metadata:
-    backbone: R-101b-D8
-    crop size: (512,1024)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 552.49
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 9.5
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 80.01
-      mIoU(ms+flip): 81.21
-  Config: configs/deeplabv3/deeplabv3_r101b-d8_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101b-d8_512x1024_80k_cityscapes/deeplabv3_r101b-d8_512x1024_80k_cityscapes_20201226_171821-8fd49503.pth
-- Name: deeplabv3_r18b-d8_4xb2-80k_cityscapes-769x769
-  In Collection: DeepLabV3
-  Metadata:
-    backbone: R-18b-D8
-    crop size: (769,769)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 172.71
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (769,769)
-    Training Memory (GB): 1.8
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 75.63
-      mIoU(ms+flip): 77.51
-  Config: configs/deeplabv3/deeplabv3_r18b-d8_4xb2-80k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18b-d8_769x769_80k_cityscapes/deeplabv3_r18b-d8_769x769_80k_cityscapes_20201225_094144-fdc985d9.pth
-- Name: deeplabv3_r50b-d8_4xb2-80k_cityscapes-769x769
-  In Collection: DeepLabV3
-  Metadata:
-    backbone: R-50b-D8
-    crop size: (769,769)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 862.07
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (769,769)
-    Training Memory (GB): 6.8
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 78.8
-      mIoU(ms+flip): 80.27
-  Config: configs/deeplabv3/deeplabv3_r50b-d8_4xb2-80k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50b-d8_769x769_80k_cityscapes/deeplabv3_r50b-d8_769x769_80k_cityscapes_20201225_155404-87fb0cf4.pth
-- Name: deeplabv3_r101b-d8_4xb2-80k_cityscapes-769x769
-  In Collection: DeepLabV3
-  Metadata:
-    backbone: R-101b-D8
-    crop size: (769,769)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 1219.51
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (769,769)
-    Training Memory (GB): 10.7
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 79.41
-      mIoU(ms+flip): 80.73
-  Config: configs/deeplabv3/deeplabv3_r101b-d8_4xb2-80k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101b-d8_769x769_80k_cityscapes/deeplabv3_r101b-d8_769x769_80k_cityscapes_20201226_190843-9142ee57.pth
-- Name: deeplabv3_r50-d8_4xb4-80k_ade20k-512x512
-  In Collection: DeepLabV3
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 67.75
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 8.9
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 42.42
-      mIoU(ms+flip): 43.28
-  Config: configs/deeplabv3/deeplabv3_r50-d8_4xb4-80k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_80k_ade20k/deeplabv3_r50-d8_512x512_80k_ade20k_20200614_185028-0bb3f844.pth
-- Name: deeplabv3_r101-d8_4xb4-80k_ade20k-512x512
-  In Collection: DeepLabV3
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 98.62
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 12.4
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 44.08
-      mIoU(ms+flip): 45.19
-  Config: configs/deeplabv3/deeplabv3_r101-d8_4xb4-80k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_80k_ade20k/deeplabv3_r101-d8_512x512_80k_ade20k_20200615_021256-d89c7fa4.pth
-- Name: deeplabv3_r50-d8_4xb4-160k_ade20k-512x512
-  In Collection: DeepLabV3
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 160000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 42.66
-      mIoU(ms+flip): 44.09
-  Config: configs/deeplabv3/deeplabv3_r50-d8_4xb4-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_160k_ade20k/deeplabv3_r50-d8_512x512_160k_ade20k_20200615_123227-5d0ee427.pth
-- Name: deeplabv3_r101-d8_4xb4-160k_ade20k-512x512
-  In Collection: DeepLabV3
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 160000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 45.0
-      mIoU(ms+flip): 46.66
-  Config: configs/deeplabv3/deeplabv3_r101-d8_4xb4-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k/deeplabv3_r101-d8_512x512_160k_ade20k_20200615_105816-b1f72b3b.pth
-- Name: deeplabv3_r50-d8_4xb4-20k_voc12aug-512x512
-  In Collection: DeepLabV3
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 20000
-    inference time (ms/im):
-    - value: 72.05
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 6.1
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal VOC 2012 + Aug
-    Metrics:
-      mIoU: 76.17
-      mIoU(ms+flip): 77.42
-  Config: configs/deeplabv3/deeplabv3_r50-d8_4xb4-20k_voc12aug-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_20k_voc12aug/deeplabv3_r50-d8_512x512_20k_voc12aug_20200617_010906-596905ef.pth
-- Name: deeplabv3_r101-d8_4xb4-20k_voc12aug-512x512
-  In Collection: DeepLabV3
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 20000
-    inference time (ms/im):
-    - value: 101.94
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 9.6
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal VOC 2012 + Aug
-    Metrics:
-      mIoU: 78.7
-      mIoU(ms+flip): 79.95
-  Config: configs/deeplabv3/deeplabv3_r101-d8_4xb4-20k_voc12aug-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_20k_voc12aug/deeplabv3_r101-d8_512x512_20k_voc12aug_20200617_010932-8d13832f.pth
-- Name: deeplabv3_r50-d8_4xb4-40k_voc12aug-512x512
-  In Collection: DeepLabV3
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 40000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal VOC 2012 + Aug
-    Metrics:
-      mIoU: 77.68
-      mIoU(ms+flip): 78.78
-  Config: configs/deeplabv3/deeplabv3_r50-d8_4xb4-40k_voc12aug-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_40k_voc12aug/deeplabv3_r50-d8_512x512_40k_voc12aug_20200613_161546-2ae96e7e.pth
-- Name: deeplabv3_r101-d8_4xb4-40k_voc12aug-512x512
-  In Collection: DeepLabV3
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 40000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal VOC 2012 + Aug
-    Metrics:
-      mIoU: 77.92
-      mIoU(ms+flip): 79.18
-  Config: configs/deeplabv3/deeplabv3_r101-d8_4xb4-40k_voc12aug-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_40k_voc12aug/deeplabv3_r101-d8_512x512_40k_voc12aug_20200613_161432-0017d784.pth
-- Name: deeplabv3_r101-d8_4xb4-40k_pascal-context-480x480
-  In Collection: DeepLabV3
-  Metadata:
-    backbone: R-101-D8
-    crop size: (480,480)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 141.04
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (480,480)
-    Training Memory (GB): 9.2
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal Context
-    Metrics:
-      mIoU: 46.55
-      mIoU(ms+flip): 47.81
-  Config: configs/deeplabv3/deeplabv3_r101-d8_4xb4-40k_pascal-context-480x480.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context/deeplabv3_r101-d8_480x480_40k_pascal_context_20200911_204118-1aa27336.pth
-- Name: deeplabv3_r101-d8_4xb4-80k_pascal-context-480x480
-  In Collection: DeepLabV3
-  Metadata:
-    backbone: R-101-D8
-    crop size: (480,480)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal Context
-    Metrics:
-      mIoU: 46.42
-      mIoU(ms+flip): 47.53
-  Config: configs/deeplabv3/deeplabv3_r101-d8_4xb4-80k_pascal-context-480x480.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context/deeplabv3_r101-d8_480x480_80k_pascal_context_20200911_170155-2a21fff3.pth
-- Name: deeplabv3_r101-d8_4xb4-40k_pascal-context-59-480x480
-  In Collection: DeepLabV3
-  Metadata:
-    backbone: R-101-D8
-    crop size: (480,480)
-    lr schd: 40000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal Context 59
-    Metrics:
-      mIoU: 52.61
-      mIoU(ms+flip): 54.28
-  Config: configs/deeplabv3/deeplabv3_r101-d8_4xb4-40k_pascal-context-59-480x480.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59/deeplabv3_r101-d8_480x480_40k_pascal_context_59_20210416_110332-cb08ea46.pth
-- Name: deeplabv3_r101-d8_4xb4-80k_pascal-context-59-480x480
-  In Collection: DeepLabV3
-  Metadata:
-    backbone: R-101-D8
-    crop size: (480,480)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal Context 59
-    Metrics:
-      mIoU: 52.46
-      mIoU(ms+flip): 54.09
-  Config: configs/deeplabv3/deeplabv3_r101-d8_4xb4-80k_pascal-context-59-480x480.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context_59/deeplabv3_r101-d8_480x480_80k_pascal_context_59_20210416_113002-26303993.pth
-- Name: deeplabv3_r50-d8_4xb4-20k_coco-stuff10k-512x512
-  In Collection: DeepLabV3
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 20000
-    inference time (ms/im):
-    - value: 92.59
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 9.6
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: COCO-Stuff 10k
-    Metrics:
-      mIoU: 34.66
-      mIoU(ms+flip): 36.08
-  Config: configs/deeplabv3/deeplabv3_r50-d8_4xb4-20k_coco-stuff10k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_20k_coco-stuff10k/deeplabv3_r50-d8_512x512_4x4_20k_coco-stuff10k_20210821_043025-b35f789d.pth
-- Name: deeplabv3_r101-d8_4xb4-20k_coco-stuff10k-512x512
-  In Collection: DeepLabV3
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 20000
-    inference time (ms/im):
-    - value: 114.94
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 13.2
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: COCO-Stuff 10k
-    Metrics:
-      mIoU: 37.3
-      mIoU(ms+flip): 38.42
-  Config: configs/deeplabv3/deeplabv3_r101-d8_4xb4-20k_coco-stuff10k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_20k_coco-stuff10k/deeplabv3_r101-d8_512x512_4x4_20k_coco-stuff10k_20210821_043025-c49752cb.pth
-- Name: deeplabv3_r50-d8_4xb4-40k_coco-stuff10k-512x512
-  In Collection: DeepLabV3
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 40000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: COCO-Stuff 10k
-    Metrics:
-      mIoU: 35.73
-      mIoU(ms+flip): 37.09
-  Config: configs/deeplabv3/deeplabv3_r50-d8_4xb4-40k_coco-stuff10k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_40k_coco-stuff10k/deeplabv3_r50-d8_512x512_4x4_40k_coco-stuff10k_20210821_043305-dc76f3ff.pth
-- Name: deeplabv3_r101-d8_4xb4-40k_coco-stuff10k-512x512
-  In Collection: DeepLabV3
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 40000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: COCO-Stuff 10k
-    Metrics:
-      mIoU: 37.81
-      mIoU(ms+flip): 38.8
-  Config: configs/deeplabv3/deeplabv3_r101-d8_4xb4-40k_coco-stuff10k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_40k_coco-stuff10k/deeplabv3_r101-d8_512x512_4x4_40k_coco-stuff10k_20210821_043305-636cb433.pth
-- Name: deeplabv3_r50-d8_4xb4-80k_coco-stuff164k-512x512
-  In Collection: DeepLabV3
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 92.59
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 9.6
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: COCO-Stuff 164k
-    Metrics:
-      mIoU: 39.38
-      mIoU(ms+flip): 40.03
-  Config: configs/deeplabv3/deeplabv3_r50-d8_4xb4-80k_coco-stuff164k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_80k_coco-stuff164k/deeplabv3_r50-d8_512x512_4x4_80k_coco-stuff164k_20210709_163016-88675c24.pth
-- Name: deeplabv3_r101-d8_4xb4-80k_coco-stuff164k-512x512
-  In Collection: DeepLabV3
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 114.94
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 13.2
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: COCO-Stuff 164k
-    Metrics:
-      mIoU: 40.87
-      mIoU(ms+flip): 41.5
-  Config: configs/deeplabv3/deeplabv3_r101-d8_4xb4-80k_coco-stuff164k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_80k_coco-stuff164k/deeplabv3_r101-d8_512x512_4x4_80k_coco-stuff164k_20210709_201252-13600dc2.pth
-- Name: deeplabv3_r50-d8_4xb4-160k_coco-stuff164k-512x512
-  In Collection: DeepLabV3
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 160000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: COCO-Stuff 164k
-    Metrics:
-      mIoU: 41.09
-      mIoU(ms+flip): 41.69
-  Config: configs/deeplabv3/deeplabv3_r50-d8_4xb4-160k_coco-stuff164k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_160k_coco-stuff164k/deeplabv3_r50-d8_512x512_4x4_160k_coco-stuff164k_20210709_163016-49f2812b.pth
-- Name: deeplabv3_r101-d8_4xb4-160k_coco-stuff164k-512x512
-  In Collection: DeepLabV3
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 160000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: COCO-Stuff 164k
-    Metrics:
-      mIoU: 41.82
-      mIoU(ms+flip): 42.49
-  Config: configs/deeplabv3/deeplabv3_r101-d8_4xb4-160k_coco-stuff164k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_160k_coco-stuff164k/deeplabv3_r101-d8_512x512_4x4_160k_coco-stuff164k_20210709_155402-f035acfd.pth
-- Name: deeplabv3_r50-d8_4xb4-320k_coco-stuff164k-512x512
-  In Collection: DeepLabV3
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 320000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: COCO-Stuff 164k
-    Metrics:
-      mIoU: 41.37
-      mIoU(ms+flip): 42.22
-  Config: configs/deeplabv3/deeplabv3_r50-d8_4xb4-320k_coco-stuff164k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_320k_coco-stuff164k/deeplabv3_r50-d8_512x512_4x4_320k_coco-stuff164k_20210709_155403-51b21115.pth
-- Name: deeplabv3_r101-d8_4xb4-320k_coco-stuff164k-512x512
-  In Collection: DeepLabV3
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 320000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: COCO-Stuff 164k
-    Metrics:
-      mIoU: 42.61
-      mIoU(ms+flip): 43.42
-  Config: configs/deeplabv3/deeplabv3_r101-d8_4xb4-320k_coco-stuff164k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k/deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k_20210709_155402-3cbca14d.pth
diff --git a/configs/deeplabv3/metafile.yaml b/configs/deeplabv3/metafile.yaml
new file mode 100644
index 0000000000..650f7d695d
--- /dev/null
+++ b/configs/deeplabv3/metafile.yaml
@@ -0,0 +1,985 @@
+Collections:
+- Name: DeepLabV3
+  License: Apache License 2.0
+  Metadata:
+    Training Data:
+    - Cityscapes
+    - ADE20K
+    - Pascal VOC 2012 + Aug
+    - Pascal Context
+    - Pascal Context 59
+    - COCO-Stuff 10k
+    - COCO-Stuff 164k
+  Paper:
+    Title: Rethinking atrous convolution for semantic image segmentation
+    URL: https://arxiv.org/abs/1706.05587
+  README: configs/deeplabv3/README.md
+  Frameworks:
+  - PyTorch
+Models:
+- Name: deeplabv3_r50-d8_4xb2-40k_cityscapes-512x1024
+  In Collection: DeepLabV3
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.09
+      mIoU(ms+flip): 80.45
+  Config: configs/deeplabv3/deeplabv3_r50-d8_4xb2-40k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - DeepLabV3
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 6.1
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x1024_40k_cityscapes/deeplabv3_r50-d8_512x1024_40k_cityscapes_20200605_022449-acadc2f8.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x1024_40k_cityscapes/deeplabv3_r50-d8_512x1024_40k_cityscapes_20200605_022449.log.json
+  Paper:
+    Title: Rethinking atrous convolution for semantic image segmentation
+    URL: https://arxiv.org/abs/1706.05587
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54
+  Framework: PyTorch
+- Name: deeplabv3_r101-d8_4xb2-40k_cityscapes-512x1024
+  In Collection: DeepLabV3
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.12
+      mIoU(ms+flip): 79.61
+  Config: configs/deeplabv3/deeplabv3_r101-d8_4xb2-40k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - DeepLabV3
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 9.6
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x1024_40k_cityscapes/deeplabv3_r101-d8_512x1024_40k_cityscapes_20200605_012241-7fd3f799.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x1024_40k_cityscapes/deeplabv3_r101-d8_512x1024_40k_cityscapes_20200605_012241.log.json
+  Paper:
+    Title: Rethinking atrous convolution for semantic image segmentation
+    URL: https://arxiv.org/abs/1706.05587
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54
+  Framework: PyTorch
+- Name: deeplabv3_r50-d8_4xb2-40k_cityscapes-769x769
+  In Collection: DeepLabV3
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.58
+      mIoU(ms+flip): 79.89
+  Config: configs/deeplabv3/deeplabv3_r50-d8_4xb2-40k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - DeepLabV3
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 6.9
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_769x769_40k_cityscapes/deeplabv3_r50-d8_769x769_40k_cityscapes_20200606_113723-7eda553c.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_769x769_40k_cityscapes/deeplabv3_r50-d8_769x769_40k_cityscapes_20200606_113723.log.json
+  Paper:
+    Title: Rethinking atrous convolution for semantic image segmentation
+    URL: https://arxiv.org/abs/1706.05587
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54
+  Framework: PyTorch
+- Name: deeplabv3_r101-d8_4xb2-40k_cityscapes-769x769
+  In Collection: DeepLabV3
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.27
+      mIoU(ms+flip): 80.11
+  Config: configs/deeplabv3/deeplabv3_r101-d8_4xb2-40k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - DeepLabV3
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 10.9
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_769x769_40k_cityscapes/deeplabv3_r101-d8_769x769_40k_cityscapes_20200606_113809-c64f889f.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_769x769_40k_cityscapes/deeplabv3_r101-d8_769x769_40k_cityscapes_20200606_113809.log.json
+  Paper:
+    Title: Rethinking atrous convolution for semantic image segmentation
+    URL: https://arxiv.org/abs/1706.05587
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54
+  Framework: PyTorch
+- Name: deeplabv3_r18-d8_4xb2-80k_cityscapes-512x1024
+  In Collection: DeepLabV3
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 76.7
+      mIoU(ms+flip): 78.27
+  Config: configs/deeplabv3/deeplabv3_r18-d8_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-18-D8
+    - DeepLabV3
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 1.7
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18-d8_512x1024_80k_cityscapes/deeplabv3_r18-d8_512x1024_80k_cityscapes_20201225_021506-23dffbe2.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18-d8_512x1024_80k_cityscapes/deeplabv3_r18-d8_512x1024_80k_cityscapes-20201225_021506.log.json
+  Paper:
+    Title: Rethinking atrous convolution for semantic image segmentation
+    URL: https://arxiv.org/abs/1706.05587
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54
+  Framework: PyTorch
+- Name: deeplabv3_r50-d8_4xb2-80k_cityscapes-512x1024
+  In Collection: DeepLabV3
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.32
+      mIoU(ms+flip): 80.57
+  Config: configs/deeplabv3/deeplabv3_r50-d8_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - DeepLabV3
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x1024_80k_cityscapes/deeplabv3_r50-d8_512x1024_80k_cityscapes_20200606_113404-b92cfdd4.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x1024_80k_cityscapes/deeplabv3_r50-d8_512x1024_80k_cityscapes_20200606_113404.log.json
+  Paper:
+    Title: Rethinking atrous convolution for semantic image segmentation
+    URL: https://arxiv.org/abs/1706.05587
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54
+  Framework: PyTorch
+- Name: deeplabv3_r101-d8_4xb2-80k_cityscapes-512x1024
+  In Collection: DeepLabV3
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 80.2
+      mIoU(ms+flip): 81.21
+  Config: configs/deeplabv3/deeplabv3_r101-d8_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - DeepLabV3
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes/deeplabv3_r101-d8_512x1024_80k_cityscapes_20200606_113503-9e428899.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes/deeplabv3_r101-d8_512x1024_80k_cityscapes_20200606_113503.log.json
+  Paper:
+    Title: Rethinking atrous convolution for semantic image segmentation
+    URL: https://arxiv.org/abs/1706.05587
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54
+  Framework: PyTorch
+- Name: deeplabv3_r101-d8_4xb2-amp-80k_cityscapes-512x1024
+  In Collection: DeepLabV3
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 80.48
+  Config: configs/deeplabv3/deeplabv3_r101-d8_4xb2-amp-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - DeepLabV3
+    - (FP16)
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 5.75
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_fp16_512x1024_80k_cityscapes/deeplabv3_r101-d8_fp16_512x1024_80k_cityscapes_20200717_230920-774d9cec.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_fp16_512x1024_80k_cityscapes/deeplabv3_r101-d8_fp16_512x1024_80k_cityscapes_20200717_230920.log.json
+  Paper:
+    Title: Rethinking atrous convolution for semantic image segmentation
+    URL: https://arxiv.org/abs/1706.05587
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54
+  Framework: PyTorch
+- Name: deeplabv3_r18-d8_4xb2-80k_cityscapes-769x769
+  In Collection: DeepLabV3
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 76.6
+      mIoU(ms+flip): 78.26
+  Config: configs/deeplabv3/deeplabv3_r18-d8_4xb2-80k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-18-D8
+    - DeepLabV3
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 1.9
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18-d8_769x769_80k_cityscapes/deeplabv3_r18-d8_769x769_80k_cityscapes_20201225_021506-6452126a.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18-d8_769x769_80k_cityscapes/deeplabv3_r18-d8_769x769_80k_cityscapes-20201225_021506.log.json
+  Paper:
+    Title: Rethinking atrous convolution for semantic image segmentation
+    URL: https://arxiv.org/abs/1706.05587
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54
+  Framework: PyTorch
+- Name: deeplabv3_r50-d8_4xb2-80k_cityscapes-769x769
+  In Collection: DeepLabV3
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.89
+      mIoU(ms+flip): 81.06
+  Config: configs/deeplabv3/deeplabv3_r50-d8_4xb2-80k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - DeepLabV3
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_769x769_80k_cityscapes/deeplabv3_r50-d8_769x769_80k_cityscapes_20200606_221338-788d6228.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_769x769_80k_cityscapes/deeplabv3_r50-d8_769x769_80k_cityscapes_20200606_221338.log.json
+  Paper:
+    Title: Rethinking atrous convolution for semantic image segmentation
+    URL: https://arxiv.org/abs/1706.05587
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54
+  Framework: PyTorch
+- Name: deeplabv3_r101-d8_4xb2-80k_cityscapes-769x769
+  In Collection: DeepLabV3
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.67
+      mIoU(ms+flip): 80.81
+  Config: configs/deeplabv3/deeplabv3_r101-d8_4xb2-80k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - DeepLabV3
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_769x769_80k_cityscapes/deeplabv3_r101-d8_769x769_80k_cityscapes_20200607_013353-60e95418.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_769x769_80k_cityscapes/deeplabv3_r101-d8_769x769_80k_cityscapes_20200607_013353.log.json
+  Paper:
+    Title: Rethinking atrous convolution for semantic image segmentation
+    URL: https://arxiv.org/abs/1706.05587
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54
+  Framework: PyTorch
+- Name: deeplabv3_r101-d16-mg124_4xb2-40k_cityscapes-512x1024
+  In Collection: DeepLabV3
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 76.71
+      mIoU(ms+flip): 78.63
+  Config: configs/deeplabv3/deeplabv3_r101-d16-mg124_4xb2-40k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D16-MG124
+    - DeepLabV3
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 4.7
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes/deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes_20200908_005644-67b0c992.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes/deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes-20200908_005644.log.json
+  Paper:
+    Title: Rethinking atrous convolution for semantic image segmentation
+    URL: https://arxiv.org/abs/1706.05587
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54
+  Framework: PyTorch
+- Name: deeplabv3_r101-d16-mg124_4xb2-80k_cityscapes-512x1024
+  In Collection: DeepLabV3
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.36
+      mIoU(ms+flip): 79.84
+  Config: configs/deeplabv3/deeplabv3_r101-d16-mg124_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D16-MG124
+    - DeepLabV3
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes_20200908_005644-57bb8425.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes-20200908_005644.log.json
+  Paper:
+    Title: Rethinking atrous convolution for semantic image segmentation
+    URL: https://arxiv.org/abs/1706.05587
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54
+  Framework: PyTorch
+- Name: deeplabv3_r18b-d8_4xb2-80k_cityscapes-512x1024
+  In Collection: DeepLabV3
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 76.26
+      mIoU(ms+flip): 77.88
+  Config: configs/deeplabv3/deeplabv3_r18b-d8_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-18b-D8
+    - DeepLabV3
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 1.6
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18b-d8_512x1024_80k_cityscapes/deeplabv3_r18b-d8_512x1024_80k_cityscapes_20201225_094144-46040cef.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18b-d8_512x1024_80k_cityscapes/deeplabv3_r18b-d8_512x1024_80k_cityscapes-20201225_094144.log.json
+  Paper:
+    Title: Rethinking atrous convolution for semantic image segmentation
+    URL: https://arxiv.org/abs/1706.05587
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54
+  Framework: PyTorch
+- Name: deeplabv3_r50b-d8_4xb2-80k_cityscapes-512x1024
+  In Collection: DeepLabV3
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.63
+      mIoU(ms+flip): 80.98
+  Config: configs/deeplabv3/deeplabv3_r50b-d8_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50b-D8
+    - DeepLabV3
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 6.0
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50b-d8_512x1024_80k_cityscapes/deeplabv3_r50b-d8_512x1024_80k_cityscapes_20201225_155148-ec368954.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50b-d8_512x1024_80k_cityscapes/deeplabv3_r50b-d8_512x1024_80k_cityscapes-20201225_155148.log.json
+  Paper:
+    Title: Rethinking atrous convolution for semantic image segmentation
+    URL: https://arxiv.org/abs/1706.05587
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54
+  Framework: PyTorch
+- Name: deeplabv3_r101b-d8_4xb2-80k_cityscapes-512x1024
+  In Collection: DeepLabV3
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 80.01
+      mIoU(ms+flip): 81.21
+  Config: configs/deeplabv3/deeplabv3_r101b-d8_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101b-D8
+    - DeepLabV3
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 9.5
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101b-d8_512x1024_80k_cityscapes/deeplabv3_r101b-d8_512x1024_80k_cityscapes_20201226_171821-8fd49503.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101b-d8_512x1024_80k_cityscapes/deeplabv3_r101b-d8_512x1024_80k_cityscapes-20201226_171821.log.json
+  Paper:
+    Title: Rethinking atrous convolution for semantic image segmentation
+    URL: https://arxiv.org/abs/1706.05587
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54
+  Framework: PyTorch
+- Name: deeplabv3_r18b-d8_4xb2-80k_cityscapes-769x769
+  In Collection: DeepLabV3
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 75.63
+      mIoU(ms+flip): 77.51
+  Config: configs/deeplabv3/deeplabv3_r18b-d8_4xb2-80k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-18b-D8
+    - DeepLabV3
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 1.8
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18b-d8_769x769_80k_cityscapes/deeplabv3_r18b-d8_769x769_80k_cityscapes_20201225_094144-fdc985d9.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18b-d8_769x769_80k_cityscapes/deeplabv3_r18b-d8_769x769_80k_cityscapes-20201225_094144.log.json
+  Paper:
+    Title: Rethinking atrous convolution for semantic image segmentation
+    URL: https://arxiv.org/abs/1706.05587
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54
+  Framework: PyTorch
+- Name: deeplabv3_r50b-d8_4xb2-80k_cityscapes-769x769
+  In Collection: DeepLabV3
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.8
+      mIoU(ms+flip): 80.27
+  Config: configs/deeplabv3/deeplabv3_r50b-d8_4xb2-80k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50b-D8
+    - DeepLabV3
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 6.8
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50b-d8_769x769_80k_cityscapes/deeplabv3_r50b-d8_769x769_80k_cityscapes_20201225_155404-87fb0cf4.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50b-d8_769x769_80k_cityscapes/deeplabv3_r50b-d8_769x769_80k_cityscapes-20201225_155404.log.json
+  Paper:
+    Title: Rethinking atrous convolution for semantic image segmentation
+    URL: https://arxiv.org/abs/1706.05587
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54
+  Framework: PyTorch
+- Name: deeplabv3_r101b-d8_4xb2-80k_cityscapes-769x769
+  In Collection: DeepLabV3
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.41
+      mIoU(ms+flip): 80.73
+  Config: configs/deeplabv3/deeplabv3_r101b-d8_4xb2-80k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101b-D8
+    - DeepLabV3
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 10.7
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101b-d8_769x769_80k_cityscapes/deeplabv3_r101b-d8_769x769_80k_cityscapes_20201226_190843-9142ee57.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101b-d8_769x769_80k_cityscapes/deeplabv3_r101b-d8_769x769_80k_cityscapes-20201226_190843.log.json
+  Paper:
+    Title: Rethinking atrous convolution for semantic image segmentation
+    URL: https://arxiv.org/abs/1706.05587
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54
+  Framework: PyTorch
+- Name: deeplabv3_r50-d8_4xb4-80k_ade20k-512x512
+  In Collection: DeepLabV3
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 42.42
+      mIoU(ms+flip): 43.28
+  Config: configs/deeplabv3/deeplabv3_r50-d8_4xb4-80k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - DeepLabV3
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 8.9
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_80k_ade20k/deeplabv3_r50-d8_512x512_80k_ade20k_20200614_185028-0bb3f844.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_80k_ade20k/deeplabv3_r50-d8_512x512_80k_ade20k_20200614_185028.log.json
+  Paper:
+    Title: Rethinking atrous convolution for semantic image segmentation
+    URL: https://arxiv.org/abs/1706.05587
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54
+  Framework: PyTorch
+- Name: deeplabv3_r101-d8_4xb4-80k_ade20k-512x512
+  In Collection: DeepLabV3
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 44.08
+      mIoU(ms+flip): 45.19
+  Config: configs/deeplabv3/deeplabv3_r101-d8_4xb4-80k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - DeepLabV3
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 12.4
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_80k_ade20k/deeplabv3_r101-d8_512x512_80k_ade20k_20200615_021256-d89c7fa4.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_80k_ade20k/deeplabv3_r101-d8_512x512_80k_ade20k_20200615_021256.log.json
+  Paper:
+    Title: Rethinking atrous convolution for semantic image segmentation
+    URL: https://arxiv.org/abs/1706.05587
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54
+  Framework: PyTorch
+- Name: deeplabv3_r50-d8_4xb4-160k_ade20k-512x512
+  In Collection: DeepLabV3
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 42.66
+      mIoU(ms+flip): 44.09
+  Config: configs/deeplabv3/deeplabv3_r50-d8_4xb4-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - DeepLabV3
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_160k_ade20k/deeplabv3_r50-d8_512x512_160k_ade20k_20200615_123227-5d0ee427.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_160k_ade20k/deeplabv3_r50-d8_512x512_160k_ade20k_20200615_123227.log.json
+  Paper:
+    Title: Rethinking atrous convolution for semantic image segmentation
+    URL: https://arxiv.org/abs/1706.05587
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54
+  Framework: PyTorch
+- Name: deeplabv3_r101-d8_4xb4-160k_ade20k-512x512
+  In Collection: DeepLabV3
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 45.0
+      mIoU(ms+flip): 46.66
+  Config: configs/deeplabv3/deeplabv3_r101-d8_4xb4-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - DeepLabV3
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k/deeplabv3_r101-d8_512x512_160k_ade20k_20200615_105816-b1f72b3b.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k/deeplabv3_r101-d8_512x512_160k_ade20k_20200615_105816.log.json
+  Paper:
+    Title: Rethinking atrous convolution for semantic image segmentation
+    URL: https://arxiv.org/abs/1706.05587
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54
+  Framework: PyTorch
+- Name: deeplabv3_r50-d8_4xb4-20k_voc12aug-512x512
+  In Collection: DeepLabV3
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 76.17
+      mIoU(ms+flip): 77.42
+  Config: configs/deeplabv3/deeplabv3_r50-d8_4xb4-20k_voc12aug-512x512.py
+  Metadata:
+    Training Data: Pascal VOC 2012 + Aug
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - DeepLabV3
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 6.1
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_20k_voc12aug/deeplabv3_r50-d8_512x512_20k_voc12aug_20200617_010906-596905ef.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_20k_voc12aug/deeplabv3_r50-d8_512x512_20k_voc12aug_20200617_010906.log.json
+  Paper:
+    Title: Rethinking atrous convolution for semantic image segmentation
+    URL: https://arxiv.org/abs/1706.05587
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54
+  Framework: PyTorch
+- Name: deeplabv3_r101-d8_4xb4-20k_voc12aug-512x512
+  In Collection: DeepLabV3
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 78.7
+      mIoU(ms+flip): 79.95
+  Config: configs/deeplabv3/deeplabv3_r101-d8_4xb4-20k_voc12aug-512x512.py
+  Metadata:
+    Training Data: Pascal VOC 2012 + Aug
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - DeepLabV3
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 9.6
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_20k_voc12aug/deeplabv3_r101-d8_512x512_20k_voc12aug_20200617_010932-8d13832f.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_20k_voc12aug/deeplabv3_r101-d8_512x512_20k_voc12aug_20200617_010932.log.json
+  Paper:
+    Title: Rethinking atrous convolution for semantic image segmentation
+    URL: https://arxiv.org/abs/1706.05587
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54
+  Framework: PyTorch
+- Name: deeplabv3_r50-d8_4xb4-40k_voc12aug-512x512
+  In Collection: DeepLabV3
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 77.68
+      mIoU(ms+flip): 78.78
+  Config: configs/deeplabv3/deeplabv3_r50-d8_4xb4-40k_voc12aug-512x512.py
+  Metadata:
+    Training Data: Pascal VOC 2012 + Aug
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - DeepLabV3
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_40k_voc12aug/deeplabv3_r50-d8_512x512_40k_voc12aug_20200613_161546-2ae96e7e.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_40k_voc12aug/deeplabv3_r50-d8_512x512_40k_voc12aug_20200613_161546.log.json
+  Paper:
+    Title: Rethinking atrous convolution for semantic image segmentation
+    URL: https://arxiv.org/abs/1706.05587
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54
+  Framework: PyTorch
+- Name: deeplabv3_r101-d8_4xb4-40k_voc12aug-512x512
+  In Collection: DeepLabV3
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 77.92
+      mIoU(ms+flip): 79.18
+  Config: configs/deeplabv3/deeplabv3_r101-d8_4xb4-40k_voc12aug-512x512.py
+  Metadata:
+    Training Data: Pascal VOC 2012 + Aug
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - DeepLabV3
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_40k_voc12aug/deeplabv3_r101-d8_512x512_40k_voc12aug_20200613_161432-0017d784.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_40k_voc12aug/deeplabv3_r101-d8_512x512_40k_voc12aug_20200613_161432.log.json
+  Paper:
+    Title: Rethinking atrous convolution for semantic image segmentation
+    URL: https://arxiv.org/abs/1706.05587
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54
+  Framework: PyTorch
+- Name: deeplabv3_r101-d8_4xb4-40k_pascal-context-480x480
+  In Collection: DeepLabV3
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal Context
+    Metrics:
+      mIoU: 46.55
+      mIoU(ms+flip): 47.81
+  Config: configs/deeplabv3/deeplabv3_r101-d8_4xb4-40k_pascal-context-480x480.py
+  Metadata:
+    Training Data: Pascal Context
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - DeepLabV3
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 9.2
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context/deeplabv3_r101-d8_480x480_40k_pascal_context_20200911_204118-1aa27336.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context/deeplabv3_r101-d8_480x480_40k_pascal_context-20200911_204118.log.json
+  Paper:
+    Title: Rethinking atrous convolution for semantic image segmentation
+    URL: https://arxiv.org/abs/1706.05587
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54
+  Framework: PyTorch
+- Name: deeplabv3_r101-d8_4xb4-80k_pascal-context-480x480
+  In Collection: DeepLabV3
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal Context
+    Metrics:
+      mIoU: 46.42
+      mIoU(ms+flip): 47.53
+  Config: configs/deeplabv3/deeplabv3_r101-d8_4xb4-80k_pascal-context-480x480.py
+  Metadata:
+    Training Data: Pascal Context
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - DeepLabV3
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context/deeplabv3_r101-d8_480x480_80k_pascal_context_20200911_170155-2a21fff3.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context/deeplabv3_r101-d8_480x480_80k_pascal_context-20200911_170155.log.json
+  Paper:
+    Title: Rethinking atrous convolution for semantic image segmentation
+    URL: https://arxiv.org/abs/1706.05587
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54
+  Framework: PyTorch
+- Name: deeplabv3_r101-d8_4xb4-40k_pascal-context-59-480x480
+  In Collection: DeepLabV3
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal Context 59
+    Metrics:
+      mIoU: 52.61
+      mIoU(ms+flip): 54.28
+  Config: configs/deeplabv3/deeplabv3_r101-d8_4xb4-40k_pascal-context-59-480x480.py
+  Metadata:
+    Training Data: Pascal Context 59
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - DeepLabV3
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59/deeplabv3_r101-d8_480x480_40k_pascal_context_59_20210416_110332-cb08ea46.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59/deeplabv3_r101-d8_480x480_40k_pascal_context_59-20210416_110332.log.json
+  Paper:
+    Title: Rethinking atrous convolution for semantic image segmentation
+    URL: https://arxiv.org/abs/1706.05587
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54
+  Framework: PyTorch
+- Name: deeplabv3_r101-d8_4xb4-80k_pascal-context-59-480x480
+  In Collection: DeepLabV3
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal Context 59
+    Metrics:
+      mIoU: 52.46
+      mIoU(ms+flip): 54.09
+  Config: configs/deeplabv3/deeplabv3_r101-d8_4xb4-80k_pascal-context-59-480x480.py
+  Metadata:
+    Training Data: Pascal Context 59
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - DeepLabV3
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context_59/deeplabv3_r101-d8_480x480_80k_pascal_context_59_20210416_113002-26303993.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context_59/deeplabv3_r101-d8_480x480_80k_pascal_context_59-20210416_113002.log.json
+  Paper:
+    Title: Rethinking atrous convolution for semantic image segmentation
+    URL: https://arxiv.org/abs/1706.05587
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54
+  Framework: PyTorch
+- Name: deeplabv3_r50-d8_4xb4-20k_coco-stuff10k-512x512
+  In Collection: DeepLabV3
+  Results:
+    Task: Semantic Segmentation
+    Dataset: COCO-Stuff 10k
+    Metrics:
+      mIoU: 34.66
+      mIoU(ms+flip): 36.08
+  Config: configs/deeplabv3/deeplabv3_r50-d8_4xb4-20k_coco-stuff10k-512x512.py
+  Metadata:
+    Training Data: COCO-Stuff 10k
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - DeepLabV3
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 9.6
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_20k_coco-stuff10k/deeplabv3_r50-d8_512x512_4x4_20k_coco-stuff10k_20210821_043025-b35f789d.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_20k_coco-stuff10k/deeplabv3_r50-d8_512x512_4x4_20k_coco-stuff10k_20210821_043025.log.json
+  Paper:
+    Title: Rethinking atrous convolution for semantic image segmentation
+    URL: https://arxiv.org/abs/1706.05587
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54
+  Framework: PyTorch
+- Name: deeplabv3_r101-d8_4xb4-20k_coco-stuff10k-512x512
+  In Collection: DeepLabV3
+  Results:
+    Task: Semantic Segmentation
+    Dataset: COCO-Stuff 10k
+    Metrics:
+      mIoU: 37.3
+      mIoU(ms+flip): 38.42
+  Config: configs/deeplabv3/deeplabv3_r101-d8_4xb4-20k_coco-stuff10k-512x512.py
+  Metadata:
+    Training Data: COCO-Stuff 10k
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - DeepLabV3
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 13.2
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_20k_coco-stuff10k/deeplabv3_r101-d8_512x512_4x4_20k_coco-stuff10k_20210821_043025-c49752cb.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_20k_coco-stuff10k/deeplabv3_r101-d8_512x512_4x4_20k_coco-stuff10k_20210821_043025.log.json
+  Paper:
+    Title: Rethinking atrous convolution for semantic image segmentation
+    URL: https://arxiv.org/abs/1706.05587
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54
+  Framework: PyTorch
+- Name: deeplabv3_r50-d8_4xb4-40k_coco-stuff10k-512x512
+  In Collection: DeepLabV3
+  Results:
+    Task: Semantic Segmentation
+    Dataset: COCO-Stuff 10k
+    Metrics:
+      mIoU: 35.73
+      mIoU(ms+flip): 37.09
+  Config: configs/deeplabv3/deeplabv3_r50-d8_4xb4-40k_coco-stuff10k-512x512.py
+  Metadata:
+    Training Data: COCO-Stuff 10k
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - DeepLabV3
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_40k_coco-stuff10k/deeplabv3_r50-d8_512x512_4x4_40k_coco-stuff10k_20210821_043305-dc76f3ff.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_40k_coco-stuff10k/deeplabv3_r50-d8_512x512_4x4_40k_coco-stuff10k_20210821_043305.log.json
+  Paper:
+    Title: Rethinking atrous convolution for semantic image segmentation
+    URL: https://arxiv.org/abs/1706.05587
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54
+  Framework: PyTorch
+- Name: deeplabv3_r101-d8_4xb4-40k_coco-stuff10k-512x512
+  In Collection: DeepLabV3
+  Results:
+    Task: Semantic Segmentation
+    Dataset: COCO-Stuff 10k
+    Metrics:
+      mIoU: 37.81
+      mIoU(ms+flip): 38.8
+  Config: configs/deeplabv3/deeplabv3_r101-d8_4xb4-40k_coco-stuff10k-512x512.py
+  Metadata:
+    Training Data: COCO-Stuff 10k
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - DeepLabV3
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_40k_coco-stuff10k/deeplabv3_r101-d8_512x512_4x4_40k_coco-stuff10k_20210821_043305-636cb433.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_40k_coco-stuff10k/deeplabv3_r101-d8_512x512_4x4_40k_coco-stuff10k_20210821_043305.log.json
+  Paper:
+    Title: Rethinking atrous convolution for semantic image segmentation
+    URL: https://arxiv.org/abs/1706.05587
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54
+  Framework: PyTorch
+- Name: deeplabv3_r50-d8_4xb4-80k_coco-stuff164k-512x512
+  In Collection: DeepLabV3
+  Results:
+    Task: Semantic Segmentation
+    Dataset: COCO-Stuff 164k
+    Metrics:
+      mIoU: 39.38
+      mIoU(ms+flip): 40.03
+  Config: configs/deeplabv3/deeplabv3_r50-d8_4xb4-80k_coco-stuff164k-512x512.py
+  Metadata:
+    Training Data: COCO-Stuff 164k
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - DeepLabV3
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 9.6
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_80k_coco-stuff164k/deeplabv3_r50-d8_512x512_4x4_80k_coco-stuff164k_20210709_163016-88675c24.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_80k_coco-stuff164k/deeplabv3_r50-d8_512x512_4x4_80k_coco-stuff164k_20210709_163016.log.json
+  Paper:
+    Title: Rethinking atrous convolution for semantic image segmentation
+    URL: https://arxiv.org/abs/1706.05587
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54
+  Framework: PyTorch
+- Name: deeplabv3_r101-d8_4xb4-80k_coco-stuff164k-512x512
+  In Collection: DeepLabV3
+  Results:
+    Task: Semantic Segmentation
+    Dataset: COCO-Stuff 164k
+    Metrics:
+      mIoU: 40.87
+      mIoU(ms+flip): 41.5
+  Config: configs/deeplabv3/deeplabv3_r101-d8_4xb4-80k_coco-stuff164k-512x512.py
+  Metadata:
+    Training Data: COCO-Stuff 164k
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - DeepLabV3
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 13.2
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_80k_coco-stuff164k/deeplabv3_r101-d8_512x512_4x4_80k_coco-stuff164k_20210709_201252-13600dc2.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_80k_coco-stuff164k/deeplabv3_r101-d8_512x512_4x4_80k_coco-stuff164k_20210709_201252.log.json
+  Paper:
+    Title: Rethinking atrous convolution for semantic image segmentation
+    URL: https://arxiv.org/abs/1706.05587
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54
+  Framework: PyTorch
+- Name: deeplabv3_r50-d8_4xb4-160k_coco-stuff164k-512x512
+  In Collection: DeepLabV3
+  Results:
+    Task: Semantic Segmentation
+    Dataset: COCO-Stuff 164k
+    Metrics:
+      mIoU: 41.09
+      mIoU(ms+flip): 41.69
+  Config: configs/deeplabv3/deeplabv3_r50-d8_4xb4-160k_coco-stuff164k-512x512.py
+  Metadata:
+    Training Data: COCO-Stuff 164k
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - DeepLabV3
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_160k_coco-stuff164k/deeplabv3_r50-d8_512x512_4x4_160k_coco-stuff164k_20210709_163016-49f2812b.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_160k_coco-stuff164k/deeplabv3_r50-d8_512x512_4x4_160k_coco-stuff164k_20210709_163016.log.json
+  Paper:
+    Title: Rethinking atrous convolution for semantic image segmentation
+    URL: https://arxiv.org/abs/1706.05587
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54
+  Framework: PyTorch
+- Name: deeplabv3_r101-d8_4xb4-160k_coco-stuff164k-512x512
+  In Collection: DeepLabV3
+  Results:
+    Task: Semantic Segmentation
+    Dataset: COCO-Stuff 164k
+    Metrics:
+      mIoU: 41.82
+      mIoU(ms+flip): 42.49
+  Config: configs/deeplabv3/deeplabv3_r101-d8_4xb4-160k_coco-stuff164k-512x512.py
+  Metadata:
+    Training Data: COCO-Stuff 164k
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - DeepLabV3
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_160k_coco-stuff164k/deeplabv3_r101-d8_512x512_4x4_160k_coco-stuff164k_20210709_155402-f035acfd.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_160k_coco-stuff164k/deeplabv3_r101-d8_512x512_4x4_160k_coco-stuff164k_20210709_155402.log.json
+  Paper:
+    Title: Rethinking atrous convolution for semantic image segmentation
+    URL: https://arxiv.org/abs/1706.05587
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54
+  Framework: PyTorch
+- Name: deeplabv3_r50-d8_4xb4-320k_coco-stuff164k-512x512
+  In Collection: DeepLabV3
+  Results:
+    Task: Semantic Segmentation
+    Dataset: COCO-Stuff 164k
+    Metrics:
+      mIoU: 41.37
+      mIoU(ms+flip): 42.22
+  Config: configs/deeplabv3/deeplabv3_r50-d8_4xb4-320k_coco-stuff164k-512x512.py
+  Metadata:
+    Training Data: COCO-Stuff 164k
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - DeepLabV3
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_320k_coco-stuff164k/deeplabv3_r50-d8_512x512_4x4_320k_coco-stuff164k_20210709_155403-51b21115.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_320k_coco-stuff164k/deeplabv3_r50-d8_512x512_4x4_320k_coco-stuff164k_20210709_155403.log.json
+  Paper:
+    Title: Rethinking atrous convolution for semantic image segmentation
+    URL: https://arxiv.org/abs/1706.05587
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54
+  Framework: PyTorch
+- Name: deeplabv3_r101-d8_4xb4-320k_coco-stuff164k-512x512
+  In Collection: DeepLabV3
+  Results:
+    Task: Semantic Segmentation
+    Dataset: COCO-Stuff 164k
+    Metrics:
+      mIoU: 42.61
+      mIoU(ms+flip): 43.42
+  Config: configs/deeplabv3/deeplabv3_r101-d8_4xb4-320k_coco-stuff164k-512x512.py
+  Metadata:
+    Training Data: COCO-Stuff 164k
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - DeepLabV3
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k/deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k_20210709_155402-3cbca14d.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k/deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k_20210709_155402.log.json
+  Paper:
+    Title: Rethinking atrous convolution for semantic image segmentation
+    URL: https://arxiv.org/abs/1706.05587
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54
+  Framework: PyTorch
diff --git a/configs/deeplabv3plus/README.md b/configs/deeplabv3plus/README.md
index f60f547391..15c51a504a 100644
--- a/configs/deeplabv3plus/README.md
+++ b/configs/deeplabv3plus/README.md
@@ -1,6 +1,6 @@
 # DeepLabV3+
 
-[Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://arxiv.org/abs/1802.02611)
+> [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://arxiv.org/abs/1802.02611)
 
 ## Introduction
 
@@ -22,107 +22,96 @@ Spatial pyramid pooling module or encode-decoder structure are used in deep neur
 <img src="https://user-images.githubusercontent.com/24582831/142900680-3e2c3098-8341-4760-bbfd-b1d7d29968ea.png" width="70%"/>
 </div>
 
-## Citation
-
-```bibtex
-@inproceedings{deeplabv3plus2018,
-  title={Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation},
-  author={Liang-Chieh Chen and Yukun Zhu and George Papandreou and Florian Schroff and Hartwig Adam},
-  booktitle={ECCV},
-  year={2018}
-}
-```
-
 ## Results and models
 
 ### Cityscapes
 
-| Method            | Backbone        | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                                                         | download                                                                                                                                                                                                                                                                                                                                                                                                                       |
-| ----------------- | --------------- | --------- | ------: | -------- | -------------- | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| DeepLabV3+        | R-50-D8         | 512x1024  |   40000 | 7.5      | 3.94           | 79.61 |         81.01 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb2-40k_cityscapes-512x1024.py)                 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_40k_cityscapes/deeplabv3plus_r50-d8_512x1024_40k_cityscapes_20200605_094610-d222ffcd.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_40k_cityscapes/deeplabv3plus_r50-d8_512x1024_40k_cityscapes_20200605_094610.log.json)                                 |
-| DeepLabV3+        | R-101-D8        | 512x1024  |   40000 | 11       | 2.60           | 80.21 |         81.82 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb2-40k_cityscapes-512x1024.py)                | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_40k_cityscapes/deeplabv3plus_r101-d8_512x1024_40k_cityscapes_20200605_094614-3769eecf.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_40k_cityscapes/deeplabv3plus_r101-d8_512x1024_40k_cityscapes_20200605_094614.log.json)                             |
-| DeepLabV3+        | R-50-D8         | 769x769   |   40000 | 8.5      | 1.72           | 78.97 |         80.46 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb2-40k_cityscapes-769x769.py)                  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_769x769_40k_cityscapes/deeplabv3plus_r50-d8_769x769_40k_cityscapes_20200606_114143-1dcb0e3c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_769x769_40k_cityscapes/deeplabv3plus_r50-d8_769x769_40k_cityscapes_20200606_114143.log.json)                                     |
-| DeepLabV3+        | R-101-D8        | 769x769   |   40000 | 12.5     | 1.15           | 79.46 |         80.50 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb2-40k_cityscapes-769x769.py)                 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_769x769_40k_cityscapes/deeplabv3plus_r101-d8_769x769_40k_cityscapes_20200606_114304-ff414b9e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_769x769_40k_cityscapes/deeplabv3plus_r101-d8_769x769_40k_cityscapes_20200606_114304.log.json)                                 |
-| DeepLabV3+        | R-18-D8         | 512x1024  |   80000 | 2.2      | 14.27          | 76.89 |         78.76 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb2-80k_cityscapes-512x1024.py)                 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x1024_80k_cityscapes/deeplabv3plus_r18-d8_512x1024_80k_cityscapes_20201226_080942-cff257fe.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x1024_80k_cityscapes/deeplabv3plus_r18-d8_512x1024_80k_cityscapes-20201226_080942.log.json)                                 |
-| DeepLabV3+        | R-50-D8         | 512x1024  |   80000 | -        | -              | 80.09 |         81.13 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb2-80k_cityscapes-512x1024.py)                 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_80k_cityscapes/deeplabv3plus_r50-d8_512x1024_80k_cityscapes_20200606_114049-f9fb496d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_80k_cityscapes/deeplabv3plus_r50-d8_512x1024_80k_cityscapes_20200606_114049.log.json)                                 |
-| DeepLabV3+        | R-101-D8        | 512x1024  |   80000 | -        | -              | 80.97 |         82.03 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb2-80k_cityscapes-512x1024.py)                | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes/deeplabv3plus_r101-d8_512x1024_80k_cityscapes_20200606_114143-068fcfe9.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes/deeplabv3plus_r101-d8_512x1024_80k_cityscapes_20200606_114143.log.json)                             |
-| DeepLabV3+ (FP16) | R-101-D8        | 512x1024  |   80000 | 6.35     | 7.87           | 80.46 |             - | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb2-amp-80k_cityscapes-512x1024.py)            | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_fp16_512x1024_80k_cityscapes/deeplabv3plus_r101-d8_fp16_512x1024_80k_cityscapes_20200717_230920-f1104f4b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_fp16_512x1024_80k_cityscapes/deeplabv3plus_r101-d8_fp16_512x1024_80k_cityscapes_20200717_230920.log.json)         |
-| DeepLabV3+        | R-18-D8         | 769x769   |   80000 | 2.5      | 5.74           | 76.26 |         77.91 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb2-80k_cityscapes-769x769.py)                  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_769x769_80k_cityscapes/deeplabv3plus_r18-d8_769x769_80k_cityscapes_20201226_083346-f326e06a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_769x769_80k_cityscapes/deeplabv3plus_r18-d8_769x769_80k_cityscapes-20201226_083346.log.json)                                     |
-| DeepLabV3+        | R-50-D8         | 769x769   |   80000 | -        | -              | 79.83 |         81.48 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb2-80k_cityscapes-769x769.py)                  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_769x769_80k_cityscapes/deeplabv3plus_r50-d8_769x769_80k_cityscapes_20200606_210233-0e9dfdc4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_769x769_80k_cityscapes/deeplabv3plus_r50-d8_769x769_80k_cityscapes_20200606_210233.log.json)                                     |
-| DeepLabV3+        | R-101-D8        | 769x769   |   80000 | -        | -              | 80.65 |         81.47 | [config<sup>\[1\]</sup>](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb2-80k_cityscapes-769x769.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_769x769_80k_cityscapes/deeplabv3plus_r101-d8_769x769_80k_cityscapes_20220406_154720-dfcc0b68.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_769x769_80k_cityscapes/deeplabv3plus_r101-d8_769x769_80k_cityscapes_20220406_154720.log.json)                                 |
-| DeepLabV3+        | R-101-D16-MG124 | 512x1024  |   40000 | 5.8      | 7.48           | 79.09 |         80.36 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/ddeeplabv3plus_r101-d16-mg124_4xb2-40k_cityscapes-512x1024.py)        | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes_20200908_005644-cf9ce186.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes-20200908_005644.log.json) |
-| DeepLabV3+        | R-101-D16-MG124 | 512x1024  |   80000 | 9.9      | -              | 79.90 |         81.33 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r101-d16-mg124_4xb2-80k_cityscapes-512x1024.py)         | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes_20200908_005644-ee6158e0.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes-20200908_005644.log.json) |
-| DeepLabV3+        | R-18b-D8        | 512x1024  |   80000 | 2.1      | 14.95          | 75.87 |         77.52 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r18b-d8_4xb2-80k_cityscapes-512x1024.py)                | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes_20201226_090828-e451abd9.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes-20201226_090828.log.json)                             |
-| DeepLabV3+        | R-50b-D8        | 512x1024  |   80000 | 7.4      | 3.94           | 80.28 |         81.44 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r50b-d8_4xb2-80k_cityscapes-512x1024.py)                | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes_20201225_213645-a97e4e43.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes-20201225_213645.log.json)                             |
-| DeepLabV3+        | R-101b-D8       | 512x1024  |   80000 | 10.9     | 2.60           | 80.16 |         81.41 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r101b-d8_4xb2-80k_cityscapes-512x1024.py)               | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes_20201226_190843-9c3c93a4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes-20201226_190843.log.json)                         |
-| DeepLabV3+        | R-18b-D8        | 769x769   |   80000 | 2.4      | 5.96           | 76.36 |         78.24 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r18b-d8_4xb2-80k_cityscapes-769x769.py)                 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18b-d8_769x769_80k_cityscapes/deeplabv3plus_r18b-d8_769x769_80k_cityscapes_20201226_151312-2c868aff.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18b-d8_769x769_80k_cityscapes/deeplabv3plus_r18b-d8_769x769_80k_cityscapes-20201226_151312.log.json)                                 |
-| DeepLabV3+        | R-50b-D8        | 769x769   |   80000 | 8.4      | 1.72           | 79.41 |         80.56 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r50b-d8_4xb2-80k_cityscapes-769x769.py)                 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50b-d8_769x769_80k_cityscapes/deeplabv3plus_r50b-d8_769x769_80k_cityscapes_20201225_224655-8b596d1c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50b-d8_769x769_80k_cityscapes/deeplabv3plus_r50b-d8_769x769_80k_cityscapes-20201225_224655.log.json)                                 |
-| DeepLabV3+        | R-101b-D8       | 769x769   |   80000 | 12.3     | 1.10           | 79.88 |         81.46 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r101b-d8_4xb2-80k_cityscapes-769x769.py)                | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101b-d8_769x769_80k_cityscapes/deeplabv3plus_r101b-d8_769x769_80k_cityscapes_20201226_205041-227cdf7c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101b-d8_769x769_80k_cityscapes/deeplabv3plus_r101b-d8_769x769_80k_cityscapes-20201226_205041.log.json)                             |
+| Method            | Backbone        | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                                                         | download                                                                                                                                                                                                                                                                                                                                                                                                                       |
+| ----------------- | --------------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| DeepLabV3+        | R-50-D8         | 512x1024  |   40000 | 7.5      | 3.94           | V100   | 79.61 |         81.01 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb2-40k_cityscapes-512x1024.py)                 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_40k_cityscapes/deeplabv3plus_r50-d8_512x1024_40k_cityscapes_20200605_094610-d222ffcd.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_40k_cityscapes/deeplabv3plus_r50-d8_512x1024_40k_cityscapes_20200605_094610.log.json)                                 |
+| DeepLabV3+        | R-101-D8        | 512x1024  |   40000 | 11       | 2.60           | V100   | 80.21 |         81.82 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb2-40k_cityscapes-512x1024.py)                | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_40k_cityscapes/deeplabv3plus_r101-d8_512x1024_40k_cityscapes_20200605_094614-3769eecf.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_40k_cityscapes/deeplabv3plus_r101-d8_512x1024_40k_cityscapes_20200605_094614.log.json)                             |
+| DeepLabV3+        | R-50-D8         | 769x769   |   40000 | 8.5      | 1.72           | V100   | 78.97 |         80.46 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb2-40k_cityscapes-769x769.py)                  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_769x769_40k_cityscapes/deeplabv3plus_r50-d8_769x769_40k_cityscapes_20200606_114143-1dcb0e3c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_769x769_40k_cityscapes/deeplabv3plus_r50-d8_769x769_40k_cityscapes_20200606_114143.log.json)                                     |
+| DeepLabV3+        | R-101-D8        | 769x769   |   40000 | 12.5     | 1.15           | V100   | 79.46 |         80.50 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb2-40k_cityscapes-769x769.py)                 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_769x769_40k_cityscapes/deeplabv3plus_r101-d8_769x769_40k_cityscapes_20200606_114304-ff414b9e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_769x769_40k_cityscapes/deeplabv3plus_r101-d8_769x769_40k_cityscapes_20200606_114304.log.json)                                 |
+| DeepLabV3+        | R-18-D8         | 512x1024  |   80000 | 2.2      | 14.27          | V100   | 76.89 |         78.76 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb2-80k_cityscapes-512x1024.py)                 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x1024_80k_cityscapes/deeplabv3plus_r18-d8_512x1024_80k_cityscapes_20201226_080942-cff257fe.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x1024_80k_cityscapes/deeplabv3plus_r18-d8_512x1024_80k_cityscapes-20201226_080942.log.json)                                 |
+| DeepLabV3+        | R-50-D8         | 512x1024  |   80000 | -        | -              | V100   | 80.09 |         81.13 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb2-80k_cityscapes-512x1024.py)                 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_80k_cityscapes/deeplabv3plus_r50-d8_512x1024_80k_cityscapes_20200606_114049-f9fb496d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_80k_cityscapes/deeplabv3plus_r50-d8_512x1024_80k_cityscapes_20200606_114049.log.json)                                 |
+| DeepLabV3+        | R-101-D8        | 512x1024  |   80000 | -        | -              | V100   | 80.97 |         82.03 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb2-80k_cityscapes-512x1024.py)                | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes/deeplabv3plus_r101-d8_512x1024_80k_cityscapes_20200606_114143-068fcfe9.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes/deeplabv3plus_r101-d8_512x1024_80k_cityscapes_20200606_114143.log.json)                             |
+| DeepLabV3+ (FP16) | R-101-D8        | 512x1024  |   80000 | 6.35     | 7.87           | V100   | 80.46 |             - | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb2-amp-80k_cityscapes-512x1024.py)            | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_fp16_512x1024_80k_cityscapes/deeplabv3plus_r101-d8_fp16_512x1024_80k_cityscapes_20200717_230920-f1104f4b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_fp16_512x1024_80k_cityscapes/deeplabv3plus_r101-d8_fp16_512x1024_80k_cityscapes_20200717_230920.log.json)         |
+| DeepLabV3+        | R-18-D8         | 769x769   |   80000 | 2.5      | 5.74           | V100   | 76.26 |         77.91 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb2-80k_cityscapes-769x769.py)                  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_769x769_80k_cityscapes/deeplabv3plus_r18-d8_769x769_80k_cityscapes_20201226_083346-f326e06a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_769x769_80k_cityscapes/deeplabv3plus_r18-d8_769x769_80k_cityscapes-20201226_083346.log.json)                                     |
+| DeepLabV3+        | R-50-D8         | 769x769   |   80000 | -        | -              | V100   | 79.83 |         81.48 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb2-80k_cityscapes-769x769.py)                  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_769x769_80k_cityscapes/deeplabv3plus_r50-d8_769x769_80k_cityscapes_20200606_210233-0e9dfdc4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_769x769_80k_cityscapes/deeplabv3plus_r50-d8_769x769_80k_cityscapes_20200606_210233.log.json)                                     |
+| DeepLabV3+        | R-101-D8        | 769x769   |   80000 | -        | -              | V100   | 80.65 |         81.47 | [config<sup>\[1\]</sup>](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb2-80k_cityscapes-769x769.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_769x769_80k_cityscapes/deeplabv3plus_r101-d8_769x769_80k_cityscapes_20220406_154720-dfcc0b68.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_769x769_80k_cityscapes/deeplabv3plus_r101-d8_769x769_80k_cityscapes_20220406_154720.log.json)                                 |
+| DeepLabV3+        | R-101-D16-MG124 | 512x1024  |   40000 | 5.8      | 7.48           | V100   | 79.09 |         80.36 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/ddeeplabv3plus_r101-d16-mg124_4xb2-40k_cityscapes-512x1024.py)        | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes_20200908_005644-cf9ce186.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes-20200908_005644.log.json) |
+| DeepLabV3+        | R-101-D16-MG124 | 512x1024  |   80000 | 9.9      | -              | V100   | 79.90 |         81.33 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r101-d16-mg124_4xb2-80k_cityscapes-512x1024.py)         | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes_20200908_005644-ee6158e0.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes-20200908_005644.log.json) |
+| DeepLabV3+        | R-18b-D8        | 512x1024  |   80000 | 2.1      | 14.95          | V100   | 75.87 |         77.52 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r18b-d8_4xb2-80k_cityscapes-512x1024.py)                | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes_20201226_090828-e451abd9.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes-20201226_090828.log.json)                             |
+| DeepLabV3+        | R-50b-D8        | 512x1024  |   80000 | 7.4      | 3.94           | V100   | 80.28 |         81.44 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r50b-d8_4xb2-80k_cityscapes-512x1024.py)                | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes_20201225_213645-a97e4e43.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes-20201225_213645.log.json)                             |
+| DeepLabV3+        | R-101b-D8       | 512x1024  |   80000 | 10.9     | 2.60           | V100   | 80.16 |         81.41 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r101b-d8_4xb2-80k_cityscapes-512x1024.py)               | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes_20201226_190843-9c3c93a4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes-20201226_190843.log.json)                         |
+| DeepLabV3+        | R-18b-D8        | 769x769   |   80000 | 2.4      | 5.96           | V100   | 76.36 |         78.24 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r18b-d8_4xb2-80k_cityscapes-769x769.py)                 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18b-d8_769x769_80k_cityscapes/deeplabv3plus_r18b-d8_769x769_80k_cityscapes_20201226_151312-2c868aff.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18b-d8_769x769_80k_cityscapes/deeplabv3plus_r18b-d8_769x769_80k_cityscapes-20201226_151312.log.json)                                 |
+| DeepLabV3+        | R-50b-D8        | 769x769   |   80000 | 8.4      | 1.72           | V100   | 79.41 |         80.56 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r50b-d8_4xb2-80k_cityscapes-769x769.py)                 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50b-d8_769x769_80k_cityscapes/deeplabv3plus_r50b-d8_769x769_80k_cityscapes_20201225_224655-8b596d1c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50b-d8_769x769_80k_cityscapes/deeplabv3plus_r50b-d8_769x769_80k_cityscapes-20201225_224655.log.json)                                 |
+| DeepLabV3+        | R-101b-D8       | 769x769   |   80000 | 12.3     | 1.10           | V100   | 79.88 |         81.46 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r101b-d8_4xb2-80k_cityscapes-769x769.py)                | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101b-d8_769x769_80k_cityscapes/deeplabv3plus_r101b-d8_769x769_80k_cityscapes_20201226_205041-227cdf7c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101b-d8_769x769_80k_cityscapes/deeplabv3plus_r101b-d8_769x769_80k_cityscapes-20201226_205041.log.json)                             |
 
 \[1\] The training of the model is sensitive to random seed, and the seed to train it is 1111.
 
 ### ADE20K
 
-| Method     | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                                      | download                                                                                                                                                                                                                                                                                                                                                                           |
-| ---------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| DeepLabV3+ | R-50-D8  | 512x512   |   80000 | 10.6     | 21.01          | 42.72 |         43.75 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-80k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_ade20k/deeplabv3plus_r50-d8_512x512_80k_ade20k_20200614_185028-bf1400d8.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_ade20k/deeplabv3plus_r50-d8_512x512_80k_ade20k_20200614_185028.log.json)         |
-| DeepLabV3+ | R-101-D8 | 512x512   |   80000 | 14.1     | 14.16          | 44.60 |         46.06 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_ade20k/deeplabv3plus_r101-d8_512x512_80k_ade20k_20200615_014139-d5730af7.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_ade20k/deeplabv3plus_r101-d8_512x512_80k_ade20k_20200615_014139.log.json)     |
-| DeepLabV3+ | R-50-D8  | 512x512   |  160000 | -        | -              | 43.95 |         44.93 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-160k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_160k_ade20k/deeplabv3plus_r50-d8_512x512_160k_ade20k_20200615_124504-6135c7e0.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_160k_ade20k/deeplabv3plus_r50-d8_512x512_160k_ade20k_20200615_124504.log.json)     |
-| DeepLabV3+ | R-101-D8 | 512x512   |  160000 | -        | -              | 45.47 |         46.35 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_160k_ade20k/deeplabv3plus_r101-d8_512x512_160k_ade20k_20200615_123232-38ed86bb.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_160k_ade20k/deeplabv3plus_r101-d8_512x512_160k_ade20k_20200615_123232.log.json) |
+| Method     | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                                      | download                                                                                                                                                                                                                                                                                                                                                                           |
+| ---------- | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| DeepLabV3+ | R-50-D8  | 512x512   |   80000 | 10.6     | 21.01          | V100   | 42.72 |         43.75 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-80k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_ade20k/deeplabv3plus_r50-d8_512x512_80k_ade20k_20200614_185028-bf1400d8.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_ade20k/deeplabv3plus_r50-d8_512x512_80k_ade20k_20200614_185028.log.json)         |
+| DeepLabV3+ | R-101-D8 | 512x512   |   80000 | 14.1     | 14.16          | V100   | 44.60 |         46.06 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_ade20k/deeplabv3plus_r101-d8_512x512_80k_ade20k_20200615_014139-d5730af7.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_ade20k/deeplabv3plus_r101-d8_512x512_80k_ade20k_20200615_014139.log.json)     |
+| DeepLabV3+ | R-50-D8  | 512x512   |  160000 | -        | -              | V100   | 43.95 |         44.93 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-160k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_160k_ade20k/deeplabv3plus_r50-d8_512x512_160k_ade20k_20200615_124504-6135c7e0.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_160k_ade20k/deeplabv3plus_r50-d8_512x512_160k_ade20k_20200615_124504.log.json)     |
+| DeepLabV3+ | R-101-D8 | 512x512   |  160000 | -        | -              | V100   | 45.47 |         46.35 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_160k_ade20k/deeplabv3plus_r101-d8_512x512_160k_ade20k_20200615_123232-38ed86bb.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_160k_ade20k/deeplabv3plus_r101-d8_512x512_160k_ade20k_20200615_123232.log.json) |
 
 ### Pascal VOC 2012 + Aug
 
-| Method     | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                                       | download                                                                                                                                                                                                                                                                                                                                                                               |
-| ---------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| DeepLabV3+ | R-50-D8  | 512x512   |   20000 | 7.6      | 21             | 75.93 |         77.50 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-20k_voc12aug-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_20k_voc12aug/deeplabv3plus_r50-d8_512x512_20k_voc12aug_20200617_102323-aad58ef1.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_20k_voc12aug/deeplabv3plus_r50-d8_512x512_20k_voc12aug_20200617_102323.log.json)     |
-| DeepLabV3+ | R-101-D8 | 512x512   |   20000 | 11       | 13.88          | 77.22 |         78.59 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-20k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_20k_voc12aug/deeplabv3plus_r101-d8_512x512_20k_voc12aug_20200617_102345-c7ff3d56.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_20k_voc12aug/deeplabv3plus_r101-d8_512x512_20k_voc12aug_20200617_102345.log.json) |
-| DeepLabV3+ | R-50-D8  | 512x512   |   40000 | -        | -              | 76.81 |         77.57 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-40k_voc12aug-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_40k_voc12aug/deeplabv3plus_r50-d8_512x512_40k_voc12aug_20200613_161759-e1b43aa9.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_40k_voc12aug/deeplabv3plus_r50-d8_512x512_40k_voc12aug_20200613_161759.log.json)     |
-| DeepLabV3+ | R-101-D8 | 512x512   |   40000 | -        | -              | 78.62 |         79.53 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-40k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_40k_voc12aug/deeplabv3plus_r101-d8_512x512_40k_voc12aug_20200613_205333-faf03387.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_40k_voc12aug/deeplabv3plus_r101-d8_512x512_40k_voc12aug_20200613_205333.log.json) |
+| Method     | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                                       | download                                                                                                                                                                                                                                                                                                                                                                               |
+| ---------- | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| DeepLabV3+ | R-50-D8  | 512x512   |   20000 | 7.6      | 21             | V100   | 75.93 |         77.50 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-20k_voc12aug-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_20k_voc12aug/deeplabv3plus_r50-d8_512x512_20k_voc12aug_20200617_102323-aad58ef1.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_20k_voc12aug/deeplabv3plus_r50-d8_512x512_20k_voc12aug_20200617_102323.log.json)     |
+| DeepLabV3+ | R-101-D8 | 512x512   |   20000 | 11       | 13.88          | V100   | 77.22 |         78.59 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-20k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_20k_voc12aug/deeplabv3plus_r101-d8_512x512_20k_voc12aug_20200617_102345-c7ff3d56.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_20k_voc12aug/deeplabv3plus_r101-d8_512x512_20k_voc12aug_20200617_102345.log.json) |
+| DeepLabV3+ | R-50-D8  | 512x512   |   40000 | -        | -              | V100   | 76.81 |         77.57 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-40k_voc12aug-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_40k_voc12aug/deeplabv3plus_r50-d8_512x512_40k_voc12aug_20200613_161759-e1b43aa9.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_40k_voc12aug/deeplabv3plus_r50-d8_512x512_40k_voc12aug_20200613_161759.log.json)     |
+| DeepLabV3+ | R-101-D8 | 512x512   |   40000 | -        | -              | V100   | 78.62 |         79.53 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-40k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_40k_voc12aug/deeplabv3plus_r101-d8_512x512_40k_voc12aug_20200613_205333-faf03387.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_40k_voc12aug/deeplabv3plus_r101-d8_512x512_40k_voc12aug_20200613_205333.log.json) |
 
 ### Pascal Context
 
-| Method     | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                                            | download                                                                                                                                                                                                                                                                                                                                                                                                       |
-| ---------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| DeepLabV3+ | R-101-D8 | 480x480   |   40000 | -        | 9.09           | 47.30 |         48.47 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-40k_pascal-context-480x480.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context/deeplabv3plus_r101-d8_480x480_40k_pascal_context_20200911_165459-d3c8a29e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context/deeplabv3plus_r101-d8_480x480_40k_pascal_context-20200911_165459.log.json) |
-| DeepLabV3+ | R-101-D8 | 480x480   |   80000 | -        | -              | 47.23 |         48.26 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-80k_pascal-context-480x480.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context/deeplabv3plus_r101-d8_480x480_80k_pascal_context_20200911_155322-145d3ee8.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context/deeplabv3plus_r101-d8_480x480_80k_pascal_context-20200911_155322.log.json) |
+| Method     | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                                            | download                                                                                                                                                                                                                                                                                                                                                                                                       |
+| ---------- | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| DeepLabV3+ | R-101-D8 | 480x480   |   40000 | -        | 9.09           | V100   | 47.30 |         48.47 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-40k_pascal-context-480x480.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context/deeplabv3plus_r101-d8_480x480_40k_pascal_context_20200911_165459-d3c8a29e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context/deeplabv3plus_r101-d8_480x480_40k_pascal_context-20200911_165459.log.json) |
+| DeepLabV3+ | R-101-D8 | 480x480   |   80000 | -        | -              | V100   | 47.23 |         48.26 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-80k_pascal-context-480x480.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context/deeplabv3plus_r101-d8_480x480_80k_pascal_context_20200911_155322-145d3ee8.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context/deeplabv3plus_r101-d8_480x480_80k_pascal_context-20200911_155322.log.json) |
 
 ### Pascal Context 59
 
-| Method     | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                                                | download                                                                                                                                                                                                                                                                                                                                                                                                                   |
-| ---------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| DeepLabV3+ | R-101-D8 | 480x480   |   40000 | -        | -              | 52.86 |         54.54 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-40k_pascal-context-59-480x480.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59_20210416_111233-ed937f15.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59-20210416_111233.log.json) |
-| DeepLabV3+ | R-101-D8 | 480x480   |   80000 | -        | -              |  53.2 |         54.67 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-80k_pascal-context-59-480x480.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59_20210416_111127-7ca0331d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59-20210416_111127.log.json) |
+| Method     | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                                                | download                                                                                                                                                                                                                                                                                                                                                                                                                   |
+| ---------- | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| DeepLabV3+ | R-101-D8 | 480x480   |   40000 | -        | -              | V100   | 52.86 |         54.54 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-40k_pascal-context-59-480x480.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59_20210416_111233-ed937f15.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59-20210416_111233.log.json) |
+| DeepLabV3+ | R-101-D8 | 480x480   |   80000 | -        | -              | V100   |  53.2 |         54.67 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-80k_pascal-context-59-480x480.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59_20210416_111127-7ca0331d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59-20210416_111127.log.json) |
 
 ### LoveDA
 
-| Method     | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                                     | download                                                                                                                                                                                                                                                                                                                                                                       |
-| ---------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| DeepLabV3+ | R-18-D8  | 512x512   |   80000 | 1.93     | 25.57          | 50.28 |         50.47 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb4-80k_loveda-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_loveda/deeplabv3plus_r18-d8_512x512_80k_loveda_20211104_132800-ce0fa0ca.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_loveda/deeplabv3plus_r18-d8_512x512_80k_loveda_20211104_132800.log.json)     |
-| DeepLabV3+ | R-50-D8  | 512x512   |   80000 | 7.37     | 6.00           | 50.99 |         50.65 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-80k_loveda-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_loveda/deeplabv3plus_r50-d8_512x512_80k_loveda_20211105_080442-f0720392.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_loveda/deeplabv3plus_r50-d8_512x512_80k_loveda_20211105_080442.log.json)     |
-| DeepLabV3+ | R-101-D8 | 512x512   |   80000 | 10.84    | 4.33           | 51.47 |         51.32 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-80k_loveda-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_loveda/deeplabv3plus_r101-d8_512x512_80k_loveda_20211105_110759-4c1f297e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_loveda/deeplabv3plus_r101-d8_512x512_80k_loveda_20211105_110759.log.json) |
+| Method     | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                                     | download                                                                                                                                                                                                                                                                                                                                                                       |
+| ---------- | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| DeepLabV3+ | R-18-D8  | 512x512   |   80000 | 1.93     | 25.57          | V100   | 50.28 |         50.47 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb4-80k_loveda-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_loveda/deeplabv3plus_r18-d8_512x512_80k_loveda_20211104_132800-ce0fa0ca.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_loveda/deeplabv3plus_r18-d8_512x512_80k_loveda_20211104_132800.log.json)     |
+| DeepLabV3+ | R-50-D8  | 512x512   |   80000 | 7.37     | 6.00           | V100   | 50.99 |         50.65 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-80k_loveda-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_loveda/deeplabv3plus_r50-d8_512x512_80k_loveda_20211105_080442-f0720392.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_loveda/deeplabv3plus_r50-d8_512x512_80k_loveda_20211105_080442.log.json)     |
+| DeepLabV3+ | R-101-D8 | 512x512   |   80000 | 10.84    | 4.33           | V100   | 51.47 |         51.32 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-80k_loveda-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_loveda/deeplabv3plus_r101-d8_512x512_80k_loveda_20211105_110759-4c1f297e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_loveda/deeplabv3plus_r101-d8_512x512_80k_loveda_20211105_110759.log.json) |
 
 ### Potsdam
 
-| Method     | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                                      | download                                                                                                                                                                                                                                                                                                                                                                           |
-| ---------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| DeepLabV3+ | R-18-D8  | 512x512   |   80000 | 1.91     | 81.68          | 77.09 |         78.44 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb4-80k_potsdam-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_potsdam/deeplabv3plus_r18-d8_512x512_80k_potsdam_20211219_020601-75fd5bc3.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_potsdam/deeplabv3plus_r18-d8_512x512_80k_potsdam_20211219_020601.log.json)     |
-| DeepLabV3+ | R-50-D8  | 512x512   |   80000 | 7.36     | 26.44          | 78.33 |         79.27 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-80k_potsdam-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_potsdam/deeplabv3plus_r50-d8_512x512_80k_potsdam_20211219_031508-7e7a2b24.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_potsdam/deeplabv3plus_r50-d8_512x512_80k_potsdam_20211219_031508.log.json)     |
-| DeepLabV3+ | R-101-D8 | 512x512   |   80000 | 10.83    | 17.56          |  78.7 |         79.47 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-80k_potsdam-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_potsdam/deeplabv3plus_r101-d8_512x512_80k_potsdam_20211219_031508-8b112708.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_potsdam/deeplabv3plus_r101-d8_512x512_80k_potsdam_20211219_031508.log.json) |
+| Method     | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                                      | download                                                                                                                                                                                                                                                                                                                                                                           |
+| ---------- | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| DeepLabV3+ | R-18-D8  | 512x512   |   80000 | 1.91     | 81.68          | V100   | 77.09 |         78.44 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb4-80k_potsdam-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_potsdam/deeplabv3plus_r18-d8_512x512_80k_potsdam_20211219_020601-75fd5bc3.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_potsdam/deeplabv3plus_r18-d8_512x512_80k_potsdam_20211219_020601.log.json)     |
+| DeepLabV3+ | R-50-D8  | 512x512   |   80000 | 7.36     | 26.44          | V100   | 78.33 |         79.27 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-80k_potsdam-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_potsdam/deeplabv3plus_r50-d8_512x512_80k_potsdam_20211219_031508-7e7a2b24.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_potsdam/deeplabv3plus_r50-d8_512x512_80k_potsdam_20211219_031508.log.json)     |
+| DeepLabV3+ | R-101-D8 | 512x512   |   80000 | 10.83    | 17.56          | V100   |  78.7 |         79.47 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-80k_potsdam-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_potsdam/deeplabv3plus_r101-d8_512x512_80k_potsdam_20211219_031508-8b112708.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_potsdam/deeplabv3plus_r101-d8_512x512_80k_potsdam_20211219_031508.log.json) |
 
 ### Vaihingen
 
-| Method     | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                                        | download                                                                                                                                                                                                                                                                                                                                                                                                   |
-| ---------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | --------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| DeepLabV3+ | R-18-D8  | 512x512   |   80000 | 1.91     | 72.79          | 72.50 |         74.13 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb4-80k_vaihingen-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_4x4_512x512_80k_vaihingen/deeplabv3plus_r18-d8_4x4_512x512_80k_vaihingen_20211231_230805-7626a263.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_4x4_512x512_80k_vaihingen/deeplabv3plus_r18-d8_4x4_512x512_80k_vaihingen_20211231_230805.log.json)     |
-| DeepLabV3+ | R-50-D8  | 512x512   |   80000 | 7.36     | 26.91          | 73.97 |         75.05 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-80k_vaihingen-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_4x4_512x512_80k_vaihingen/deeplabv3plus_r50-d8_4x4_512x512_80k_vaihingen_20211231_230816-5040938d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_4x4_512x512_80k_vaihingen/deeplabv3plus_r50-d8_4x4_512x512_80k_vaihingen_20211231_230816.log.json)     |
-| DeepLabV3+ | R-101-D8 | 512x512   |   80000 | 10.83    | 18.59          | 73.06 |         74.14 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-80k_vaihingen-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_4x4_512x512_80k_vaihingen/deeplabv3plus_r101-d8_4x4_512x512_80k_vaihingen_20211231_230816-8a095afa.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_4x4_512x512_80k_vaihingen/deeplabv3plus_r101-d8_4x4_512x512_80k_vaihingen_20211231_230816.log.json) |
+| Method     | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                                        | download                                                                                                                                                                                                                                                                                                                                                                                                   |
+| ---------- | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | --------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| DeepLabV3+ | R-18-D8  | 512x512   |   80000 | 1.91     | 72.79          | V100   | 72.50 |         74.13 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb4-80k_vaihingen-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_4x4_512x512_80k_vaihingen/deeplabv3plus_r18-d8_4x4_512x512_80k_vaihingen_20211231_230805-7626a263.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_4x4_512x512_80k_vaihingen/deeplabv3plus_r18-d8_4x4_512x512_80k_vaihingen_20211231_230805.log.json)     |
+| DeepLabV3+ | R-50-D8  | 512x512   |   80000 | 7.36     | 26.91          | V100   | 73.97 |         75.05 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-80k_vaihingen-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_4x4_512x512_80k_vaihingen/deeplabv3plus_r50-d8_4x4_512x512_80k_vaihingen_20211231_230816-5040938d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_4x4_512x512_80k_vaihingen/deeplabv3plus_r50-d8_4x4_512x512_80k_vaihingen_20211231_230816.log.json)     |
+| DeepLabV3+ | R-101-D8 | 512x512   |   80000 | 10.83    | 18.59          | V100   | 73.06 |         74.14 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-80k_vaihingen-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_4x4_512x512_80k_vaihingen/deeplabv3plus_r101-d8_4x4_512x512_80k_vaihingen_20211231_230816-8a095afa.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_4x4_512x512_80k_vaihingen/deeplabv3plus_r101-d8_4x4_512x512_80k_vaihingen_20211231_230816.log.json) |
 
 ### iSAID
 
-| Method     | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                                   | download                                                                                                                                                                                                                                                                                                                                                                               |
-| ---------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ---------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| DeepLabV3+ | R-18-D8  | 896x896   |   80000 | 6.19     | 24.81          | 61.35 |         62.61 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb4-80k_isaid-896x896.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_4x4_896x896_80k_isaid/deeplabv3plus_r18-d8_4x4_896x896_80k_isaid_20220110_180526-7059991d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_4x4_896x896_80k_isaid/deeplabv3plus_r18-d8_4x4_896x896_80k_isaid_20220110_180526.log.json) |
-| DeepLabV3+ | R-50-D8  | 896x896   |   80000 | 21.45    | 8.42           | 67.06 |         68.02 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-80k_isaid-896x896.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_4x4_896x896_80k_isaid/deeplabv3plus_r50-d8_4x4_896x896_80k_isaid_20220110_180526-598be439.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_4x4_896x896_80k_isaid/deeplabv3plus_r50-d8_4x4_896x896_80k_isaid_20220110_180526.log.json) |
+| Method     | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                                   | download                                                                                                                                                                                                                                                                                                                                                                               |
+| ---------- | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ---------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| DeepLabV3+ | R-18-D8  | 896x896   |   80000 | 6.19     | 24.81          | V100   | 61.35 |         62.61 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb4-80k_isaid-896x896.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_4x4_896x896_80k_isaid/deeplabv3plus_r18-d8_4x4_896x896_80k_isaid_20220110_180526-7059991d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_4x4_896x896_80k_isaid/deeplabv3plus_r18-d8_4x4_896x896_80k_isaid_20220110_180526.log.json) |
+| DeepLabV3+ | R-50-D8  | 896x896   |   80000 | 21.45    | 8.42           | V100   | 67.06 |         68.02 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-80k_isaid-896x896.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_4x4_896x896_80k_isaid/deeplabv3plus_r50-d8_4x4_896x896_80k_isaid_20220110_180526-598be439.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_4x4_896x896_80k_isaid/deeplabv3plus_r50-d8_4x4_896x896_80k_isaid_20220110_180526.log.json) |
 
 ### Mapillary Vistas v1.2
 
@@ -136,3 +125,14 @@ Note:
 - `MG-124` stands for multi-grid dilation in the last stage of ResNet.
 - `FP16` means Mixed Precision (FP16) is adopted in training.
 - `896x896` is the Crop Size of iSAID dataset, which is followed by the implementation of [PointFlow: Flowing Semantics Through Points for Aerial Image Segmentation](https://arxiv.org/pdf/2103.06564.pdf)
+
+## Citation
+
+```bibtex
+@inproceedings{deeplabv3plus2018,
+  title={Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation},
+  author={Liang-Chieh Chen and Yukun Zhu and George Papandreou and Florian Schroff and Hartwig Adam},
+  booktitle={ECCV},
+  year={2018}
+}
+```
diff --git a/configs/deeplabv3plus/deeplabv3plus.yml b/configs/deeplabv3plus/deeplabv3plus.yml
deleted file mode 100644
index 949cbcecc0..0000000000
--- a/configs/deeplabv3plus/deeplabv3plus.yml
+++ /dev/null
@@ -1,872 +0,0 @@
-Collections:
-- Name: DeepLabV3+
-  Metadata:
-    Training Data:
-    - Cityscapes
-    - ADE20K
-    - Pascal VOC 2012 + Aug
-    - Pascal Context
-    - Pascal Context 59
-    - LoveDA
-    - Potsdam
-    - Vaihingen
-    - iSAID
-    - Mapillary Vistas v1.2
-  Paper:
-    URL: https://arxiv.org/abs/1802.02611
-    Title: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
-  README: configs/deeplabv3plus/README.md
-  Code:
-    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/sep_aspp_head.py#L30
-    Version: v0.17.0
-  Converted From:
-    Code: https://github.com/tensorflow/models/tree/master/research/deeplab
-Models:
-- Name: deeplabv3plus_r50-d8_4xb2-40k_cityscapes-512x1024
-  In Collection: DeepLabV3+
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,1024)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 253.81
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 7.5
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 79.61
-      mIoU(ms+flip): 81.01
-  Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb2-40k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_40k_cityscapes/deeplabv3plus_r50-d8_512x1024_40k_cityscapes_20200605_094610-d222ffcd.pth
-- Name: deeplabv3plus_r101-d8_4xb2-40k_cityscapes-512x1024
-  In Collection: DeepLabV3+
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,1024)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 384.62
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 11.0
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 80.21
-      mIoU(ms+flip): 81.82
-  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb2-40k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_40k_cityscapes/deeplabv3plus_r101-d8_512x1024_40k_cityscapes_20200605_094614-3769eecf.pth
-- Name: deeplabv3plus_r50-d8_4xb2-40k_cityscapes-769x769
-  In Collection: DeepLabV3+
-  Metadata:
-    backbone: R-50-D8
-    crop size: (769,769)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 581.4
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (769,769)
-    Training Memory (GB): 8.5
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 78.97
-      mIoU(ms+flip): 80.46
-  Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb2-40k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_769x769_40k_cityscapes/deeplabv3plus_r50-d8_769x769_40k_cityscapes_20200606_114143-1dcb0e3c.pth
-- Name: deeplabv3plus_r101-d8_4xb2-40k_cityscapes-769x769
-  In Collection: DeepLabV3+
-  Metadata:
-    backbone: R-101-D8
-    crop size: (769,769)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 869.57
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (769,769)
-    Training Memory (GB): 12.5
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 79.46
-      mIoU(ms+flip): 80.5
-  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb2-40k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_769x769_40k_cityscapes/deeplabv3plus_r101-d8_769x769_40k_cityscapes_20200606_114304-ff414b9e.pth
-- Name: deeplabv3plus_r18-d8_4xb2-80k_cityscapes-512x1024
-  In Collection: DeepLabV3+
-  Metadata:
-    backbone: R-18-D8
-    crop size: (512,1024)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 70.08
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 2.2
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 76.89
-      mIoU(ms+flip): 78.76
-  Config: configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x1024_80k_cityscapes/deeplabv3plus_r18-d8_512x1024_80k_cityscapes_20201226_080942-cff257fe.pth
-- Name: deeplabv3plus_r50-d8_4xb2-80k_cityscapes-512x1024
-  In Collection: DeepLabV3+
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,1024)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 80.09
-      mIoU(ms+flip): 81.13
-  Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_80k_cityscapes/deeplabv3plus_r50-d8_512x1024_80k_cityscapes_20200606_114049-f9fb496d.pth
-- Name: deeplabv3plus_r101-d8_4xb2-80k_cityscapes-512x1024
-  In Collection: DeepLabV3+
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,1024)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 80.97
-      mIoU(ms+flip): 82.03
-  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes/deeplabv3plus_r101-d8_512x1024_80k_cityscapes_20200606_114143-068fcfe9.pth
-- Name: deeplabv3plus_r101-d8_4xb2-amp-80k_cityscapes-512x1024
-  In Collection: DeepLabV3+
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,1024)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 127.06
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: AMP
-      resolution: (512,1024)
-    Training Memory (GB): 6.35
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 80.46
-  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb2-amp-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_fp16_512x1024_80k_cityscapes/deeplabv3plus_r101-d8_fp16_512x1024_80k_cityscapes_20200717_230920-f1104f4b.pth
-- Name: deeplabv3plus_r18-d8_4xb2-80k_cityscapes-769x769
-  In Collection: DeepLabV3+
-  Metadata:
-    backbone: R-18-D8
-    crop size: (769,769)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 174.22
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (769,769)
-    Training Memory (GB): 2.5
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 76.26
-      mIoU(ms+flip): 77.91
-  Config: configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb2-80k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_769x769_80k_cityscapes/deeplabv3plus_r18-d8_769x769_80k_cityscapes_20201226_083346-f326e06a.pth
-- Name: deeplabv3plus_r50-d8_4xb2-80k_cityscapes-769x769
-  In Collection: DeepLabV3+
-  Metadata:
-    backbone: R-50-D8
-    crop size: (769,769)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 79.83
-      mIoU(ms+flip): 81.48
-  Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb2-80k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_769x769_80k_cityscapes/deeplabv3plus_r50-d8_769x769_80k_cityscapes_20200606_210233-0e9dfdc4.pth
-- Name: deeplabv3plus_r101-d8_4xb2-80k_cityscapes-769x769
-  In Collection: DeepLabV3+
-  Metadata:
-    backbone: R-101-D8
-    crop size: (769,769)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 80.65
-      mIoU(ms+flip): 81.47
-  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb2-80k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_769x769_80k_cityscapes/deeplabv3plus_r101-d8_769x769_80k_cityscapes_20220406_154720-dfcc0b68.pth
-- Name: deeplabv3plus_r101-d16-mg124_4xb2-40k_cityscapes-512x1024
-  In Collection: DeepLabV3+
-  Metadata:
-    backbone: R-101-D16-MG124
-    crop size: (512,1024)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 133.69
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 5.8
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 79.09
-      mIoU(ms+flip): 80.36
-  Config: configs/deeplabv3plus/deeplabv3plus_r101-d16-mg124_4xb2-40k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes_20200908_005644-cf9ce186.pth
-- Name: deeplabv3plus_r101-d16-mg124_4xb2-80k_cityscapes-512x1024
-  In Collection: DeepLabV3+
-  Metadata:
-    backbone: R-101-D16-MG124
-    crop size: (512,1024)
-    lr schd: 80000
-    Training Memory (GB): 9.9
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 79.9
-      mIoU(ms+flip): 81.33
-  Config: configs/deeplabv3plus/deeplabv3plus_r101-d16-mg124_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes_20200908_005644-ee6158e0.pth
-- Name: deeplabv3plus_r18b-d8_4xb2-80k_cityscapes-512x1024
-  In Collection: DeepLabV3+
-  Metadata:
-    backbone: R-18b-D8
-    crop size: (512,1024)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 66.89
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 2.1
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 75.87
-      mIoU(ms+flip): 77.52
-  Config: configs/deeplabv3plus/deeplabv3plus_r18b-d8_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes_20201226_090828-e451abd9.pth
-- Name: deeplabv3plus_r50b-d8_4xb2-80k_cityscapes-512x1024
-  In Collection: DeepLabV3+
-  Metadata:
-    backbone: R-50b-D8
-    crop size: (512,1024)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 253.81
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 7.4
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 80.28
-      mIoU(ms+flip): 81.44
-  Config: configs/deeplabv3plus/deeplabv3plus_r50b-d8_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes_20201225_213645-a97e4e43.pth
-- Name: deeplabv3plus_r101b-d8_4xb2-80k_cityscapes-512x1024
-  In Collection: DeepLabV3+
-  Metadata:
-    backbone: R-101b-D8
-    crop size: (512,1024)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 384.62
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 10.9
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 80.16
-      mIoU(ms+flip): 81.41
-  Config: configs/deeplabv3plus/deeplabv3plus_r101b-d8_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes_20201226_190843-9c3c93a4.pth
-- Name: deeplabv3plus_r18b-d8_4xb2-80k_cityscapes-769x769
-  In Collection: DeepLabV3+
-  Metadata:
-    backbone: R-18b-D8
-    crop size: (769,769)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 167.79
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (769,769)
-    Training Memory (GB): 2.4
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 76.36
-      mIoU(ms+flip): 78.24
-  Config: configs/deeplabv3plus/deeplabv3plus_r18b-d8_4xb2-80k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18b-d8_769x769_80k_cityscapes/deeplabv3plus_r18b-d8_769x769_80k_cityscapes_20201226_151312-2c868aff.pth
-- Name: deeplabv3plus_r50b-d8_4xb2-80k_cityscapes-769x769
-  In Collection: DeepLabV3+
-  Metadata:
-    backbone: R-50b-D8
-    crop size: (769,769)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 581.4
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (769,769)
-    Training Memory (GB): 8.4
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 79.41
-      mIoU(ms+flip): 80.56
-  Config: configs/deeplabv3plus/deeplabv3plus_r50b-d8_4xb2-80k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50b-d8_769x769_80k_cityscapes/deeplabv3plus_r50b-d8_769x769_80k_cityscapes_20201225_224655-8b596d1c.pth
-- Name: deeplabv3plus_r101b-d8_4xb2-80k_cityscapes-769x769
-  In Collection: DeepLabV3+
-  Metadata:
-    backbone: R-101b-D8
-    crop size: (769,769)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 909.09
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (769,769)
-    Training Memory (GB): 12.3
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 79.88
-      mIoU(ms+flip): 81.46
-  Config: configs/deeplabv3plus/deeplabv3plus_r101b-d8_4xb2-80k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101b-d8_769x769_80k_cityscapes/deeplabv3plus_r101b-d8_769x769_80k_cityscapes_20201226_205041-227cdf7c.pth
-- Name: deeplabv3plus_r50-d8_4xb4-80k_ade20k-512x512
-  In Collection: DeepLabV3+
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 47.6
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 10.6
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 42.72
-      mIoU(ms+flip): 43.75
-  Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-80k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_ade20k/deeplabv3plus_r50-d8_512x512_80k_ade20k_20200614_185028-bf1400d8.pth
-- Name: deeplabv3plus_r101-d8_4xb4-160k_ade20k-512x512
-  In Collection: DeepLabV3+
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 70.62
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 14.1
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 44.6
-      mIoU(ms+flip): 46.06
-  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_ade20k/deeplabv3plus_r101-d8_512x512_80k_ade20k_20200615_014139-d5730af7.pth
-- Name: deeplabv3plus_r50-d8_4xb4-160k_ade20k-512x512
-  In Collection: DeepLabV3+
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 160000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 43.95
-      mIoU(ms+flip): 44.93
-  Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_160k_ade20k/deeplabv3plus_r50-d8_512x512_160k_ade20k_20200615_124504-6135c7e0.pth
-- Name: deeplabv3plus_r101-d8_4xb4-160k_ade20k-512x512
-  In Collection: DeepLabV3+
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 160000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 45.47
-      mIoU(ms+flip): 46.35
-  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_160k_ade20k/deeplabv3plus_r101-d8_512x512_160k_ade20k_20200615_123232-38ed86bb.pth
-- Name: deeplabv3plus_r50-d8_4xb4-20k_voc12aug-512x512
-  In Collection: DeepLabV3+
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 20000
-    inference time (ms/im):
-    - value: 47.62
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 7.6
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal VOC 2012 + Aug
-    Metrics:
-      mIoU: 75.93
-      mIoU(ms+flip): 77.5
-  Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-20k_voc12aug-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_20k_voc12aug/deeplabv3plus_r50-d8_512x512_20k_voc12aug_20200617_102323-aad58ef1.pth
-- Name: deeplabv3plus_r101-d8_4xb4-20k_voc12aug-512x512
-  In Collection: DeepLabV3+
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 20000
-    inference time (ms/im):
-    - value: 72.05
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 11.0
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal VOC 2012 + Aug
-    Metrics:
-      mIoU: 77.22
-      mIoU(ms+flip): 78.59
-  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-20k_voc12aug-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_20k_voc12aug/deeplabv3plus_r101-d8_512x512_20k_voc12aug_20200617_102345-c7ff3d56.pth
-- Name: deeplabv3plus_r50-d8_4xb4-40k_voc12aug-512x512
-  In Collection: DeepLabV3+
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 40000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal VOC 2012 + Aug
-    Metrics:
-      mIoU: 76.81
-      mIoU(ms+flip): 77.57
-  Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-40k_voc12aug-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_40k_voc12aug/deeplabv3plus_r50-d8_512x512_40k_voc12aug_20200613_161759-e1b43aa9.pth
-- Name: deeplabv3plus_r101-d8_4xb4-40k_voc12aug-512x512
-  In Collection: DeepLabV3+
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 40000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal VOC 2012 + Aug
-    Metrics:
-      mIoU: 78.62
-      mIoU(ms+flip): 79.53
-  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-40k_voc12aug-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_40k_voc12aug/deeplabv3plus_r101-d8_512x512_40k_voc12aug_20200613_205333-faf03387.pth
-- Name: deeplabv3plus_r50-d8_4xb4-40k_pascal-context-480x480
-  In Collection: DeepLabV3+
-  Metadata:
-    backbone: R-101-D8
-    crop size: (480,480)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 110.01
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (480,480)
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal Context
-    Metrics:
-      mIoU: 47.3
-      mIoU(ms+flip): 48.47
-  Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-40k_pascal-context-480x480.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context/deeplabv3plus_r101-d8_480x480_40k_pascal_context_20200911_165459-d3c8a29e.pth
-- Name: deeplabv3plus_r50-d8_4xb4-80k_pascal-context-480x480
-  In Collection: DeepLabV3+
-  Metadata:
-    backbone: R-101-D8
-    crop size: (480,480)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal Context
-    Metrics:
-      mIoU: 47.23
-      mIoU(ms+flip): 48.26
-  Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-80k_pascal-context-480x480.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context/deeplabv3plus_r101-d8_480x480_80k_pascal_context_20200911_155322-145d3ee8.pth
-- Name: deeplabv3plus_r101-d8_4xb4-40k_pascal-context-59-480x480
-  In Collection: DeepLabV3+
-  Metadata:
-    backbone: R-101-D8
-    crop size: (480,480)
-    lr schd: 40000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal Context 59
-    Metrics:
-      mIoU: 52.86
-      mIoU(ms+flip): 54.54
-  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-40k_pascal-context-59-480x480.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59_20210416_111233-ed937f15.pth
-- Name: deeplabv3plus_r101-d8_4xb4-80k_pascal-context-59-480x480
-  In Collection: DeepLabV3+
-  Metadata:
-    backbone: R-101-D8
-    crop size: (480,480)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal Context 59
-    Metrics:
-      mIoU: 53.2
-      mIoU(ms+flip): 54.67
-  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-80k_pascal-context-59-480x480.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59_20210416_111127-7ca0331d.pth
-- Name: deeplabv3plus_r18-d8_4xb4-80k_loveda-512x512
-  In Collection: DeepLabV3+
-  Metadata:
-    backbone: R-18-D8
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 39.11
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 1.93
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: LoveDA
-    Metrics:
-      mIoU: 50.28
-      mIoU(ms+flip): 50.47
-  Config: configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb4-80k_loveda-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_loveda/deeplabv3plus_r18-d8_512x512_80k_loveda_20211104_132800-ce0fa0ca.pth
-- Name: deeplabv3plus_r50-d8_4xb4-80k_loveda-512x512
-  In Collection: DeepLabV3+
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 166.67
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 7.37
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: LoveDA
-    Metrics:
-      mIoU: 50.99
-      mIoU(ms+flip): 50.65
-  Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-80k_loveda-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_loveda/deeplabv3plus_r50-d8_512x512_80k_loveda_20211105_080442-f0720392.pth
-- Name: deeplabv3plus_r101-d8_4xb4-80k_loveda-512x512
-  In Collection: DeepLabV3+
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 230.95
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 10.84
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: LoveDA
-    Metrics:
-      mIoU: 51.47
-      mIoU(ms+flip): 51.32
-  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-80k_loveda-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_loveda/deeplabv3plus_r101-d8_512x512_80k_loveda_20211105_110759-4c1f297e.pth
-- Name: deeplabv3plus_r18-d8_4xb4-80k_potsdam-512x512
-  In Collection: DeepLabV3+
-  Metadata:
-    backbone: R-18-D8
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 12.24
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 1.91
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Potsdam
-    Metrics:
-      mIoU: 77.09
-      mIoU(ms+flip): 78.44
-  Config: configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb4-80k_potsdam-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_potsdam/deeplabv3plus_r18-d8_512x512_80k_potsdam_20211219_020601-75fd5bc3.pth
-- Name: deeplabv3plus_r50-d8_4xb4-80k_potsdam-512x512
-  In Collection: DeepLabV3+
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 37.82
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 7.36
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Potsdam
-    Metrics:
-      mIoU: 78.33
-      mIoU(ms+flip): 79.27
-  Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-80k_potsdam-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_potsdam/deeplabv3plus_r50-d8_512x512_80k_potsdam_20211219_031508-7e7a2b24.pth
-- Name: deeplabv3plus_r101-d8_4xb4-80k_potsdam-512x512
-  In Collection: DeepLabV3+
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 56.95
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 10.83
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Potsdam
-    Metrics:
-      mIoU: 78.7
-      mIoU(ms+flip): 79.47
-  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-80k_potsdam-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_potsdam/deeplabv3plus_r101-d8_512x512_80k_potsdam_20211219_031508-8b112708.pth
-- Name: deeplabv3plus_r18-d8_4xb4-80k_vaihingen-512x512
-  In Collection: DeepLabV3+
-  Metadata:
-    backbone: R-18-D8
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 13.74
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 1.91
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Vaihingen
-    Metrics:
-      mIoU: 72.5
-      mIoU(ms+flip): 74.13
-  Config: configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb4-80k_vaihingen-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_4x4_512x512_80k_vaihingen/deeplabv3plus_r18-d8_4x4_512x512_80k_vaihingen_20211231_230805-7626a263.pth
-- Name: deeplabv3plus_r50-d8_4xb4-80k_vaihingen-512x512
-  In Collection: DeepLabV3+
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 37.16
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 7.36
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Vaihingen
-    Metrics:
-      mIoU: 73.97
-      mIoU(ms+flip): 75.05
-  Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-80k_vaihingen-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_4x4_512x512_80k_vaihingen/deeplabv3plus_r50-d8_4x4_512x512_80k_vaihingen_20211231_230816-5040938d.pth
-- Name: deeplabv3plus_r101-d8_4xb4-80k_vaihingen-512x512
-  In Collection: DeepLabV3+
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 53.79
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 10.83
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Vaihingen
-    Metrics:
-      mIoU: 73.06
-      mIoU(ms+flip): 74.14
-  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-80k_vaihingen-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_4x4_512x512_80k_vaihingen/deeplabv3plus_r101-d8_4x4_512x512_80k_vaihingen_20211231_230816-8a095afa.pth
-- Name: deeplabv3plus_r18-d8_4xb4-80k_isaid-896x896
-  In Collection: DeepLabV3+
-  Metadata:
-    backbone: R-18-D8
-    crop size: (896,896)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 40.31
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (896,896)
-    Training Memory (GB): 6.19
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: iSAID
-    Metrics:
-      mIoU: 61.35
-      mIoU(ms+flip): 62.61
-  Config: configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb4-80k_isaid-896x896.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_4x4_896x896_80k_isaid/deeplabv3plus_r18-d8_4x4_896x896_80k_isaid_20220110_180526-7059991d.pth
-- Name: deeplabv3plus_r50-d8_4xb4-80k_isaid-896x896
-  In Collection: DeepLabV3+
-  Metadata:
-    backbone: R-50-D8
-    crop size: (896,896)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 118.76
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (896,896)
-    Training Memory (GB): 21.45
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: iSAID
-    Metrics:
-      mIoU: 67.06
-      mIoU(ms+flip): 68.02
-  Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-80k_isaid-896x896.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_4x4_896x896_80k_isaid/deeplabv3plus_r50-d8_4x4_896x896_80k_isaid_20220110_180526-598be439.pth
-- Name: deeplabv3plus_r50-d8_4xb2-300k_mapillay_v1_65-1280x1280
-  In Collection: DeepLabV3+
-  Metadata:
-    backbone: R-50-D8
-    crop size: (1280,1280)
-    lr schd: 300000
-    inference time (ms/im):
-    - value: 55.8
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (1280,1280)
-    Training Memory (GB): 24.04
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Mapillary Vistas v1.2
-    Metrics:
-      mIoU: 47.35
-  Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb2-300k_mapillay_v1_65-1280x1280.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_4xb2-300k_mapillay_v1_65-1280x1280/deeplabv3plus_r50-d8_4xb2-300k_mapillay_v1_65-1280x1280_20230301_110504-655f8e43.pth
diff --git a/configs/deeplabv3plus/metafile.yaml b/configs/deeplabv3plus/metafile.yaml
new file mode 100644
index 0000000000..b41de4dee2
--- /dev/null
+++ b/configs/deeplabv3plus/metafile.yaml
@@ -0,0 +1,1041 @@
+Collections:
+- Name: DeepLabV3+
+  License: Apache License 2.0
+  Metadata:
+    Training Data:
+    - Cityscapes
+    - ADE20K
+    - Pascal VOC 2012 + Aug
+    - Pascal Context
+    - Pascal Context 59
+    - LoveDA
+    - Potsdam
+    - Vaihingen
+    - iSAID
+    - Mapillary Vistas v1.2
+  Paper:
+    Title: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
+    URL: https://arxiv.org/abs/1802.02611
+  README: configs/deeplabv3plus/README.md
+  Frameworks:
+  - PyTorch
+Models:
+- Name: deeplabv3plus_r50-d8_4xb2-40k_cityscapes-512x1024
+  In Collection: DeepLabV3+
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.61
+      mIoU(ms+flip): 81.01
+  Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb2-40k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - DeepLabV3+
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 7.5
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_40k_cityscapes/deeplabv3plus_r50-d8_512x1024_40k_cityscapes_20200605_094610-d222ffcd.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_40k_cityscapes/deeplabv3plus_r50-d8_512x1024_40k_cityscapes_20200605_094610.log.json
+  Paper:
+    Title: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
+    URL: https://arxiv.org/abs/1802.02611
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/sep_aspp_head.py#L30
+  Framework: PyTorch
+- Name: deeplabv3plus_r101-d8_4xb2-40k_cityscapes-512x1024
+  In Collection: DeepLabV3+
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 80.21
+      mIoU(ms+flip): 81.82
+  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb2-40k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - DeepLabV3+
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 11.0
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_40k_cityscapes/deeplabv3plus_r101-d8_512x1024_40k_cityscapes_20200605_094614-3769eecf.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_40k_cityscapes/deeplabv3plus_r101-d8_512x1024_40k_cityscapes_20200605_094614.log.json
+  Paper:
+    Title: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
+    URL: https://arxiv.org/abs/1802.02611
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/sep_aspp_head.py#L30
+  Framework: PyTorch
+- Name: deeplabv3plus_r50-d8_4xb2-40k_cityscapes-769x769
+  In Collection: DeepLabV3+
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.97
+      mIoU(ms+flip): 80.46
+  Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb2-40k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - DeepLabV3+
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 8.5
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_769x769_40k_cityscapes/deeplabv3plus_r50-d8_769x769_40k_cityscapes_20200606_114143-1dcb0e3c.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_769x769_40k_cityscapes/deeplabv3plus_r50-d8_769x769_40k_cityscapes_20200606_114143.log.json
+  Paper:
+    Title: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
+    URL: https://arxiv.org/abs/1802.02611
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/sep_aspp_head.py#L30
+  Framework: PyTorch
+- Name: deeplabv3plus_r101-d8_4xb2-40k_cityscapes-769x769
+  In Collection: DeepLabV3+
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.46
+      mIoU(ms+flip): 80.5
+  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb2-40k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - DeepLabV3+
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 12.5
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_769x769_40k_cityscapes/deeplabv3plus_r101-d8_769x769_40k_cityscapes_20200606_114304-ff414b9e.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_769x769_40k_cityscapes/deeplabv3plus_r101-d8_769x769_40k_cityscapes_20200606_114304.log.json
+  Paper:
+    Title: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
+    URL: https://arxiv.org/abs/1802.02611
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/sep_aspp_head.py#L30
+  Framework: PyTorch
+- Name: deeplabv3plus_r18-d8_4xb2-80k_cityscapes-512x1024
+  In Collection: DeepLabV3+
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 76.89
+      mIoU(ms+flip): 78.76
+  Config: configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-18-D8
+    - DeepLabV3+
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 2.2
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x1024_80k_cityscapes/deeplabv3plus_r18-d8_512x1024_80k_cityscapes_20201226_080942-cff257fe.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x1024_80k_cityscapes/deeplabv3plus_r18-d8_512x1024_80k_cityscapes-20201226_080942.log.json
+  Paper:
+    Title: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
+    URL: https://arxiv.org/abs/1802.02611
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/sep_aspp_head.py#L30
+  Framework: PyTorch
+- Name: deeplabv3plus_r50-d8_4xb2-80k_cityscapes-512x1024
+  In Collection: DeepLabV3+
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 80.09
+      mIoU(ms+flip): 81.13
+  Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - DeepLabV3+
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_80k_cityscapes/deeplabv3plus_r50-d8_512x1024_80k_cityscapes_20200606_114049-f9fb496d.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_80k_cityscapes/deeplabv3plus_r50-d8_512x1024_80k_cityscapes_20200606_114049.log.json
+  Paper:
+    Title: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
+    URL: https://arxiv.org/abs/1802.02611
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/sep_aspp_head.py#L30
+  Framework: PyTorch
+- Name: deeplabv3plus_r101-d8_4xb2-80k_cityscapes-512x1024
+  In Collection: DeepLabV3+
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 80.97
+      mIoU(ms+flip): 82.03
+  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - DeepLabV3+
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes/deeplabv3plus_r101-d8_512x1024_80k_cityscapes_20200606_114143-068fcfe9.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes/deeplabv3plus_r101-d8_512x1024_80k_cityscapes_20200606_114143.log.json
+  Paper:
+    Title: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
+    URL: https://arxiv.org/abs/1802.02611
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/sep_aspp_head.py#L30
+  Framework: PyTorch
+- Name: deeplabv3plus_r101-d8_4xb2-amp-80k_cityscapes-512x1024
+  In Collection: DeepLabV3+
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 80.46
+  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb2-amp-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - DeepLabV3+
+    - (FP16)
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 6.35
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_fp16_512x1024_80k_cityscapes/deeplabv3plus_r101-d8_fp16_512x1024_80k_cityscapes_20200717_230920-f1104f4b.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_fp16_512x1024_80k_cityscapes/deeplabv3plus_r101-d8_fp16_512x1024_80k_cityscapes_20200717_230920.log.json
+  Paper:
+    Title: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
+    URL: https://arxiv.org/abs/1802.02611
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/sep_aspp_head.py#L30
+  Framework: PyTorch
+- Name: deeplabv3plus_r18-d8_4xb2-80k_cityscapes-769x769
+  In Collection: DeepLabV3+
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 76.26
+      mIoU(ms+flip): 77.91
+  Config: configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb2-80k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-18-D8
+    - DeepLabV3+
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 2.5
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_769x769_80k_cityscapes/deeplabv3plus_r18-d8_769x769_80k_cityscapes_20201226_083346-f326e06a.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_769x769_80k_cityscapes/deeplabv3plus_r18-d8_769x769_80k_cityscapes-20201226_083346.log.json
+  Paper:
+    Title: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
+    URL: https://arxiv.org/abs/1802.02611
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/sep_aspp_head.py#L30
+  Framework: PyTorch
+- Name: deeplabv3plus_r50-d8_4xb2-80k_cityscapes-769x769
+  In Collection: DeepLabV3+
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.83
+      mIoU(ms+flip): 81.48
+  Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb2-80k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - DeepLabV3+
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_769x769_80k_cityscapes/deeplabv3plus_r50-d8_769x769_80k_cityscapes_20200606_210233-0e9dfdc4.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_769x769_80k_cityscapes/deeplabv3plus_r50-d8_769x769_80k_cityscapes_20200606_210233.log.json
+  Paper:
+    Title: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
+    URL: https://arxiv.org/abs/1802.02611
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/sep_aspp_head.py#L30
+  Framework: PyTorch
+- Name: deeplabv3plus_r101-d8_4xb2-80k_cityscapes-769x769
+  In Collection: DeepLabV3+
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 80.65
+      mIoU(ms+flip): 81.47
+  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb2-80k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - DeepLabV3+
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_769x769_80k_cityscapes/deeplabv3plus_r101-d8_769x769_80k_cityscapes_20220406_154720-dfcc0b68.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_769x769_80k_cityscapes/deeplabv3plus_r101-d8_769x769_80k_cityscapes_20220406_154720.log.json
+  Paper:
+    Title: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
+    URL: https://arxiv.org/abs/1802.02611
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/sep_aspp_head.py#L30
+  Framework: PyTorch
+- Name: ddeeplabv3plus_r101-d16-mg124_4xb2-40k_cityscapes-512x1024
+  In Collection: DeepLabV3+
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.09
+      mIoU(ms+flip): 80.36
+  Config: configs/deeplabv3plus/ddeeplabv3plus_r101-d16-mg124_4xb2-40k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D16-MG124
+    - DeepLabV3+
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 5.8
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes_20200908_005644-cf9ce186.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes-20200908_005644.log.json
+  Paper:
+    Title: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
+    URL: https://arxiv.org/abs/1802.02611
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/sep_aspp_head.py#L30
+  Framework: PyTorch
+- Name: deeplabv3plus_r101-d16-mg124_4xb2-80k_cityscapes-512x1024
+  In Collection: DeepLabV3+
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.9
+      mIoU(ms+flip): 81.33
+  Config: configs/deeplabv3plus/deeplabv3plus_r101-d16-mg124_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D16-MG124
+    - DeepLabV3+
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 9.9
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes_20200908_005644-ee6158e0.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes-20200908_005644.log.json
+  Paper:
+    Title: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
+    URL: https://arxiv.org/abs/1802.02611
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/sep_aspp_head.py#L30
+  Framework: PyTorch
+- Name: deeplabv3plus_r18b-d8_4xb2-80k_cityscapes-512x1024
+  In Collection: DeepLabV3+
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 75.87
+      mIoU(ms+flip): 77.52
+  Config: configs/deeplabv3plus/deeplabv3plus_r18b-d8_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-18b-D8
+    - DeepLabV3+
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 2.1
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes_20201226_090828-e451abd9.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes-20201226_090828.log.json
+  Paper:
+    Title: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
+    URL: https://arxiv.org/abs/1802.02611
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/sep_aspp_head.py#L30
+  Framework: PyTorch
+- Name: deeplabv3plus_r50b-d8_4xb2-80k_cityscapes-512x1024
+  In Collection: DeepLabV3+
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 80.28
+      mIoU(ms+flip): 81.44
+  Config: configs/deeplabv3plus/deeplabv3plus_r50b-d8_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50b-D8
+    - DeepLabV3+
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 7.4
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes_20201225_213645-a97e4e43.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes-20201225_213645.log.json
+  Paper:
+    Title: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
+    URL: https://arxiv.org/abs/1802.02611
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/sep_aspp_head.py#L30
+  Framework: PyTorch
+- Name: deeplabv3plus_r101b-d8_4xb2-80k_cityscapes-512x1024
+  In Collection: DeepLabV3+
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 80.16
+      mIoU(ms+flip): 81.41
+  Config: configs/deeplabv3plus/deeplabv3plus_r101b-d8_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101b-D8
+    - DeepLabV3+
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 10.9
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes_20201226_190843-9c3c93a4.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes-20201226_190843.log.json
+  Paper:
+    Title: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
+    URL: https://arxiv.org/abs/1802.02611
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/sep_aspp_head.py#L30
+  Framework: PyTorch
+- Name: deeplabv3plus_r18b-d8_4xb2-80k_cityscapes-769x769
+  In Collection: DeepLabV3+
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 76.36
+      mIoU(ms+flip): 78.24
+  Config: configs/deeplabv3plus/deeplabv3plus_r18b-d8_4xb2-80k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-18b-D8
+    - DeepLabV3+
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 2.4
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18b-d8_769x769_80k_cityscapes/deeplabv3plus_r18b-d8_769x769_80k_cityscapes_20201226_151312-2c868aff.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18b-d8_769x769_80k_cityscapes/deeplabv3plus_r18b-d8_769x769_80k_cityscapes-20201226_151312.log.json
+  Paper:
+    Title: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
+    URL: https://arxiv.org/abs/1802.02611
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/sep_aspp_head.py#L30
+  Framework: PyTorch
+- Name: deeplabv3plus_r50b-d8_4xb2-80k_cityscapes-769x769
+  In Collection: DeepLabV3+
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.41
+      mIoU(ms+flip): 80.56
+  Config: configs/deeplabv3plus/deeplabv3plus_r50b-d8_4xb2-80k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50b-D8
+    - DeepLabV3+
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 8.4
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50b-d8_769x769_80k_cityscapes/deeplabv3plus_r50b-d8_769x769_80k_cityscapes_20201225_224655-8b596d1c.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50b-d8_769x769_80k_cityscapes/deeplabv3plus_r50b-d8_769x769_80k_cityscapes-20201225_224655.log.json
+  Paper:
+    Title: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
+    URL: https://arxiv.org/abs/1802.02611
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/sep_aspp_head.py#L30
+  Framework: PyTorch
+- Name: deeplabv3plus_r101b-d8_4xb2-80k_cityscapes-769x769
+  In Collection: DeepLabV3+
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.88
+      mIoU(ms+flip): 81.46
+  Config: configs/deeplabv3plus/deeplabv3plus_r101b-d8_4xb2-80k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101b-D8
+    - DeepLabV3+
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 12.3
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101b-d8_769x769_80k_cityscapes/deeplabv3plus_r101b-d8_769x769_80k_cityscapes_20201226_205041-227cdf7c.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101b-d8_769x769_80k_cityscapes/deeplabv3plus_r101b-d8_769x769_80k_cityscapes-20201226_205041.log.json
+  Paper:
+    Title: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
+    URL: https://arxiv.org/abs/1802.02611
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/sep_aspp_head.py#L30
+  Framework: PyTorch
+- Name: deeplabv3plus_r50-d8_4xb4-80k_ade20k-512x512
+  In Collection: DeepLabV3+
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 42.72
+      mIoU(ms+flip): 43.75
+  Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-80k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - DeepLabV3+
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 10.6
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_ade20k/deeplabv3plus_r50-d8_512x512_80k_ade20k_20200614_185028-bf1400d8.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_ade20k/deeplabv3plus_r50-d8_512x512_80k_ade20k_20200614_185028.log.json
+  Paper:
+    Title: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
+    URL: https://arxiv.org/abs/1802.02611
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/sep_aspp_head.py#L30
+  Framework: PyTorch
+- Name: deeplabv3plus_r101-d8_4xb4-160k_ade20k-512x512
+  In Collection: DeepLabV3+
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 44.6
+      mIoU(ms+flip): 46.06
+  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - DeepLabV3+
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 14.1
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_ade20k/deeplabv3plus_r101-d8_512x512_80k_ade20k_20200615_014139-d5730af7.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_ade20k/deeplabv3plus_r101-d8_512x512_80k_ade20k_20200615_014139.log.json
+  Paper:
+    Title: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
+    URL: https://arxiv.org/abs/1802.02611
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/sep_aspp_head.py#L30
+  Framework: PyTorch
+- Name: deeplabv3plus_r50-d8_4xb4-160k_ade20k-512x512
+  In Collection: DeepLabV3+
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 43.95
+      mIoU(ms+flip): 44.93
+  Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - DeepLabV3+
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_160k_ade20k/deeplabv3plus_r50-d8_512x512_160k_ade20k_20200615_124504-6135c7e0.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_160k_ade20k/deeplabv3plus_r50-d8_512x512_160k_ade20k_20200615_124504.log.json
+  Paper:
+    Title: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
+    URL: https://arxiv.org/abs/1802.02611
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/sep_aspp_head.py#L30
+  Framework: PyTorch
+- Name: deeplabv3plus_r101-d8_4xb4-160k_ade20k-512x512
+  In Collection: DeepLabV3+
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 45.47
+      mIoU(ms+flip): 46.35
+  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - DeepLabV3+
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_160k_ade20k/deeplabv3plus_r101-d8_512x512_160k_ade20k_20200615_123232-38ed86bb.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_160k_ade20k/deeplabv3plus_r101-d8_512x512_160k_ade20k_20200615_123232.log.json
+  Paper:
+    Title: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
+    URL: https://arxiv.org/abs/1802.02611
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/sep_aspp_head.py#L30
+  Framework: PyTorch
+- Name: deeplabv3plus_r50-d8_4xb4-20k_voc12aug-512x512
+  In Collection: DeepLabV3+
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 75.93
+      mIoU(ms+flip): 77.5
+  Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-20k_voc12aug-512x512.py
+  Metadata:
+    Training Data: Pascal VOC 2012 + Aug
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - DeepLabV3+
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 7.6
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_20k_voc12aug/deeplabv3plus_r50-d8_512x512_20k_voc12aug_20200617_102323-aad58ef1.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_20k_voc12aug/deeplabv3plus_r50-d8_512x512_20k_voc12aug_20200617_102323.log.json
+  Paper:
+    Title: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
+    URL: https://arxiv.org/abs/1802.02611
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/sep_aspp_head.py#L30
+  Framework: PyTorch
+- Name: deeplabv3plus_r101-d8_4xb4-20k_voc12aug-512x512
+  In Collection: DeepLabV3+
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 77.22
+      mIoU(ms+flip): 78.59
+  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-20k_voc12aug-512x512.py
+  Metadata:
+    Training Data: Pascal VOC 2012 + Aug
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - DeepLabV3+
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 11.0
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_20k_voc12aug/deeplabv3plus_r101-d8_512x512_20k_voc12aug_20200617_102345-c7ff3d56.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_20k_voc12aug/deeplabv3plus_r101-d8_512x512_20k_voc12aug_20200617_102345.log.json
+  Paper:
+    Title: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
+    URL: https://arxiv.org/abs/1802.02611
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/sep_aspp_head.py#L30
+  Framework: PyTorch
+- Name: deeplabv3plus_r50-d8_4xb4-40k_voc12aug-512x512
+  In Collection: DeepLabV3+
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 76.81
+      mIoU(ms+flip): 77.57
+  Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-40k_voc12aug-512x512.py
+  Metadata:
+    Training Data: Pascal VOC 2012 + Aug
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - DeepLabV3+
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_40k_voc12aug/deeplabv3plus_r50-d8_512x512_40k_voc12aug_20200613_161759-e1b43aa9.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_40k_voc12aug/deeplabv3plus_r50-d8_512x512_40k_voc12aug_20200613_161759.log.json
+  Paper:
+    Title: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
+    URL: https://arxiv.org/abs/1802.02611
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/sep_aspp_head.py#L30
+  Framework: PyTorch
+- Name: deeplabv3plus_r101-d8_4xb4-40k_voc12aug-512x512
+  In Collection: DeepLabV3+
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 78.62
+      mIoU(ms+flip): 79.53
+  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-40k_voc12aug-512x512.py
+  Metadata:
+    Training Data: Pascal VOC 2012 + Aug
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - DeepLabV3+
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_40k_voc12aug/deeplabv3plus_r101-d8_512x512_40k_voc12aug_20200613_205333-faf03387.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_40k_voc12aug/deeplabv3plus_r101-d8_512x512_40k_voc12aug_20200613_205333.log.json
+  Paper:
+    Title: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
+    URL: https://arxiv.org/abs/1802.02611
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/sep_aspp_head.py#L30
+  Framework: PyTorch
+- Name: deeplabv3plus_r50-d8_4xb4-40k_pascal-context-480x480
+  In Collection: DeepLabV3+
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal Context
+    Metrics:
+      mIoU: 47.3
+      mIoU(ms+flip): 48.47
+  Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-40k_pascal-context-480x480.py
+  Metadata:
+    Training Data: Pascal Context
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - DeepLabV3+
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context/deeplabv3plus_r101-d8_480x480_40k_pascal_context_20200911_165459-d3c8a29e.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context/deeplabv3plus_r101-d8_480x480_40k_pascal_context-20200911_165459.log.json
+  Paper:
+    Title: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
+    URL: https://arxiv.org/abs/1802.02611
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/sep_aspp_head.py#L30
+  Framework: PyTorch
+- Name: deeplabv3plus_r50-d8_4xb4-80k_pascal-context-480x480
+  In Collection: DeepLabV3+
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal Context
+    Metrics:
+      mIoU: 47.23
+      mIoU(ms+flip): 48.26
+  Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-80k_pascal-context-480x480.py
+  Metadata:
+    Training Data: Pascal Context
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - DeepLabV3+
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context/deeplabv3plus_r101-d8_480x480_80k_pascal_context_20200911_155322-145d3ee8.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context/deeplabv3plus_r101-d8_480x480_80k_pascal_context-20200911_155322.log.json
+  Paper:
+    Title: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
+    URL: https://arxiv.org/abs/1802.02611
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/sep_aspp_head.py#L30
+  Framework: PyTorch
+- Name: deeplabv3plus_r101-d8_4xb4-40k_pascal-context-59-480x480
+  In Collection: DeepLabV3+
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal Context 59
+    Metrics:
+      mIoU: 52.86
+      mIoU(ms+flip): 54.54
+  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-40k_pascal-context-59-480x480.py
+  Metadata:
+    Training Data: Pascal Context 59
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - DeepLabV3+
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59_20210416_111233-ed937f15.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59-20210416_111233.log.json
+  Paper:
+    Title: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
+    URL: https://arxiv.org/abs/1802.02611
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/sep_aspp_head.py#L30
+  Framework: PyTorch
+- Name: deeplabv3plus_r101-d8_4xb4-80k_pascal-context-59-480x480
+  In Collection: DeepLabV3+
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal Context 59
+    Metrics:
+      mIoU: 53.2
+      mIoU(ms+flip): 54.67
+  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-80k_pascal-context-59-480x480.py
+  Metadata:
+    Training Data: Pascal Context 59
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - DeepLabV3+
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59_20210416_111127-7ca0331d.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59-20210416_111127.log.json
+  Paper:
+    Title: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
+    URL: https://arxiv.org/abs/1802.02611
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/sep_aspp_head.py#L30
+  Framework: PyTorch
+- Name: deeplabv3plus_r18-d8_4xb4-80k_loveda-512x512
+  In Collection: DeepLabV3+
+  Results:
+    Task: Semantic Segmentation
+    Dataset: LoveDA
+    Metrics:
+      mIoU: 50.28
+      mIoU(ms+flip): 50.47
+  Config: configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb4-80k_loveda-512x512.py
+  Metadata:
+    Training Data: LoveDA
+    Batch Size: 16
+    Architecture:
+    - R-18-D8
+    - DeepLabV3+
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 1.93
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_loveda/deeplabv3plus_r18-d8_512x512_80k_loveda_20211104_132800-ce0fa0ca.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_loveda/deeplabv3plus_r18-d8_512x512_80k_loveda_20211104_132800.log.json
+  Paper:
+    Title: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
+    URL: https://arxiv.org/abs/1802.02611
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/sep_aspp_head.py#L30
+  Framework: PyTorch
+- Name: deeplabv3plus_r50-d8_4xb4-80k_loveda-512x512
+  In Collection: DeepLabV3+
+  Results:
+    Task: Semantic Segmentation
+    Dataset: LoveDA
+    Metrics:
+      mIoU: 50.99
+      mIoU(ms+flip): 50.65
+  Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-80k_loveda-512x512.py
+  Metadata:
+    Training Data: LoveDA
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - DeepLabV3+
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 7.37
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_loveda/deeplabv3plus_r50-d8_512x512_80k_loveda_20211105_080442-f0720392.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_loveda/deeplabv3plus_r50-d8_512x512_80k_loveda_20211105_080442.log.json
+  Paper:
+    Title: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
+    URL: https://arxiv.org/abs/1802.02611
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/sep_aspp_head.py#L30
+  Framework: PyTorch
+- Name: deeplabv3plus_r101-d8_4xb4-80k_loveda-512x512
+  In Collection: DeepLabV3+
+  Results:
+    Task: Semantic Segmentation
+    Dataset: LoveDA
+    Metrics:
+      mIoU: 51.47
+      mIoU(ms+flip): 51.32
+  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-80k_loveda-512x512.py
+  Metadata:
+    Training Data: LoveDA
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - DeepLabV3+
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 10.84
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_loveda/deeplabv3plus_r101-d8_512x512_80k_loveda_20211105_110759-4c1f297e.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_loveda/deeplabv3plus_r101-d8_512x512_80k_loveda_20211105_110759.log.json
+  Paper:
+    Title: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
+    URL: https://arxiv.org/abs/1802.02611
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/sep_aspp_head.py#L30
+  Framework: PyTorch
+- Name: deeplabv3plus_r18-d8_4xb4-80k_potsdam-512x512
+  In Collection: DeepLabV3+
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Potsdam
+    Metrics:
+      mIoU: 77.09
+      mIoU(ms+flip): 78.44
+  Config: configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb4-80k_potsdam-512x512.py
+  Metadata:
+    Training Data: Potsdam
+    Batch Size: 16
+    Architecture:
+    - R-18-D8
+    - DeepLabV3+
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 1.91
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_potsdam/deeplabv3plus_r18-d8_512x512_80k_potsdam_20211219_020601-75fd5bc3.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_potsdam/deeplabv3plus_r18-d8_512x512_80k_potsdam_20211219_020601.log.json
+  Paper:
+    Title: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
+    URL: https://arxiv.org/abs/1802.02611
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/sep_aspp_head.py#L30
+  Framework: PyTorch
+- Name: deeplabv3plus_r50-d8_4xb4-80k_potsdam-512x512
+  In Collection: DeepLabV3+
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Potsdam
+    Metrics:
+      mIoU: 78.33
+      mIoU(ms+flip): 79.27
+  Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-80k_potsdam-512x512.py
+  Metadata:
+    Training Data: Potsdam
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - DeepLabV3+
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 7.36
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_potsdam/deeplabv3plus_r50-d8_512x512_80k_potsdam_20211219_031508-7e7a2b24.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_potsdam/deeplabv3plus_r50-d8_512x512_80k_potsdam_20211219_031508.log.json
+  Paper:
+    Title: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
+    URL: https://arxiv.org/abs/1802.02611
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/sep_aspp_head.py#L30
+  Framework: PyTorch
+- Name: deeplabv3plus_r101-d8_4xb4-80k_potsdam-512x512
+  In Collection: DeepLabV3+
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Potsdam
+    Metrics:
+      mIoU: 78.7
+      mIoU(ms+flip): 79.47
+  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-80k_potsdam-512x512.py
+  Metadata:
+    Training Data: Potsdam
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - DeepLabV3+
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 10.83
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_potsdam/deeplabv3plus_r101-d8_512x512_80k_potsdam_20211219_031508-8b112708.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_potsdam/deeplabv3plus_r101-d8_512x512_80k_potsdam_20211219_031508.log.json
+  Paper:
+    Title: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
+    URL: https://arxiv.org/abs/1802.02611
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/sep_aspp_head.py#L30
+  Framework: PyTorch
+- Name: deeplabv3plus_r18-d8_4xb4-80k_vaihingen-512x512
+  In Collection: DeepLabV3+
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Vaihingen
+    Metrics:
+      mIoU: 72.5
+      mIoU(ms+flip): 74.13
+  Config: configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb4-80k_vaihingen-512x512.py
+  Metadata:
+    Training Data: Vaihingen
+    Batch Size: 16
+    Architecture:
+    - R-18-D8
+    - DeepLabV3+
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 1.91
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_4x4_512x512_80k_vaihingen/deeplabv3plus_r18-d8_4x4_512x512_80k_vaihingen_20211231_230805-7626a263.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_4x4_512x512_80k_vaihingen/deeplabv3plus_r18-d8_4x4_512x512_80k_vaihingen_20211231_230805.log.json
+  Paper:
+    Title: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
+    URL: https://arxiv.org/abs/1802.02611
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/sep_aspp_head.py#L30
+  Framework: PyTorch
+- Name: deeplabv3plus_r50-d8_4xb4-80k_vaihingen-512x512
+  In Collection: DeepLabV3+
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Vaihingen
+    Metrics:
+      mIoU: 73.97
+      mIoU(ms+flip): 75.05
+  Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-80k_vaihingen-512x512.py
+  Metadata:
+    Training Data: Vaihingen
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - DeepLabV3+
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 7.36
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_4x4_512x512_80k_vaihingen/deeplabv3plus_r50-d8_4x4_512x512_80k_vaihingen_20211231_230816-5040938d.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_4x4_512x512_80k_vaihingen/deeplabv3plus_r50-d8_4x4_512x512_80k_vaihingen_20211231_230816.log.json
+  Paper:
+    Title: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
+    URL: https://arxiv.org/abs/1802.02611
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/sep_aspp_head.py#L30
+  Framework: PyTorch
+- Name: deeplabv3plus_r101-d8_4xb4-80k_vaihingen-512x512
+  In Collection: DeepLabV3+
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Vaihingen
+    Metrics:
+      mIoU: 73.06
+      mIoU(ms+flip): 74.14
+  Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-80k_vaihingen-512x512.py
+  Metadata:
+    Training Data: Vaihingen
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - DeepLabV3+
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 10.83
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_4x4_512x512_80k_vaihingen/deeplabv3plus_r101-d8_4x4_512x512_80k_vaihingen_20211231_230816-8a095afa.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_4x4_512x512_80k_vaihingen/deeplabv3plus_r101-d8_4x4_512x512_80k_vaihingen_20211231_230816.log.json
+  Paper:
+    Title: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
+    URL: https://arxiv.org/abs/1802.02611
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/sep_aspp_head.py#L30
+  Framework: PyTorch
+- Name: deeplabv3plus_r18-d8_4xb4-80k_isaid-896x896
+  In Collection: DeepLabV3+
+  Results:
+    Task: Semantic Segmentation
+    Dataset: iSAID
+    Metrics:
+      mIoU: 61.35
+      mIoU(ms+flip): 62.61
+  Config: configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb4-80k_isaid-896x896.py
+  Metadata:
+    Training Data: iSAID
+    Batch Size: 16
+    Architecture:
+    - R-18-D8
+    - DeepLabV3+
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 6.19
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_4x4_896x896_80k_isaid/deeplabv3plus_r18-d8_4x4_896x896_80k_isaid_20220110_180526-7059991d.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_4x4_896x896_80k_isaid/deeplabv3plus_r18-d8_4x4_896x896_80k_isaid_20220110_180526.log.json
+  Paper:
+    Title: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
+    URL: https://arxiv.org/abs/1802.02611
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/sep_aspp_head.py#L30
+  Framework: PyTorch
+- Name: deeplabv3plus_r50-d8_4xb4-80k_isaid-896x896
+  In Collection: DeepLabV3+
+  Results:
+    Task: Semantic Segmentation
+    Dataset: iSAID
+    Metrics:
+      mIoU: 67.06
+      mIoU(ms+flip): 68.02
+  Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-80k_isaid-896x896.py
+  Metadata:
+    Training Data: iSAID
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - DeepLabV3+
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 21.45
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_4x4_896x896_80k_isaid/deeplabv3plus_r50-d8_4x4_896x896_80k_isaid_20220110_180526-598be439.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_4x4_896x896_80k_isaid/deeplabv3plus_r50-d8_4x4_896x896_80k_isaid_20220110_180526.log.json
+  Paper:
+    Title: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
+    URL: https://arxiv.org/abs/1802.02611
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/sep_aspp_head.py#L30
+  Framework: PyTorch
+- Name: deeplabv3plus_r50-d8_4xb2-300k_mapillay_v1_65-1280x1280
+  In Collection: DeepLabV3+
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Mapillary Vistas v1.2
+    Metrics:
+      mIoU: 47.35
+  Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb2-300k_mapillay_v1_65-1280x1280.py
+  Metadata:
+    Training Data: Mapillary Vistas v1.2
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - DeepLabV3+
+    Training Resources: 4x A100 GPUS
+    Memory (GB): 24.04
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_4xb2-300k_mapillay_v1_65-1280x1280/deeplabv3plus_r50-d8_4xb2-300k_mapillay_v1_65-1280x1280_20230301_110504-655f8e43.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_4xb2-300k_mapillay_v1_65-1280x1280/deeplabv3plus_r50-d8_4xb2-300k_mapillay_v1_65-1280x1280_20230301_110504.json
+  Paper:
+    Title: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
+    URL: https://arxiv.org/abs/1802.02611
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/sep_aspp_head.py#L30
+  Framework: PyTorch
diff --git a/configs/dmnet/README.md b/configs/dmnet/README.md
index 535740ddd3..6004c06ccb 100644
--- a/configs/dmnet/README.md
+++ b/configs/dmnet/README.md
@@ -1,6 +1,6 @@
 # DMNet
 
-[Dynamic Multi-scale Filters for Semantic Segmentation](https://openaccess.thecvf.com/content_ICCV_2019/papers/He_Dynamic_Multi-Scale_Filters_for_Semantic_Segmentation_ICCV_2019_paper.pdf)
+> [Dynamic Multi-scale Filters for Semantic Segmentation](https://openaccess.thecvf.com/content_ICCV_2019/papers/He_Dynamic_Multi-Scale_Filters_for_Semantic_Segmentation_ICCV_2019_paper.pdf)
 
 ## Introduction
 
@@ -22,6 +22,30 @@ Multi-scale representation provides an effective way toaddress scale variation o
 <img src="https://user-images.githubusercontent.com/24582831/142900781-6215763f-8b71-4e0b-a6b1-c41372db2aa0.png" width="70%"/>
 </div>
 
+## Results and models
+
+### Cityscapes
+
+| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                          | download                                                                                                                                                                                                                                                                                                                                           |
+| ------ | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| DMNet  | R-50-D8  | 512x1024  |   40000 | 7.0      | 3.66           | V100   | 77.78 |         79.14 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/dmnet/dmnet_r50-d8_4xb2-40k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x1024_40k_cityscapes/dmnet_r50-d8_512x1024_40k_cityscapes_20201215_042326-615373cf.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x1024_40k_cityscapes/dmnet_r50-d8_512x1024_40k_cityscapes-20201215_042326.log.json)     |
+| DMNet  | R-101-D8 | 512x1024  |   40000 | 10.6     | 2.54           | V100   | 78.37 |         79.72 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/dmnet/dmnet_r101-d8_4xb2-40k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x1024_40k_cityscapes/dmnet_r101-d8_512x1024_40k_cityscapes_20201215_043100-8291e976.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x1024_40k_cityscapes/dmnet_r101-d8_512x1024_40k_cityscapes-20201215_043100.log.json) |
+| DMNet  | R-50-D8  | 769x769   |   40000 | 7.9      | 1.57           | V100   | 78.49 |         80.27 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/dmnet/dmnet_r50-d8_4xb2-40k_cityscapes-769x769.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_769x769_40k_cityscapes/dmnet_r50-d8_769x769_40k_cityscapes_20201215_093706-e7f0e23e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_769x769_40k_cityscapes/dmnet_r50-d8_769x769_40k_cityscapes-20201215_093706.log.json)         |
+| DMNet  | R-101-D8 | 769x769   |   40000 | 12.0     | 1.01           | V100   | 77.62 |         78.94 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/dmnet/dmnet_r101-d8_4xb2-40k_cityscapes-769x769.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_769x769_40k_cityscapes/dmnet_r101-d8_769x769_40k_cityscapes_20201215_081348-a74261f6.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_769x769_40k_cityscapes/dmnet_r101-d8_769x769_40k_cityscapes-20201215_081348.log.json)     |
+| DMNet  | R-50-D8  | 512x1024  |   80000 | -        | -              | V100   | 79.07 |         80.22 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/dmnet/dmnet_r50-d8_4xb2-80k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x1024_80k_cityscapes/dmnet_r50-d8_512x1024_80k_cityscapes_20201215_053728-3c8893b9.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x1024_80k_cityscapes/dmnet_r50-d8_512x1024_80k_cityscapes-20201215_053728.log.json)     |
+| DMNet  | R-101-D8 | 512x1024  |   80000 | -        | -              | V100   | 79.64 |         80.67 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/dmnet/dmnet_r101-d8_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x1024_80k_cityscapes/dmnet_r101-d8_512x1024_80k_cityscapes_20201215_031718-fa081cb8.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x1024_80k_cityscapes/dmnet_r101-d8_512x1024_80k_cityscapes-20201215_031718.log.json) |
+| DMNet  | R-50-D8  | 769x769   |   80000 | -        | -              | V100   | 79.22 |         80.55 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/dmnet/dmnet_r50-d8_4xb2-80k_cityscapes-769x769.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_769x769_80k_cityscapes/dmnet_r50-d8_769x769_80k_cityscapes_20201215_034006-6060840e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_769x769_80k_cityscapes/dmnet_r50-d8_769x769_80k_cityscapes-20201215_034006.log.json)         |
+| DMNet  | R-101-D8 | 769x769   |   80000 | -        | -              | V100   | 79.19 |         80.65 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/dmnet/dmnet_r101-d8_4xb2-80k_cityscapes-769x769.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_769x769_80k_cityscapes/dmnet_r101-d8_769x769_80k_cityscapes_20201215_082810-7f0de59a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_769x769_80k_cityscapes/dmnet_r101-d8_769x769_80k_cityscapes-20201215_082810.log.json)     |
+
+### ADE20K
+
+| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                      | download                                                                                                                                                                                                                                                                                                                           |
+| ------ | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | --------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| DMNet  | R-50-D8  | 512x512   |   80000 | 9.4      | 20.95          | V100   | 42.37 |         43.62 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/dmnet/dmnet_r50-d8_4xb4-80k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x512_80k_ade20k/dmnet_r50-d8_512x512_80k_ade20k_20201215_144744-f89092a6.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x512_80k_ade20k/dmnet_r50-d8_512x512_80k_ade20k-20201215_144744.log.json)         |
+| DMNet  | R-101-D8 | 512x512   |   80000 | 13.0     | 13.88          | V100   | 45.34 |         46.13 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/dmnet/dmnet_r101-d8_4xb4-80k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x512_80k_ade20k/dmnet_r101-d8_512x512_80k_ade20k_20201215_104812-bfa45311.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x512_80k_ade20k/dmnet_r101-d8_512x512_80k_ade20k-20201215_104812.log.json)     |
+| DMNet  | R-50-D8  | 512x512   |  160000 | -        | -              | V100   | 43.15 |         44.17 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/dmnet/dmnet_r50-d8_4xb4-160k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x512_160k_ade20k/dmnet_r50-d8_512x512_160k_ade20k_20201215_115313-025ab3f9.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x512_160k_ade20k/dmnet_r50-d8_512x512_160k_ade20k-20201215_115313.log.json)     |
+| DMNet  | R-101-D8 | 512x512   |  160000 | -        | -              | V100   | 45.42 |         46.76 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/dmnet/dmnet_r101-d8_4xb4-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x512_160k_ade20k/dmnet_r101-d8_512x512_160k_ade20k_20201215_111145-a0bc02ef.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x512_160k_ade20k/dmnet_r101-d8_512x512_160k_ade20k-20201215_111145.log.json) |
+
 ## Citation
 
 ```bibtex
@@ -33,27 +57,3 @@ month = {October},
 year = {2019}
 }
 ```
-
-## Results and models
-
-### Cityscapes
-
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                          | download                                                                                                                                                                                                                                                                                                                                           |
-| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| DMNet  | R-50-D8  | 512x1024  |   40000 | 7.0      | 3.66           | 77.78 |         79.14 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/dmnet/dmnet_r50-d8_4xb2-40k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x1024_40k_cityscapes/dmnet_r50-d8_512x1024_40k_cityscapes_20201215_042326-615373cf.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x1024_40k_cityscapes/dmnet_r50-d8_512x1024_40k_cityscapes-20201215_042326.log.json)     |
-| DMNet  | R-101-D8 | 512x1024  |   40000 | 10.6     | 2.54           | 78.37 |         79.72 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/dmnet/dmnet_r101-d8_4xb2-40k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x1024_40k_cityscapes/dmnet_r101-d8_512x1024_40k_cityscapes_20201215_043100-8291e976.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x1024_40k_cityscapes/dmnet_r101-d8_512x1024_40k_cityscapes-20201215_043100.log.json) |
-| DMNet  | R-50-D8  | 769x769   |   40000 | 7.9      | 1.57           | 78.49 |         80.27 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/dmnet/dmnet_r50-d8_4xb2-40k_cityscapes-769x769.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_769x769_40k_cityscapes/dmnet_r50-d8_769x769_40k_cityscapes_20201215_093706-e7f0e23e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_769x769_40k_cityscapes/dmnet_r50-d8_769x769_40k_cityscapes-20201215_093706.log.json)         |
-| DMNet  | R-101-D8 | 769x769   |   40000 | 12.0     | 1.01           | 77.62 |         78.94 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/dmnet/dmnet_r101-d8_4xb2-40k_cityscapes-769x769.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_769x769_40k_cityscapes/dmnet_r101-d8_769x769_40k_cityscapes_20201215_081348-a74261f6.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_769x769_40k_cityscapes/dmnet_r101-d8_769x769_40k_cityscapes-20201215_081348.log.json)     |
-| DMNet  | R-50-D8  | 512x1024  |   80000 | -        | -              | 79.07 |         80.22 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/dmnet/dmnet_r50-d8_4xb2-80k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x1024_80k_cityscapes/dmnet_r50-d8_512x1024_80k_cityscapes_20201215_053728-3c8893b9.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x1024_80k_cityscapes/dmnet_r50-d8_512x1024_80k_cityscapes-20201215_053728.log.json)     |
-| DMNet  | R-101-D8 | 512x1024  |   80000 | -        | -              | 79.64 |         80.67 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/dmnet/dmnet_r101-d8_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x1024_80k_cityscapes/dmnet_r101-d8_512x1024_80k_cityscapes_20201215_031718-fa081cb8.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x1024_80k_cityscapes/dmnet_r101-d8_512x1024_80k_cityscapes-20201215_031718.log.json) |
-| DMNet  | R-50-D8  | 769x769   |   80000 | -        | -              | 79.22 |         80.55 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/dmnet/dmnet_r50-d8_4xb2-80k_cityscapes-769x769.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_769x769_80k_cityscapes/dmnet_r50-d8_769x769_80k_cityscapes_20201215_034006-6060840e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_769x769_80k_cityscapes/dmnet_r50-d8_769x769_80k_cityscapes-20201215_034006.log.json)         |
-| DMNet  | R-101-D8 | 769x769   |   80000 | -        | -              | 79.19 |         80.65 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/dmnet/dmnet_r101-d8_4xb2-80k_cityscapes-769x769.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_769x769_80k_cityscapes/dmnet_r101-d8_769x769_80k_cityscapes_20201215_082810-7f0de59a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_769x769_80k_cityscapes/dmnet_r101-d8_769x769_80k_cityscapes-20201215_082810.log.json)     |
-
-### ADE20K
-
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                      | download                                                                                                                                                                                                                                                                                                                           |
-| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | --------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| DMNet  | R-50-D8  | 512x512   |   80000 | 9.4      | 20.95          | 42.37 |         43.62 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/dmnet/dmnet_r50-d8_4xb4-80k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x512_80k_ade20k/dmnet_r50-d8_512x512_80k_ade20k_20201215_144744-f89092a6.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x512_80k_ade20k/dmnet_r50-d8_512x512_80k_ade20k-20201215_144744.log.json)         |
-| DMNet  | R-101-D8 | 512x512   |   80000 | 13.0     | 13.88          | 45.34 |         46.13 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/dmnet/dmnet_r101-d8_4xb4-80k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x512_80k_ade20k/dmnet_r101-d8_512x512_80k_ade20k_20201215_104812-bfa45311.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x512_80k_ade20k/dmnet_r101-d8_512x512_80k_ade20k-20201215_104812.log.json)     |
-| DMNet  | R-50-D8  | 512x512   |  160000 | -        | -              | 43.15 |         44.17 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/dmnet/dmnet_r50-d8_4xb4-160k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x512_160k_ade20k/dmnet_r50-d8_512x512_160k_ade20k_20201215_115313-025ab3f9.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x512_160k_ade20k/dmnet_r50-d8_512x512_160k_ade20k-20201215_115313.log.json)     |
-| DMNet  | R-101-D8 | 512x512   |  160000 | -        | -              | 45.42 |         46.76 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/dmnet/dmnet_r101-d8_4xb4-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x512_160k_ade20k/dmnet_r101-d8_512x512_160k_ade20k_20201215_111145-a0bc02ef.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x512_160k_ade20k/dmnet_r101-d8_512x512_160k_ade20k-20201215_111145.log.json) |
diff --git a/configs/dmnet/dmnet.yml b/configs/dmnet/dmnet.yml
deleted file mode 100644
index dfb80ba7e1..0000000000
--- a/configs/dmnet/dmnet.yml
+++ /dev/null
@@ -1,232 +0,0 @@
-Collections:
-- Name: DMNet
-  Metadata:
-    Training Data:
-    - Cityscapes
-    - ADE20K
-  Paper:
-    URL: https://openaccess.thecvf.com/content_ICCV_2019/papers/He_Dynamic_Multi-Scale_Filters_for_Semantic_Segmentation_ICCV_2019_paper.pdf
-    Title: Dynamic Multi-scale Filters for Semantic Segmentation
-  README: configs/dmnet/README.md
-  Code:
-    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/dm_head.py#L93
-    Version: v0.17.0
-  Converted From:
-    Code: https://github.com/Junjun2016/DMNet
-Models:
-- Name: dmnet_r50-d8_4xb2-40k_cityscapes-512x1024
-  In Collection: DMNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,1024)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 273.22
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 7.0
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 77.78
-      mIoU(ms+flip): 79.14
-  Config: configs/dmnet/dmnet_r50-d8_4xb2-40k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x1024_40k_cityscapes/dmnet_r50-d8_512x1024_40k_cityscapes_20201215_042326-615373cf.pth
-- Name: dmnet_r101-d8_4xb2-40k_cityscapes-512x1024
-  In Collection: DMNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,1024)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 393.7
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 10.6
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 78.37
-      mIoU(ms+flip): 79.72
-  Config: configs/dmnet/dmnet_r101-d8_4xb2-40k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x1024_40k_cityscapes/dmnet_r101-d8_512x1024_40k_cityscapes_20201215_043100-8291e976.pth
-- Name: dmnet_r50-d8_4xb2-40k_cityscapes-769x769
-  In Collection: DMNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (769,769)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 636.94
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (769,769)
-    Training Memory (GB): 7.9
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 78.49
-      mIoU(ms+flip): 80.27
-  Config: configs/dmnet/dmnet_r50-d8_4xb2-40k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_769x769_40k_cityscapes/dmnet_r50-d8_769x769_40k_cityscapes_20201215_093706-e7f0e23e.pth
-- Name: dmnet_r101-d8_4xb2-40k_cityscapes-769x769
-  In Collection: DMNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (769,769)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 990.1
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (769,769)
-    Training Memory (GB): 12.0
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 77.62
-      mIoU(ms+flip): 78.94
-  Config: configs/dmnet/dmnet_r101-d8_4xb2-40k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_769x769_40k_cityscapes/dmnet_r101-d8_769x769_40k_cityscapes_20201215_081348-a74261f6.pth
-- Name: dmnet_r50-d8_4xb2-80k_cityscapes-512x1024
-  In Collection: DMNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,1024)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 79.07
-      mIoU(ms+flip): 80.22
-  Config: configs/dmnet/dmnet_r50-d8_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x1024_80k_cityscapes/dmnet_r50-d8_512x1024_80k_cityscapes_20201215_053728-3c8893b9.pth
-- Name: dmnet_r101-d8_4xb2-80k_cityscapes-512x1024
-  In Collection: DMNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,1024)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 79.64
-      mIoU(ms+flip): 80.67
-  Config: configs/dmnet/dmnet_r101-d8_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x1024_80k_cityscapes/dmnet_r101-d8_512x1024_80k_cityscapes_20201215_031718-fa081cb8.pth
-- Name: dmnet_r50-d8_4xb2-80k_cityscapes-769x769
-  In Collection: DMNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (769,769)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 79.22
-      mIoU(ms+flip): 80.55
-  Config: configs/dmnet/dmnet_r50-d8_4xb2-80k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_769x769_80k_cityscapes/dmnet_r50-d8_769x769_80k_cityscapes_20201215_034006-6060840e.pth
-- Name: dmnet_r101-d8_4xb2-80k_cityscapes-769x769
-  In Collection: DMNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (769,769)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 79.19
-      mIoU(ms+flip): 80.65
-  Config: configs/dmnet/dmnet_r101-d8_4xb2-80k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_769x769_80k_cityscapes/dmnet_r101-d8_769x769_80k_cityscapes_20201215_082810-7f0de59a.pth
-- Name: dmnet_r50-d8_4xb4-80k_ade20k-512x512
-  In Collection: DMNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 47.73
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 9.4
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 42.37
-      mIoU(ms+flip): 43.62
-  Config: configs/dmnet/dmnet_r50-d8_4xb4-80k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x512_80k_ade20k/dmnet_r50-d8_512x512_80k_ade20k_20201215_144744-f89092a6.pth
-- Name: dmnet_r101-d8_4xb4-80k_ade20k-512x512
-  In Collection: DMNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 72.05
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 13.0
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 45.34
-      mIoU(ms+flip): 46.13
-  Config: configs/dmnet/dmnet_r101-d8_4xb4-80k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x512_80k_ade20k/dmnet_r101-d8_512x512_80k_ade20k_20201215_104812-bfa45311.pth
-- Name: dmnet_r50-d8_4xb4-160k_ade20k-512x512
-  In Collection: DMNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 160000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 43.15
-      mIoU(ms+flip): 44.17
-  Config: configs/dmnet/dmnet_r50-d8_4xb4-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x512_160k_ade20k/dmnet_r50-d8_512x512_160k_ade20k_20201215_115313-025ab3f9.pth
-- Name: dmnet_r101-d8_4xb4-160k_ade20k-512x512
-  In Collection: DMNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 160000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 45.42
-      mIoU(ms+flip): 46.76
-  Config: configs/dmnet/dmnet_r101-d8_4xb4-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x512_160k_ade20k/dmnet_r101-d8_512x512_160k_ade20k_20201215_111145-a0bc02ef.pth
diff --git a/configs/dmnet/metafile.yaml b/configs/dmnet/metafile.yaml
new file mode 100644
index 0000000000..7f5e536753
--- /dev/null
+++ b/configs/dmnet/metafile.yaml
@@ -0,0 +1,296 @@
+Collections:
+- Name: DMNet
+  License: Apache License 2.0
+  Metadata:
+    Training Data:
+    - Cityscapes
+    - ADE20K
+  Paper:
+    Title: Dynamic Multi-scale Filters for Semantic Segmentation
+    URL: https://openaccess.thecvf.com/content_ICCV_2019/papers/He_Dynamic_Multi-Scale_Filters_for_Semantic_Segmentation_ICCV_2019_paper.pdf
+  README: configs/dmnet/README.md
+  Frameworks:
+  - PyTorch
+Models:
+- Name: dmnet_r50-d8_4xb2-40k_cityscapes-512x1024
+  In Collection: DMNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.78
+      mIoU(ms+flip): 79.14
+  Config: configs/dmnet/dmnet_r50-d8_4xb2-40k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - DMNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 7.0
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x1024_40k_cityscapes/dmnet_r50-d8_512x1024_40k_cityscapes_20201215_042326-615373cf.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x1024_40k_cityscapes/dmnet_r50-d8_512x1024_40k_cityscapes-20201215_042326.log.json
+  Paper:
+    Title: Dynamic Multi-scale Filters for Semantic Segmentation
+    URL: https://openaccess.thecvf.com/content_ICCV_2019/papers/He_Dynamic_Multi-Scale_Filters_for_Semantic_Segmentation_ICCV_2019_paper.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/dm_head.py#L93
+  Framework: PyTorch
+- Name: dmnet_r101-d8_4xb2-40k_cityscapes-512x1024
+  In Collection: DMNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.37
+      mIoU(ms+flip): 79.72
+  Config: configs/dmnet/dmnet_r101-d8_4xb2-40k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - DMNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 10.6
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x1024_40k_cityscapes/dmnet_r101-d8_512x1024_40k_cityscapes_20201215_043100-8291e976.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x1024_40k_cityscapes/dmnet_r101-d8_512x1024_40k_cityscapes-20201215_043100.log.json
+  Paper:
+    Title: Dynamic Multi-scale Filters for Semantic Segmentation
+    URL: https://openaccess.thecvf.com/content_ICCV_2019/papers/He_Dynamic_Multi-Scale_Filters_for_Semantic_Segmentation_ICCV_2019_paper.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/dm_head.py#L93
+  Framework: PyTorch
+- Name: dmnet_r50-d8_4xb2-40k_cityscapes-769x769
+  In Collection: DMNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.49
+      mIoU(ms+flip): 80.27
+  Config: configs/dmnet/dmnet_r50-d8_4xb2-40k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - DMNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 7.9
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_769x769_40k_cityscapes/dmnet_r50-d8_769x769_40k_cityscapes_20201215_093706-e7f0e23e.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_769x769_40k_cityscapes/dmnet_r50-d8_769x769_40k_cityscapes-20201215_093706.log.json
+  Paper:
+    Title: Dynamic Multi-scale Filters for Semantic Segmentation
+    URL: https://openaccess.thecvf.com/content_ICCV_2019/papers/He_Dynamic_Multi-Scale_Filters_for_Semantic_Segmentation_ICCV_2019_paper.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/dm_head.py#L93
+  Framework: PyTorch
+- Name: dmnet_r101-d8_4xb2-40k_cityscapes-769x769
+  In Collection: DMNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.62
+      mIoU(ms+flip): 78.94
+  Config: configs/dmnet/dmnet_r101-d8_4xb2-40k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - DMNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 12.0
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_769x769_40k_cityscapes/dmnet_r101-d8_769x769_40k_cityscapes_20201215_081348-a74261f6.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_769x769_40k_cityscapes/dmnet_r101-d8_769x769_40k_cityscapes-20201215_081348.log.json
+  Paper:
+    Title: Dynamic Multi-scale Filters for Semantic Segmentation
+    URL: https://openaccess.thecvf.com/content_ICCV_2019/papers/He_Dynamic_Multi-Scale_Filters_for_Semantic_Segmentation_ICCV_2019_paper.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/dm_head.py#L93
+  Framework: PyTorch
+- Name: dmnet_r50-d8_4xb2-80k_cityscapes-512x1024
+  In Collection: DMNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.07
+      mIoU(ms+flip): 80.22
+  Config: configs/dmnet/dmnet_r50-d8_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - DMNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x1024_80k_cityscapes/dmnet_r50-d8_512x1024_80k_cityscapes_20201215_053728-3c8893b9.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x1024_80k_cityscapes/dmnet_r50-d8_512x1024_80k_cityscapes-20201215_053728.log.json
+  Paper:
+    Title: Dynamic Multi-scale Filters for Semantic Segmentation
+    URL: https://openaccess.thecvf.com/content_ICCV_2019/papers/He_Dynamic_Multi-Scale_Filters_for_Semantic_Segmentation_ICCV_2019_paper.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/dm_head.py#L93
+  Framework: PyTorch
+- Name: dmnet_r101-d8_4xb2-80k_cityscapes-512x1024
+  In Collection: DMNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.64
+      mIoU(ms+flip): 80.67
+  Config: configs/dmnet/dmnet_r101-d8_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - DMNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x1024_80k_cityscapes/dmnet_r101-d8_512x1024_80k_cityscapes_20201215_031718-fa081cb8.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x1024_80k_cityscapes/dmnet_r101-d8_512x1024_80k_cityscapes-20201215_031718.log.json
+  Paper:
+    Title: Dynamic Multi-scale Filters for Semantic Segmentation
+    URL: https://openaccess.thecvf.com/content_ICCV_2019/papers/He_Dynamic_Multi-Scale_Filters_for_Semantic_Segmentation_ICCV_2019_paper.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/dm_head.py#L93
+  Framework: PyTorch
+- Name: dmnet_r50-d8_4xb2-80k_cityscapes-769x769
+  In Collection: DMNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.22
+      mIoU(ms+flip): 80.55
+  Config: configs/dmnet/dmnet_r50-d8_4xb2-80k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - DMNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_769x769_80k_cityscapes/dmnet_r50-d8_769x769_80k_cityscapes_20201215_034006-6060840e.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_769x769_80k_cityscapes/dmnet_r50-d8_769x769_80k_cityscapes-20201215_034006.log.json
+  Paper:
+    Title: Dynamic Multi-scale Filters for Semantic Segmentation
+    URL: https://openaccess.thecvf.com/content_ICCV_2019/papers/He_Dynamic_Multi-Scale_Filters_for_Semantic_Segmentation_ICCV_2019_paper.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/dm_head.py#L93
+  Framework: PyTorch
+- Name: dmnet_r101-d8_4xb2-80k_cityscapes-769x769
+  In Collection: DMNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.19
+      mIoU(ms+flip): 80.65
+  Config: configs/dmnet/dmnet_r101-d8_4xb2-80k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - DMNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_769x769_80k_cityscapes/dmnet_r101-d8_769x769_80k_cityscapes_20201215_082810-7f0de59a.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_769x769_80k_cityscapes/dmnet_r101-d8_769x769_80k_cityscapes-20201215_082810.log.json
+  Paper:
+    Title: Dynamic Multi-scale Filters for Semantic Segmentation
+    URL: https://openaccess.thecvf.com/content_ICCV_2019/papers/He_Dynamic_Multi-Scale_Filters_for_Semantic_Segmentation_ICCV_2019_paper.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/dm_head.py#L93
+  Framework: PyTorch
+- Name: dmnet_r50-d8_4xb4-80k_ade20k-512x512
+  In Collection: DMNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 42.37
+      mIoU(ms+flip): 43.62
+  Config: configs/dmnet/dmnet_r50-d8_4xb4-80k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - DMNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 9.4
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x512_80k_ade20k/dmnet_r50-d8_512x512_80k_ade20k_20201215_144744-f89092a6.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x512_80k_ade20k/dmnet_r50-d8_512x512_80k_ade20k-20201215_144744.log.json
+  Paper:
+    Title: Dynamic Multi-scale Filters for Semantic Segmentation
+    URL: https://openaccess.thecvf.com/content_ICCV_2019/papers/He_Dynamic_Multi-Scale_Filters_for_Semantic_Segmentation_ICCV_2019_paper.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/dm_head.py#L93
+  Framework: PyTorch
+- Name: dmnet_r101-d8_4xb4-80k_ade20k-512x512
+  In Collection: DMNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 45.34
+      mIoU(ms+flip): 46.13
+  Config: configs/dmnet/dmnet_r101-d8_4xb4-80k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - DMNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 13.0
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x512_80k_ade20k/dmnet_r101-d8_512x512_80k_ade20k_20201215_104812-bfa45311.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x512_80k_ade20k/dmnet_r101-d8_512x512_80k_ade20k-20201215_104812.log.json
+  Paper:
+    Title: Dynamic Multi-scale Filters for Semantic Segmentation
+    URL: https://openaccess.thecvf.com/content_ICCV_2019/papers/He_Dynamic_Multi-Scale_Filters_for_Semantic_Segmentation_ICCV_2019_paper.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/dm_head.py#L93
+  Framework: PyTorch
+- Name: dmnet_r50-d8_4xb4-160k_ade20k-512x512
+  In Collection: DMNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 43.15
+      mIoU(ms+flip): 44.17
+  Config: configs/dmnet/dmnet_r50-d8_4xb4-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - DMNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x512_160k_ade20k/dmnet_r50-d8_512x512_160k_ade20k_20201215_115313-025ab3f9.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x512_160k_ade20k/dmnet_r50-d8_512x512_160k_ade20k-20201215_115313.log.json
+  Paper:
+    Title: Dynamic Multi-scale Filters for Semantic Segmentation
+    URL: https://openaccess.thecvf.com/content_ICCV_2019/papers/He_Dynamic_Multi-Scale_Filters_for_Semantic_Segmentation_ICCV_2019_paper.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/dm_head.py#L93
+  Framework: PyTorch
+- Name: dmnet_r101-d8_4xb4-160k_ade20k-512x512
+  In Collection: DMNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 45.42
+      mIoU(ms+flip): 46.76
+  Config: configs/dmnet/dmnet_r101-d8_4xb4-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - DMNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x512_160k_ade20k/dmnet_r101-d8_512x512_160k_ade20k_20201215_111145-a0bc02ef.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x512_160k_ade20k/dmnet_r101-d8_512x512_160k_ade20k-20201215_111145.log.json
+  Paper:
+    Title: Dynamic Multi-scale Filters for Semantic Segmentation
+    URL: https://openaccess.thecvf.com/content_ICCV_2019/papers/He_Dynamic_Multi-Scale_Filters_for_Semantic_Segmentation_ICCV_2019_paper.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/dm_head.py#L93
+  Framework: PyTorch
diff --git a/configs/dnlnet/README.md b/configs/dnlnet/README.md
index ab24549ed6..12338c730c 100644
--- a/configs/dnlnet/README.md
+++ b/configs/dnlnet/README.md
@@ -1,6 +1,6 @@
 # DNLNet
 
-[Disentangled Non-Local Neural Networks](https://arxiv.org/abs/2006.06668)
+> [Disentangled Non-Local Neural Networks](https://arxiv.org/abs/2006.06668)
 
 ## Introduction
 
@@ -22,7 +22,31 @@ The non-local block is a popular module for strengthening the context modeling a
 <img src="https://user-images.githubusercontent.com/24582831/142900944-b8d93301-d2ce-488e-a461-b0813f96be49.png" width="70%"/>
 </div>
 
-## Citation
+## Results and models (in progress)
+
+### Cityscapes
+
+| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                         | download                                                                                                                                                                                                                                                                                                                                     |
+| ------ | -------- | --------- | ------: | -------: | -------------- | ------ | ----: | ------------- | ------------------------------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| DNLNet | R-50-D8  | 512x1024  |   40000 |      7.3 | 2.56           | V100   | 78.61 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/dnlnet/dnl_r50-d8_4xb2-40k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x1024_40k_cityscapes/dnl_r50-d8_512x1024_40k_cityscapes_20200904_233629-53d4ea93.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x1024_40k_cityscapes/dnl_r50-d8_512x1024_40k_cityscapes-20200904_233629.log.json)     |
+| DNLNet | R-101-D8 | 512x1024  |   40000 |     10.9 | 1.96           | V100   | 78.31 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/dnlnet/dnl_r101-d8_4xb2-40k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x1024_40k_cityscapes/dnl_r101-d8_512x1024_40k_cityscapes_20200904_233629-9928ffef.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x1024_40k_cityscapes/dnl_r101-d8_512x1024_40k_cityscapes-20200904_233629.log.json) |
+| DNLNet | R-50-D8  | 769x769   |   40000 |      9.2 | 1.50           | V100   | 78.44 | 80.27         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/dnlnet/dnl_r50-d8_4xb2-40k_cityscapes-769x769.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_769x769_40k_cityscapes/dnl_r50-d8_769x769_40k_cityscapes_20200820_232206-0f283785.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_769x769_40k_cityscapes/dnl_r50-d8_769x769_40k_cityscapes-20200820_232206.log.json)         |
+| DNLNet | R-101-D8 | 769x769   |   40000 |     12.6 | 1.02           | V100   | 76.39 | 77.77         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/dnlnet/dnl_r101-d8_4xb2-40k_cityscapes-769x769.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_769x769_40k_cityscapes/dnl_r101-d8_769x769_40k_cityscapes_20200820_171256-76c596df.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_769x769_40k_cityscapes/dnl_r101-d8_769x769_40k_cityscapes-20200820_171256.log.json)     |
+| DNLNet | R-50-D8  | 512x1024  |   80000 |        - | -              | V100   | 79.33 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/dnlnet/dnl_r50-d8_4xb2-80k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x1024_80k_cityscapes/dnl_r50-d8_512x1024_80k_cityscapes_20200904_233629-58b2f778.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x1024_80k_cityscapes/dnl_r50-d8_512x1024_80k_cityscapes-20200904_233629.log.json)     |
+| DNLNet | R-101-D8 | 512x1024  |   80000 |        - | -              | V100   | 80.41 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/dnlnet/dnl_r101-d8_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x1024_80k_cityscapes/dnl_r101-d8_512x1024_80k_cityscapes_20200904_233629-758e2dd4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x1024_80k_cityscapes/dnl_r101-d8_512x1024_80k_cityscapes-20200904_233629.log.json) |
+| DNLNet | R-50-D8  | 769x769   |   80000 |        - | -              | V100   | 79.36 | 80.70         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/dnlnet/dnl_r50-d8_4xb2-80k_cityscapes-769x769.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_769x769_80k_cityscapes/dnl_r50-d8_769x769_80k_cityscapes_20200820_011925-366bc4c7.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_769x769_80k_cityscapes/dnl_r50-d8_769x769_80k_cityscapes-20200820_011925.log.json)         |
+| DNLNet | R-101-D8 | 769x769   |   80000 |        - | -              | V100   | 79.41 | 80.68         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/dnlnet/dnl_r101-d8_4xb2-80k_cityscapes-769x769.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_769x769_80k_cityscapes/dnl_r101-d8_769x769_80k_cityscapes_20200821_051111-95ff84ab.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_769x769_80k_cityscapes/dnl_r101-d8_769x769_80k_cityscapes-20200821_051111.log.json)     |
+
+### ADE20K
+
+| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                     | download                                                                                                                                                                                                                                                                                                                     |
+| ------ | -------- | --------- | ------: | -------: | -------------- | ------ | ----: | ------------- | -------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| DNLNet | R-50-D8  | 512x512   |   80000 |      8.8 | 20.66          | V100   | 41.76 | 42.99         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/dnlnet/dnl_r50-d8_4xb4-80k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x512_80k_ade20k/dnl_r50-d8_512x512_80k_ade20k_20200826_183354-1cf6e0c1.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x512_80k_ade20k/dnl_r50-d8_512x512_80k_ade20k-20200826_183354.log.json)         |
+| DNLNet | R-101-D8 | 512x512   |   80000 |     12.8 | 12.54          | V100   | 43.76 | 44.91         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/dnlnet/dnl_r101-d8_4xb4-80k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x512_80k_ade20k/dnl_r101-d8_512x512_80k_ade20k_20200826_183354-d820d6ea.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x512_80k_ade20k/dnl_r101-d8_512x512_80k_ade20k-20200826_183354.log.json)     |
+| DNLNet | R-50-D8  | 512x512   |  160000 |        - | -              | V100   | 41.87 | 43.01         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/dnlnet/dnl_r50-d8_4xb4-160k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x512_160k_ade20k/dnl_r50-d8_512x512_160k_ade20k_20200826_183350-37837798.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x512_160k_ade20k/dnl_r50-d8_512x512_160k_ade20k-20200826_183350.log.json)     |
+| DNLNet | R-101-D8 | 512x512   |  160000 |        - | -              | V100   | 44.25 | 45.78         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/dnlnet/dnl_r101-d8_4xb4-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x512_160k_ade20k/dnl_r101-d8_512x512_160k_ade20k_20200826_183350-ed522c61.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x512_160k_ade20k/dnl_r101-d8_512x512_160k_ade20k-20200826_183350.log.json) |
+
+## Notes
 
 This example is to reproduce ["Disentangled Non-Local Neural Networks"](https://arxiv.org/abs/2006.06668) for semantic segmentation. It is still in progress.
 
@@ -36,27 +60,3 @@ This example is to reproduce ["Disentangled Non-Local Neural Networks"](https://
     booktitle={ECCV}
 }
 ```
-
-## Results and models (in progress)
-
-### Cityscapes
-
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                         | download                                                                                                                                                                                                                                                                                                                                     |
-| ------ | -------- | --------- | ------: | -------: | -------------- | ----: | ------------- | ------------------------------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| DNLNet | R-50-D8  | 512x1024  |   40000 |      7.3 | 2.56           | 78.61 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/dnlnet/dnl_r50-d8_4xb2-40k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x1024_40k_cityscapes/dnl_r50-d8_512x1024_40k_cityscapes_20200904_233629-53d4ea93.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x1024_40k_cityscapes/dnl_r50-d8_512x1024_40k_cityscapes-20200904_233629.log.json)     |
-| DNLNet | R-101-D8 | 512x1024  |   40000 |     10.9 | 1.96           | 78.31 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/dnlnet/dnl_r101-d8_4xb2-40k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x1024_40k_cityscapes/dnl_r101-d8_512x1024_40k_cityscapes_20200904_233629-9928ffef.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x1024_40k_cityscapes/dnl_r101-d8_512x1024_40k_cityscapes-20200904_233629.log.json) |
-| DNLNet | R-50-D8  | 769x769   |   40000 |      9.2 | 1.50           | 78.44 | 80.27         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/dnlnet/dnl_r50-d8_4xb2-40k_cityscapes-769x769.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_769x769_40k_cityscapes/dnl_r50-d8_769x769_40k_cityscapes_20200820_232206-0f283785.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_769x769_40k_cityscapes/dnl_r50-d8_769x769_40k_cityscapes-20200820_232206.log.json)         |
-| DNLNet | R-101-D8 | 769x769   |   40000 |     12.6 | 1.02           | 76.39 | 77.77         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/dnlnet/dnl_r101-d8_4xb2-40k_cityscapes-769x769.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_769x769_40k_cityscapes/dnl_r101-d8_769x769_40k_cityscapes_20200820_171256-76c596df.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_769x769_40k_cityscapes/dnl_r101-d8_769x769_40k_cityscapes-20200820_171256.log.json)     |
-| DNLNet | R-50-D8  | 512x1024  |   80000 |        - | -              | 79.33 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/dnlnet/dnl_r50-d8_4xb2-80k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x1024_80k_cityscapes/dnl_r50-d8_512x1024_80k_cityscapes_20200904_233629-58b2f778.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x1024_80k_cityscapes/dnl_r50-d8_512x1024_80k_cityscapes-20200904_233629.log.json)     |
-| DNLNet | R-101-D8 | 512x1024  |   80000 |        - | -              | 80.41 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/dnlnet/dnl_r101-d8_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x1024_80k_cityscapes/dnl_r101-d8_512x1024_80k_cityscapes_20200904_233629-758e2dd4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x1024_80k_cityscapes/dnl_r101-d8_512x1024_80k_cityscapes-20200904_233629.log.json) |
-| DNLNet | R-50-D8  | 769x769   |   80000 |        - | -              | 79.36 | 80.70         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/dnlnet/dnl_r50-d8_4xb2-80k_cityscapes-769x769.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_769x769_80k_cityscapes/dnl_r50-d8_769x769_80k_cityscapes_20200820_011925-366bc4c7.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_769x769_80k_cityscapes/dnl_r50-d8_769x769_80k_cityscapes-20200820_011925.log.json)         |
-| DNLNet | R-101-D8 | 769x769   |   80000 |        - | -              | 79.41 | 80.68         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/dnlnet/dnl_r101-d8_4xb2-80k_cityscapes-769x769.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_769x769_80k_cityscapes/dnl_r101-d8_769x769_80k_cityscapes_20200821_051111-95ff84ab.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_769x769_80k_cityscapes/dnl_r101-d8_769x769_80k_cityscapes-20200821_051111.log.json)     |
-
-### ADE20K
-
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                     | download                                                                                                                                                                                                                                                                                                                     |
-| ------ | -------- | --------- | ------: | -------: | -------------- | ----: | ------------- | -------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| DNLNet | R-50-D8  | 512x512   |   80000 |      8.8 | 20.66          | 41.76 | 42.99         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/dnlnet/dnl_r50-d8_4xb4-80k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x512_80k_ade20k/dnl_r50-d8_512x512_80k_ade20k_20200826_183354-1cf6e0c1.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x512_80k_ade20k/dnl_r50-d8_512x512_80k_ade20k-20200826_183354.log.json)         |
-| DNLNet | R-101-D8 | 512x512   |   80000 |     12.8 | 12.54          | 43.76 | 44.91         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/dnlnet/dnl_r101-d8_4xb4-80k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x512_80k_ade20k/dnl_r101-d8_512x512_80k_ade20k_20200826_183354-d820d6ea.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x512_80k_ade20k/dnl_r101-d8_512x512_80k_ade20k-20200826_183354.log.json)     |
-| DNLNet | R-50-D8  | 512x512   |  160000 |        - | -              | 41.87 | 43.01         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/dnlnet/dnl_r50-d8_4xb4-160k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x512_160k_ade20k/dnl_r50-d8_512x512_160k_ade20k_20200826_183350-37837798.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x512_160k_ade20k/dnl_r50-d8_512x512_160k_ade20k-20200826_183350.log.json)     |
-| DNLNet | R-101-D8 | 512x512   |  160000 |        - | -              | 44.25 | 45.78         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/dnlnet/dnl_r101-d8_4xb4-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x512_160k_ade20k/dnl_r101-d8_512x512_160k_ade20k_20200826_183350-ed522c61.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x512_160k_ade20k/dnl_r101-d8_512x512_160k_ade20k-20200826_183350.log.json) |
diff --git a/configs/dnlnet/dnlnet.yml b/configs/dnlnet/dnlnet.yml
deleted file mode 100644
index ae65dbbaca..0000000000
--- a/configs/dnlnet/dnlnet.yml
+++ /dev/null
@@ -1,228 +0,0 @@
-Collections:
-- Name: DNLNet
-  Metadata:
-    Training Data:
-    - Cityscapes
-    - ADE20K
-  Paper:
-    URL: https://arxiv.org/abs/2006.06668
-    Title: Disentangled Non-Local Neural Networks
-  README: configs/dnlnet/README.md
-  Code:
-    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/dnl_head.py#L88
-    Version: v0.17.0
-  Converted From:
-    Code: https://github.com/yinmh17/DNL-Semantic-Segmentation
-Models:
-- Name: dnl_r50-d8_4xb2-40k_cityscapes-512x1024
-  In Collection: DNLNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,1024)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 390.62
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 7.3
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 78.61
-  Config: configs/dnlnet/dnl_r50-d8_4xb2-40k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x1024_40k_cityscapes/dnl_r50-d8_512x1024_40k_cityscapes_20200904_233629-53d4ea93.pth
-- Name: dnl_r101-d8_4xb2-40k_cityscapes-512x1024
-  In Collection: DNLNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,1024)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 510.2
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 10.9
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 78.31
-  Config: configs/dnlnet/dnl_r101-d8_4xb2-40k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x1024_40k_cityscapes/dnl_r101-d8_512x1024_40k_cityscapes_20200904_233629-9928ffef.pth
-- Name: dnl_r50-d8_4xb2-40k_cityscapes-769x769
-  In Collection: DNLNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (769,769)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 666.67
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (769,769)
-    Training Memory (GB): 9.2
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 78.44
-      mIoU(ms+flip): 80.27
-  Config: configs/dnlnet/dnl_r50-d8_4xb2-40k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_769x769_40k_cityscapes/dnl_r50-d8_769x769_40k_cityscapes_20200820_232206-0f283785.pth
-- Name: dnl_r101-d8_4xb2-40k_cityscapes-769x769
-  In Collection: DNLNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (769,769)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 980.39
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (769,769)
-    Training Memory (GB): 12.6
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 76.39
-      mIoU(ms+flip): 77.77
-  Config: configs/dnlnet/dnl_r101-d8_4xb2-40k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_769x769_40k_cityscapes/dnl_r101-d8_769x769_40k_cityscapes_20200820_171256-76c596df.pth
-- Name: dnl_r50-d8_4xb2-80k_cityscapes-512x1024
-  In Collection: DNLNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,1024)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 79.33
-  Config: configs/dnlnet/dnl_r50-d8_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x1024_80k_cityscapes/dnl_r50-d8_512x1024_80k_cityscapes_20200904_233629-58b2f778.pth
-- Name: dnl_r101-d8_4xb2-80k_cityscapes-512x1024
-  In Collection: DNLNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,1024)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 80.41
-  Config: configs/dnlnet/dnl_r101-d8_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x1024_80k_cityscapes/dnl_r101-d8_512x1024_80k_cityscapes_20200904_233629-758e2dd4.pth
-- Name: dnl_r50-d8_4xb2-80k_cityscapes-769x769
-  In Collection: DNLNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (769,769)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 79.36
-      mIoU(ms+flip): 80.7
-  Config: configs/dnlnet/dnl_r50-d8_4xb2-80k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_769x769_80k_cityscapes/dnl_r50-d8_769x769_80k_cityscapes_20200820_011925-366bc4c7.pth
-- Name: dnl_r101-d8_4xb2-80k_cityscapes-769x769
-  In Collection: DNLNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (769,769)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 79.41
-      mIoU(ms+flip): 80.68
-  Config: configs/dnlnet/dnl_r101-d8_4xb2-80k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_769x769_80k_cityscapes/dnl_r101-d8_769x769_80k_cityscapes_20200821_051111-95ff84ab.pth
-- Name: dnl_r50-d8_4xb4-80k_ade20k-512x512
-  In Collection: DNLNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 48.4
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 8.8
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 41.76
-      mIoU(ms+flip): 42.99
-  Config: configs/dnlnet/dnl_r50-d8_4xb4-80k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x512_80k_ade20k/dnl_r50-d8_512x512_80k_ade20k_20200826_183354-1cf6e0c1.pth
-- Name: dnl_r101-d8_4xb4-80k_ade20k-512x512
-  In Collection: DNLNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 79.74
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 12.8
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 43.76
-      mIoU(ms+flip): 44.91
-  Config: configs/dnlnet/dnl_r101-d8_4xb4-80k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x512_80k_ade20k/dnl_r101-d8_512x512_80k_ade20k_20200826_183354-d820d6ea.pth
-- Name: dnl_r50-d8_4xb4-160k_ade20k-512x512
-  In Collection: DNLNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 160000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 41.87
-      mIoU(ms+flip): 43.01
-  Config: configs/dnlnet/dnl_r50-d8_4xb4-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x512_160k_ade20k/dnl_r50-d8_512x512_160k_ade20k_20200826_183350-37837798.pth
-- Name: dnl_r101-d8_4xb4-160k_ade20k-512x512
-  In Collection: DNLNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 160000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 44.25
-      mIoU(ms+flip): 45.78
-  Config: configs/dnlnet/dnl_r101-d8_4xb4-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x512_160k_ade20k/dnl_r101-d8_512x512_160k_ade20k_20200826_183350-ed522c61.pth
diff --git a/configs/dnlnet/metafile.yaml b/configs/dnlnet/metafile.yaml
new file mode 100644
index 0000000000..22e48d3dc5
--- /dev/null
+++ b/configs/dnlnet/metafile.yaml
@@ -0,0 +1,292 @@
+Collections:
+- Name: DNLNet
+  License: Apache License 2.0
+  Metadata:
+    Training Data:
+    - Cityscapes
+    - ADE20K
+  Paper:
+    Title: Disentangled Non-Local Neural Networks
+    URL: https://arxiv.org/abs/2006.06668
+  README: configs/dnlnet/README.md
+  Frameworks:
+  - PyTorch
+Models:
+- Name: dnl_r50-d8_4xb2-40k_cityscapes-512x1024
+  In Collection: DNLNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.61
+  Config: configs/dnlnet/dnl_r50-d8_4xb2-40k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - DNLNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 7.3
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x1024_40k_cityscapes/dnl_r50-d8_512x1024_40k_cityscapes_20200904_233629-53d4ea93.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x1024_40k_cityscapes/dnl_r50-d8_512x1024_40k_cityscapes-20200904_233629.log.json
+  Paper:
+    Title: Disentangled Non-Local Neural Networks
+    URL: https://arxiv.org/abs/2006.06668
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/dnl_head.py#L88
+  Framework: PyTorch
+- Name: dnl_r101-d8_4xb2-40k_cityscapes-512x1024
+  In Collection: DNLNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.31
+  Config: configs/dnlnet/dnl_r101-d8_4xb2-40k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - DNLNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 10.9
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x1024_40k_cityscapes/dnl_r101-d8_512x1024_40k_cityscapes_20200904_233629-9928ffef.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x1024_40k_cityscapes/dnl_r101-d8_512x1024_40k_cityscapes-20200904_233629.log.json
+  Paper:
+    Title: Disentangled Non-Local Neural Networks
+    URL: https://arxiv.org/abs/2006.06668
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/dnl_head.py#L88
+  Framework: PyTorch
+- Name: dnl_r50-d8_4xb2-40k_cityscapes-769x769
+  In Collection: DNLNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.44
+      mIoU(ms+flip): 80.27
+  Config: configs/dnlnet/dnl_r50-d8_4xb2-40k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - DNLNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 9.2
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_769x769_40k_cityscapes/dnl_r50-d8_769x769_40k_cityscapes_20200820_232206-0f283785.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_769x769_40k_cityscapes/dnl_r50-d8_769x769_40k_cityscapes-20200820_232206.log.json
+  Paper:
+    Title: Disentangled Non-Local Neural Networks
+    URL: https://arxiv.org/abs/2006.06668
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/dnl_head.py#L88
+  Framework: PyTorch
+- Name: dnl_r101-d8_4xb2-40k_cityscapes-769x769
+  In Collection: DNLNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 76.39
+      mIoU(ms+flip): 77.77
+  Config: configs/dnlnet/dnl_r101-d8_4xb2-40k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - DNLNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 12.6
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_769x769_40k_cityscapes/dnl_r101-d8_769x769_40k_cityscapes_20200820_171256-76c596df.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_769x769_40k_cityscapes/dnl_r101-d8_769x769_40k_cityscapes-20200820_171256.log.json
+  Paper:
+    Title: Disentangled Non-Local Neural Networks
+    URL: https://arxiv.org/abs/2006.06668
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/dnl_head.py#L88
+  Framework: PyTorch
+- Name: dnl_r50-d8_4xb2-80k_cityscapes-512x1024
+  In Collection: DNLNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.33
+  Config: configs/dnlnet/dnl_r50-d8_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - DNLNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x1024_80k_cityscapes/dnl_r50-d8_512x1024_80k_cityscapes_20200904_233629-58b2f778.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x1024_80k_cityscapes/dnl_r50-d8_512x1024_80k_cityscapes-20200904_233629.log.json
+  Paper:
+    Title: Disentangled Non-Local Neural Networks
+    URL: https://arxiv.org/abs/2006.06668
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/dnl_head.py#L88
+  Framework: PyTorch
+- Name: dnl_r101-d8_4xb2-80k_cityscapes-512x1024
+  In Collection: DNLNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 80.41
+  Config: configs/dnlnet/dnl_r101-d8_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - DNLNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x1024_80k_cityscapes/dnl_r101-d8_512x1024_80k_cityscapes_20200904_233629-758e2dd4.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x1024_80k_cityscapes/dnl_r101-d8_512x1024_80k_cityscapes-20200904_233629.log.json
+  Paper:
+    Title: Disentangled Non-Local Neural Networks
+    URL: https://arxiv.org/abs/2006.06668
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/dnl_head.py#L88
+  Framework: PyTorch
+- Name: dnl_r50-d8_4xb2-80k_cityscapes-769x769
+  In Collection: DNLNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.36
+      mIoU(ms+flip): 80.7
+  Config: configs/dnlnet/dnl_r50-d8_4xb2-80k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - DNLNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_769x769_80k_cityscapes/dnl_r50-d8_769x769_80k_cityscapes_20200820_011925-366bc4c7.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_769x769_80k_cityscapes/dnl_r50-d8_769x769_80k_cityscapes-20200820_011925.log.json
+  Paper:
+    Title: Disentangled Non-Local Neural Networks
+    URL: https://arxiv.org/abs/2006.06668
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/dnl_head.py#L88
+  Framework: PyTorch
+- Name: dnl_r101-d8_4xb2-80k_cityscapes-769x769
+  In Collection: DNLNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.41
+      mIoU(ms+flip): 80.68
+  Config: configs/dnlnet/dnl_r101-d8_4xb2-80k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - DNLNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_769x769_80k_cityscapes/dnl_r101-d8_769x769_80k_cityscapes_20200821_051111-95ff84ab.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_769x769_80k_cityscapes/dnl_r101-d8_769x769_80k_cityscapes-20200821_051111.log.json
+  Paper:
+    Title: Disentangled Non-Local Neural Networks
+    URL: https://arxiv.org/abs/2006.06668
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/dnl_head.py#L88
+  Framework: PyTorch
+- Name: dnl_r50-d8_4xb4-80k_ade20k-512x512
+  In Collection: DNLNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 41.76
+      mIoU(ms+flip): 42.99
+  Config: configs/dnlnet/dnl_r50-d8_4xb4-80k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - DNLNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 8.8
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x512_80k_ade20k/dnl_r50-d8_512x512_80k_ade20k_20200826_183354-1cf6e0c1.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x512_80k_ade20k/dnl_r50-d8_512x512_80k_ade20k-20200826_183354.log.json
+  Paper:
+    Title: Disentangled Non-Local Neural Networks
+    URL: https://arxiv.org/abs/2006.06668
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/dnl_head.py#L88
+  Framework: PyTorch
+- Name: dnl_r101-d8_4xb4-80k_ade20k-512x512
+  In Collection: DNLNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 43.76
+      mIoU(ms+flip): 44.91
+  Config: configs/dnlnet/dnl_r101-d8_4xb4-80k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - DNLNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 12.8
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x512_80k_ade20k/dnl_r101-d8_512x512_80k_ade20k_20200826_183354-d820d6ea.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x512_80k_ade20k/dnl_r101-d8_512x512_80k_ade20k-20200826_183354.log.json
+  Paper:
+    Title: Disentangled Non-Local Neural Networks
+    URL: https://arxiv.org/abs/2006.06668
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/dnl_head.py#L88
+  Framework: PyTorch
+- Name: dnl_r50-d8_4xb4-160k_ade20k-512x512
+  In Collection: DNLNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 41.87
+      mIoU(ms+flip): 43.01
+  Config: configs/dnlnet/dnl_r50-d8_4xb4-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - DNLNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x512_160k_ade20k/dnl_r50-d8_512x512_160k_ade20k_20200826_183350-37837798.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x512_160k_ade20k/dnl_r50-d8_512x512_160k_ade20k-20200826_183350.log.json
+  Paper:
+    Title: Disentangled Non-Local Neural Networks
+    URL: https://arxiv.org/abs/2006.06668
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/dnl_head.py#L88
+  Framework: PyTorch
+- Name: dnl_r101-d8_4xb4-160k_ade20k-512x512
+  In Collection: DNLNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 44.25
+      mIoU(ms+flip): 45.78
+  Config: configs/dnlnet/dnl_r101-d8_4xb4-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - DNLNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x512_160k_ade20k/dnl_r101-d8_512x512_160k_ade20k_20200826_183350-ed522c61.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x512_160k_ade20k/dnl_r101-d8_512x512_160k_ade20k-20200826_183350.log.json
+  Paper:
+    Title: Disentangled Non-Local Neural Networks
+    URL: https://arxiv.org/abs/2006.06668
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/dnl_head.py#L88
+  Framework: PyTorch
diff --git a/configs/dpt/README.md b/configs/dpt/README.md
index 41d73ea57a..432d1dd226 100644
--- a/configs/dpt/README.md
+++ b/configs/dpt/README.md
@@ -1,6 +1,6 @@
 # DPT
 
-[Vision Transformer for Dense Prediction](https://arxiv.org/abs/2103.13413)
+> [Vision Transformer for Dense Prediction](https://arxiv.org/abs/2103.13413)
 
 ## Introduction
 
@@ -22,24 +22,6 @@ We introduce dense vision transformers, an architecture that leverages vision tr
 <img src="https://user-images.githubusercontent.com/24582831/142901057-00aabea5-dab4-43d3-a14a-5f73eb5dd9b9.png" width="80%"/>
 </div>
 
-## Citation
-
-```bibtex
-@article{dosoViTskiy2020,
-  title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
-  author={DosoViTskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and  Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil},
-  journal={arXiv preprint arXiv:2010.11929},
-  year={2020}
-}
-
-@article{Ranftl2021,
-  author    = {Ren\'{e} Ranftl and Alexey Bochkovskiy and Vladlen Koltun},
-  title     = {Vision Transformers for Dense Prediction},
-  journal   = {ArXiv preprint},
-  year      = {2021},
-}
-```
-
 ## Usage
 
 To use other repositories' pre-trained models, it is necessary to convert keys.
@@ -62,6 +44,24 @@ This script convert model from `PRETRAIN_PATH` and store the converted model in
 
 ### ADE20K
 
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                  | download                                                                                                                                                                                                                                                                                               |
-| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| DPT    | ViT-B    | 512x512   |  160000 | 8.09     | 10.41          | 46.97 |         48.34 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/dpt/dpt_vit-b16_8xb2-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dpt/dpt_vit-b16_512x512_160k_ade20k/dpt_vit-b16_512x512_160k_ade20k-db31cf52.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/dpt/dpt_vit-b16_512x512_160k_ade20k/dpt_vit-b16_512x512_160k_ade20k-20210809_172025.log.json) |
+| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                  | download                                                                                                                                                                                                                                                                                               |
+| ------ | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| DPT    | ViT-B    | 512x512   |  160000 | 8.09     | 10.41          | V100   | 46.97 |         48.34 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/dpt/dpt_vit-b16_8xb2-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dpt/dpt_vit-b16_512x512_160k_ade20k/dpt_vit-b16_512x512_160k_ade20k-db31cf52.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/dpt/dpt_vit-b16_512x512_160k_ade20k/dpt_vit-b16_512x512_160k_ade20k-20210809_172025.log.json) |
+
+## Citation
+
+```bibtex
+@article{dosoViTskiy2020,
+  title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
+  author={DosoViTskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and  Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil},
+  journal={arXiv preprint arXiv:2010.11929},
+  year={2020}
+}
+
+@article{Ranftl2021,
+  author    = {Ren\'{e} Ranftl and Alexey Bochkovskiy and Vladlen Koltun},
+  title     = {Vision Transformers for Dense Prediction},
+  journal   = {ArXiv preprint},
+  year      = {2021},
+}
+```
diff --git a/configs/dpt/dpt.yml b/configs/dpt/dpt.yml
deleted file mode 100644
index 32324d3459..0000000000
--- a/configs/dpt/dpt.yml
+++ /dev/null
@@ -1,37 +0,0 @@
-Collections:
-- Name: DPT
-  Metadata:
-    Training Data:
-    - ADE20K
-  Paper:
-    URL: https://arxiv.org/abs/2103.13413
-    Title: Vision Transformer for Dense Prediction
-  README: configs/dpt/README.md
-  Code:
-    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/dpt_head.py#L215
-    Version: v0.17.0
-  Converted From:
-    Code: https://github.com/isl-org/DPT
-Models:
-- Name: dpt_vit-b16_8xb2-160k_ade20k-512x512
-  In Collection: DPT
-  Metadata:
-    backbone: ViT-B
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 96.06
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 8.09
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 46.97
-      mIoU(ms+flip): 48.34
-  Config: configs/dpt/dpt_vit-b16_8xb2-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dpt/dpt_vit-b16_512x512_160k_ade20k/dpt_vit-b16_512x512_160k_ade20k-db31cf52.pth
diff --git a/configs/dpt/metafile.yaml b/configs/dpt/metafile.yaml
new file mode 100644
index 0000000000..b721e041b6
--- /dev/null
+++ b/configs/dpt/metafile.yaml
@@ -0,0 +1,37 @@
+Collections:
+- Name: DPT
+  License: Apache License 2.0
+  Metadata:
+    Training Data:
+    - ADE20K
+  Paper:
+    Title: Vision Transformer for Dense Prediction
+    URL: https://arxiv.org/abs/2103.13413
+  README: configs/dpt/README.md
+  Frameworks:
+  - PyTorch
+Models:
+- Name: dpt_vit-b16_8xb2-160k_ade20k-512x512
+  In Collection: DPT
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 46.97
+      mIoU(ms+flip): 48.34
+  Config: configs/dpt/dpt_vit-b16_8xb2-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - ViT-B
+    - DPT
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 8.09
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dpt/dpt_vit-b16_512x512_160k_ade20k/dpt_vit-b16_512x512_160k_ade20k-db31cf52.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/dpt/dpt_vit-b16_512x512_160k_ade20k/dpt_vit-b16_512x512_160k_ade20k-20210809_172025.log.json
+  Paper:
+    Title: Vision Transformer for Dense Prediction
+    URL: https://arxiv.org/abs/2103.13413
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/dpt_head.py#L215
+  Framework: PyTorch
diff --git a/configs/emanet/README.md b/configs/emanet/README.md
index 5a9bfc326a..8ffaf471ca 100644
--- a/configs/emanet/README.md
+++ b/configs/emanet/README.md
@@ -1,6 +1,6 @@
 # EMANet
 
-[Expectation-Maximization Attention Networks for Semantic Segmentation](https://arxiv.org/abs/1907.13426)
+> [Expectation-Maximization Attention Networks for Semantic Segmentation](https://arxiv.org/abs/1907.13426)
 
 ## Introduction
 
@@ -22,6 +22,17 @@ Self-attention mechanism has been widely used for various tasks. It is designed
 <img src="https://user-images.githubusercontent.com/24582831/142901186-7bfe15e2-805a-420e-81b0-74f214f20a36.png" width="80%"/>
 </div>
 
+## Results and models
+
+### Cityscapes
+
+| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                           | download                                                                                                                                                                                                                                                                                                                                                 |
+| ------ | -------- | --------- | ------: | -------: | -------------- | ------ | ----: | ------------- | -------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| EMANet | R-50-D8  | 512x1024  |   80000 |      5.4 | 4.58           | V100   | 77.59 | 79.44         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/emanet/eemanet_r50-d8_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r50-d8_512x1024_80k_cityscapes/emanet_r50-d8_512x1024_80k_cityscapes_20200901_100301-c43fcef1.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r50-d8_512x1024_80k_cityscapes/emanet_r50-d8_512x1024_80k_cityscapes-20200901_100301.log.json)     |
+| EMANet | R-101-D8 | 512x1024  |   80000 |      6.2 | 2.87           | V100   | 79.10 | 81.21         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/emanet/emanet_r101-d8_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r101-d8_512x1024_80k_cityscapes/emanet_r101-d8_512x1024_80k_cityscapes_20200901_100301-2d970745.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r101-d8_512x1024_80k_cityscapes/emanet_r101-d8_512x1024_80k_cityscapes-20200901_100301.log.json) |
+| EMANet | R-50-D8  | 769x769   |   80000 |      8.9 | 1.97           | V100   | 79.33 | 80.49         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/emanet/emanet_r50-d8_4xb2-80k_cityscapes-769x769.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r50-d8_769x769_80k_cityscapes/emanet_r50-d8_769x769_80k_cityscapes_20200901_100301-16f8de52.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r50-d8_769x769_80k_cityscapes/emanet_r50-d8_769x769_80k_cityscapes-20200901_100301.log.json)         |
+| EMANet | R-101-D8 | 769x769   |   80000 |     10.1 | 1.22           | V100   | 79.62 | 81.00         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/emanet/emanet_r101-d8_4xb2-80k_cityscapes-769x769.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r101-d8_769x769_80k_cityscapes/emanet_r101-d8_769x769_80k_cityscapes_20200901_100301-47a324ce.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r101-d8_769x769_80k_cityscapes/emanet_r101-d8_769x769_80k_cityscapes-20200901_100301.log.json)     |
+
 ## Citation
 
 ```bibtex
@@ -33,14 +44,3 @@ Self-attention mechanism has been widely used for various tasks. It is designed
   year={2019}
 }
 ```
-
-## Results and models
-
-### Cityscapes
-
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                           | download                                                                                                                                                                                                                                                                                                                                                 |
-| ------ | -------- | --------- | ------: | -------: | -------------- | ----: | ------------- | -------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| EMANet | R-50-D8  | 512x1024  |   80000 |      5.4 | 4.58           | 77.59 | 79.44         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/emanet/eemanet_r50-d8_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r50-d8_512x1024_80k_cityscapes/emanet_r50-d8_512x1024_80k_cityscapes_20200901_100301-c43fcef1.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r50-d8_512x1024_80k_cityscapes/emanet_r50-d8_512x1024_80k_cityscapes-20200901_100301.log.json)     |
-| EMANet | R-101-D8 | 512x1024  |   80000 |      6.2 | 2.87           | 79.10 | 81.21         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/emanet/emanet_r101-d8_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r101-d8_512x1024_80k_cityscapes/emanet_r101-d8_512x1024_80k_cityscapes_20200901_100301-2d970745.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r101-d8_512x1024_80k_cityscapes/emanet_r101-d8_512x1024_80k_cityscapes-20200901_100301.log.json) |
-| EMANet | R-50-D8  | 769x769   |   80000 |      8.9 | 1.97           | 79.33 | 80.49         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/emanet/emanet_r50-d8_4xb2-80k_cityscapes-769x769.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r50-d8_769x769_80k_cityscapes/emanet_r50-d8_769x769_80k_cityscapes_20200901_100301-16f8de52.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r50-d8_769x769_80k_cityscapes/emanet_r50-d8_769x769_80k_cityscapes-20200901_100301.log.json)         |
-| EMANet | R-101-D8 | 769x769   |   80000 |     10.1 | 1.22           | 79.62 | 81.00         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/emanet/emanet_r101-d8_4xb2-80k_cityscapes-769x769.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r101-d8_769x769_80k_cityscapes/emanet_r101-d8_769x769_80k_cityscapes_20200901_100301-47a324ce.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r101-d8_769x769_80k_cityscapes/emanet_r101-d8_769x769_80k_cityscapes-20200901_100301.log.json)     |
diff --git a/configs/emanet/emanet.yml b/configs/emanet/emanet.yml
deleted file mode 100644
index ac194f2a0f..0000000000
--- a/configs/emanet/emanet.yml
+++ /dev/null
@@ -1,103 +0,0 @@
-Collections:
-- Name: EMANet
-  Metadata:
-    Training Data:
-    - Cityscapes
-  Paper:
-    URL: https://arxiv.org/abs/1907.13426
-    Title: Expectation-Maximization Attention Networks for Semantic Segmentation
-  README: configs/emanet/README.md
-  Code:
-    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ema_head.py#L80
-    Version: v0.17.0
-  Converted From:
-    Code: https://xialipku.github.io/EMANet
-Models:
-- Name: emanet_r50-d8_4xb2-80k_cityscapes-512x1024
-  In Collection: EMANet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,1024)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 218.34
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 5.4
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 77.59
-      mIoU(ms+flip): 79.44
-  Config: configs/emanet/emanet_r50-d8_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r50-d8_512x1024_80k_cityscapes/emanet_r50-d8_512x1024_80k_cityscapes_20200901_100301-c43fcef1.pth
-- Name: emanet_r101-d8_4xb2-80k_cityscapes-512x1024
-  In Collection: EMANet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,1024)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 348.43
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 6.2
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 79.1
-      mIoU(ms+flip): 81.21
-  Config: configs/emanet/emanet_r101-d8_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r101-d8_512x1024_80k_cityscapes/emanet_r101-d8_512x1024_80k_cityscapes_20200901_100301-2d970745.pth
-- Name: emanet_r50-d8_4xb2-80k_cityscapes-769x769
-  In Collection: EMANet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (769,769)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 507.61
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (769,769)
-    Training Memory (GB): 8.9
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 79.33
-      mIoU(ms+flip): 80.49
-  Config: configs/emanet/emanet_r50-d8_4xb2-80k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r50-d8_769x769_80k_cityscapes/emanet_r50-d8_769x769_80k_cityscapes_20200901_100301-16f8de52.pth
-- Name: emanet_r101-d8_4xb2-80k_cityscapes-769x769
-  In Collection: EMANet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (769,769)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 819.67
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (769,769)
-    Training Memory (GB): 10.1
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 79.62
-      mIoU(ms+flip): 81.0
-  Config: configs/emanet/emanet_r101-d8_4xb2-80k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r101-d8_769x769_80k_cityscapes/emanet_r101-d8_769x769_80k_cityscapes_20200901_100301-47a324ce.pth
diff --git a/configs/emanet/metafile.yaml b/configs/emanet/metafile.yaml
new file mode 100644
index 0000000000..b2a6b09ed7
--- /dev/null
+++ b/configs/emanet/metafile.yaml
@@ -0,0 +1,109 @@
+Collections:
+- Name: EMANet
+  License: Apache License 2.0
+  Metadata:
+    Training Data:
+    - Cityscapes
+  Paper:
+    Title: Expectation-Maximization Attention Networks for Semantic Segmentation
+    URL: https://arxiv.org/abs/1907.13426
+  README: configs/emanet/README.md
+  Frameworks:
+  - PyTorch
+Models:
+- Name: eemanet_r50-d8_4xb2-80k_cityscapes-512x1024
+  In Collection: EMANet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.59
+      mIoU(ms+flip): 79.44
+  Config: configs/emanet/eemanet_r50-d8_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - EMANet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 5.4
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r50-d8_512x1024_80k_cityscapes/emanet_r50-d8_512x1024_80k_cityscapes_20200901_100301-c43fcef1.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r50-d8_512x1024_80k_cityscapes/emanet_r50-d8_512x1024_80k_cityscapes-20200901_100301.log.json
+  Paper:
+    Title: Expectation-Maximization Attention Networks for Semantic Segmentation
+    URL: https://arxiv.org/abs/1907.13426
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ema_head.py#L80
+  Framework: PyTorch
+- Name: emanet_r101-d8_4xb2-80k_cityscapes-512x1024
+  In Collection: EMANet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.1
+      mIoU(ms+flip): 81.21
+  Config: configs/emanet/emanet_r101-d8_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - EMANet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 6.2
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r101-d8_512x1024_80k_cityscapes/emanet_r101-d8_512x1024_80k_cityscapes_20200901_100301-2d970745.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r101-d8_512x1024_80k_cityscapes/emanet_r101-d8_512x1024_80k_cityscapes-20200901_100301.log.json
+  Paper:
+    Title: Expectation-Maximization Attention Networks for Semantic Segmentation
+    URL: https://arxiv.org/abs/1907.13426
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ema_head.py#L80
+  Framework: PyTorch
+- Name: emanet_r50-d8_4xb2-80k_cityscapes-769x769
+  In Collection: EMANet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.33
+      mIoU(ms+flip): 80.49
+  Config: configs/emanet/emanet_r50-d8_4xb2-80k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - EMANet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 8.9
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r50-d8_769x769_80k_cityscapes/emanet_r50-d8_769x769_80k_cityscapes_20200901_100301-16f8de52.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r50-d8_769x769_80k_cityscapes/emanet_r50-d8_769x769_80k_cityscapes-20200901_100301.log.json
+  Paper:
+    Title: Expectation-Maximization Attention Networks for Semantic Segmentation
+    URL: https://arxiv.org/abs/1907.13426
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ema_head.py#L80
+  Framework: PyTorch
+- Name: emanet_r101-d8_4xb2-80k_cityscapes-769x769
+  In Collection: EMANet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.62
+      mIoU(ms+flip): 81.0
+  Config: configs/emanet/emanet_r101-d8_4xb2-80k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - EMANet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 10.1
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r101-d8_769x769_80k_cityscapes/emanet_r101-d8_769x769_80k_cityscapes_20200901_100301-47a324ce.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r101-d8_769x769_80k_cityscapes/emanet_r101-d8_769x769_80k_cityscapes-20200901_100301.log.json
+  Paper:
+    Title: Expectation-Maximization Attention Networks for Semantic Segmentation
+    URL: https://arxiv.org/abs/1907.13426
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ema_head.py#L80
+  Framework: PyTorch
diff --git a/configs/encnet/README.md b/configs/encnet/README.md
index 7be0c6d926..31bbd98c6b 100644
--- a/configs/encnet/README.md
+++ b/configs/encnet/README.md
@@ -1,6 +1,6 @@
 # EncNet
 
-[Context Encoding for Semantic Segmentation](https://arxiv.org/abs/1803.08904)
+> [Context Encoding for Semantic Segmentation](https://arxiv.org/abs/1803.08904)
 
 ## Introduction
 
@@ -22,6 +22,30 @@ Recent work has made significant progress in improving spatial resolution for pi
 <img src="https://user-images.githubusercontent.com/24582831/142901276-b364fbbf-3bdb-4000-9d31-b9a135e30935.png" width="70%"/>
 </div>
 
+## Results and models
+
+### Cityscapes
+
+| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                            | download                                                                                                                                                                                                                                                                                                                                                 |
+| ------ | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | --------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| EncNet | R-50-D8  | 512x1024  |   40000 | 8.6      | 4.58           | V100   | 75.67 |         77.08 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/encnet/encnet_r50-d8_4xb2-40k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x1024_40k_cityscapes/encnet_r50-d8_512x1024_40k_cityscapes_20200621_220958-68638a47.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x1024_40k_cityscapes/encnet_r50-d8_512x1024_40k_cityscapes-20200621_220958.log.json)     |
+| EncNet | R-101-D8 | 512x1024  |   40000 | 12.1     | 2.66           | V100   | 75.81 |         77.21 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/encnet/encnet_r101-d8_4xb2-40k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x1024_40k_cityscapes/encnet_r101-d8_512x1024_40k_cityscapes_20200621_220933-35e0a3e8.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x1024_40k_cityscapes/encnet_r101-d8_512x1024_40k_cityscapes-20200621_220933.log.json) |
+| EncNet | R-50-D8  | 769x769   |   40000 | 9.8      | 1.82           | V100   | 76.24 |         77.85 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/encnet/encnet_r50-d8_4xb2-40k_cityscapes-769x769.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_769x769_40k_cityscapes/encnet_r50-d8_769x769_40k_cityscapes_20200621_220958-3bcd2884.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_769x769_40k_cityscapes/encnet_r50-d8_769x769_40k_cityscapes-20200621_220958.log.json)         |
+| EncNet | R-101-D8 | 769x769   |   40000 | 13.7     | 1.26           | V100   | 74.25 |         76.25 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/encnet/encnet_r101-d8_4xb2-40k_cityscapes-769x769.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_769x769_40k_cityscapes/encnet_r101-d8_769x769_40k_cityscapes_20200621_220933-2fafed55.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_769x769_40k_cityscapes/encnet_r101-d8_769x769_40k_cityscapes-20200621_220933.log.json)     |
+| EncNet | R-50-D8  | 512x1024  |   80000 | -        | -              | V100   | 77.94 |         79.13 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/encnet/encnet_r50-d8_4xb2-80k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x1024_80k_cityscapes/encnet_r50-d8_512x1024_80k_cityscapes_20200622_003554-fc5c5624.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x1024_80k_cityscapes/encnet_r50-d8_512x1024_80k_cityscapes-20200622_003554.log.json)     |
+| EncNet | R-101-D8 | 512x1024  |   80000 | -        | -              | V100   | 78.55 |         79.47 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/encnet/encnet_r101-d8_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x1024_80k_cityscapes/encnet_r101-d8_512x1024_80k_cityscapes_20200622_003555-1de64bec.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x1024_80k_cityscapes/encnet_r101-d8_512x1024_80k_cityscapes-20200622_003555.log.json) |
+| EncNet | R-50-D8  | 769x769   |   80000 | -        | -              | V100   | 77.44 |         78.72 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/encnet/encnet_r50-d8_4xb2-80k_cityscapes-769x769.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_769x769_80k_cityscapes/encnet_r50-d8_769x769_80k_cityscapes_20200622_003554-55096dcb.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_769x769_80k_cityscapes/encnet_r50-d8_769x769_80k_cityscapes-20200622_003554.log.json)         |
+| EncNet | R-101-D8 | 769x769   |   80000 | -        | -              | V100   | 76.10 |         76.97 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/encnet/encnet_r101-d8_4xb2-80k_cityscapes-769x769.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_769x769_80k_cityscapes/encnet_r101-d8_769x769_80k_cityscapes_20200622_003555-470ef79d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_769x769_80k_cityscapes/encnet_r101-d8_769x769_80k_cityscapes-20200622_003555.log.json)     |
+
+### ADE20K
+
+| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                        | download                                                                                                                                                                                                                                                                                                                                 |
+| ------ | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| EncNet | R-50-D8  | 512x512   |   80000 | 10.1     | 22.81          | V100   | 39.53 |         41.17 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/encnet/encnet_r50-d8_4xb4-80k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x512_80k_ade20k/encnet_r50-d8_512x512_80k_ade20k_20200622_042412-44b46b04.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x512_80k_ade20k/encnet_r50-d8_512x512_80k_ade20k-20200622_042412.log.json)         |
+| EncNet | R-101-D8 | 512x512   |   80000 | 13.6     | 14.87          | V100   | 42.11 |         43.61 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/encnet/encnet_r101-d8_4xb4-80k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x512_80k_ade20k/encnet_r101-d8_512x512_80k_ade20k_20200622_101128-dd35e237.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x512_80k_ade20k/encnet_r101-d8_512x512_80k_ade20k-20200622_101128.log.json)     |
+| EncNet | R-50-D8  | 512x512   |  160000 | -        | -              | V100   | 40.10 |         41.71 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/encnet/encnet_r50-d8_4xb4-160k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x512_160k_ade20k/encnet_r50-d8_512x512_160k_ade20k_20200622_101059-b2db95e0.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x512_160k_ade20k/encnet_r50-d8_512x512_160k_ade20k-20200622_101059.log.json)     |
+| EncNet | R-101-D8 | 512x512   |  160000 | -        | -              | V100   | 42.61 |         44.01 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/encnet/encnet_r101-d8_4xb4-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x512_160k_ade20k/encnet_r101-d8_512x512_160k_ade20k_20200622_073348-7989641f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x512_160k_ade20k/encnet_r101-d8_512x512_160k_ade20k-20200622_073348.log.json) |
+
 ## Citation
 
 ```bibtex
@@ -33,27 +57,3 @@ month = {June},
 year = {2018}
 }
 ```
-
-## Results and models
-
-### Cityscapes
-
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                            | download                                                                                                                                                                                                                                                                                                                                                 |
-| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | --------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| EncNet | R-50-D8  | 512x1024  |   40000 | 8.6      | 4.58           | 75.67 |         77.08 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/encnet/encnet_r50-d8_4xb2-40k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x1024_40k_cityscapes/encnet_r50-d8_512x1024_40k_cityscapes_20200621_220958-68638a47.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x1024_40k_cityscapes/encnet_r50-d8_512x1024_40k_cityscapes-20200621_220958.log.json)     |
-| EncNet | R-101-D8 | 512x1024  |   40000 | 12.1     | 2.66           | 75.81 |         77.21 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/encnet/encnet_r101-d8_4xb2-40k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x1024_40k_cityscapes/encnet_r101-d8_512x1024_40k_cityscapes_20200621_220933-35e0a3e8.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x1024_40k_cityscapes/encnet_r101-d8_512x1024_40k_cityscapes-20200621_220933.log.json) |
-| EncNet | R-50-D8  | 769x769   |   40000 | 9.8      | 1.82           | 76.24 |         77.85 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/encnet/encnet_r50-d8_4xb2-40k_cityscapes-769x769.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_769x769_40k_cityscapes/encnet_r50-d8_769x769_40k_cityscapes_20200621_220958-3bcd2884.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_769x769_40k_cityscapes/encnet_r50-d8_769x769_40k_cityscapes-20200621_220958.log.json)         |
-| EncNet | R-101-D8 | 769x769   |   40000 | 13.7     | 1.26           | 74.25 |         76.25 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/encnet/encnet_r101-d8_4xb2-40k_cityscapes-769x769.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_769x769_40k_cityscapes/encnet_r101-d8_769x769_40k_cityscapes_20200621_220933-2fafed55.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_769x769_40k_cityscapes/encnet_r101-d8_769x769_40k_cityscapes-20200621_220933.log.json)     |
-| EncNet | R-50-D8  | 512x1024  |   80000 | -        | -              | 77.94 |         79.13 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/encnet/encnet_r50-d8_4xb2-80k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x1024_80k_cityscapes/encnet_r50-d8_512x1024_80k_cityscapes_20200622_003554-fc5c5624.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x1024_80k_cityscapes/encnet_r50-d8_512x1024_80k_cityscapes-20200622_003554.log.json)     |
-| EncNet | R-101-D8 | 512x1024  |   80000 | -        | -              | 78.55 |         79.47 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/encnet/encnet_r101-d8_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x1024_80k_cityscapes/encnet_r101-d8_512x1024_80k_cityscapes_20200622_003555-1de64bec.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x1024_80k_cityscapes/encnet_r101-d8_512x1024_80k_cityscapes-20200622_003555.log.json) |
-| EncNet | R-50-D8  | 769x769   |   80000 | -        | -              | 77.44 |         78.72 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/encnet/encnet_r50-d8_4xb2-80k_cityscapes-769x769.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_769x769_80k_cityscapes/encnet_r50-d8_769x769_80k_cityscapes_20200622_003554-55096dcb.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_769x769_80k_cityscapes/encnet_r50-d8_769x769_80k_cityscapes-20200622_003554.log.json)         |
-| EncNet | R-101-D8 | 769x769   |   80000 | -        | -              | 76.10 |         76.97 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/encnet/encnet_r101-d8_4xb2-80k_cityscapes-769x769.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_769x769_80k_cityscapes/encnet_r101-d8_769x769_80k_cityscapes_20200622_003555-470ef79d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_769x769_80k_cityscapes/encnet_r101-d8_769x769_80k_cityscapes-20200622_003555.log.json)     |
-
-### ADE20K
-
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                        | download                                                                                                                                                                                                                                                                                                                                 |
-| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| EncNet | R-50-D8  | 512x512   |   80000 | 10.1     | 22.81          | 39.53 |         41.17 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/encnet/encnet_r50-d8_4xb4-80k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x512_80k_ade20k/encnet_r50-d8_512x512_80k_ade20k_20200622_042412-44b46b04.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x512_80k_ade20k/encnet_r50-d8_512x512_80k_ade20k-20200622_042412.log.json)         |
-| EncNet | R-101-D8 | 512x512   |   80000 | 13.6     | 14.87          | 42.11 |         43.61 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/encnet/encnet_r101-d8_4xb4-80k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x512_80k_ade20k/encnet_r101-d8_512x512_80k_ade20k_20200622_101128-dd35e237.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x512_80k_ade20k/encnet_r101-d8_512x512_80k_ade20k-20200622_101128.log.json)     |
-| EncNet | R-50-D8  | 512x512   |  160000 | -        | -              | 40.10 |         41.71 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/encnet/encnet_r50-d8_4xb4-160k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x512_160k_ade20k/encnet_r50-d8_512x512_160k_ade20k_20200622_101059-b2db95e0.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x512_160k_ade20k/encnet_r50-d8_512x512_160k_ade20k-20200622_101059.log.json)     |
-| EncNet | R-101-D8 | 512x512   |  160000 | -        | -              | 42.61 |         44.01 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/encnet/encnet_r101-d8_4xb4-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x512_160k_ade20k/encnet_r101-d8_512x512_160k_ade20k_20200622_073348-7989641f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x512_160k_ade20k/encnet_r101-d8_512x512_160k_ade20k-20200622_073348.log.json) |
diff --git a/configs/encnet/encnet.yml b/configs/encnet/encnet.yml
deleted file mode 100644
index bea147b286..0000000000
--- a/configs/encnet/encnet.yml
+++ /dev/null
@@ -1,232 +0,0 @@
-Collections:
-- Name: EncNet
-  Metadata:
-    Training Data:
-    - Cityscapes
-    - ADE20K
-  Paper:
-    URL: https://arxiv.org/abs/1803.08904
-    Title: Context Encoding for Semantic Segmentation
-  README: configs/encnet/README.md
-  Code:
-    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/enc_head.py#L63
-    Version: v0.17.0
-  Converted From:
-    Code: https://github.com/zhanghang1989/PyTorch-Encoding
-Models:
-- Name: encnet_r50-d8_4xb2-40k_cityscapes-512x1024
-  In Collection: EncNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,1024)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 218.34
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 8.6
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 75.67
-      mIoU(ms+flip): 77.08
-  Config: configs/encnet/encnet_r50-d8_4xb2-40k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x1024_40k_cityscapes/encnet_r50-d8_512x1024_40k_cityscapes_20200621_220958-68638a47.pth
-- Name: encnet_r101-d8_4xb2-40k_cityscapes-512x1024
-  In Collection: EncNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,1024)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 375.94
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 12.1
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 75.81
-      mIoU(ms+flip): 77.21
-  Config: configs/encnet/encnet_r101-d8_4xb2-40k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x1024_40k_cityscapes/encnet_r101-d8_512x1024_40k_cityscapes_20200621_220933-35e0a3e8.pth
-- Name: encnet_r50-d8_4xb2-40k_cityscapes-769x769
-  In Collection: EncNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (769,769)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 549.45
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (769,769)
-    Training Memory (GB): 9.8
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 76.24
-      mIoU(ms+flip): 77.85
-  Config: configs/encnet/encnet_r50-d8_4xb2-40k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_769x769_40k_cityscapes/encnet_r50-d8_769x769_40k_cityscapes_20200621_220958-3bcd2884.pth
-- Name: encnet_r101-d8_4xb2-40k_cityscapes-769x769
-  In Collection: EncNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (769,769)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 793.65
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (769,769)
-    Training Memory (GB): 13.7
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 74.25
-      mIoU(ms+flip): 76.25
-  Config: configs/encnet/encnet_r101-d8_4xb2-40k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_769x769_40k_cityscapes/encnet_r101-d8_769x769_40k_cityscapes_20200621_220933-2fafed55.pth
-- Name: encnet_r50-d8_4xb2-80k_cityscapes-512x1024
-  In Collection: EncNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,1024)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 77.94
-      mIoU(ms+flip): 79.13
-  Config: configs/encnet/encnet_r50-d8_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x1024_80k_cityscapes/encnet_r50-d8_512x1024_80k_cityscapes_20200622_003554-fc5c5624.pth
-- Name: encnet_r101-d8_4xb2-80k_cityscapes-512x1024
-  In Collection: EncNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,1024)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 78.55
-      mIoU(ms+flip): 79.47
-  Config: configs/encnet/encnet_r101-d8_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x1024_80k_cityscapes/encnet_r101-d8_512x1024_80k_cityscapes_20200622_003555-1de64bec.pth
-- Name: encnet_r50-d8_4xb2-80k_cityscapes-769x769
-  In Collection: EncNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (769,769)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 77.44
-      mIoU(ms+flip): 78.72
-  Config: configs/encnet/encnet_r50-d8_4xb2-80k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_769x769_80k_cityscapes/encnet_r50-d8_769x769_80k_cityscapes_20200622_003554-55096dcb.pth
-- Name: encnet_r101-d8_4xb2-80k_cityscapes-769x769
-  In Collection: EncNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (769,769)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 76.1
-      mIoU(ms+flip): 76.97
-  Config: configs/encnet/encnet_r101-d8_4xb2-80k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_769x769_80k_cityscapes/encnet_r101-d8_769x769_80k_cityscapes_20200622_003555-470ef79d.pth
-- Name: encnet_r50-d8_4xb4-80k_ade20k-512x512
-  In Collection: EncNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 43.84
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 10.1
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 39.53
-      mIoU(ms+flip): 41.17
-  Config: configs/encnet/encnet_r50-d8_4xb4-80k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x512_80k_ade20k/encnet_r50-d8_512x512_80k_ade20k_20200622_042412-44b46b04.pth
-- Name: encnet_r101-d8_4xb4-80k_ade20k-512x512
-  In Collection: EncNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 67.25
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 13.6
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 42.11
-      mIoU(ms+flip): 43.61
-  Config: configs/encnet/encnet_r101-d8_4xb4-80k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x512_80k_ade20k/encnet_r101-d8_512x512_80k_ade20k_20200622_101128-dd35e237.pth
-- Name: encnet_r50-d8_4xb4-160k_ade20k-512x512
-  In Collection: EncNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 160000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 40.1
-      mIoU(ms+flip): 41.71
-  Config: configs/encnet/encnet_r50-d8_4xb4-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x512_160k_ade20k/encnet_r50-d8_512x512_160k_ade20k_20200622_101059-b2db95e0.pth
-- Name: encnet_r101-d8_4xb4-160k_ade20k-512x512
-  In Collection: EncNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 160000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 42.61
-      mIoU(ms+flip): 44.01
-  Config: configs/encnet/encnet_r101-d8_4xb4-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x512_160k_ade20k/encnet_r101-d8_512x512_160k_ade20k_20200622_073348-7989641f.pth
diff --git a/configs/encnet/metafile.yaml b/configs/encnet/metafile.yaml
new file mode 100644
index 0000000000..0dbdcfaab3
--- /dev/null
+++ b/configs/encnet/metafile.yaml
@@ -0,0 +1,296 @@
+Collections:
+- Name: EncNet
+  License: Apache License 2.0
+  Metadata:
+    Training Data:
+    - Cityscapes
+    - ADE20K
+  Paper:
+    Title: Context Encoding for Semantic Segmentation
+    URL: https://arxiv.org/abs/1803.08904
+  README: configs/encnet/README.md
+  Frameworks:
+  - PyTorch
+Models:
+- Name: encnet_r50-d8_4xb2-40k_cityscapes-512x1024
+  In Collection: EncNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 75.67
+      mIoU(ms+flip): 77.08
+  Config: configs/encnet/encnet_r50-d8_4xb2-40k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - EncNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 8.6
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x1024_40k_cityscapes/encnet_r50-d8_512x1024_40k_cityscapes_20200621_220958-68638a47.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x1024_40k_cityscapes/encnet_r50-d8_512x1024_40k_cityscapes-20200621_220958.log.json
+  Paper:
+    Title: Context Encoding for Semantic Segmentation
+    URL: https://arxiv.org/abs/1803.08904
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/enc_head.py#L63
+  Framework: PyTorch
+- Name: encnet_r101-d8_4xb2-40k_cityscapes-512x1024
+  In Collection: EncNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 75.81
+      mIoU(ms+flip): 77.21
+  Config: configs/encnet/encnet_r101-d8_4xb2-40k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - EncNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 12.1
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x1024_40k_cityscapes/encnet_r101-d8_512x1024_40k_cityscapes_20200621_220933-35e0a3e8.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x1024_40k_cityscapes/encnet_r101-d8_512x1024_40k_cityscapes-20200621_220933.log.json
+  Paper:
+    Title: Context Encoding for Semantic Segmentation
+    URL: https://arxiv.org/abs/1803.08904
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/enc_head.py#L63
+  Framework: PyTorch
+- Name: encnet_r50-d8_4xb2-40k_cityscapes-769x769
+  In Collection: EncNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 76.24
+      mIoU(ms+flip): 77.85
+  Config: configs/encnet/encnet_r50-d8_4xb2-40k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - EncNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 9.8
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_769x769_40k_cityscapes/encnet_r50-d8_769x769_40k_cityscapes_20200621_220958-3bcd2884.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_769x769_40k_cityscapes/encnet_r50-d8_769x769_40k_cityscapes-20200621_220958.log.json
+  Paper:
+    Title: Context Encoding for Semantic Segmentation
+    URL: https://arxiv.org/abs/1803.08904
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/enc_head.py#L63
+  Framework: PyTorch
+- Name: encnet_r101-d8_4xb2-40k_cityscapes-769x769
+  In Collection: EncNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 74.25
+      mIoU(ms+flip): 76.25
+  Config: configs/encnet/encnet_r101-d8_4xb2-40k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - EncNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 13.7
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_769x769_40k_cityscapes/encnet_r101-d8_769x769_40k_cityscapes_20200621_220933-2fafed55.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_769x769_40k_cityscapes/encnet_r101-d8_769x769_40k_cityscapes-20200621_220933.log.json
+  Paper:
+    Title: Context Encoding for Semantic Segmentation
+    URL: https://arxiv.org/abs/1803.08904
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/enc_head.py#L63
+  Framework: PyTorch
+- Name: encnet_r50-d8_4xb2-80k_cityscapes-512x1024
+  In Collection: EncNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.94
+      mIoU(ms+flip): 79.13
+  Config: configs/encnet/encnet_r50-d8_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - EncNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x1024_80k_cityscapes/encnet_r50-d8_512x1024_80k_cityscapes_20200622_003554-fc5c5624.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x1024_80k_cityscapes/encnet_r50-d8_512x1024_80k_cityscapes-20200622_003554.log.json
+  Paper:
+    Title: Context Encoding for Semantic Segmentation
+    URL: https://arxiv.org/abs/1803.08904
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/enc_head.py#L63
+  Framework: PyTorch
+- Name: encnet_r101-d8_4xb2-80k_cityscapes-512x1024
+  In Collection: EncNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.55
+      mIoU(ms+flip): 79.47
+  Config: configs/encnet/encnet_r101-d8_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - EncNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x1024_80k_cityscapes/encnet_r101-d8_512x1024_80k_cityscapes_20200622_003555-1de64bec.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x1024_80k_cityscapes/encnet_r101-d8_512x1024_80k_cityscapes-20200622_003555.log.json
+  Paper:
+    Title: Context Encoding for Semantic Segmentation
+    URL: https://arxiv.org/abs/1803.08904
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/enc_head.py#L63
+  Framework: PyTorch
+- Name: encnet_r50-d8_4xb2-80k_cityscapes-769x769
+  In Collection: EncNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.44
+      mIoU(ms+flip): 78.72
+  Config: configs/encnet/encnet_r50-d8_4xb2-80k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - EncNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_769x769_80k_cityscapes/encnet_r50-d8_769x769_80k_cityscapes_20200622_003554-55096dcb.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_769x769_80k_cityscapes/encnet_r50-d8_769x769_80k_cityscapes-20200622_003554.log.json
+  Paper:
+    Title: Context Encoding for Semantic Segmentation
+    URL: https://arxiv.org/abs/1803.08904
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/enc_head.py#L63
+  Framework: PyTorch
+- Name: encnet_r101-d8_4xb2-80k_cityscapes-769x769
+  In Collection: EncNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 76.1
+      mIoU(ms+flip): 76.97
+  Config: configs/encnet/encnet_r101-d8_4xb2-80k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - EncNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_769x769_80k_cityscapes/encnet_r101-d8_769x769_80k_cityscapes_20200622_003555-470ef79d.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_769x769_80k_cityscapes/encnet_r101-d8_769x769_80k_cityscapes-20200622_003555.log.json
+  Paper:
+    Title: Context Encoding for Semantic Segmentation
+    URL: https://arxiv.org/abs/1803.08904
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/enc_head.py#L63
+  Framework: PyTorch
+- Name: encnet_r50-d8_4xb4-80k_ade20k-512x512
+  In Collection: EncNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 39.53
+      mIoU(ms+flip): 41.17
+  Config: configs/encnet/encnet_r50-d8_4xb4-80k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - EncNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 10.1
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x512_80k_ade20k/encnet_r50-d8_512x512_80k_ade20k_20200622_042412-44b46b04.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x512_80k_ade20k/encnet_r50-d8_512x512_80k_ade20k-20200622_042412.log.json
+  Paper:
+    Title: Context Encoding for Semantic Segmentation
+    URL: https://arxiv.org/abs/1803.08904
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/enc_head.py#L63
+  Framework: PyTorch
+- Name: encnet_r101-d8_4xb4-80k_ade20k-512x512
+  In Collection: EncNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 42.11
+      mIoU(ms+flip): 43.61
+  Config: configs/encnet/encnet_r101-d8_4xb4-80k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - EncNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 13.6
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x512_80k_ade20k/encnet_r101-d8_512x512_80k_ade20k_20200622_101128-dd35e237.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x512_80k_ade20k/encnet_r101-d8_512x512_80k_ade20k-20200622_101128.log.json
+  Paper:
+    Title: Context Encoding for Semantic Segmentation
+    URL: https://arxiv.org/abs/1803.08904
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/enc_head.py#L63
+  Framework: PyTorch
+- Name: encnet_r50-d8_4xb4-160k_ade20k-512x512
+  In Collection: EncNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 40.1
+      mIoU(ms+flip): 41.71
+  Config: configs/encnet/encnet_r50-d8_4xb4-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - EncNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x512_160k_ade20k/encnet_r50-d8_512x512_160k_ade20k_20200622_101059-b2db95e0.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x512_160k_ade20k/encnet_r50-d8_512x512_160k_ade20k-20200622_101059.log.json
+  Paper:
+    Title: Context Encoding for Semantic Segmentation
+    URL: https://arxiv.org/abs/1803.08904
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/enc_head.py#L63
+  Framework: PyTorch
+- Name: encnet_r101-d8_4xb4-160k_ade20k-512x512
+  In Collection: EncNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 42.61
+      mIoU(ms+flip): 44.01
+  Config: configs/encnet/encnet_r101-d8_4xb4-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - EncNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x512_160k_ade20k/encnet_r101-d8_512x512_160k_ade20k_20200622_073348-7989641f.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x512_160k_ade20k/encnet_r101-d8_512x512_160k_ade20k-20200622_073348.log.json
+  Paper:
+    Title: Context Encoding for Semantic Segmentation
+    URL: https://arxiv.org/abs/1803.08904
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/enc_head.py#L63
+  Framework: PyTorch
diff --git a/configs/erfnet/README.md b/configs/erfnet/README.md
index 4f7d21572a..e29afc485d 100644
--- a/configs/erfnet/README.md
+++ b/configs/erfnet/README.md
@@ -1,6 +1,6 @@
 # ERFNet
 
-[ERFNet: Efficient Residual Factorized ConvNet for Real-time Semantic Segmentation](http://www.robesafe.uah.es/personal/eduardo.romera/pdfs/Romera17tits.pdf)
+> [ERFNet: Efficient Residual Factorized ConvNet for Real-time Semantic Segmentation](http://www.robesafe.uah.es/personal/eduardo.romera/pdfs/Romera17tits.pdf)
 
 ## Introduction
 
@@ -22,6 +22,22 @@ Semantic segmentation is a challenging task that addresses most of the perceptio
 <img src="https://user-images.githubusercontent.com/24582831/143479729-ea7951f6-1a3c-47d6-aaee-62c5759c0638.png" width="60%"/>
 </div>
 
+## Results and models
+
+### Cityscapes
+
+| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device | mIoU | mIoU(ms+flip) | config                                                                                                                         | download                                                                                                                                                                                                                                                                                                                                                     |
+| ------ | -------- | --------- | ------: | -------- | -------------- | ------ | ---: | ------------- | ------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| ERFNet | ERFNet   | 512x1024  |  160000 | 6.04     | 15.26          | V100   | 72.5 | 74.75         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/erfnet/erfnet_fcn_4xb4-160k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/erfnet/erfnet_fcn_4x4_512x1024_160k_cityscapes/erfnet_fcn_4x4_512x1024_160k_cityscapes_20220704_162145-dc90157a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/erfnet/erfnet_fcn_4x4_512x1024_160k_cityscapes/erfnet_fcn_4x4_512x1024_160k_cityscapes_20220704_162145.log.json) |
+
+Note:
+
+- The model is trained from scratch.
+
+- Last deconvolution layer in the [original paper](https://github.com/Eromera/erfnet_pytorch/blob/master/train/erfnet.py#L123) is replaced by a naive `FCNHead` decoder head and a bilinear upsampling layer, found more effective and efficient.
+
+- This model performance is sensitive to the seed values used, please refer to the log file for the specific settings of the seed. If you choose a different seed, the results might differ from the table results.
+
 ## Citation
 
 ```bibtex
@@ -36,19 +52,3 @@ Semantic segmentation is a challenging task that addresses most of the perceptio
   publisher={IEEE}
 }
 ```
-
-## Results and models
-
-### Cityscapes
-
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config                                                                                                                         | download                                                                                                                                                                                                                                                                                                                                                     |
-| ------ | -------- | --------- | ------: | -------- | -------------- | ---: | ------------- | ------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| ERFNet | ERFNet   | 512x1024  |  160000 | 6.04     | 15.26          | 72.5 | 74.75         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/erfnet/erfnet_fcn_4xb4-160k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/erfnet/erfnet_fcn_4x4_512x1024_160k_cityscapes/erfnet_fcn_4x4_512x1024_160k_cityscapes_20220704_162145-dc90157a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/erfnet/erfnet_fcn_4x4_512x1024_160k_cityscapes/erfnet_fcn_4x4_512x1024_160k_cityscapes_20220704_162145.log.json) |
-
-Note:
-
-- The model is trained from scratch.
-
-- Last deconvolution layer in the [original paper](https://github.com/Eromera/erfnet_pytorch/blob/master/train/erfnet.py#L123) is replaced by a naive `FCNHead` decoder head and a bilinear upsampling layer, found more effective and efficient.
-
-- This model performance is sensitive to the seed values used, please refer to the log file for the specific settings of the seed. If you choose a different seed, the results might differ from the table results.
diff --git a/configs/erfnet/erfnet.yml b/configs/erfnet/erfnet.yml
deleted file mode 100644
index 5f87f020cf..0000000000
--- a/configs/erfnet/erfnet.yml
+++ /dev/null
@@ -1,37 +0,0 @@
-Collections:
-- Name: ERFNet
-  Metadata:
-    Training Data:
-    - Cityscapes
-  Paper:
-    URL: http://www.robesafe.uah.es/personal/eduardo.romera/pdfs/Romera17tits.pdf
-    Title: 'ERFNet: Efficient Residual Factorized ConvNet for Real-time Semantic Segmentation'
-  README: configs/erfnet/README.md
-  Code:
-    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.20.0/mmseg/models/backbones/erfnet.py#L321
-    Version: v0.20.0
-  Converted From:
-    Code: https://github.com/Eromera/erfnet_pytorch
-Models:
-- Name: erfnet_fcn_4xb4-160k_cityscapes-512x1024
-  In Collection: ERFNet
-  Metadata:
-    backbone: ERFNet
-    crop size: (512,1024)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 65.53
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 6.04
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 72.5
-      mIoU(ms+flip): 74.75
-  Config: configs/erfnet/erfnet_fcn_4xb4-160k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/erfnet/erfnet_fcn_4x4_512x1024_160k_cityscapes/erfnet_fcn_4x4_512x1024_160k_cityscapes_20220704_162145-dc90157a.pth
diff --git a/configs/erfnet/metafile.yaml b/configs/erfnet/metafile.yaml
new file mode 100644
index 0000000000..bf514124ee
--- /dev/null
+++ b/configs/erfnet/metafile.yaml
@@ -0,0 +1,37 @@
+Collections:
+- Name: ERFNet
+  License: Apache License 2.0
+  Metadata:
+    Training Data:
+    - Cityscapes
+  Paper:
+    Title: 'ERFNet: Efficient Residual Factorized ConvNet for Real-time Semantic Segmentation'
+    URL: http://www.robesafe.uah.es/personal/eduardo.romera/pdfs/Romera17tits.pdf
+  README: configs/erfnet/README.md
+  Frameworks:
+  - PyTorch
+Models:
+- Name: erfnet_fcn_4xb4-160k_cityscapes-512x1024
+  In Collection: ERFNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 72.5
+      mIoU(ms+flip): 74.75
+  Config: configs/erfnet/erfnet_fcn_4xb4-160k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 16
+    Architecture:
+    - ERFNet
+    - ERFNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 6.04
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/erfnet/erfnet_fcn_4x4_512x1024_160k_cityscapes/erfnet_fcn_4x4_512x1024_160k_cityscapes_20220704_162145-dc90157a.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/erfnet/erfnet_fcn_4x4_512x1024_160k_cityscapes/erfnet_fcn_4x4_512x1024_160k_cityscapes_20220704_162145.log.json
+  Paper:
+    Title: 'ERFNet: Efficient Residual Factorized ConvNet for Real-time Semantic Segmentation'
+    URL: http://www.robesafe.uah.es/personal/eduardo.romera/pdfs/Romera17tits.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.20.0/mmseg/models/backbones/erfnet.py#L321
+  Framework: PyTorch
diff --git a/configs/fastfcn/README.md b/configs/fastfcn/README.md
index feedfa33a8..0b26a33edd 100644
--- a/configs/fastfcn/README.md
+++ b/configs/fastfcn/README.md
@@ -1,6 +1,6 @@
 # FastFCN
 
-[FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation](https://arxiv.org/abs/1903.11816)
+> [FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation](https://arxiv.org/abs/1903.11816)
 
 ## Introduction
 
@@ -22,42 +22,42 @@ Modern approaches for semantic segmentation usually employ dilated convolutions
 <img src="https://user-images.githubusercontent.com/24582831/142901374-6e0252ab-6e0f-4acd-86ad-1e9f49be3185.png" width="70%"/>
 </div>
 
-## Citation
-
-```bibtex
-@article{wu2019fastfcn,
-title={Fastfcn: Rethinking dilated convolution in the backbone for semantic segmentation},
-author={Wu, Huikai and Zhang, Junge and Huang, Kaiqi and Liang, Kongming and Yu, Yizhou},
-journal={arXiv preprint arXiv:1903.11816},
-year={2019}
-}
-```
-
 ## Results and models
 
 ### Cityscapes
 
-| Method                    | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                                       | download                                                                                                                                                                                                                                                                                                                                                                                                           |
-| ------------------------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------- | -------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| FastFCN + DeepLabV3       | R-50-D32 | 512x1024  |   80000 | 5.67     | 2.64           | 79.12 | 80.58         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fastfcn/fastfcn_r50-d32_jpu_aspp_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes_20210928_053722-5d1a2648.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes_20210928_053722.log.json)                 |
-| FastFCN + DeepLabV3 (4x4) | R-50-D32 | 512x1024  |   80000 | 9.79     | -              | 79.52 | 80.91         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fastfcn/fastfcn_r50-d32_jpu_aspp_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes_20210924_214357-72220849.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes_20210924_214357.log.json) |
-| FastFCN + PSPNet          | R-50-D32 | 512x1024  |   80000 | 5.67     | 4.40           | 79.26 | 80.86         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fastfcn/fastfcn_r50-d32_jpu_psp_4xb2-80k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes_20210928_053722-57749bed.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes_20210928_053722.log.json)                     |
-| FastFCN + PSPNet (4x4)    | R-50-D32 | 512x1024  |   80000 | 9.94     | -              | 78.76 | 80.03         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fastfcn/fastfcn_r50-d32_jpu_psp_4xb2-80k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes_20210925_061841-77e87b0a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes_20210925_061841.log.json)     |
-| FastFCN + EncNet          | R-50-D32 | 512x1024  |   80000 | 8.15     | 4.77           | 77.97 | 79.92         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fastfcn/fastfcn_r50-d32_jpu_enc_4xb2-80k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes_20210928_030036-78da5046.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes_20210928_030036.log.json)                     |
-| FastFCN + EncNet (4x4)    | R-50-D32 | 512x1024  |   80000 | 15.45    | -              |  78.6 | 80.25         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fastfcn/fastfcn_r50-d32_jpu_enc_4xb2-80k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes_20210926_093217-e1eb6dbb.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes_20210926_093217.log.json)     |
+| Method              | Backbone       | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                                       | download                                                                                                                                                                                                                                                                                                                                                                                                           |
+| ------------------- | -------------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------- | -------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| FastFCN + DeepLabV3 | R-50-D32       | 512x1024  |   80000 | 5.67     | 2.64           | V100   | 79.12 | 80.58         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fastfcn/fastfcn_r50-d32_jpu_aspp_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes_20210928_053722-5d1a2648.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes_20210928_053722.log.json)                 |
+| FastFCN + DeepLabV3 | R-50-D32 (4x4) | 512x1024  |   80000 | 9.79     | -              | V100   | 79.52 | 80.91         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fastfcn/fastfcn_r50-d32_jpu_aspp_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes_20210924_214357-72220849.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes_20210924_214357.log.json) |
+| FastFCN + PSPNet    | R-50-D32       | 512x1024  |   80000 | 5.67     | 4.40           | V100   | 79.26 | 80.86         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fastfcn/fastfcn_r50-d32_jpu_psp_4xb2-80k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes_20210928_053722-57749bed.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes_20210928_053722.log.json)                     |
+| FastFCN + PSPNet    | R-50-D32 (4x4) | 512x1024  |   80000 | 9.94     | -              | V100   | 78.76 | 80.03         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fastfcn/fastfcn_r50-d32_jpu_psp_4xb2-80k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes_20210925_061841-77e87b0a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes_20210925_061841.log.json)     |
+| FastFCN + EncNet    | R-50-D32       | 512x1024  |   80000 | 8.15     | 4.77           | V100   | 77.97 | 79.92         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fastfcn/fastfcn_r50-d32_jpu_enc_4xb2-80k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes_20210928_030036-78da5046.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes_20210928_030036.log.json)                     |
+| FastFCN + EncNet    | R-50-D32 (4x4) | 512x1024  |   80000 | 15.45    | -              | V100   |  78.6 | 80.25         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fastfcn/fastfcn_r50-d32_jpu_enc_4xb2-80k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes_20210926_093217-e1eb6dbb.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes_20210926_093217.log.json)     |
 
 ### ADE20K
 
-| Method              | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                                   | download                                                                                                                                                                                                                                                                                                                                                                           |
-| ------------------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------- | ---------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| FastFCN + DeepLabV3 | R-50-D32 | 512x1024  |   80000 | 8.46     | 12.06          | 41.88 | 42.91         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fastfcn/fastfcn_r50-d32_jpu_aspp_4xb4-80k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_80k_ade20k/fastfcn_r50-d32_jpu_aspp_512x512_80k_ade20k_20211013_190619-3aa40f2d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_80k_ade20k/fastfcn_r50-d32_jpu_aspp_512x512_80k_ade20k_20211013_190619.log.json)     |
-| FastFCN + DeepLabV3 | R-50-D32 | 512x1024  |  160000 | -        | -              | 43.58 | 44.92         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fastfcn/fastfcn_r50-d32_jpu_aspp_4xb4-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_160k_ade20k/fastfcn_r50-d32_jpu_aspp_512x512_160k_ade20k_20211008_152246-27036aee.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_160k_ade20k/fastfcn_r50-d32_jpu_aspp_512x512_160k_ade20k_20211008_152246.log.json) |
-| FastFCN + PSPNet    | R-50-D32 | 512x1024  |   80000 | 8.02     | 19.21          | 41.40 | 42.12         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fastfcn/fastfcn_r50-d32_jpu_psp_4xb4-80k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_512x512_80k_ade20k/fastfcn_r50-d32_jpu_psp_512x512_80k_ade20k_20210930_225137-993d07c8.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_512x512_80k_ade20k/fastfcn_r50-d32_jpu_psp_512x512_80k_ade20k_20210930_225137.log.json)         |
-| FastFCN + PSPNet    | R-50-D32 | 512x1024  |  160000 | -        | -              | 42.63 | 43.71         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fastfcn/fastfcn_r50-d32_jpu_psp_4xb4-160k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_512x512_160k_ade20k/fastfcn_r50-d32_jpu_psp_512x512_160k_ade20k_20211008_105455-e8f5a2fd.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_512x512_160k_ade20k/fastfcn_r50-d32_jpu_psp_512x512_160k_ade20k_20211008_105455.log.json)     |
-| FastFCN + EncNet    | R-50-D32 | 512x1024  |   80000 | 9.67     | 17.23          | 40.88 | 42.36         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fastfcn/fastfcn_r50-d32_jpu_enc_4xb4-80k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_512x512_80k_ade20k/fastfcn_r50-d32_jpu_enc_512x512_80k_ade20k_20210930_225214-65aef6dd.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_512x512_80k_ade20k/fastfcn_r50-d32_jpu_enc_512x512_80k_ade20k_20210930_225214.log.json)         |
-| FastFCN + EncNet    | R-50-D32 | 512x1024  |  160000 | -        | -              | 42.50 | 44.21         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fastfcn/fastfcn_r50-d32_jpu_enc_4xb4-160k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_512x512_160k_ade20k/fastfcn_r50-d32_jpu_enc_512x512_160k_ade20k_20211008_105456-d875ce3c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_512x512_160k_ade20k/fastfcn_r50-d32_jpu_enc_512x512_160k_ade20k_20211008_105456.log.json)     |
+| Method              | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                                   | download                                                                                                                                                                                                                                                                                                                                                                           |
+| ------------------- | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------- | ---------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| FastFCN + DeepLabV3 | R-50-D32 | 512x1024  |   80000 | 8.46     | 12.06          | V100   | 41.88 | 42.91         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fastfcn/fastfcn_r50-d32_jpu_aspp_4xb4-80k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_80k_ade20k/fastfcn_r50-d32_jpu_aspp_512x512_80k_ade20k_20211013_190619-3aa40f2d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_80k_ade20k/fastfcn_r50-d32_jpu_aspp_512x512_80k_ade20k_20211013_190619.log.json)     |
+| FastFCN + DeepLabV3 | R-50-D32 | 512x1024  |  160000 | -        | -              | V100   | 43.58 | 44.92         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fastfcn/fastfcn_r50-d32_jpu_aspp_4xb4-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_160k_ade20k/fastfcn_r50-d32_jpu_aspp_512x512_160k_ade20k_20211008_152246-27036aee.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_160k_ade20k/fastfcn_r50-d32_jpu_aspp_512x512_160k_ade20k_20211008_152246.log.json) |
+| FastFCN + PSPNet    | R-50-D32 | 512x1024  |   80000 | 8.02     | 19.21          | V100   | 41.40 | 42.12         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fastfcn/fastfcn_r50-d32_jpu_psp_4xb4-80k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_512x512_80k_ade20k/fastfcn_r50-d32_jpu_psp_512x512_80k_ade20k_20210930_225137-993d07c8.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_512x512_80k_ade20k/fastfcn_r50-d32_jpu_psp_512x512_80k_ade20k_20210930_225137.log.json)         |
+| FastFCN + PSPNet    | R-50-D32 | 512x1024  |  160000 | -        | -              | V100   | 42.63 | 43.71         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fastfcn/fastfcn_r50-d32_jpu_psp_4xb4-160k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_512x512_160k_ade20k/fastfcn_r50-d32_jpu_psp_512x512_160k_ade20k_20211008_105455-e8f5a2fd.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_512x512_160k_ade20k/fastfcn_r50-d32_jpu_psp_512x512_160k_ade20k_20211008_105455.log.json)     |
+| FastFCN + EncNet    | R-50-D32 | 512x1024  |   80000 | 9.67     | 17.23          | V100   | 40.88 | 42.36         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fastfcn/fastfcn_r50-d32_jpu_enc_4xb4-80k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_512x512_80k_ade20k/fastfcn_r50-d32_jpu_enc_512x512_80k_ade20k_20210930_225214-65aef6dd.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_512x512_80k_ade20k/fastfcn_r50-d32_jpu_enc_512x512_80k_ade20k_20210930_225214.log.json)         |
+| FastFCN + EncNet    | R-50-D32 | 512x1024  |  160000 | -        | -              | V100   | 42.50 | 44.21         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fastfcn/fastfcn_r50-d32_jpu_enc_4xb4-160k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_512x512_160k_ade20k/fastfcn_r50-d32_jpu_enc_512x512_160k_ade20k_20211008_105456-d875ce3c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_512x512_160k_ade20k/fastfcn_r50-d32_jpu_enc_512x512_160k_ade20k_20211008_105456.log.json)     |
 
 Note:
 
 - `4x4` means 4 GPUs with 4 samples per GPU in training, default setting is 4 GPUs with 2 samples per GPU in training.
 - Results of [DeepLabV3 (mIoU: 79.32)](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/deeplabv3), [PSPNet (mIoU: 78.55)](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet) and [ENCNet (mIoU: 77.94)](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/encnet) can be found in each original repository.
+
+## Citation
+
+```bibtex
+@article{wu2019fastfcn,
+title={Fastfcn: Rethinking dilated convolution in the backbone for semantic segmentation},
+author={Wu, Huikai and Zhang, Junge and Huang, Kaiqi and Liang, Kongming and Yu, Yizhou},
+journal={arXiv preprint arXiv:1903.11816},
+year={2019}
+}
+```
diff --git a/configs/fastfcn/fastfcn.yml b/configs/fastfcn/fastfcn.yml
deleted file mode 100644
index 6b4d3bc121..0000000000
--- a/configs/fastfcn/fastfcn.yml
+++ /dev/null
@@ -1,235 +0,0 @@
-Collections:
-- Name: FastFCN
-  Metadata:
-    Training Data:
-    - Cityscapes
-    - ADE20K
-  Paper:
-    URL: https://arxiv.org/abs/1903.11816
-    Title: 'FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation'
-  README: configs/fastfcn/README.md
-  Code:
-    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/jpu.py#L12
-    Version: v0.18.0
-  Converted From:
-    Code: https://github.com/wuhuikai/FastFCN
-Models:
-- Name: fastfcn_r50-d32_jpu_aspp_4xb2-80k_cityscapes-512x1024
-  In Collection: FastFCN
-  Metadata:
-    backbone: R-50-D32
-    crop size: (512,1024)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 378.79
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 5.67
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 79.12
-      mIoU(ms+flip): 80.58
-  Config: configs/fastfcn/fastfcn_r50-d32_jpu_aspp_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes_20210928_053722-5d1a2648.pth
-- Name: fastfcn_r50-d32_jpu_aspp_4xb2-80k_cityscapes-512x1024
-  In Collection: FastFCN
-  Metadata:
-    backbone: R-50-D32
-    crop size: (512,1024)
-    lr schd: 80000
-    Training Memory (GB): 9.79
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 79.52
-      mIoU(ms+flip): 80.91
-  Config: configs/fastfcn/fastfcn_r50-d32_jpu_aspp_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes_20210924_214357-72220849.pth
-- Name: fastfcn_r50-d32_jpu_psp_4xb2-80k_cityscapes-512x1024
-  In Collection: FastFCN
-  Metadata:
-    backbone: R-50-D32
-    crop size: (512,1024)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 227.27
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 5.67
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 79.26
-      mIoU(ms+flip): 80.86
-  Config: configs/fastfcn/fastfcn_r50-d32_jpu_psp_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes_20210928_053722-57749bed.pth
-- Name: fastfcn_r50-d32_jpu_psp_4xb2-80k_cityscapes-512x1024
-  In Collection: FastFCN
-  Metadata:
-    backbone: R-50-D32
-    crop size: (512,1024)
-    lr schd: 80000
-    Training Memory (GB): 9.94
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 78.76
-      mIoU(ms+flip): 80.03
-  Config: configs/fastfcn/fastfcn_r50-d32_jpu_psp_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes_20210925_061841-77e87b0a.pth
-- Name: fastfcn_r50-d32_jpu_enc_4xb2-80k_cityscapes-512x1024
-  In Collection: FastFCN
-  Metadata:
-    backbone: R-50-D32
-    crop size: (512,1024)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 209.64
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 8.15
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 77.97
-      mIoU(ms+flip): 79.92
-  Config: configs/fastfcn/fastfcn_r50-d32_jpu_enc_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes_20210928_030036-78da5046.pth
-- Name: fastfcn_r50-d32_jpu_enc_4xb2-80k_cityscapes-512x1024
-  In Collection: FastFCN
-  Metadata:
-    backbone: R-50-D32
-    crop size: (512,1024)
-    lr schd: 80000
-    Training Memory (GB): 15.45
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 78.6
-      mIoU(ms+flip): 80.25
-  Config: configs/fastfcn/fastfcn_r50-d32_jpu_enc_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes_20210926_093217-e1eb6dbb.pth
-- Name: fastfcn_r50-d32_jpu_aspp_4xb4-80k_ade20k-512x512
-  In Collection: FastFCN
-  Metadata:
-    backbone: R-50-D32
-    crop size: (512,1024)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 82.92
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 8.46
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 41.88
-      mIoU(ms+flip): 42.91
-  Config: configs/fastfcn/fastfcn_r50-d32_jpu_aspp_4xb4-80k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_80k_ade20k/fastfcn_r50-d32_jpu_aspp_512x512_80k_ade20k_20211013_190619-3aa40f2d.pth
-- Name: fastfcn_r50-d32_jpu_aspp_4xb4-160k_ade20k-512x512
-  In Collection: FastFCN
-  Metadata:
-    backbone: R-50-D32
-    crop size: (512,1024)
-    lr schd: 160000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 43.58
-      mIoU(ms+flip): 44.92
-  Config: configs/fastfcn/fastfcn_r50-d32_jpu_aspp_4xb4-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_160k_ade20k/fastfcn_r50-d32_jpu_aspp_512x512_160k_ade20k_20211008_152246-27036aee.pth
-- Name: fastfcn_r50-d32_jpu_psp_4xb4-80k_ade20k-512x512
-  In Collection: FastFCN
-  Metadata:
-    backbone: R-50-D32
-    crop size: (512,1024)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 52.06
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 8.02
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 41.4
-      mIoU(ms+flip): 42.12
-  Config: configs/fastfcn/fastfcn_r50-d32_jpu_psp_4xb4-80k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_512x512_80k_ade20k/fastfcn_r50-d32_jpu_psp_512x512_80k_ade20k_20210930_225137-993d07c8.pth
-- Name: fastfcn_r50-d32_jpu_psp_4xb4-160k_ade20k-512x512
-  In Collection: FastFCN
-  Metadata:
-    backbone: R-50-D32
-    crop size: (512,1024)
-    lr schd: 160000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 42.63
-      mIoU(ms+flip): 43.71
-  Config: configs/fastfcn/fastfcn_r50-d32_jpu_psp_4xb4-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_512x512_160k_ade20k/fastfcn_r50-d32_jpu_psp_512x512_160k_ade20k_20211008_105455-e8f5a2fd.pth
-- Name: fastfcn_r50-d32_jpu_enc_4xb4-80k_ade20k-512x512
-  In Collection: FastFCN
-  Metadata:
-    backbone: R-50-D32
-    crop size: (512,1024)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 58.04
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 9.67
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 40.88
-      mIoU(ms+flip): 42.36
-  Config: configs/fastfcn/fastfcn_r50-d32_jpu_enc_4xb4-80k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_512x512_80k_ade20k/fastfcn_r50-d32_jpu_enc_512x512_80k_ade20k_20210930_225214-65aef6dd.pth
-- Name: fastfcn_r50-d32_jpu_enc_4xb4-160k_ade20k-512x512
-  In Collection: FastFCN
-  Metadata:
-    backbone: R-50-D32
-    crop size: (512,1024)
-    lr schd: 160000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 42.5
-      mIoU(ms+flip): 44.21
-  Config: configs/fastfcn/fastfcn_r50-d32_jpu_enc_4xb4-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_512x512_160k_ade20k/fastfcn_r50-d32_jpu_enc_512x512_160k_ade20k_20211008_105456-d875ce3c.pth
diff --git a/configs/fastfcn/metafile.yaml b/configs/fastfcn/metafile.yaml
new file mode 100644
index 0000000000..f5fe03ca45
--- /dev/null
+++ b/configs/fastfcn/metafile.yaml
@@ -0,0 +1,311 @@
+Collections:
+- Name: FastFCN
+  License: Apache License 2.0
+  Metadata:
+    Training Data:
+    - Cityscapes
+    - ADE20K
+  Paper:
+    Title: 'FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation'
+    URL: https://arxiv.org/abs/1903.11816
+  README: configs/fastfcn/README.md
+  Frameworks:
+  - PyTorch
+Models:
+- Name: fastfcn_r50-d32_jpu_aspp_4xb2-80k_cityscapes-512x1024
+  In Collection: FastFCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.12
+      mIoU(ms+flip): 80.58
+  Config: configs/fastfcn/fastfcn_r50-d32_jpu_aspp_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D32
+    - FastFCN
+    - DeepLabV3
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 5.67
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes_20210928_053722-5d1a2648.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes_20210928_053722.log.json
+  Paper:
+    Title: 'FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation'
+    URL: https://arxiv.org/abs/1903.11816
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/jpu.py#L12
+  Framework: PyTorch
+- Name: fastfcn_r50-d32_jpu_aspp_4xb2-80k_cityscapes-512x1024
+  In Collection: FastFCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.52
+      mIoU(ms+flip): 80.91
+  Config: configs/fastfcn/fastfcn_r50-d32_jpu_aspp_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D32
+    - FastFCN
+    - DeepLabV3
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 9.79
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes_20210924_214357-72220849.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes_20210924_214357.log.json
+  Paper:
+    Title: 'FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation'
+    URL: https://arxiv.org/abs/1903.11816
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/jpu.py#L12
+  Framework: PyTorch
+- Name: fastfcn_r50-d32_jpu_psp_4xb2-80k_cityscapes-512x1024
+  In Collection: FastFCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.26
+      mIoU(ms+flip): 80.86
+  Config: configs/fastfcn/fastfcn_r50-d32_jpu_psp_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D32
+    - FastFCN
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 5.67
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes_20210928_053722-57749bed.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes_20210928_053722.log.json
+  Paper:
+    Title: 'FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation'
+    URL: https://arxiv.org/abs/1903.11816
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/jpu.py#L12
+  Framework: PyTorch
+- Name: fastfcn_r50-d32_jpu_psp_4xb2-80k_cityscapes-512x1024
+  In Collection: FastFCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.76
+      mIoU(ms+flip): 80.03
+  Config: configs/fastfcn/fastfcn_r50-d32_jpu_psp_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D32
+    - FastFCN
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 9.94
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes_20210925_061841-77e87b0a.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes_20210925_061841.log.json
+  Paper:
+    Title: 'FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation'
+    URL: https://arxiv.org/abs/1903.11816
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/jpu.py#L12
+  Framework: PyTorch
+- Name: fastfcn_r50-d32_jpu_enc_4xb2-80k_cityscapes-512x1024
+  In Collection: FastFCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.97
+      mIoU(ms+flip): 79.92
+  Config: configs/fastfcn/fastfcn_r50-d32_jpu_enc_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D32
+    - FastFCN
+    - EncNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 8.15
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes_20210928_030036-78da5046.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes_20210928_030036.log.json
+  Paper:
+    Title: 'FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation'
+    URL: https://arxiv.org/abs/1903.11816
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/jpu.py#L12
+  Framework: PyTorch
+- Name: fastfcn_r50-d32_jpu_enc_4xb2-80k_cityscapes-512x1024
+  In Collection: FastFCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.6
+      mIoU(ms+flip): 80.25
+  Config: configs/fastfcn/fastfcn_r50-d32_jpu_enc_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D32
+    - FastFCN
+    - EncNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 15.45
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes_20210926_093217-e1eb6dbb.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes_20210926_093217.log.json
+  Paper:
+    Title: 'FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation'
+    URL: https://arxiv.org/abs/1903.11816
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/jpu.py#L12
+  Framework: PyTorch
+- Name: fastfcn_r50-d32_jpu_aspp_4xb4-80k_ade20k-512x512
+  In Collection: FastFCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 41.88
+      mIoU(ms+flip): 42.91
+  Config: configs/fastfcn/fastfcn_r50-d32_jpu_aspp_4xb4-80k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-50-D32
+    - FastFCN
+    - DeepLabV3
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 8.46
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_80k_ade20k/fastfcn_r50-d32_jpu_aspp_512x512_80k_ade20k_20211013_190619-3aa40f2d.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_80k_ade20k/fastfcn_r50-d32_jpu_aspp_512x512_80k_ade20k_20211013_190619.log.json
+  Paper:
+    Title: 'FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation'
+    URL: https://arxiv.org/abs/1903.11816
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/jpu.py#L12
+  Framework: PyTorch
+- Name: fastfcn_r50-d32_jpu_aspp_4xb4-160k_ade20k-512x512
+  In Collection: FastFCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 43.58
+      mIoU(ms+flip): 44.92
+  Config: configs/fastfcn/fastfcn_r50-d32_jpu_aspp_4xb4-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-50-D32
+    - FastFCN
+    - DeepLabV3
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_160k_ade20k/fastfcn_r50-d32_jpu_aspp_512x512_160k_ade20k_20211008_152246-27036aee.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_160k_ade20k/fastfcn_r50-d32_jpu_aspp_512x512_160k_ade20k_20211008_152246.log.json
+  Paper:
+    Title: 'FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation'
+    URL: https://arxiv.org/abs/1903.11816
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/jpu.py#L12
+  Framework: PyTorch
+- Name: fastfcn_r50-d32_jpu_psp_4xb4-80k_ade20k-512x512
+  In Collection: FastFCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 41.4
+      mIoU(ms+flip): 42.12
+  Config: configs/fastfcn/fastfcn_r50-d32_jpu_psp_4xb4-80k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-50-D32
+    - FastFCN
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 8.02
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_512x512_80k_ade20k/fastfcn_r50-d32_jpu_psp_512x512_80k_ade20k_20210930_225137-993d07c8.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_512x512_80k_ade20k/fastfcn_r50-d32_jpu_psp_512x512_80k_ade20k_20210930_225137.log.json
+  Paper:
+    Title: 'FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation'
+    URL: https://arxiv.org/abs/1903.11816
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/jpu.py#L12
+  Framework: PyTorch
+- Name: fastfcn_r50-d32_jpu_psp_4xb4-160k_ade20k-512x512
+  In Collection: FastFCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 42.63
+      mIoU(ms+flip): 43.71
+  Config: configs/fastfcn/fastfcn_r50-d32_jpu_psp_4xb4-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-50-D32
+    - FastFCN
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_512x512_160k_ade20k/fastfcn_r50-d32_jpu_psp_512x512_160k_ade20k_20211008_105455-e8f5a2fd.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_512x512_160k_ade20k/fastfcn_r50-d32_jpu_psp_512x512_160k_ade20k_20211008_105455.log.json
+  Paper:
+    Title: 'FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation'
+    URL: https://arxiv.org/abs/1903.11816
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/jpu.py#L12
+  Framework: PyTorch
+- Name: fastfcn_r50-d32_jpu_enc_4xb4-80k_ade20k-512x512
+  In Collection: FastFCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 40.88
+      mIoU(ms+flip): 42.36
+  Config: configs/fastfcn/fastfcn_r50-d32_jpu_enc_4xb4-80k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-50-D32
+    - FastFCN
+    - EncNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 9.67
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_512x512_80k_ade20k/fastfcn_r50-d32_jpu_enc_512x512_80k_ade20k_20210930_225214-65aef6dd.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_512x512_80k_ade20k/fastfcn_r50-d32_jpu_enc_512x512_80k_ade20k_20210930_225214.log.json
+  Paper:
+    Title: 'FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation'
+    URL: https://arxiv.org/abs/1903.11816
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/jpu.py#L12
+  Framework: PyTorch
+- Name: fastfcn_r50-d32_jpu_enc_4xb4-160k_ade20k-512x512
+  In Collection: FastFCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 42.5
+      mIoU(ms+flip): 44.21
+  Config: configs/fastfcn/fastfcn_r50-d32_jpu_enc_4xb4-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-50-D32
+    - FastFCN
+    - EncNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_512x512_160k_ade20k/fastfcn_r50-d32_jpu_enc_512x512_160k_ade20k_20211008_105456-d875ce3c.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_512x512_160k_ade20k/fastfcn_r50-d32_jpu_enc_512x512_160k_ade20k_20211008_105456.log.json
+  Paper:
+    Title: 'FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation'
+    URL: https://arxiv.org/abs/1903.11816
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/jpu.py#L12
+  Framework: PyTorch
diff --git a/configs/fastscnn/README.md b/configs/fastscnn/README.md
index 3e06903ae5..ea3449fd2c 100644
--- a/configs/fastscnn/README.md
+++ b/configs/fastscnn/README.md
@@ -1,6 +1,6 @@
 # Fast-SCNN
 
-[Fast-SCNN for Semantic Segmentation](https://arxiv.org/abs/1902.04502)
+> [Fast-SCNN for Semantic Segmentation](https://arxiv.org/abs/1902.04502)
 
 ## Introduction
 
@@ -22,6 +22,14 @@ The encoder-decoder framework is state-of-the-art for offline semantic image seg
 <img src="https://user-images.githubusercontent.com/24582831/142901444-705b4ff4-6d1e-409b-899a-37bf3a6b69ce.png" width="80%"/>
 </div>
 
+## Results and models
+
+### Cityscapes
+
+| Method   | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                          | download                                                                                                                                                                                                                                                                                                                                               |
+| -------- | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------- | ------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| FastSCNN | FastSCNN | 512x1024  |  160000 | 3.3      | 56.45          | V100   | 70.96 | 72.65         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fastscnn/fast_scnn_8xb4-160k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fast_scnn/fast_scnn_lr0.12_8x4_160k_cityscapes/fast_scnn_lr0.12_8x4_160k_cityscapes_20210630_164853-0cec9937.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fast_scnn/fast_scnn_lr0.12_8x4_160k_cityscapes/fast_scnn_lr0.12_8x4_160k_cityscapes_20210630_164853.log.json) |
+
 ## Citation
 
 ```bibtex
@@ -32,11 +40,3 @@ The encoder-decoder framework is state-of-the-art for offline semantic image seg
   year={2019}
 }
 ```
-
-## Results and models
-
-### Cityscapes
-
-| Method   | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                          | download                                                                                                                                                                                                                                                                                                                                               |
-| -------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------- | ------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| FastSCNN | FastSCNN | 512x1024  |  160000 | 3.3      | 56.45          | 70.96 | 72.65         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fastscnn/fast_scnn_8xb4-160k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fast_scnn/fast_scnn_lr0.12_8x4_160k_cityscapes/fast_scnn_lr0.12_8x4_160k_cityscapes_20210630_164853-0cec9937.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fast_scnn/fast_scnn_lr0.12_8x4_160k_cityscapes/fast_scnn_lr0.12_8x4_160k_cityscapes_20210630_164853.log.json) |
diff --git a/configs/fastscnn/fastscnn.yml b/configs/fastscnn/metafile.yaml
similarity index 50%
rename from configs/fastscnn/fastscnn.yml
rename to configs/fastscnn/metafile.yaml
index 13215c2fb1..9e33c902db 100644
--- a/configs/fastscnn/fastscnn.yml
+++ b/configs/fastscnn/metafile.yaml
@@ -1,35 +1,37 @@
 Collections:
 - Name: FastSCNN
+  License: Apache License 2.0
   Metadata:
     Training Data:
     - Cityscapes
   Paper:
-    URL: https://arxiv.org/abs/1902.04502
     Title: Fast-SCNN for Semantic Segmentation
+    URL: https://arxiv.org/abs/1902.04502
   README: configs/fastscnn/README.md
-  Code:
-    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/fast_scnn.py#L272
-    Version: v0.17.0
+  Frameworks:
+  - PyTorch
 Models:
 - Name: fast_scnn_8xb4-160k_cityscapes-512x1024
   In Collection: FastSCNN
-  Metadata:
-    backbone: FastSCNN
-    crop size: (512,1024)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 17.71
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 3.3
   Results:
-  - Task: Semantic Segmentation
+    Task: Semantic Segmentation
     Dataset: Cityscapes
     Metrics:
       mIoU: 70.96
       mIoU(ms+flip): 72.65
   Config: configs/fastscnn/fast_scnn_8xb4-160k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 32
+    Architecture:
+    - FastSCNN
+    - FastSCNN
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 3.3
   Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fast_scnn/fast_scnn_lr0.12_8x4_160k_cityscapes/fast_scnn_lr0.12_8x4_160k_cityscapes_20210630_164853-0cec9937.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fast_scnn/fast_scnn_lr0.12_8x4_160k_cityscapes/fast_scnn_lr0.12_8x4_160k_cityscapes_20210630_164853.log.json
+  Paper:
+    Title: Fast-SCNN for Semantic Segmentation
+    URL: https://arxiv.org/abs/1902.04502
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/fast_scnn.py#L272
+  Framework: PyTorch
diff --git a/configs/fcn/README.md b/configs/fcn/README.md
index 4b887f19e4..6134933710 100644
--- a/configs/fcn/README.md
+++ b/configs/fcn/README.md
@@ -1,6 +1,6 @@
 # FCN
 
-[Fully Convolutional Networks for Semantic Segmentation](https://arxiv.org/abs/1411.4038)
+> [Fully Convolutional Networks for Semantic Segmentation](https://arxiv.org/abs/1411.4038)
 
 ## Introduction
 
@@ -22,90 +22,90 @@ Convolutional networks are powerful visual models that yield hierarchies of feat
 <img src="https://user-images.githubusercontent.com/24582831/142901525-fd0d2bf4-9a47-4143-85f5-3cee8849eaa4.png" width="70%"/>
 </div>
 
-## Citation
-
-```bibtex
-@article{shelhamer2017fully,
-  title={Fully convolutional networks for semantic segmentation},
-  author={Shelhamer, Evan and Long, Jonathan and Darrell, Trevor},
-  journal={IEEE transactions on pattern analysis and machine intelligence},
-  volume={39},
-  number={4},
-  pages={640--651},
-  year={2017},
-  publisher={IEEE Trans Pattern Anal Mach Intell}
-}
-```
-
 ## Results and models
 
 ### Cityscapes
 
-| Method     | Backbone   | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                           | download                                                                                                                                                                                                                                                                                                                                                   |
-| ---------- | ---------- | --------- | ------: | -------- | -------------- | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| FCN        | R-50-D8    | 512x1024  |   40000 | 5.7      | 4.17           | 72.25 |         73.36 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn_r50-d8_4xb2-40k_cityscapes-512x1024.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x1024_40k_cityscapes/fcn_r50-d8_512x1024_40k_cityscapes_20200604_192608-efe53f0d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x1024_40k_cityscapes/fcn_r50-d8_512x1024_40k_cityscapes_20200604_192608.log.json)                         |
-| FCN        | R-101-D8   | 512x1024  |   40000 | 9.2      | 2.66           | 75.45 |         76.58 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn_r101-d8_4xb2-40k_cityscapes-512x1024.py)      | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x1024_40k_cityscapes/fcn_r101-d8_512x1024_40k_cityscapes_20200604_181852-a883d3a1.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x1024_40k_cityscapes/fcn_r101-d8_512x1024_40k_cityscapes_20200604_181852.log.json)                     |
-| FCN        | R-50-D8    | 769x769   |   40000 | 6.5      | 1.80           | 71.47 |         72.54 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn_r50-d8_4xb2-40k_cityscapes-769x769.py)        | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_769x769_40k_cityscapes/fcn_r50-d8_769x769_40k_cityscapes_20200606_113104-977b5d02.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_769x769_40k_cityscapes/fcn_r50-d8_769x769_40k_cityscapes_20200606_113104.log.json)                             |
-| FCN        | R-101-D8   | 769x769   |   40000 | 10.4     | 1.19           | 73.93 |         75.14 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn_r101-d8_4xb2-40k_cityscapes-769x769.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_769x769_40k_cityscapes/fcn_r101-d8_769x769_40k_cityscapes_20200606_113208-7d4ab69c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_769x769_40k_cityscapes/fcn_r101-d8_769x769_40k_cityscapes_20200606_113208.log.json)                         |
-| FCN        | R-18-D8    | 512x1024  |   80000 | 1.7      | 14.65          | 71.11 |         72.91 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn_r18-d8_4xb2-80k_cityscapes-512x1024.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18-d8_512x1024_80k_cityscapes/fcn_r18-d8_512x1024_80k_cityscapes_20201225_021327-6c50f8b4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18-d8_512x1024_80k_cityscapes/fcn_r18-d8_512x1024_80k_cityscapes-20201225_021327.log.json)                         |
-| FCN        | R-50-D8    | 512x1024  |   80000 | -        |                | 73.61 |         74.24 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn_r50-d8_4xb2-80k_cityscapes-512x1024.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x1024_80k_cityscapes/fcn_r50-d8_512x1024_80k_cityscapes_20200606_113019-03aa804d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x1024_80k_cityscapes/fcn_r50-d8_512x1024_80k_cityscapes_20200606_113019.log.json)                         |
-| FCN        | R-101-D8   | 512x1024  |   80000 | -        | -              | 75.13 |         75.94 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn_r101-d8_4xb2-80k_cityscapes-512x1024.py)      | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x1024_80k_cityscapes/fcn_r101-d8_512x1024_80k_cityscapes_20200606_113038-3fb937eb.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x1024_80k_cityscapes/fcn_r101-d8_512x1024_80k_cityscapes_20200606_113038.log.json)                     |
-| FCN (FP16) | R-101-D8   | 512x1024  |   80000 | 5.37     | 8.64           | 76.80 |             - | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn_r101-d8_4xb2-amp-80k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_fp16_512x1024_80k_cityscapes/fcn_r101-d8_fp16_512x1024_80k_cityscapes_20200717_230921-fb13e883.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_fp16_512x1024_80k_cityscapes/fcn_r101-d8_fp16_512x1024_80k_cityscapes_20200717_230921.log.json) |
-| FCN        | R-18-D8    | 769x769   |   80000 | 1.9      | 6.40           | 70.80 |         73.16 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn_r18-d8_4xb2-80k_cityscapes-769x769.py)        | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18-d8_769x769_80k_cityscapes/fcn_r18-d8_769x769_80k_cityscapes_20201225_021451-9739d1b8.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18-d8_769x769_80k_cityscapes/fcn_r18-d8_769x769_80k_cityscapes-20201225_021451.log.json)                             |
-| FCN        | R-50-D8    | 769x769   |   80000 | -        | -              | 72.64 |         73.32 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn_r50-d8_4xb2-80k_cityscapes-769x769.py)        | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_769x769_80k_cityscapes/fcn_r50-d8_769x769_80k_cityscapes_20200606_195749-f5caeabc.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_769x769_80k_cityscapes/fcn_r50-d8_769x769_80k_cityscapes_20200606_195749.log.json)                             |
-| FCN        | R-101-D8   | 769x769   |   80000 | -        | -              | 75.52 |         76.61 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn_r101-d8_4xb2-80k_cityscapes-769x769.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_769x769_80k_cityscapes/fcn_r101-d8_769x769_80k_cityscapes_20200606_214354-45cbac68.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_769x769_80k_cityscapes/fcn_r101-d8_769x769_80k_cityscapes_20200606_214354.log.json)                         |
-| FCN        | R-18b-D8   | 512x1024  |   80000 | 1.6      | 16.74          | 70.24 |         72.77 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn_r18b-d8_4xb2-80k_cityscapes-512x1024.py)      | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18b-d8_512x1024_80k_cityscapes/fcn_r18b-d8_512x1024_80k_cityscapes_20201225_230143-92c0f445.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18b-d8_512x1024_80k_cityscapes/fcn_r18b-d8_512x1024_80k_cityscapes-20201225_230143.log.json)                     |
-| FCN        | R-50b-D8   | 512x1024  |   80000 | 5.6      | 4.20           | 75.65 |         77.59 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn_r50b-d8_4xb2-80k_cityscapes-512x1024.py)      | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50b-d8_512x1024_80k_cityscapes/fcn_r50b-d8_512x1024_80k_cityscapes_20201225_094221-82957416.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50b-d8_512x1024_80k_cityscapes/fcn_r50b-d8_512x1024_80k_cityscapes-20201225_094221.log.json)                     |
-| FCN        | R-101b-D8  | 512x1024  |   80000 | 9.1      | 2.73           | 77.37 |         78.77 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn_r101b-d8_4xb2-80k_cityscapes-512x1024.py)     | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101b-d8_512x1024_80k_cityscapes/fcn_r101b-d8_512x1024_80k_cityscapes_20201226_160213-4543858f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101b-d8_512x1024_80k_cityscapes/fcn_r101b-d8_512x1024_80k_cityscapes-20201226_160213.log.json)                 |
-| FCN        | R-18b-D8   | 769x769   |   80000 | 1.7      | 6.70           | 69.66 |         72.07 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn_r18b-d8_4xb2-80k_cityscapes-769x769.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18b-d8_769x769_80k_cityscapes/fcn_r18b-d8_769x769_80k_cityscapes_20201226_004430-32d504e5.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18b-d8_769x769_80k_cityscapes/fcn_r18b-d8_769x769_80k_cityscapes-20201226_004430.log.json)                         |
-| FCN        | R-50b-D8   | 769x769   |   80000 | 6.3      | 1.82           | 73.83 |         76.60 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn_r50b-d8_4xb2-80k_cityscapes-769x769.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50b-d8_769x769_80k_cityscapes/fcn_r50b-d8_769x769_80k_cityscapes_20201225_094223-94552d38.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50b-d8_769x769_80k_cityscapes/fcn_r50b-d8_769x769_80k_cityscapes-20201225_094223.log.json)                         |
-| FCN        | R-101b-D8  | 769x769   |   80000 | 10.3     | 1.15           | 77.02 |         78.67 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn_r101b-d8_4xb2-80k_cityscapes-769x769.py)      | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101b-d8_769x769_80k_cityscapes/fcn_r101b-d8_769x769_80k_cityscapes_20201226_170012-82be37e2.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101b-d8_769x769_80k_cityscapes/fcn_r101b-d8_769x769_80k_cityscapes-20201226_170012.log.json)                     |
-| FCN (D6)   | R-50-D16   | 512x1024  |   40000 | 3.4      | 10.22          | 77.06 |         78.85 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn-d6_r50-d16_4xb2-40k_cityscapes-512x1024.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_512x1024_40k_cityscapes/fcn_d6_r50-d16_512x1024_40k_cityscapes_20210305_130133-98d5d1bc.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_512x1024_40k_cityscapes/fcn_d6_r50-d16_512x1024_40k_cityscapes-20210305_130133.log.json)         |
-| FCN (D6)   | R-50-D16   | 512x1024  |   80000 | -        | 10.35          | 77.27 |         78.88 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn-d6_r50-d16_4xb2-80k_cityscapes-512x1024.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_512x1024_80k_cityscapes/fcn_d6_r50-d16_512x1024_80k_cityscapes_20210306_115604-133c292f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_512x1024_80k_cityscapes/fcn_d6_r50-d16_512x1024_80k_cityscapes-20210306_115604.log.json)         |
-| FCN (D6)   | R-50-D16   | 769x769   |   40000 | 3.7      | 4.17           | 76.82 |         78.22 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn-d6_r50-d16_4xb2-40k_cityscapes-769x769.py)    | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_769x769_40k_cityscapes/fcn_d6_r50-d16_769x769_40k_cityscapes_20210305_185744-1aab18ed.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_769x769_40k_cityscapes/fcn_d6_r50-d16_769x769_40k_cityscapes-20210305_185744.log.json)             |
-| FCN (D6)   | R-50-D16   | 769x769   |   80000 | -        | 4.15           | 77.04 |         78.40 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn-d6_r50-d16_4xb2-80k_cityscapes-769x769.py)    | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_769x769_80k_cityscapes/fcn_d6_r50-d16_769x769_80k_cityscapes_20210305_200413-109d88eb.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_769x769_80k_cityscapes/fcn_d6_r50-d16_769x769_80k_cityscapes-20210305_200413.log.json)             |
-| FCN (D6)   | R-101-D16  | 512x1024  |   40000 | 4.5      | 8.04           | 77.36 |         79.18 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn-d6_r101-d16_4xb2-40k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_512x1024_40k_cityscapes/fcn_d6_r101-d16_512x1024_40k_cityscapes_20210305_130337-9cf2b450.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_512x1024_40k_cityscapes/fcn_d6_r101-d16_512x1024_40k_cityscapes-20210305_130337.log.json)     |
-| FCN (D6)   | R-101-D16  | 512x1024  |   80000 | -        | 8.26           | 78.46 |         80.42 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn-d6_r101-d16_4xb2-80k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_512x1024_80k_cityscapes/fcn_d6_r101-d16_512x1024_80k_cityscapes_20210308_102747-cb336445.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_512x1024_80k_cityscapes/fcn_d6_r101-d16_512x1024_80k_cityscapes-20210308_102747.log.json)     |
-| FCN (D6)   | R-101-D16  | 769x769   |   40000 | 5.0      | 3.12           | 77.28 |         78.95 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn-d6_r101-d16_4xb2-40k_cityscapes-769x769.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_40k_cityscapes/fcn_d6_r101-d16_769x769_40k_cityscapes_20210308_102453-60b114e9.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_40k_cityscapes/fcn_d6_r101-d16_769x769_40k_cityscapes-20210308_102453.log.json)         |
-| FCN (D6)   | R-101-D16  | 769x769   |   80000 | -        | 3.21           | 78.06 |         79.58 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn-d6_r101-d16_4xb2-80k_cityscapes-769x769.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_80k_cityscapes/fcn_d6_r101-d16_769x769_80k_cityscapes_20210306_120016-e33adc4f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_80k_cityscapes/fcn_d6_r101-d16_769x769_80k_cityscapes-20210306_120016.log.json)         |
-| FCN (D6)   | R-50b-D16  | 512x1024  |   80000 | 3.2      | 10.16          | 76.99 |         79.03 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn-d6_r50b-d16_4xb2-80k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50b-d16_512x1024_80k_cityscapes/fcn_d6_r50b-d16_512x1024_80k_cityscapes_20210311_125550-6a0b62e9.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50b_d16_512x1024_80k_cityscapes/fcn_d6_r50b_d16_512x1024_80k_cityscapes-20210311_125550.log.json)     |
-| FCN (D6)   | R-50b-D16  | 769x769   |   80000 | 3.6      | 4.17           | 76.86 |         78.52 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn-d6_r50b-d16_4xb2-80k_cityscapes-769x769.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50b-d16_769x769_80k_cityscapes/fcn_d6_r50b-d16_769x769_80k_cityscapes_20210311_131012-d665f231.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50b_d16_769x769_80k_cityscapes/fcn_d6_r50b_d16_769x769_80k_cityscapes-20210311_131012.log.json)         |
-| FCN (D6)   | R-101b-D16 | 512x1024  |   80000 | 4.3      | 8.46           | 77.72 |         79.53 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn-d6_r101b-d16_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101b-d16_512x1024_80k_cityscapes/fcn_d6_r101b-d16_512x1024_80k_cityscapes_20210311_144305-3f2eb5b4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101b_d16_512x1024_80k_cityscapes/fcn_d6_r101b_d16_512x1024_80k_cityscapes-20210311_144305.log.json) |
-| FCN (D6)   | R-101b-D16 | 769x769   |   80000 | 4.8      | 3.32           | 77.34 |         78.91 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn-d6_r101b-d16_4xb2-80k_cityscapes-769x769.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101b-d16_769x769_80k_cityscapes/fcn_d6_r101b-d16_769x769_80k_cityscapes_20210311_154527-c4d8bfbc.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101b_d16_769x769_80k_cityscapes/fcn_d6_r101b_d16_769x769_80k_cityscapes-20210311_154527.log.json)     |
+| Method     | Backbone   | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device   |  mIoU | mIoU(ms+flip) | config                                                                                                                           | download                                                                                                                                                                                                                                                                                                                                                   |
+| ---------- | ---------- | --------- | ------: | -------- | -------------- | -------- | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| FCN        | R-50-D8    | 512x1024  |   40000 | 5.7      | 4.17           | V100     | 72.25 |         73.36 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn_r50-d8_4xb2-40k_cityscapes-512x1024.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x1024_40k_cityscapes/fcn_r50-d8_512x1024_40k_cityscapes_20200604_192608-efe53f0d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x1024_40k_cityscapes/fcn_r50-d8_512x1024_40k_cityscapes_20200604_192608.log.json)                         |
+| FCN        | R-101-D8   | 512x1024  |   40000 | 9.2      | 2.66           | V100     | 75.45 |         76.58 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn_r101-d8_4xb2-40k_cityscapes-512x1024.py)      | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x1024_40k_cityscapes/fcn_r101-d8_512x1024_40k_cityscapes_20200604_181852-a883d3a1.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x1024_40k_cityscapes/fcn_r101-d8_512x1024_40k_cityscapes_20200604_181852.log.json)                     |
+| FCN        | R-50-D8    | 769x769   |   40000 | 6.5      | 1.80           | V100     | 71.47 |         72.54 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn_r50-d8_4xb2-40k_cityscapes-769x769.py)        | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_769x769_40k_cityscapes/fcn_r50-d8_769x769_40k_cityscapes_20200606_113104-977b5d02.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_769x769_40k_cityscapes/fcn_r50-d8_769x769_40k_cityscapes_20200606_113104.log.json)                             |
+| FCN        | R-101-D8   | 769x769   |   40000 | 10.4     | 1.19           | V100     | 73.93 |         75.14 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn_r101-d8_4xb2-40k_cityscapes-769x769.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_769x769_40k_cityscapes/fcn_r101-d8_769x769_40k_cityscapes_20200606_113208-7d4ab69c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_769x769_40k_cityscapes/fcn_r101-d8_769x769_40k_cityscapes_20200606_113208.log.json)                         |
+| FCN        | R-18-D8    | 512x1024  |   80000 | 1.7      | 14.65          | V100     | 71.11 |         72.91 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn_r18-d8_4xb2-80k_cityscapes-512x1024.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18-d8_512x1024_80k_cityscapes/fcn_r18-d8_512x1024_80k_cityscapes_20201225_021327-6c50f8b4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18-d8_512x1024_80k_cityscapes/fcn_r18-d8_512x1024_80k_cityscapes-20201225_021327.log.json)                         |
+| FCN        | R-50-D8    | 512x1024  |   80000 | -        |                | V100     | 73.61 |         74.24 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn_r50-d8_4xb2-80k_cityscapes-512x1024.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x1024_80k_cityscapes/fcn_r50-d8_512x1024_80k_cityscapes_20200606_113019-03aa804d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x1024_80k_cityscapes/fcn_r50-d8_512x1024_80k_cityscapes_20200606_113019.log.json)                         |
+| FCN        | R-101-D8   | 512x1024  |   80000 | -        | -              | V100     | 75.13 |         75.94 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn_r101-d8_4xb2-80k_cityscapes-512x1024.py)      | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x1024_80k_cityscapes/fcn_r101-d8_512x1024_80k_cityscapes_20200606_113038-3fb937eb.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x1024_80k_cityscapes/fcn_r101-d8_512x1024_80k_cityscapes_20200606_113038.log.json)                     |
+| FCN (FP16) | R-101-D8   | 512x1024  |   80000 | 5.37     | 8.64           | V100     | 76.80 |             - | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn_r101-d8_4xb2-amp-80k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_fp16_512x1024_80k_cityscapes/fcn_r101-d8_fp16_512x1024_80k_cityscapes_20200717_230921-fb13e883.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_fp16_512x1024_80k_cityscapes/fcn_r101-d8_fp16_512x1024_80k_cityscapes_20200717_230921.log.json) |
+| FCN        | R-18-D8    | 769x769   |   80000 | 1.9      | 6.40           | V100     | 70.80 |         73.16 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn_r18-d8_4xb2-80k_cityscapes-769x769.py)        | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18-d8_769x769_80k_cityscapes/fcn_r18-d8_769x769_80k_cityscapes_20201225_021451-9739d1b8.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18-d8_769x769_80k_cityscapes/fcn_r18-d8_769x769_80k_cityscapes-20201225_021451.log.json)                             |
+| FCN        | R-50-D8    | 769x769   |   80000 | -        | -              | V100     | 72.64 |         73.32 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn_r50-d8_4xb2-80k_cityscapes-769x769.py)        | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_769x769_80k_cityscapes/fcn_r50-d8_769x769_80k_cityscapes_20200606_195749-f5caeabc.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_769x769_80k_cityscapes/fcn_r50-d8_769x769_80k_cityscapes_20200606_195749.log.json)                             |
+| FCN        | R-101-D8   | 769x769   |   80000 | -        | -              | V100     | 75.52 |         76.61 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn_r101-d8_4xb2-80k_cityscapes-769x769.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_769x769_80k_cityscapes/fcn_r101-d8_769x769_80k_cityscapes_20200606_214354-45cbac68.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_769x769_80k_cityscapes/fcn_r101-d8_769x769_80k_cityscapes_20200606_214354.log.json)                         |
+| FCN        | R-18b-D8   | 512x1024  |   80000 | 1.6      | 16.74          | V100     | 70.24 |         72.77 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn_r18b-d8_4xb2-80k_cityscapes-512x1024.py)      | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18b-d8_512x1024_80k_cityscapes/fcn_r18b-d8_512x1024_80k_cityscapes_20201225_230143-92c0f445.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18b-d8_512x1024_80k_cityscapes/fcn_r18b-d8_512x1024_80k_cityscapes-20201225_230143.log.json)                     |
+| FCN        | R-50b-D8   | 512x1024  |   80000 | 5.6      | 4.20           | V100     | 75.65 |         77.59 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn_r50b-d8_4xb2-80k_cityscapes-512x1024.py)      | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50b-d8_512x1024_80k_cityscapes/fcn_r50b-d8_512x1024_80k_cityscapes_20201225_094221-82957416.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50b-d8_512x1024_80k_cityscapes/fcn_r50b-d8_512x1024_80k_cityscapes-20201225_094221.log.json)                     |
+| FCN        | R-101b-D8  | 512x1024  |   80000 | 9.1      | 2.73           | V100     | 77.37 |         78.77 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn_r101b-d8_4xb2-80k_cityscapes-512x1024.py)     | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101b-d8_512x1024_80k_cityscapes/fcn_r101b-d8_512x1024_80k_cityscapes_20201226_160213-4543858f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101b-d8_512x1024_80k_cityscapes/fcn_r101b-d8_512x1024_80k_cityscapes-20201226_160213.log.json)                 |
+| FCN        | R-18b-D8   | 769x769   |   80000 | 1.7      | 6.70           | V100     | 69.66 |         72.07 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn_r18b-d8_4xb2-80k_cityscapes-769x769.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18b-d8_769x769_80k_cityscapes/fcn_r18b-d8_769x769_80k_cityscapes_20201226_004430-32d504e5.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18b-d8_769x769_80k_cityscapes/fcn_r18b-d8_769x769_80k_cityscapes-20201226_004430.log.json)                         |
+| FCN        | R-50b-D8   | 769x769   |   80000 | 6.3      | 1.82           | V100     | 73.83 |         76.60 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn_r50b-d8_4xb2-80k_cityscapes-769x769.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50b-d8_769x769_80k_cityscapes/fcn_r50b-d8_769x769_80k_cityscapes_20201225_094223-94552d38.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50b-d8_769x769_80k_cityscapes/fcn_r50b-d8_769x769_80k_cityscapes-20201225_094223.log.json)                         |
+| FCN        | R-101b-D8  | 769x769   |   80000 | 10.3     | 1.15           | V100     | 77.02 |         78.67 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn_r101b-d8_4xb2-80k_cityscapes-769x769.py)      | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101b-d8_769x769_80k_cityscapes/fcn_r101b-d8_769x769_80k_cityscapes_20201226_170012-82be37e2.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101b-d8_769x769_80k_cityscapes/fcn_r101b-d8_769x769_80k_cityscapes-20201226_170012.log.json)                     |
+| FCN (D6)   | R-50-D16   | 512x1024  |   40000 | 3.4      | 10.22          | TITAN Xp | 77.06 |         78.85 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn-d6_r50-d16_4xb2-40k_cityscapes-512x1024.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_512x1024_40k_cityscapes/fcn_d6_r50-d16_512x1024_40k_cityscapes_20210305_130133-98d5d1bc.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_512x1024_40k_cityscapes/fcn_d6_r50-d16_512x1024_40k_cityscapes-20210305_130133.log.json)         |
+| FCN (D6)   | R-50-D16   | 512x1024  |   80000 | -        | 10.35          | TITAN Xp | 77.27 |         78.88 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn-d6_r50-d16_4xb2-80k_cityscapes-512x1024.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_512x1024_80k_cityscapes/fcn_d6_r50-d16_512x1024_80k_cityscapes_20210306_115604-133c292f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_512x1024_80k_cityscapes/fcn_d6_r50-d16_512x1024_80k_cityscapes-20210306_115604.log.json)         |
+| FCN (D6)   | R-50-D16   | 769x769   |   40000 | 3.7      | 4.17           | TITAN Xp | 76.82 |         78.22 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn-d6_r50-d16_4xb2-40k_cityscapes-769x769.py)    | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_769x769_40k_cityscapes/fcn_d6_r50-d16_769x769_40k_cityscapes_20210305_185744-1aab18ed.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_769x769_40k_cityscapes/fcn_d6_r50-d16_769x769_40k_cityscapes-20210305_185744.log.json)             |
+| FCN (D6)   | R-50-D16   | 769x769   |   80000 | -        | 4.15           | TITAN Xp | 77.04 |         78.40 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn-d6_r50-d16_4xb2-80k_cityscapes-769x769.py)    | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_769x769_80k_cityscapes/fcn_d6_r50-d16_769x769_80k_cityscapes_20210305_200413-109d88eb.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_769x769_80k_cityscapes/fcn_d6_r50-d16_769x769_80k_cityscapes-20210305_200413.log.json)             |
+| FCN (D6)   | R-101-D16  | 512x1024  |   40000 | 4.5      | 8.04           | TITAN Xp | 77.36 |         79.18 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn-d6_r101-d16_4xb2-40k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_512x1024_40k_cityscapes/fcn_d6_r101-d16_512x1024_40k_cityscapes_20210305_130337-9cf2b450.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_512x1024_40k_cityscapes/fcn_d6_r101-d16_512x1024_40k_cityscapes-20210305_130337.log.json)     |
+| FCN (D6)   | R-101-D16  | 512x1024  |   80000 | -        | 8.26           | TITAN Xp | 78.46 |         80.42 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn-d6_r101-d16_4xb2-80k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_512x1024_80k_cityscapes/fcn_d6_r101-d16_512x1024_80k_cityscapes_20210308_102747-cb336445.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_512x1024_80k_cityscapes/fcn_d6_r101-d16_512x1024_80k_cityscapes-20210308_102747.log.json)     |
+| FCN (D6)   | R-101-D16  | 769x769   |   40000 | 5.0      | 3.12           | TITAN Xp | 77.28 |         78.95 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn-d6_r101-d16_4xb2-40k_cityscapes-769x769.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_40k_cityscapes/fcn_d6_r101-d16_769x769_40k_cityscapes_20210308_102453-60b114e9.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_40k_cityscapes/fcn_d6_r101-d16_769x769_40k_cityscapes-20210308_102453.log.json)         |
+| FCN (D6)   | R-101-D16  | 769x769   |   80000 | -        | 3.21           | TITAN Xp | 78.06 |         79.58 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn-d6_r101-d16_4xb2-80k_cityscapes-769x769.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_80k_cityscapes/fcn_d6_r101-d16_769x769_80k_cityscapes_20210306_120016-e33adc4f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_80k_cityscapes/fcn_d6_r101-d16_769x769_80k_cityscapes-20210306_120016.log.json)         |
+| FCN (D6)   | R-50b-D16  | 512x1024  |   80000 | 3.2      | 10.16          | TITAN Xp | 76.99 |         79.03 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn-d6_r50b-d16_4xb2-80k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50b-d16_512x1024_80k_cityscapes/fcn_d6_r50b-d16_512x1024_80k_cityscapes_20210311_125550-6a0b62e9.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50b_d16_512x1024_80k_cityscapes/fcn_d6_r50b_d16_512x1024_80k_cityscapes-20210311_125550.log.json)     |
+| FCN (D6)   | R-50b-D16  | 769x769   |   80000 | 3.6      | 4.17           | TITAN Xp | 76.86 |         78.52 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn-d6_r50b-d16_4xb2-80k_cityscapes-769x769.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50b-d16_769x769_80k_cityscapes/fcn_d6_r50b-d16_769x769_80k_cityscapes_20210311_131012-d665f231.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50b_d16_769x769_80k_cityscapes/fcn_d6_r50b_d16_769x769_80k_cityscapes-20210311_131012.log.json)         |
+| FCN (D6)   | R-101b-D16 | 512x1024  |   80000 | 4.3      | 8.46           | TITAN Xp | 77.72 |         79.53 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn-d6_r101b-d16_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101b-d16_512x1024_80k_cityscapes/fcn_d6_r101b-d16_512x1024_80k_cityscapes_20210311_144305-3f2eb5b4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101b_d16_512x1024_80k_cityscapes/fcn_d6_r101b_d16_512x1024_80k_cityscapes-20210311_144305.log.json) |
+| FCN (D6)   | R-101b-D16 | 769x769   |   80000 | 4.8      | 3.32           | TITAN Xp | 77.34 |         78.91 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn-d6_r101b-d16_4xb2-80k_cityscapes-769x769.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101b-d16_769x769_80k_cityscapes/fcn_d6_r101b-d16_769x769_80k_cityscapes_20210311_154527-c4d8bfbc.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101b_d16_769x769_80k_cityscapes/fcn_d6_r101b_d16_769x769_80k_cityscapes-20210311_154527.log.json)     |
 
 ### ADE20K
 
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                  | download                                                                                                                                                                                                                                                                                                               |
-| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| FCN    | R-50-D8  | 512x512   |   80000 | 8.5      | 23.49          | 35.94 |         37.94 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn_r50-d8_4xb4-80k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_80k_ade20k/fcn_r50-d8_512x512_80k_ade20k_20200614_144016-f8ac5082.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_80k_ade20k/fcn_r50-d8_512x512_80k_ade20k_20200614_144016.log.json)         |
-| FCN    | R-101-D8 | 512x512   |   80000 | 12       | 14.78          | 39.61 |         40.83 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn_r101-d8_4xb4-80k_ade20k-512x512.pyy) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_80k_ade20k/fcn_r101-d8_512x512_80k_ade20k_20200615_014143-bc1809f7.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_80k_ade20k/fcn_r101-d8_512x512_80k_ade20k_20200615_014143.log.json)     |
-| FCN    | R-50-D8  | 512x512   |  160000 | -        | -              | 36.10 |         38.08 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn_r50-d8_4xb4-160k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_160k_ade20k/fcn_r50-d8_512x512_160k_ade20k_20200615_100713-4edbc3b4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_160k_ade20k/fcn_r50-d8_512x512_160k_ade20k_20200615_100713.log.json)     |
-| FCN    | R-101-D8 | 512x512   |  160000 | -        | -              | 39.91 |         41.40 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn_r101-d8_4xb4-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_160k_ade20k/fcn_r101-d8_512x512_160k_ade20k_20200615_105816-fd192bd5.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_160k_ade20k/fcn_r101-d8_512x512_160k_ade20k_20200615_105816.log.json) |
+| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                  | download                                                                                                                                                                                                                                                                                                               |
+| ------ | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| FCN    | R-50-D8  | 512x512   |   80000 | 8.5      | 23.49          | V100   | 35.94 |         37.94 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn_r50-d8_4xb4-80k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_80k_ade20k/fcn_r50-d8_512x512_80k_ade20k_20200614_144016-f8ac5082.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_80k_ade20k/fcn_r50-d8_512x512_80k_ade20k_20200614_144016.log.json)         |
+| FCN    | R-101-D8 | 512x512   |   80000 | 12       | 14.78          | V100   | 39.61 |         40.83 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn_r101-d8_4xb4-80k_ade20k-512x512.pyy) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_80k_ade20k/fcn_r101-d8_512x512_80k_ade20k_20200615_014143-bc1809f7.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_80k_ade20k/fcn_r101-d8_512x512_80k_ade20k_20200615_014143.log.json)     |
+| FCN    | R-50-D8  | 512x512   |  160000 | -        | -              | V100   | 36.10 |         38.08 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn_r50-d8_4xb4-160k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_160k_ade20k/fcn_r50-d8_512x512_160k_ade20k_20200615_100713-4edbc3b4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_160k_ade20k/fcn_r50-d8_512x512_160k_ade20k_20200615_100713.log.json)     |
+| FCN    | R-101-D8 | 512x512   |  160000 | -        | -              | V100   | 39.91 |         41.40 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn_r101-d8_4xb4-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_160k_ade20k/fcn_r101-d8_512x512_160k_ade20k_20200615_105816-fd192bd5.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_160k_ade20k/fcn_r101-d8_512x512_160k_ade20k_20200615_105816.log.json) |
 
 ### Pascal VOC 2012 + Aug
 
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                   | download                                                                                                                                                                                                                                                                                                                   |
-| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| FCN    | R-50-D8  | 512x512   |   20000 | 5.7      | 23.28          | 67.08 |         69.94 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn_r50-d8_4xb4-20k_voc12aug-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_20k_voc12aug/fcn_r50-d8_512x512_20k_voc12aug_20200617_010715-52dc5306.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_20k_voc12aug/fcn_r50-d8_512x512_20k_voc12aug_20200617_010715.log.json)     |
-| FCN    | R-101-D8 | 512x512   |   20000 | 9.2      | 14.81          | 71.16 |         73.57 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn_r101-d8_4xb4-20k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_20k_voc12aug/fcn_r101-d8_512x512_20k_voc12aug_20200617_010842-0bb4e798.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_20k_voc12aug/fcn_r101-d8_512x512_20k_voc12aug_20200617_010842.log.json) |
-| FCN    | R-50-D8  | 512x512   |   40000 | -        | -              | 66.97 |         69.04 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn_r50-d8_4xb4-40k_voc12aug-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_40k_voc12aug/fcn_r50-d8_512x512_40k_voc12aug_20200613_161222-5e2dbf40.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_40k_voc12aug/fcn_r50-d8_512x512_40k_voc12aug_20200613_161222.log.json)     |
-| FCN    | R-101-D8 | 512x512   |   40000 | -        | -              | 69.91 |         72.38 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn_r101-d8_4xb4-40k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_40k_voc12aug/fcn_r101-d8_512x512_40k_voc12aug_20200613_161240-4c8bcefd.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_40k_voc12aug/fcn_r101-d8_512x512_40k_voc12aug_20200613_161240.log.json) |
+| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                   | download                                                                                                                                                                                                                                                                                                                   |
+| ------ | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| FCN    | R-50-D8  | 512x512   |   20000 | 5.7      | 23.28          | V100   | 67.08 |         69.94 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn_r50-d8_4xb4-20k_voc12aug-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_20k_voc12aug/fcn_r50-d8_512x512_20k_voc12aug_20200617_010715-52dc5306.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_20k_voc12aug/fcn_r50-d8_512x512_20k_voc12aug_20200617_010715.log.json)     |
+| FCN    | R-101-D8 | 512x512   |   20000 | 9.2      | 14.81          | V100   | 71.16 |         73.57 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn_r101-d8_4xb4-20k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_20k_voc12aug/fcn_r101-d8_512x512_20k_voc12aug_20200617_010842-0bb4e798.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_20k_voc12aug/fcn_r101-d8_512x512_20k_voc12aug_20200617_010842.log.json) |
+| FCN    | R-50-D8  | 512x512   |   40000 | -        | -              | V100   | 66.97 |         69.04 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn_r50-d8_4xb4-40k_voc12aug-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_40k_voc12aug/fcn_r50-d8_512x512_40k_voc12aug_20200613_161222-5e2dbf40.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_40k_voc12aug/fcn_r50-d8_512x512_40k_voc12aug_20200613_161222.log.json)     |
+| FCN    | R-101-D8 | 512x512   |   40000 | -        | -              | V100   | 69.91 |         72.38 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn_r101-d8_4xb4-40k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_40k_voc12aug/fcn_r101-d8_512x512_40k_voc12aug_20200613_161240-4c8bcefd.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_40k_voc12aug/fcn_r101-d8_512x512_40k_voc12aug_20200613_161240.log.json) |
 
 ### Pascal Context
 
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                         | download                                                                                                                                                                                                                                                                                                                                           |
-| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| FCN    | R-101-D8 | 480x480   |   40000 | -        | 9.93           | 44.43 |         45.63 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn_r101-d8_4xb4-40k_pascal-context-480x480.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_40k_pascal_context/fcn_r101-d8_480x480_40k_pascal_context_20210421_154757-b5e97937.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_40k_pascal_context/fcn_r101-d8_480x480_40k_pascal_context-20210421_154757.log.json) |
-| FCN    | R-101-D8 | 480x480   |   80000 | -        | -              | 44.13 |         45.26 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn_r101-d8_4xb4-80k_pascal-context-480x480.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_80k_pascal_context/fcn_r101-d8_480x480_80k_pascal_context_20210421_163310-4711813f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_80k_pascal_context/fcn_r101-d8_480x480_80k_pascal_context-20210421_163310.log.json) |
+| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                         | download                                                                                                                                                                                                                                                                                                                                           |
+| ------ | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| FCN    | R-101-D8 | 480x480   |   40000 | -        | 9.93           | V100   | 44.43 |         45.63 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn_r101-d8_4xb4-40k_pascal-context-480x480.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_40k_pascal_context/fcn_r101-d8_480x480_40k_pascal_context_20210421_154757-b5e97937.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_40k_pascal_context/fcn_r101-d8_480x480_40k_pascal_context-20210421_154757.log.json) |
+| FCN    | R-101-D8 | 480x480   |   80000 | -        | -              | V100   | 44.13 |         45.26 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn_r101-d8_4xb4-80k_pascal-context-480x480.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_80k_pascal_context/fcn_r101-d8_480x480_80k_pascal_context_20210421_163310-4711813f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_80k_pascal_context/fcn_r101-d8_480x480_80k_pascal_context-20210421_163310.log.json) |
 
 ### Pascal Context 59
 
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                            | download                                                                                                                                                                                                                                                                                                                                                       |
-| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | --------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| FCN    | R-101-D8 | 480x480   |   40000 | -        | -              | 48.42 |          50.4 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn_r101-d8_4xb4-40k_pascal-context-59-480x480.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_40k_pascal_context_59/fcn_r101-d8_480x480_40k_pascal_context_59_20210415_230724-8cf83682.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_40k_pascal_context_59/fcn_r101-d8_480x480_40k_pascal_context_59-20210415_230724.log.json) |
-| FCN    | R-101-D8 | 480x480   |   80000 | -        | -              | 49.35 |         51.38 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn_r101-d8_4xb4-80k_pascal-context-59-480x480.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_80k_pascal_context_59/fcn_r101-d8_480x480_80k_pascal_context_59_20210416_110804-9a6f2c94.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_80k_pascal_context_59/fcn_r101-d8_480x480_80k_pascal_context_59-20210416_110804.log.json) |
+| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                            | download                                                                                                                                                                                                                                                                                                                                                       |
+| ------ | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | --------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| FCN    | R-101-D8 | 480x480   |   40000 | -        | -              | V100   | 48.42 |          50.4 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn_r101-d8_4xb4-40k_pascal-context-59-480x480.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_40k_pascal_context_59/fcn_r101-d8_480x480_40k_pascal_context_59_20210415_230724-8cf83682.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_40k_pascal_context_59/fcn_r101-d8_480x480_40k_pascal_context_59-20210415_230724.log.json) |
+| FCN    | R-101-D8 | 480x480   |   80000 | -        | -              | V100   | 49.35 |         51.38 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn_r101-d8_4xb4-80k_pascal-context-59-480x480.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_80k_pascal_context_59/fcn_r101-d8_480x480_80k_pascal_context_59_20210416_110804-9a6f2c94.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_80k_pascal_context_59/fcn_r101-d8_480x480_80k_pascal_context_59-20210416_110804.log.json) |
 
 Note:
 
 - `FP16` means Mixed Precision (FP16) is adopted in training.
 - `FCN D6` means dilation rate of convolution operator in FCN is 6.
+
+## Citation
+
+```bibtex
+@article{shelhamer2017fully,
+  title={Fully convolutional networks for semantic segmentation},
+  author={Shelhamer, Evan and Long, Jonathan and Darrell, Trevor},
+  journal={IEEE transactions on pattern analysis and machine intelligence},
+  volume={39},
+  number={4},
+  pages={640--651},
+  year={2017},
+  publisher={IEEE Trans Pattern Anal Mach Intell}
+}
+```
diff --git a/configs/fcn/fcn.yml b/configs/fcn/fcn.yml
deleted file mode 100644
index 71c4b2d122..0000000000
--- a/configs/fcn/fcn.yml
+++ /dev/null
@@ -1,827 +0,0 @@
-Collections:
-- Name: FCN
-  Metadata:
-    Training Data:
-    - Cityscapes
-    - ADE20K
-    - Pascal VOC 2012 + Aug
-    - Pascal Context
-    - Pascal Context 59
-  Paper:
-    URL: https://arxiv.org/abs/1411.4038
-    Title: Fully Convolutional Networks for Semantic Segmentation
-  README: configs/fcn/README.md
-  Code:
-    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11
-    Version: v0.17.0
-  Converted From:
-    Code: https://github.com/BVLC/caffe/wiki/Model-Zoo#fcn
-Models:
-- Name: fcn_r50-d8_4xb2-40k_cityscapes-512x1024
-  In Collection: FCN
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,1024)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 239.81
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 5.7
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 72.25
-      mIoU(ms+flip): 73.36
-  Config: configs/fcn/fcn_r50-d8_4xb2-40k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x1024_40k_cityscapes/fcn_r50-d8_512x1024_40k_cityscapes_20200604_192608-efe53f0d.pth
-- Name: fcn_r101-d8_4xb2-40k_cityscapes-512x1024
-  In Collection: FCN
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,1024)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 375.94
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 9.2
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 75.45
-      mIoU(ms+flip): 76.58
-  Config: configs/fcn/fcn_r101-d8_4xb2-40k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x1024_40k_cityscapes/fcn_r101-d8_512x1024_40k_cityscapes_20200604_181852-a883d3a1.pth
-- Name: fcn_r50-d8_4xb2-40k_cityscapes-769x769
-  In Collection: FCN
-  Metadata:
-    backbone: R-50-D8
-    crop size: (769,769)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 555.56
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (769,769)
-    Training Memory (GB): 6.5
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 71.47
-      mIoU(ms+flip): 72.54
-  Config: configs/fcn/fcn_r50-d8_4xb2-40k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_769x769_40k_cityscapes/fcn_r50-d8_769x769_40k_cityscapes_20200606_113104-977b5d02.pth
-- Name: fcn_r101-d8_4xb2-40k_cityscapes-769x769
-  In Collection: FCN
-  Metadata:
-    backbone: R-101-D8
-    crop size: (769,769)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 840.34
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (769,769)
-    Training Memory (GB): 10.4
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 73.93
-      mIoU(ms+flip): 75.14
-  Config: configs/fcn/fcn_r101-d8_4xb2-40k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_769x769_40k_cityscapes/fcn_r101-d8_769x769_40k_cityscapes_20200606_113208-7d4ab69c.pth
-- Name: fcn_r18-d8_4xb2-80k_cityscapes-512x1024
-  In Collection: FCN
-  Metadata:
-    backbone: R-18-D8
-    crop size: (512,1024)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 68.26
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 1.7
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 71.11
-      mIoU(ms+flip): 72.91
-  Config: configs/fcn/fcn_r18-d8_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18-d8_512x1024_80k_cityscapes/fcn_r18-d8_512x1024_80k_cityscapes_20201225_021327-6c50f8b4.pth
-- Name: fcn_r50-d8_4xb2-80k_cityscapes-512x1024
-  In Collection: FCN
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,1024)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 73.61
-      mIoU(ms+flip): 74.24
-  Config: configs/fcn/fcn_r50-d8_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x1024_80k_cityscapes/fcn_r50-d8_512x1024_80k_cityscapes_20200606_113019-03aa804d.pth
-- Name: fcn_r101-d8_4xb2-80k_cityscapes-512x1024
-  In Collection: FCN
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,1024)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 75.13
-      mIoU(ms+flip): 75.94
-  Config: configs/fcn/fcn_r101-d8_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x1024_80k_cityscapes/fcn_r101-d8_512x1024_80k_cityscapes_20200606_113038-3fb937eb.pth
-- Name: fcn_r101-d8_4xb2-amp-80k_cityscapes-512x1024
-  In Collection: FCN
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,1024)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 115.74
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: AMP
-      resolution: (512,1024)
-    Training Memory (GB): 5.37
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 76.8
-  Config: configs/fcn/fcn_r101-d8_4xb2-amp-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_fp16_512x1024_80k_cityscapes/fcn_r101-d8_fp16_512x1024_80k_cityscapes_20200717_230921-fb13e883.pth
-- Name: fcn_r18-d8_4xb2-80k_cityscapes-769x769
-  In Collection: FCN
-  Metadata:
-    backbone: R-18-D8
-    crop size: (769,769)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 156.25
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (769,769)
-    Training Memory (GB): 1.9
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 70.8
-      mIoU(ms+flip): 73.16
-  Config: configs/fcn/fcn_r18-d8_4xb2-80k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18-d8_769x769_80k_cityscapes/fcn_r18-d8_769x769_80k_cityscapes_20201225_021451-9739d1b8.pth
-- Name: fcn_r50-d8_4xb2-80k_cityscapes-769x769
-  In Collection: FCN
-  Metadata:
-    backbone: R-50-D8
-    crop size: (769,769)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 72.64
-      mIoU(ms+flip): 73.32
-  Config: configs/fcn/fcn_r50-d8_4xb2-80k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_769x769_80k_cityscapes/fcn_r50-d8_769x769_80k_cityscapes_20200606_195749-f5caeabc.pth
-- Name: fcn_r101-d8_4xb2-80k_cityscapes-769x769
-  In Collection: FCN
-  Metadata:
-    backbone: R-101-D8
-    crop size: (769,769)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 75.52
-      mIoU(ms+flip): 76.61
-  Config: configs/fcn/fcn_r101-d8_4xb2-80k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_769x769_80k_cityscapes/fcn_r101-d8_769x769_80k_cityscapes_20200606_214354-45cbac68.pth
-- Name: fcn_r18b-d8_4xb2-80k_cityscapes-512x1024
-  In Collection: FCN
-  Metadata:
-    backbone: R-18b-D8
-    crop size: (512,1024)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 59.74
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 1.6
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 70.24
-      mIoU(ms+flip): 72.77
-  Config: configs/fcn/fcn_r18b-d8_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18b-d8_512x1024_80k_cityscapes/fcn_r18b-d8_512x1024_80k_cityscapes_20201225_230143-92c0f445.pth
-- Name: fcn_r50b-d8_4xb2-80k_cityscapes-512x1024
-  In Collection: FCN
-  Metadata:
-    backbone: R-50b-D8
-    crop size: (512,1024)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 238.1
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 5.6
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 75.65
-      mIoU(ms+flip): 77.59
-  Config: configs/fcn/fcn_r50b-d8_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50b-d8_512x1024_80k_cityscapes/fcn_r50b-d8_512x1024_80k_cityscapes_20201225_094221-82957416.pth
-- Name: fcn_r101b-d8_4xb2-80k_cityscapes-512x1024
-  In Collection: FCN
-  Metadata:
-    backbone: R-101b-D8
-    crop size: (512,1024)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 366.3
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 9.1
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 77.37
-      mIoU(ms+flip): 78.77
-  Config: configs/fcn/fcn_r101b-d8_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101b-d8_512x1024_80k_cityscapes/fcn_r101b-d8_512x1024_80k_cityscapes_20201226_160213-4543858f.pth
-- Name: fcn_r18b-d8_4xb2-80k_cityscapes-769x769
-  In Collection: FCN
-  Metadata:
-    backbone: R-18b-D8
-    crop size: (769,769)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 149.25
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (769,769)
-    Training Memory (GB): 1.7
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 69.66
-      mIoU(ms+flip): 72.07
-  Config: configs/fcn/fcn_r18b-d8_4xb2-80k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18b-d8_769x769_80k_cityscapes/fcn_r18b-d8_769x769_80k_cityscapes_20201226_004430-32d504e5.pth
-- Name: fcn_r50b-d8_4xb2-80k_cityscapes-769x769
-  In Collection: FCN
-  Metadata:
-    backbone: R-50b-D8
-    crop size: (769,769)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 549.45
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (769,769)
-    Training Memory (GB): 6.3
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 73.83
-      mIoU(ms+flip): 76.6
-  Config: configs/fcn/fcn_r50b-d8_4xb2-80k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50b-d8_769x769_80k_cityscapes/fcn_r50b-d8_769x769_80k_cityscapes_20201225_094223-94552d38.pth
-- Name: fcn_r101b-d8_4xb2-80k_cityscapes-769x769
-  In Collection: FCN
-  Metadata:
-    backbone: R-101b-D8
-    crop size: (769,769)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 869.57
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (769,769)
-    Training Memory (GB): 10.3
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 77.02
-      mIoU(ms+flip): 78.67
-  Config: configs/fcn/fcn_r101b-d8_4xb2-80k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101b-d8_769x769_80k_cityscapes/fcn_r101b-d8_769x769_80k_cityscapes_20201226_170012-82be37e2.pth
-- Name: fcn-d6_r50-d16_4xb2-40k_cityscapes-512x1024
-  In Collection: FCN
-  Metadata:
-    backbone: R-50-D16
-    crop size: (512,1024)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 97.85
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 3.4
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 77.06
-      mIoU(ms+flip): 78.85
-  Config: configs/fcn/fcn-d6_r50-d16_4xb2-40k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_512x1024_40k_cityscapes/fcn_d6_r50-d16_512x1024_40k_cityscapes_20210305_130133-98d5d1bc.pth
-- Name: fcn-d6_r50-d16_4xb2-80k_cityscapes-512x1024
-  In Collection: FCN
-  Metadata:
-    backbone: R-50-D16
-    crop size: (512,1024)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 96.62
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 77.27
-      mIoU(ms+flip): 78.88
-  Config: configs/fcn/fcn-d6_r50-d16_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_512x1024_80k_cityscapes/fcn_d6_r50-d16_512x1024_80k_cityscapes_20210306_115604-133c292f.pth
-- Name: fcn-d6_r50-d16_4xb2-40k_cityscapes-769x769
-  In Collection: FCN
-  Metadata:
-    backbone: R-50-D16
-    crop size: (769,769)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 239.81
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (769,769)
-    Training Memory (GB): 3.7
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 76.82
-      mIoU(ms+flip): 78.22
-  Config: configs/fcn/fcn-d6_r50-d16_4xb2-40k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_769x769_40k_cityscapes/fcn_d6_r50-d16_769x769_40k_cityscapes_20210305_185744-1aab18ed.pth
-- Name: fcn-d6_r50-d16_4xb2-80k_cityscapes-769x769
-  In Collection: FCN
-  Metadata:
-    backbone: R-50-D16
-    crop size: (769,769)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 240.96
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (769,769)
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 77.04
-      mIoU(ms+flip): 78.4
-  Config: configs/fcn/fcn-d6_r50-d16_4xb2-80k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_769x769_80k_cityscapes/fcn_d6_r50-d16_769x769_80k_cityscapes_20210305_200413-109d88eb.pth
-- Name: fcn-d6_r101-d16_4xb2-40k_cityscapes-512x1024
-  In Collection: FCN
-  Metadata:
-    backbone: R-101-D16
-    crop size: (512,1024)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 124.38
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 4.5
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 77.36
-      mIoU(ms+flip): 79.18
-  Config: configs/fcn/fcn-d6_r101-d16_4xb2-40k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_512x1024_40k_cityscapes/fcn_d6_r101-d16_512x1024_40k_cityscapes_20210305_130337-9cf2b450.pth
-- Name: fcn-d6_r101-d16_4xb2-80k_cityscapes-512x1024
-  In Collection: FCN
-  Metadata:
-    backbone: R-101-D16
-    crop size: (512,1024)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 121.07
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 78.46
-      mIoU(ms+flip): 80.42
-  Config: configs/fcn/fcn-d6_r101-d16_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_512x1024_80k_cityscapes/fcn_d6_r101-d16_512x1024_80k_cityscapes_20210308_102747-cb336445.pth
-- Name: fcn-d6_r101-d16_4xb2-40k_cityscapes-769x769
-  In Collection: FCN
-  Metadata:
-    backbone: R-101-D16
-    crop size: (769,769)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 320.51
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (769,769)
-    Training Memory (GB): 5.0
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 77.28
-      mIoU(ms+flip): 78.95
-  Config: configs/fcn/fcn-d6_r101-d16_4xb2-40k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_40k_cityscapes/fcn_d6_r101-d16_769x769_40k_cityscapes_20210308_102453-60b114e9.pth
-- Name: fcn-d6_r101-d16_4xb2-80k_cityscapes-769x769
-  In Collection: FCN
-  Metadata:
-    backbone: R-101-D16
-    crop size: (769,769)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 311.53
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (769,769)
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 78.06
-      mIoU(ms+flip): 79.58
-  Config: configs/fcn/fcn-d6_r101-d16_4xb2-80k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_80k_cityscapes/fcn_d6_r101-d16_769x769_80k_cityscapes_20210306_120016-e33adc4f.pth
-- Name: fcn-d6_r50b-d16_4xb2-80k_cityscapes-512x1024
-  In Collection: FCN
-  Metadata:
-    backbone: R-50b-D16
-    crop size: (512,1024)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 98.43
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 3.2
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 76.99
-      mIoU(ms+flip): 79.03
-  Config: configs/fcn/fcn-d6_r50b-d16_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50b-d16_512x1024_80k_cityscapes/fcn_d6_r50b-d16_512x1024_80k_cityscapes_20210311_125550-6a0b62e9.pth
-- Name: fcn-d6_r50b-d16_4xb2-80k_cityscapes-769x769
-  In Collection: FCN
-  Metadata:
-    backbone: R-50b-D16
-    crop size: (769,769)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 239.81
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (769,769)
-    Training Memory (GB): 3.6
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 76.86
-      mIoU(ms+flip): 78.52
-  Config: configs/fcn/fcn-d6_r50b-d16_4xb2-80k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50b-d16_769x769_80k_cityscapes/fcn_d6_r50b-d16_769x769_80k_cityscapes_20210311_131012-d665f231.pth
-- Name: fcn-d6_r101b-d16_4xb2-80k_cityscapes-512x1024
-  In Collection: FCN
-  Metadata:
-    backbone: R-101b-D16
-    crop size: (512,1024)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 118.2
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 4.3
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 77.72
-      mIoU(ms+flip): 79.53
-  Config: configs/fcn/fcn-d6_r101b-d16_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101b-d16_512x1024_80k_cityscapes/fcn_d6_r101b-d16_512x1024_80k_cityscapes_20210311_144305-3f2eb5b4.pth
-- Name: fcn-d6_r101b-d16_4xb2-80k_cityscapes-769x769
-  In Collection: FCN
-  Metadata:
-    backbone: R-101b-D16
-    crop size: (769,769)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 301.2
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (769,769)
-    Training Memory (GB): 4.8
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 77.34
-      mIoU(ms+flip): 78.91
-  Config: configs/fcn/fcn-d6_r101b-d16_4xb2-80k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101b-d16_769x769_80k_cityscapes/fcn_d6_r101b-d16_769x769_80k_cityscapes_20210311_154527-c4d8bfbc.pth
-- Name: fcn_r50-d8_4xb4-80k_ade20k-512x512
-  In Collection: FCN
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 42.57
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 8.5
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 35.94
-      mIoU(ms+flip): 37.94
-  Config: configs/fcn/fcn_r50-d8_4xb4-80k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_80k_ade20k/fcn_r50-d8_512x512_80k_ade20k_20200614_144016-f8ac5082.pth
-- Name: fcn_r101-d8_4xb4-80k_ade20k-512x512
-  In Collection: FCN
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 67.66
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 12.0
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 39.61
-      mIoU(ms+flip): 40.83
-  Config: configs/fcn/fcn_r101-d8_4xb4-80k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_80k_ade20k/fcn_r101-d8_512x512_80k_ade20k_20200615_014143-bc1809f7.pth
-- Name: fcn_r50-d8_4xb4-160k_ade20k-512x512
-  In Collection: FCN
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 160000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 36.1
-      mIoU(ms+flip): 38.08
-  Config: configs/fcn/fcn_r50-d8_4xb4-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_160k_ade20k/fcn_r50-d8_512x512_160k_ade20k_20200615_100713-4edbc3b4.pth
-- Name: fcn_r101-d8_4xb4-160k_ade20k-512x512
-  In Collection: FCN
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 160000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 39.91
-      mIoU(ms+flip): 41.4
-  Config: configs/fcn/fcn_r101-d8_4xb4-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_160k_ade20k/fcn_r101-d8_512x512_160k_ade20k_20200615_105816-fd192bd5.pth
-- Name: fcn_r50-d8_4xb4-20k_voc12aug-512x512
-  In Collection: FCN
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 20000
-    inference time (ms/im):
-    - value: 42.96
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 5.7
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal VOC 2012 + Aug
-    Metrics:
-      mIoU: 67.08
-      mIoU(ms+flip): 69.94
-  Config: configs/fcn/fcn_r50-d8_4xb4-20k_voc12aug-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_20k_voc12aug/fcn_r50-d8_512x512_20k_voc12aug_20200617_010715-52dc5306.pth
-- Name: fcn_r101-d8_4xb4-20k_voc12aug-512x512
-  In Collection: FCN
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 20000
-    inference time (ms/im):
-    - value: 67.52
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 9.2
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal VOC 2012 + Aug
-    Metrics:
-      mIoU: 71.16
-      mIoU(ms+flip): 73.57
-  Config: configs/fcn/fcn_r101-d8_4xb4-20k_voc12aug-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_20k_voc12aug/fcn_r101-d8_512x512_20k_voc12aug_20200617_010842-0bb4e798.pth
-- Name: fcn_r50-d8_4xb4-40k_voc12aug-512x512
-  In Collection: FCN
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 40000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal VOC 2012 + Aug
-    Metrics:
-      mIoU: 66.97
-      mIoU(ms+flip): 69.04
-  Config: configs/fcn/fcn_r50-d8_4xb4-40k_voc12aug-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_40k_voc12aug/fcn_r50-d8_512x512_40k_voc12aug_20200613_161222-5e2dbf40.pth
-- Name: fcn_r101-d8_4xb4-40k_voc12aug-512x512
-  In Collection: FCN
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 40000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal VOC 2012 + Aug
-    Metrics:
-      mIoU: 69.91
-      mIoU(ms+flip): 72.38
-  Config: configs/fcn/fcn_r101-d8_4xb4-40k_voc12aug-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_40k_voc12aug/fcn_r101-d8_512x512_40k_voc12aug_20200613_161240-4c8bcefd.pth
-- Name: fcn_r101-d8_4xb4-40k_pascal-context-480x480
-  In Collection: FCN
-  Metadata:
-    backbone: R-101-D8
-    crop size: (480,480)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 100.7
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (480,480)
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal Context
-    Metrics:
-      mIoU: 44.43
-      mIoU(ms+flip): 45.63
-  Config: configs/fcn/fcn_r101-d8_4xb4-40k_pascal-context-480x480.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_40k_pascal_context/fcn_r101-d8_480x480_40k_pascal_context_20210421_154757-b5e97937.pth
-- Name: fcn_r101-d8_4xb4-80k_pascal-context-480x480
-  In Collection: FCN
-  Metadata:
-    backbone: R-101-D8
-    crop size: (480,480)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal Context
-    Metrics:
-      mIoU: 44.13
-      mIoU(ms+flip): 45.26
-  Config: configs/fcn/fcn_r101-d8_4xb4-80k_pascal-context-480x480.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_80k_pascal_context/fcn_r101-d8_480x480_80k_pascal_context_20210421_163310-4711813f.pth
-- Name: fcn_r101-d8_4xb4-40k_pascal-context-59-480x480
-  In Collection: FCN
-  Metadata:
-    backbone: R-101-D8
-    crop size: (480,480)
-    lr schd: 40000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal Context 59
-    Metrics:
-      mIoU: 48.42
-      mIoU(ms+flip): 50.4
-  Config: configs/fcn/fcn_r101-d8_4xb4-40k_pascal-context-59-480x480.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_40k_pascal_context_59/fcn_r101-d8_480x480_40k_pascal_context_59_20210415_230724-8cf83682.pth
-- Name: fcn_r101-d8_4xb4-80k_pascal-context-59-480x480
-  In Collection: FCN
-  Metadata:
-    backbone: R-101-D8
-    crop size: (480,480)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal Context 59
-    Metrics:
-      mIoU: 49.35
-      mIoU(ms+flip): 51.38
-  Config: configs/fcn/fcn_r101-d8_4xb4-80k_pascal-context-59-480x480.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_80k_pascal_context_59/fcn_r101-d8_480x480_80k_pascal_context_59_20210416_110804-9a6f2c94.pth
diff --git a/configs/fcn/metafile.yaml b/configs/fcn/metafile.yaml
new file mode 100644
index 0000000000..f3d80f652e
--- /dev/null
+++ b/configs/fcn/metafile.yaml
@@ -0,0 +1,997 @@
+Collections:
+- Name: FCN
+  License: Apache License 2.0
+  Metadata:
+    Training Data:
+    - Cityscapes
+    - ADE20K
+    - Pascal VOC 2012 + Aug
+    - Pascal Context
+    - Pascal Context 59
+  Paper:
+    Title: Fully Convolutional Networks for Semantic Segmentation
+    URL: https://arxiv.org/abs/1411.4038
+  README: configs/fcn/README.md
+  Frameworks:
+  - PyTorch
+Models:
+- Name: fcn_r50-d8_4xb2-40k_cityscapes-512x1024
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 72.25
+      mIoU(ms+flip): 73.36
+  Config: configs/fcn/fcn_r50-d8_4xb2-40k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - FCN
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 5.7
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x1024_40k_cityscapes/fcn_r50-d8_512x1024_40k_cityscapes_20200604_192608-efe53f0d.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x1024_40k_cityscapes/fcn_r50-d8_512x1024_40k_cityscapes_20200604_192608.log.json
+  Paper:
+    Title: Fully Convolutional Networks for Semantic Segmentation
+    URL: https://arxiv.org/abs/1411.4038
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11
+  Framework: PyTorch
+- Name: fcn_r101-d8_4xb2-40k_cityscapes-512x1024
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 75.45
+      mIoU(ms+flip): 76.58
+  Config: configs/fcn/fcn_r101-d8_4xb2-40k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - FCN
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 9.2
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x1024_40k_cityscapes/fcn_r101-d8_512x1024_40k_cityscapes_20200604_181852-a883d3a1.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x1024_40k_cityscapes/fcn_r101-d8_512x1024_40k_cityscapes_20200604_181852.log.json
+  Paper:
+    Title: Fully Convolutional Networks for Semantic Segmentation
+    URL: https://arxiv.org/abs/1411.4038
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11
+  Framework: PyTorch
+- Name: fcn_r50-d8_4xb2-40k_cityscapes-769x769
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 71.47
+      mIoU(ms+flip): 72.54
+  Config: configs/fcn/fcn_r50-d8_4xb2-40k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - FCN
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 6.5
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_769x769_40k_cityscapes/fcn_r50-d8_769x769_40k_cityscapes_20200606_113104-977b5d02.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_769x769_40k_cityscapes/fcn_r50-d8_769x769_40k_cityscapes_20200606_113104.log.json
+  Paper:
+    Title: Fully Convolutional Networks for Semantic Segmentation
+    URL: https://arxiv.org/abs/1411.4038
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11
+  Framework: PyTorch
+- Name: fcn_r101-d8_4xb2-40k_cityscapes-769x769
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 73.93
+      mIoU(ms+flip): 75.14
+  Config: configs/fcn/fcn_r101-d8_4xb2-40k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - FCN
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 10.4
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_769x769_40k_cityscapes/fcn_r101-d8_769x769_40k_cityscapes_20200606_113208-7d4ab69c.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_769x769_40k_cityscapes/fcn_r101-d8_769x769_40k_cityscapes_20200606_113208.log.json
+  Paper:
+    Title: Fully Convolutional Networks for Semantic Segmentation
+    URL: https://arxiv.org/abs/1411.4038
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11
+  Framework: PyTorch
+- Name: fcn_r18-d8_4xb2-80k_cityscapes-512x1024
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 71.11
+      mIoU(ms+flip): 72.91
+  Config: configs/fcn/fcn_r18-d8_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-18-D8
+    - FCN
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 1.7
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18-d8_512x1024_80k_cityscapes/fcn_r18-d8_512x1024_80k_cityscapes_20201225_021327-6c50f8b4.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18-d8_512x1024_80k_cityscapes/fcn_r18-d8_512x1024_80k_cityscapes-20201225_021327.log.json
+  Paper:
+    Title: Fully Convolutional Networks for Semantic Segmentation
+    URL: https://arxiv.org/abs/1411.4038
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11
+  Framework: PyTorch
+- Name: fcn_r50-d8_4xb2-80k_cityscapes-512x1024
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 73.61
+      mIoU(ms+flip): 74.24
+  Config: configs/fcn/fcn_r50-d8_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - FCN
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x1024_80k_cityscapes/fcn_r50-d8_512x1024_80k_cityscapes_20200606_113019-03aa804d.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x1024_80k_cityscapes/fcn_r50-d8_512x1024_80k_cityscapes_20200606_113019.log.json
+  Paper:
+    Title: Fully Convolutional Networks for Semantic Segmentation
+    URL: https://arxiv.org/abs/1411.4038
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11
+  Framework: PyTorch
+- Name: fcn_r101-d8_4xb2-80k_cityscapes-512x1024
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 75.13
+      mIoU(ms+flip): 75.94
+  Config: configs/fcn/fcn_r101-d8_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - FCN
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x1024_80k_cityscapes/fcn_r101-d8_512x1024_80k_cityscapes_20200606_113038-3fb937eb.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x1024_80k_cityscapes/fcn_r101-d8_512x1024_80k_cityscapes_20200606_113038.log.json
+  Paper:
+    Title: Fully Convolutional Networks for Semantic Segmentation
+    URL: https://arxiv.org/abs/1411.4038
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11
+  Framework: PyTorch
+- Name: fcn_r101-d8_4xb2-amp-80k_cityscapes-512x1024
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 76.8
+  Config: configs/fcn/fcn_r101-d8_4xb2-amp-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - FCN
+    - (FP16)
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 5.37
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_fp16_512x1024_80k_cityscapes/fcn_r101-d8_fp16_512x1024_80k_cityscapes_20200717_230921-fb13e883.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_fp16_512x1024_80k_cityscapes/fcn_r101-d8_fp16_512x1024_80k_cityscapes_20200717_230921.log.json
+  Paper:
+    Title: Fully Convolutional Networks for Semantic Segmentation
+    URL: https://arxiv.org/abs/1411.4038
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11
+  Framework: PyTorch
+- Name: fcn_r18-d8_4xb2-80k_cityscapes-769x769
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 70.8
+      mIoU(ms+flip): 73.16
+  Config: configs/fcn/fcn_r18-d8_4xb2-80k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-18-D8
+    - FCN
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 1.9
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18-d8_769x769_80k_cityscapes/fcn_r18-d8_769x769_80k_cityscapes_20201225_021451-9739d1b8.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18-d8_769x769_80k_cityscapes/fcn_r18-d8_769x769_80k_cityscapes-20201225_021451.log.json
+  Paper:
+    Title: Fully Convolutional Networks for Semantic Segmentation
+    URL: https://arxiv.org/abs/1411.4038
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11
+  Framework: PyTorch
+- Name: fcn_r50-d8_4xb2-80k_cityscapes-769x769
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 72.64
+      mIoU(ms+flip): 73.32
+  Config: configs/fcn/fcn_r50-d8_4xb2-80k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - FCN
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_769x769_80k_cityscapes/fcn_r50-d8_769x769_80k_cityscapes_20200606_195749-f5caeabc.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_769x769_80k_cityscapes/fcn_r50-d8_769x769_80k_cityscapes_20200606_195749.log.json
+  Paper:
+    Title: Fully Convolutional Networks for Semantic Segmentation
+    URL: https://arxiv.org/abs/1411.4038
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11
+  Framework: PyTorch
+- Name: fcn_r101-d8_4xb2-80k_cityscapes-769x769
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 75.52
+      mIoU(ms+flip): 76.61
+  Config: configs/fcn/fcn_r101-d8_4xb2-80k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - FCN
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_769x769_80k_cityscapes/fcn_r101-d8_769x769_80k_cityscapes_20200606_214354-45cbac68.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_769x769_80k_cityscapes/fcn_r101-d8_769x769_80k_cityscapes_20200606_214354.log.json
+  Paper:
+    Title: Fully Convolutional Networks for Semantic Segmentation
+    URL: https://arxiv.org/abs/1411.4038
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11
+  Framework: PyTorch
+- Name: fcn_r18b-d8_4xb2-80k_cityscapes-512x1024
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 70.24
+      mIoU(ms+flip): 72.77
+  Config: configs/fcn/fcn_r18b-d8_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-18b-D8
+    - FCN
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 1.6
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18b-d8_512x1024_80k_cityscapes/fcn_r18b-d8_512x1024_80k_cityscapes_20201225_230143-92c0f445.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18b-d8_512x1024_80k_cityscapes/fcn_r18b-d8_512x1024_80k_cityscapes-20201225_230143.log.json
+  Paper:
+    Title: Fully Convolutional Networks for Semantic Segmentation
+    URL: https://arxiv.org/abs/1411.4038
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11
+  Framework: PyTorch
+- Name: fcn_r50b-d8_4xb2-80k_cityscapes-512x1024
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 75.65
+      mIoU(ms+flip): 77.59
+  Config: configs/fcn/fcn_r50b-d8_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50b-D8
+    - FCN
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 5.6
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50b-d8_512x1024_80k_cityscapes/fcn_r50b-d8_512x1024_80k_cityscapes_20201225_094221-82957416.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50b-d8_512x1024_80k_cityscapes/fcn_r50b-d8_512x1024_80k_cityscapes-20201225_094221.log.json
+  Paper:
+    Title: Fully Convolutional Networks for Semantic Segmentation
+    URL: https://arxiv.org/abs/1411.4038
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11
+  Framework: PyTorch
+- Name: fcn_r101b-d8_4xb2-80k_cityscapes-512x1024
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.37
+      mIoU(ms+flip): 78.77
+  Config: configs/fcn/fcn_r101b-d8_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101b-D8
+    - FCN
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 9.1
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101b-d8_512x1024_80k_cityscapes/fcn_r101b-d8_512x1024_80k_cityscapes_20201226_160213-4543858f.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101b-d8_512x1024_80k_cityscapes/fcn_r101b-d8_512x1024_80k_cityscapes-20201226_160213.log.json
+  Paper:
+    Title: Fully Convolutional Networks for Semantic Segmentation
+    URL: https://arxiv.org/abs/1411.4038
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11
+  Framework: PyTorch
+- Name: fcn_r18b-d8_4xb2-80k_cityscapes-769x769
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 69.66
+      mIoU(ms+flip): 72.07
+  Config: configs/fcn/fcn_r18b-d8_4xb2-80k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-18b-D8
+    - FCN
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 1.7
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18b-d8_769x769_80k_cityscapes/fcn_r18b-d8_769x769_80k_cityscapes_20201226_004430-32d504e5.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18b-d8_769x769_80k_cityscapes/fcn_r18b-d8_769x769_80k_cityscapes-20201226_004430.log.json
+  Paper:
+    Title: Fully Convolutional Networks for Semantic Segmentation
+    URL: https://arxiv.org/abs/1411.4038
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11
+  Framework: PyTorch
+- Name: fcn_r50b-d8_4xb2-80k_cityscapes-769x769
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 73.83
+      mIoU(ms+flip): 76.6
+  Config: configs/fcn/fcn_r50b-d8_4xb2-80k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50b-D8
+    - FCN
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 6.3
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50b-d8_769x769_80k_cityscapes/fcn_r50b-d8_769x769_80k_cityscapes_20201225_094223-94552d38.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50b-d8_769x769_80k_cityscapes/fcn_r50b-d8_769x769_80k_cityscapes-20201225_094223.log.json
+  Paper:
+    Title: Fully Convolutional Networks for Semantic Segmentation
+    URL: https://arxiv.org/abs/1411.4038
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11
+  Framework: PyTorch
+- Name: fcn_r101b-d8_4xb2-80k_cityscapes-769x769
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.02
+      mIoU(ms+flip): 78.67
+  Config: configs/fcn/fcn_r101b-d8_4xb2-80k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101b-D8
+    - FCN
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 10.3
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101b-d8_769x769_80k_cityscapes/fcn_r101b-d8_769x769_80k_cityscapes_20201226_170012-82be37e2.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101b-d8_769x769_80k_cityscapes/fcn_r101b-d8_769x769_80k_cityscapes-20201226_170012.log.json
+  Paper:
+    Title: Fully Convolutional Networks for Semantic Segmentation
+    URL: https://arxiv.org/abs/1411.4038
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11
+  Framework: PyTorch
+- Name: fcn-d6_r50-d16_4xb2-40k_cityscapes-512x1024
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.06
+      mIoU(ms+flip): 78.85
+  Config: configs/fcn/fcn-d6_r50-d16_4xb2-40k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D16
+    - FCN
+    - (D6)
+    Training Resources: 4x TITAN Xp GPUS
+    Memory (GB): 3.4
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_512x1024_40k_cityscapes/fcn_d6_r50-d16_512x1024_40k_cityscapes_20210305_130133-98d5d1bc.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_512x1024_40k_cityscapes/fcn_d6_r50-d16_512x1024_40k_cityscapes-20210305_130133.log.json
+  Paper:
+    Title: Fully Convolutional Networks for Semantic Segmentation
+    URL: https://arxiv.org/abs/1411.4038
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11
+  Framework: PyTorch
+- Name: fcn-d6_r50-d16_4xb2-80k_cityscapes-512x1024
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.27
+      mIoU(ms+flip): 78.88
+  Config: configs/fcn/fcn-d6_r50-d16_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D16
+    - FCN
+    - (D6)
+    Training Resources: 4x TITAN Xp GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_512x1024_80k_cityscapes/fcn_d6_r50-d16_512x1024_80k_cityscapes_20210306_115604-133c292f.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_512x1024_80k_cityscapes/fcn_d6_r50-d16_512x1024_80k_cityscapes-20210306_115604.log.json
+  Paper:
+    Title: Fully Convolutional Networks for Semantic Segmentation
+    URL: https://arxiv.org/abs/1411.4038
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11
+  Framework: PyTorch
+- Name: fcn-d6_r50-d16_4xb2-40k_cityscapes-769x769
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 76.82
+      mIoU(ms+flip): 78.22
+  Config: configs/fcn/fcn-d6_r50-d16_4xb2-40k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D16
+    - FCN
+    - (D6)
+    Training Resources: 4x TITAN Xp GPUS
+    Memory (GB): 3.7
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_769x769_40k_cityscapes/fcn_d6_r50-d16_769x769_40k_cityscapes_20210305_185744-1aab18ed.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_769x769_40k_cityscapes/fcn_d6_r50-d16_769x769_40k_cityscapes-20210305_185744.log.json
+  Paper:
+    Title: Fully Convolutional Networks for Semantic Segmentation
+    URL: https://arxiv.org/abs/1411.4038
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11
+  Framework: PyTorch
+- Name: fcn-d6_r50-d16_4xb2-80k_cityscapes-769x769
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.04
+      mIoU(ms+flip): 78.4
+  Config: configs/fcn/fcn-d6_r50-d16_4xb2-80k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D16
+    - FCN
+    - (D6)
+    Training Resources: 4x TITAN Xp GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_769x769_80k_cityscapes/fcn_d6_r50-d16_769x769_80k_cityscapes_20210305_200413-109d88eb.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_769x769_80k_cityscapes/fcn_d6_r50-d16_769x769_80k_cityscapes-20210305_200413.log.json
+  Paper:
+    Title: Fully Convolutional Networks for Semantic Segmentation
+    URL: https://arxiv.org/abs/1411.4038
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11
+  Framework: PyTorch
+- Name: fcn-d6_r101-d16_4xb2-40k_cityscapes-512x1024
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.36
+      mIoU(ms+flip): 79.18
+  Config: configs/fcn/fcn-d6_r101-d16_4xb2-40k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D16
+    - FCN
+    - (D6)
+    Training Resources: 4x TITAN Xp GPUS
+    Memory (GB): 4.5
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_512x1024_40k_cityscapes/fcn_d6_r101-d16_512x1024_40k_cityscapes_20210305_130337-9cf2b450.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_512x1024_40k_cityscapes/fcn_d6_r101-d16_512x1024_40k_cityscapes-20210305_130337.log.json
+  Paper:
+    Title: Fully Convolutional Networks for Semantic Segmentation
+    URL: https://arxiv.org/abs/1411.4038
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11
+  Framework: PyTorch
+- Name: fcn-d6_r101-d16_4xb2-80k_cityscapes-512x1024
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.46
+      mIoU(ms+flip): 80.42
+  Config: configs/fcn/fcn-d6_r101-d16_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D16
+    - FCN
+    - (D6)
+    Training Resources: 4x TITAN Xp GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_512x1024_80k_cityscapes/fcn_d6_r101-d16_512x1024_80k_cityscapes_20210308_102747-cb336445.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_512x1024_80k_cityscapes/fcn_d6_r101-d16_512x1024_80k_cityscapes-20210308_102747.log.json
+  Paper:
+    Title: Fully Convolutional Networks for Semantic Segmentation
+    URL: https://arxiv.org/abs/1411.4038
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11
+  Framework: PyTorch
+- Name: fcn-d6_r101-d16_4xb2-40k_cityscapes-769x769
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.28
+      mIoU(ms+flip): 78.95
+  Config: configs/fcn/fcn-d6_r101-d16_4xb2-40k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D16
+    - FCN
+    - (D6)
+    Training Resources: 4x TITAN Xp GPUS
+    Memory (GB): 5.0
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_40k_cityscapes/fcn_d6_r101-d16_769x769_40k_cityscapes_20210308_102453-60b114e9.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_40k_cityscapes/fcn_d6_r101-d16_769x769_40k_cityscapes-20210308_102453.log.json
+  Paper:
+    Title: Fully Convolutional Networks for Semantic Segmentation
+    URL: https://arxiv.org/abs/1411.4038
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11
+  Framework: PyTorch
+- Name: fcn-d6_r101-d16_4xb2-80k_cityscapes-769x769
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.06
+      mIoU(ms+flip): 79.58
+  Config: configs/fcn/fcn-d6_r101-d16_4xb2-80k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D16
+    - FCN
+    - (D6)
+    Training Resources: 4x TITAN Xp GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_80k_cityscapes/fcn_d6_r101-d16_769x769_80k_cityscapes_20210306_120016-e33adc4f.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_80k_cityscapes/fcn_d6_r101-d16_769x769_80k_cityscapes-20210306_120016.log.json
+  Paper:
+    Title: Fully Convolutional Networks for Semantic Segmentation
+    URL: https://arxiv.org/abs/1411.4038
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11
+  Framework: PyTorch
+- Name: fcn-d6_r50b-d16_4xb2-80k_cityscapes-512x1024
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 76.99
+      mIoU(ms+flip): 79.03
+  Config: configs/fcn/fcn-d6_r50b-d16_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50b-D16
+    - FCN
+    - (D6)
+    Training Resources: 4x TITAN Xp GPUS
+    Memory (GB): 3.2
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50b-d16_512x1024_80k_cityscapes/fcn_d6_r50b-d16_512x1024_80k_cityscapes_20210311_125550-6a0b62e9.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50b_d16_512x1024_80k_cityscapes/fcn_d6_r50b_d16_512x1024_80k_cityscapes-20210311_125550.log.json
+  Paper:
+    Title: Fully Convolutional Networks for Semantic Segmentation
+    URL: https://arxiv.org/abs/1411.4038
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11
+  Framework: PyTorch
+- Name: fcn-d6_r50b-d16_4xb2-80k_cityscapes-769x769
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 76.86
+      mIoU(ms+flip): 78.52
+  Config: configs/fcn/fcn-d6_r50b-d16_4xb2-80k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50b-D16
+    - FCN
+    - (D6)
+    Training Resources: 4x TITAN Xp GPUS
+    Memory (GB): 3.6
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50b-d16_769x769_80k_cityscapes/fcn_d6_r50b-d16_769x769_80k_cityscapes_20210311_131012-d665f231.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50b_d16_769x769_80k_cityscapes/fcn_d6_r50b_d16_769x769_80k_cityscapes-20210311_131012.log.json
+  Paper:
+    Title: Fully Convolutional Networks for Semantic Segmentation
+    URL: https://arxiv.org/abs/1411.4038
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11
+  Framework: PyTorch
+- Name: fcn-d6_r101b-d16_4xb2-80k_cityscapes-512x1024
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.72
+      mIoU(ms+flip): 79.53
+  Config: configs/fcn/fcn-d6_r101b-d16_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101b-D16
+    - FCN
+    - (D6)
+    Training Resources: 4x TITAN Xp GPUS
+    Memory (GB): 4.3
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101b-d16_512x1024_80k_cityscapes/fcn_d6_r101b-d16_512x1024_80k_cityscapes_20210311_144305-3f2eb5b4.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101b_d16_512x1024_80k_cityscapes/fcn_d6_r101b_d16_512x1024_80k_cityscapes-20210311_144305.log.json
+  Paper:
+    Title: Fully Convolutional Networks for Semantic Segmentation
+    URL: https://arxiv.org/abs/1411.4038
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11
+  Framework: PyTorch
+- Name: fcn-d6_r101b-d16_4xb2-80k_cityscapes-769x769
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.34
+      mIoU(ms+flip): 78.91
+  Config: configs/fcn/fcn-d6_r101b-d16_4xb2-80k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101b-D16
+    - FCN
+    - (D6)
+    Training Resources: 4x TITAN Xp GPUS
+    Memory (GB): 4.8
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101b-d16_769x769_80k_cityscapes/fcn_d6_r101b-d16_769x769_80k_cityscapes_20210311_154527-c4d8bfbc.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101b_d16_769x769_80k_cityscapes/fcn_d6_r101b_d16_769x769_80k_cityscapes-20210311_154527.log.json
+  Paper:
+    Title: Fully Convolutional Networks for Semantic Segmentation
+    URL: https://arxiv.org/abs/1411.4038
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11
+  Framework: PyTorch
+- Name: fcn_r50-d8_4xb4-80k_ade20k-512x512
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 35.94
+      mIoU(ms+flip): 37.94
+  Config: configs/fcn/fcn_r50-d8_4xb4-80k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - FCN
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 8.5
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_80k_ade20k/fcn_r50-d8_512x512_80k_ade20k_20200614_144016-f8ac5082.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_80k_ade20k/fcn_r50-d8_512x512_80k_ade20k_20200614_144016.log.json
+  Paper:
+    Title: Fully Convolutional Networks for Semantic Segmentation
+    URL: https://arxiv.org/abs/1411.4038
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11
+  Framework: PyTorch
+- Name: fcn_r101-d8_4xb4-80k_ade20k-512x512
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 39.61
+      mIoU(ms+flip): 40.83
+  Config: configs/fcn/fcn_r101-d8_4xb4-80k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - FCN
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 12.0
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_80k_ade20k/fcn_r101-d8_512x512_80k_ade20k_20200615_014143-bc1809f7.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_80k_ade20k/fcn_r101-d8_512x512_80k_ade20k_20200615_014143.log.json
+  Paper:
+    Title: Fully Convolutional Networks for Semantic Segmentation
+    URL: https://arxiv.org/abs/1411.4038
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11
+  Framework: PyTorch
+- Name: fcn_r50-d8_4xb4-160k_ade20k-512x512
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 36.1
+      mIoU(ms+flip): 38.08
+  Config: configs/fcn/fcn_r50-d8_4xb4-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - FCN
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_160k_ade20k/fcn_r50-d8_512x512_160k_ade20k_20200615_100713-4edbc3b4.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_160k_ade20k/fcn_r50-d8_512x512_160k_ade20k_20200615_100713.log.json
+  Paper:
+    Title: Fully Convolutional Networks for Semantic Segmentation
+    URL: https://arxiv.org/abs/1411.4038
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11
+  Framework: PyTorch
+- Name: fcn_r101-d8_4xb4-160k_ade20k-512x512
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 39.91
+      mIoU(ms+flip): 41.4
+  Config: configs/fcn/fcn_r101-d8_4xb4-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - FCN
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_160k_ade20k/fcn_r101-d8_512x512_160k_ade20k_20200615_105816-fd192bd5.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_160k_ade20k/fcn_r101-d8_512x512_160k_ade20k_20200615_105816.log.json
+  Paper:
+    Title: Fully Convolutional Networks for Semantic Segmentation
+    URL: https://arxiv.org/abs/1411.4038
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11
+  Framework: PyTorch
+- Name: fcn_r50-d8_4xb4-20k_voc12aug-512x512
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 67.08
+      mIoU(ms+flip): 69.94
+  Config: configs/fcn/fcn_r50-d8_4xb4-20k_voc12aug-512x512.py
+  Metadata:
+    Training Data: Pascal VOC 2012 + Aug
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - FCN
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 5.7
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_20k_voc12aug/fcn_r50-d8_512x512_20k_voc12aug_20200617_010715-52dc5306.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_20k_voc12aug/fcn_r50-d8_512x512_20k_voc12aug_20200617_010715.log.json
+  Paper:
+    Title: Fully Convolutional Networks for Semantic Segmentation
+    URL: https://arxiv.org/abs/1411.4038
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11
+  Framework: PyTorch
+- Name: fcn_r101-d8_4xb4-20k_voc12aug-512x512
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 71.16
+      mIoU(ms+flip): 73.57
+  Config: configs/fcn/fcn_r101-d8_4xb4-20k_voc12aug-512x512.py
+  Metadata:
+    Training Data: Pascal VOC 2012 + Aug
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - FCN
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 9.2
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_20k_voc12aug/fcn_r101-d8_512x512_20k_voc12aug_20200617_010842-0bb4e798.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_20k_voc12aug/fcn_r101-d8_512x512_20k_voc12aug_20200617_010842.log.json
+  Paper:
+    Title: Fully Convolutional Networks for Semantic Segmentation
+    URL: https://arxiv.org/abs/1411.4038
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11
+  Framework: PyTorch
+- Name: fcn_r50-d8_4xb4-40k_voc12aug-512x512
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 66.97
+      mIoU(ms+flip): 69.04
+  Config: configs/fcn/fcn_r50-d8_4xb4-40k_voc12aug-512x512.py
+  Metadata:
+    Training Data: Pascal VOC 2012 + Aug
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - FCN
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_40k_voc12aug/fcn_r50-d8_512x512_40k_voc12aug_20200613_161222-5e2dbf40.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_40k_voc12aug/fcn_r50-d8_512x512_40k_voc12aug_20200613_161222.log.json
+  Paper:
+    Title: Fully Convolutional Networks for Semantic Segmentation
+    URL: https://arxiv.org/abs/1411.4038
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11
+  Framework: PyTorch
+- Name: fcn_r101-d8_4xb4-40k_voc12aug-512x512
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 69.91
+      mIoU(ms+flip): 72.38
+  Config: configs/fcn/fcn_r101-d8_4xb4-40k_voc12aug-512x512.py
+  Metadata:
+    Training Data: Pascal VOC 2012 + Aug
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - FCN
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_40k_voc12aug/fcn_r101-d8_512x512_40k_voc12aug_20200613_161240-4c8bcefd.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_40k_voc12aug/fcn_r101-d8_512x512_40k_voc12aug_20200613_161240.log.json
+  Paper:
+    Title: Fully Convolutional Networks for Semantic Segmentation
+    URL: https://arxiv.org/abs/1411.4038
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11
+  Framework: PyTorch
+- Name: fcn_r101-d8_4xb4-40k_pascal-context-480x480
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal Context
+    Metrics:
+      mIoU: 44.43
+      mIoU(ms+flip): 45.63
+  Config: configs/fcn/fcn_r101-d8_4xb4-40k_pascal-context-480x480.py
+  Metadata:
+    Training Data: Pascal Context
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - FCN
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_40k_pascal_context/fcn_r101-d8_480x480_40k_pascal_context_20210421_154757-b5e97937.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_40k_pascal_context/fcn_r101-d8_480x480_40k_pascal_context-20210421_154757.log.json
+  Paper:
+    Title: Fully Convolutional Networks for Semantic Segmentation
+    URL: https://arxiv.org/abs/1411.4038
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11
+  Framework: PyTorch
+- Name: fcn_r101-d8_4xb4-80k_pascal-context-480x480
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal Context
+    Metrics:
+      mIoU: 44.13
+      mIoU(ms+flip): 45.26
+  Config: configs/fcn/fcn_r101-d8_4xb4-80k_pascal-context-480x480.py
+  Metadata:
+    Training Data: Pascal Context
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - FCN
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_80k_pascal_context/fcn_r101-d8_480x480_80k_pascal_context_20210421_163310-4711813f.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_80k_pascal_context/fcn_r101-d8_480x480_80k_pascal_context-20210421_163310.log.json
+  Paper:
+    Title: Fully Convolutional Networks for Semantic Segmentation
+    URL: https://arxiv.org/abs/1411.4038
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11
+  Framework: PyTorch
+- Name: fcn_r101-d8_4xb4-40k_pascal-context-59-480x480
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal Context 59
+    Metrics:
+      mIoU: 48.42
+      mIoU(ms+flip): 50.4
+  Config: configs/fcn/fcn_r101-d8_4xb4-40k_pascal-context-59-480x480.py
+  Metadata:
+    Training Data: Pascal Context 59
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - FCN
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_40k_pascal_context_59/fcn_r101-d8_480x480_40k_pascal_context_59_20210415_230724-8cf83682.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_40k_pascal_context_59/fcn_r101-d8_480x480_40k_pascal_context_59-20210415_230724.log.json
+  Paper:
+    Title: Fully Convolutional Networks for Semantic Segmentation
+    URL: https://arxiv.org/abs/1411.4038
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11
+  Framework: PyTorch
+- Name: fcn_r101-d8_4xb4-80k_pascal-context-59-480x480
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal Context 59
+    Metrics:
+      mIoU: 49.35
+      mIoU(ms+flip): 51.38
+  Config: configs/fcn/fcn_r101-d8_4xb4-80k_pascal-context-59-480x480.py
+  Metadata:
+    Training Data: Pascal Context 59
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - FCN
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_80k_pascal_context_59/fcn_r101-d8_480x480_80k_pascal_context_59_20210416_110804-9a6f2c94.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_80k_pascal_context_59/fcn_r101-d8_480x480_80k_pascal_context_59-20210416_110804.log.json
+  Paper:
+    Title: Fully Convolutional Networks for Semantic Segmentation
+    URL: https://arxiv.org/abs/1411.4038
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11
+  Framework: PyTorch
diff --git a/configs/gcnet/README.md b/configs/gcnet/README.md
index fa37f76468..26a478808f 100644
--- a/configs/gcnet/README.md
+++ b/configs/gcnet/README.md
@@ -1,6 +1,6 @@
 # GCNet
 
-[GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond](https://arxiv.org/abs/1904.11492)
+> [GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond](https://arxiv.org/abs/1904.11492)
 
 ## Introduction
 
@@ -22,6 +22,39 @@ The Non-Local Network (NLNet) presents a pioneering approach for capturing long-
 <img src="https://user-images.githubusercontent.com/24582831/142901601-ad17922e-2538-4b48-9f51-84a57d44b12b.png" width="80%"/>
 </div>
 
+## Results and models
+
+### Cityscapes
+
+| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                           | download                                                                                                                                                                                                                                                                                                                                           |
+| ------ | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| GCNet  | R-50-D8  | 512x1024  |   40000 | 5.8      | 3.93           | V100   | 77.69 |         78.56 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/gcnet/gcnet_r50-d8_4xb2-40k_cityscapes-512x1024.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x1024_40k_cityscapes/gcnet_r50-d8_512x1024_40k_cityscapes_20200618_074436-4b0fd17b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x1024_40k_cityscapes/gcnet_r50-d8_512x1024_40k_cityscapes_20200618_074436.log.json)     |
+| GCNet  | R-101-D8 | 512x1024  |   40000 | 9.2      | 2.61           | V100   | 78.28 |         79.34 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/gcnet/gcnet_r101-d8_4xb2-40k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x1024_40k_cityscapes/gcnet_r101-d8_512x1024_40k_cityscapes_20200618_074436-5e62567f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x1024_40k_cityscapes/gcnet_r101-d8_512x1024_40k_cityscapes_20200618_074436.log.json) |
+| GCNet  | R-50-D8  | 769x769   |   40000 | 6.5      | 1.67           | V100   | 78.12 |         80.09 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/gcnet/gcnet_r50-d8_4xb2-40k_cityscapes-769x769.py)    | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_769x769_40k_cityscapes/gcnet_r50-d8_769x769_40k_cityscapes_20200618_182814-a26f4471.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_769x769_40k_cityscapes/gcnet_r50-d8_769x769_40k_cityscapes_20200618_182814.log.json)         |
+| GCNet  | R-101-D8 | 769x769   |   40000 | 10.5     | 1.13           | V100   | 78.95 |         80.71 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/gcnet/gcnet_r101-d8_4xb2-40k_cityscapes-769x769.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_769x769_40k_cityscapes/gcnet_r101-d8_769x769_40k_cityscapes_20200619_092550-ca4f0a84.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_769x769_40k_cityscapes/gcnet_r101-d8_769x769_40k_cityscapes_20200619_092550.log.json)     |
+| GCNet  | R-50-D8  | 512x1024  |   80000 | -        | -              | V100   | 78.48 |         80.01 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/gcnet/gcnet_r50-d8_4xb2-80k_cityscapes-512x1024.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x1024_80k_cityscapes/gcnet_r50-d8_512x1024_80k_cityscapes_20200618_074450-ef8f069b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x1024_80k_cityscapes/gcnet_r50-d8_512x1024_80k_cityscapes_20200618_074450.log.json)     |
+| GCNet  | R-101-D8 | 512x1024  |   80000 | -        | -              | V100   | 79.03 |         79.84 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/gcnet/gcnet_r101-d8_4xb2-80k_cityscapes-512x1024.pyy) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x1024_80k_cityscapes/gcnet_r101-d8_512x1024_80k_cityscapes_20200618_074450-778ebf69.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x1024_80k_cityscapes/gcnet_r101-d8_512x1024_80k_cityscapes_20200618_074450.log.json) |
+| GCNet  | R-50-D8  | 769x769   |   80000 | -        | -              | V100   | 78.68 |         80.66 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/gcnet/gcnet_r50-d8_4xb2-80k_cityscapes-769x769.py)    | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_769x769_80k_cityscapes/gcnet_r50-d8_769x769_80k_cityscapes_20200619_092516-4839565b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_769x769_80k_cityscapes/gcnet_r50-d8_769x769_80k_cityscapes_20200619_092516.log.json)         |
+| GCNet  | R-101-D8 | 769x769   |   80000 | -        | -              | V100   | 79.18 |         80.71 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/gcnet/gcnet_r101-d8_4xb2-80k_cityscapes-769x769.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_769x769_80k_cityscapes/gcnet_r101-d8_769x769_80k_cityscapes_20200619_092628-8e043423.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_769x769_80k_cityscapes/gcnet_r101-d8_769x769_80k_cityscapes_20200619_092628.log.json)     |
+
+### ADE20K
+
+| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                      | download                                                                                                                                                                                                                                                                                                                           |
+| ------ | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | --------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| GCNet  | R-50-D8  | 512x512   |   80000 | 8.5      | 23.38          | V100   | 41.47 |         42.85 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/gcnet/gcnet_r50-d8_4xb4-80k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_80k_ade20k/gcnet_r50-d8_512x512_80k_ade20k_20200614_185146-91a6da41.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_80k_ade20k/gcnet_r50-d8_512x512_80k_ade20k_20200614_185146.log.json)         |
+| GCNet  | R-101-D8 | 512x512   |   80000 | 12       | 15.20          | V100   | 42.82 |         44.54 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/gcnet/gcnet_r101-d8_4xb4-80k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_80k_ade20k/gcnet_r101-d8_512x512_80k_ade20k_20200615_020811-c3fcb6dd.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_80k_ade20k/gcnet_r101-d8_512x512_80k_ade20k_20200615_020811.log.json)     |
+| GCNet  | R-50-D8  | 512x512   |  160000 | -        | -              | V100   | 42.37 |         43.52 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/gcnet/gcnet_r50-d8_4xb4-160k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_160k_ade20k/gcnet_r50-d8_512x512_160k_ade20k_20200615_224122-d95f3e1f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_160k_ade20k/gcnet_r50-d8_512x512_160k_ade20k_20200615_224122.log.json)     |
+| GCNet  | R-101-D8 | 512x512   |  160000 | -        | -              | V100   | 43.69 |         45.21 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/gcnet/gcnet_r101-d8_4xb4-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_160k_ade20k/gcnet_r101-d8_512x512_160k_ade20k_20200615_225406-615528d7.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_160k_ade20k/gcnet_r101-d8_512x512_160k_ade20k_20200615_225406.log.json) |
+
+### Pascal VOC 2012 + Aug
+
+| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                       | download                                                                                                                                                                                                                                                                                                                               |
+| ------ | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ---------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| GCNet  | R-50-D8  | 512x512   |   20000 | 5.8      | 23.35          | V100   | 76.42 |         77.51 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/gcnet/gcnet_r50-d8_4xb4-20k_voc12aug-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_20k_voc12aug/gcnet_r50-d8_512x512_20k_voc12aug_20200617_165701-3cbfdab1.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_20k_voc12aug/gcnet_r50-d8_512x512_20k_voc12aug_20200617_165701.log.json)     |
+| GCNet  | R-101-D8 | 512x512   |   20000 | 9.2      | 14.80          | V100   | 77.41 |         78.56 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/gcnet/gcnet_r101-d8_4xb4-20k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_20k_voc12aug/gcnet_r101-d8_512x512_20k_voc12aug_20200617_165713-6c720aa9.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_20k_voc12aug/gcnet_r101-d8_512x512_20k_voc12aug_20200617_165713.log.json) |
+| GCNet  | R-50-D8  | 512x512   |   40000 | -        | -              | V100   | 76.24 |         77.63 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/gcnet/gcnet_r50-d8_4xb4-40k_voc12aug-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_40k_voc12aug/gcnet_r50-d8_512x512_40k_voc12aug_20200613_195105-9797336d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_40k_voc12aug/gcnet_r50-d8_512x512_40k_voc12aug_20200613_195105.log.json)     |
+| GCNet  | R-101-D8 | 512x512   |   40000 | -        | -              | V100   | 77.84 |         78.59 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/gcnet/gcnet_r101-d8_4xb4-40k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_40k_voc12aug/gcnet_r101-d8_512x512_40k_voc12aug_20200613_185806-1e38208d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_40k_voc12aug/gcnet_r101-d8_512x512_40k_voc12aug_20200613_185806.log.json) |
+
 ## Citation
 
 ```bibtex
@@ -33,36 +66,3 @@ The Non-Local Network (NLNet) presents a pioneering approach for capturing long-
   year={2019}
 }
 ```
-
-## Results and models
-
-### Cityscapes
-
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                           | download                                                                                                                                                                                                                                                                                                                                           |
-| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| GCNet  | R-50-D8  | 512x1024  |   40000 | 5.8      | 3.93           | 77.69 |         78.56 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/gcnet/gcnet_r50-d8_4xb2-40k_cityscapes-512x1024.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x1024_40k_cityscapes/gcnet_r50-d8_512x1024_40k_cityscapes_20200618_074436-4b0fd17b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x1024_40k_cityscapes/gcnet_r50-d8_512x1024_40k_cityscapes_20200618_074436.log.json)     |
-| GCNet  | R-101-D8 | 512x1024  |   40000 | 9.2      | 2.61           | 78.28 |         79.34 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/gcnet/gcnet_r101-d8_4xb2-40k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x1024_40k_cityscapes/gcnet_r101-d8_512x1024_40k_cityscapes_20200618_074436-5e62567f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x1024_40k_cityscapes/gcnet_r101-d8_512x1024_40k_cityscapes_20200618_074436.log.json) |
-| GCNet  | R-50-D8  | 769x769   |   40000 | 6.5      | 1.67           | 78.12 |         80.09 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/gcnet/gcnet_r50-d8_4xb2-40k_cityscapes-769x769.py)    | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_769x769_40k_cityscapes/gcnet_r50-d8_769x769_40k_cityscapes_20200618_182814-a26f4471.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_769x769_40k_cityscapes/gcnet_r50-d8_769x769_40k_cityscapes_20200618_182814.log.json)         |
-| GCNet  | R-101-D8 | 769x769   |   40000 | 10.5     | 1.13           | 78.95 |         80.71 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/gcnet/gcnet_r101-d8_4xb2-40k_cityscapes-769x769.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_769x769_40k_cityscapes/gcnet_r101-d8_769x769_40k_cityscapes_20200619_092550-ca4f0a84.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_769x769_40k_cityscapes/gcnet_r101-d8_769x769_40k_cityscapes_20200619_092550.log.json)     |
-| GCNet  | R-50-D8  | 512x1024  |   80000 | -        | -              | 78.48 |         80.01 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/gcnet/gcnet_r50-d8_4xb2-80k_cityscapes-512x1024.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x1024_80k_cityscapes/gcnet_r50-d8_512x1024_80k_cityscapes_20200618_074450-ef8f069b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x1024_80k_cityscapes/gcnet_r50-d8_512x1024_80k_cityscapes_20200618_074450.log.json)     |
-| GCNet  | R-101-D8 | 512x1024  |   80000 | -        | -              | 79.03 |         79.84 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/gcnet/gcnet_r101-d8_4xb2-80k_cityscapes-512x1024.pyy) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x1024_80k_cityscapes/gcnet_r101-d8_512x1024_80k_cityscapes_20200618_074450-778ebf69.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x1024_80k_cityscapes/gcnet_r101-d8_512x1024_80k_cityscapes_20200618_074450.log.json) |
-| GCNet  | R-50-D8  | 769x769   |   80000 | -        | -              | 78.68 |         80.66 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/gcnet/gcnet_r50-d8_4xb2-80k_cityscapes-769x769.py)    | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_769x769_80k_cityscapes/gcnet_r50-d8_769x769_80k_cityscapes_20200619_092516-4839565b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_769x769_80k_cityscapes/gcnet_r50-d8_769x769_80k_cityscapes_20200619_092516.log.json)         |
-| GCNet  | R-101-D8 | 769x769   |   80000 | -        | -              | 79.18 |         80.71 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/gcnet/gcnet_r101-d8_4xb2-80k_cityscapes-769x769.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_769x769_80k_cityscapes/gcnet_r101-d8_769x769_80k_cityscapes_20200619_092628-8e043423.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_769x769_80k_cityscapes/gcnet_r101-d8_769x769_80k_cityscapes_20200619_092628.log.json)     |
-
-### ADE20K
-
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                      | download                                                                                                                                                                                                                                                                                                                           |
-| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | --------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| GCNet  | R-50-D8  | 512x512   |   80000 | 8.5      | 23.38          | 41.47 |         42.85 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/gcnet/gcnet_r50-d8_4xb4-80k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_80k_ade20k/gcnet_r50-d8_512x512_80k_ade20k_20200614_185146-91a6da41.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_80k_ade20k/gcnet_r50-d8_512x512_80k_ade20k_20200614_185146.log.json)         |
-| GCNet  | R-101-D8 | 512x512   |   80000 | 12       | 15.20          | 42.82 |         44.54 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/gcnet/gcnet_r101-d8_4xb4-80k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_80k_ade20k/gcnet_r101-d8_512x512_80k_ade20k_20200615_020811-c3fcb6dd.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_80k_ade20k/gcnet_r101-d8_512x512_80k_ade20k_20200615_020811.log.json)     |
-| GCNet  | R-50-D8  | 512x512   |  160000 | -        | -              | 42.37 |         43.52 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/gcnet/gcnet_r50-d8_4xb4-160k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_160k_ade20k/gcnet_r50-d8_512x512_160k_ade20k_20200615_224122-d95f3e1f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_160k_ade20k/gcnet_r50-d8_512x512_160k_ade20k_20200615_224122.log.json)     |
-| GCNet  | R-101-D8 | 512x512   |  160000 | -        | -              | 43.69 |         45.21 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/gcnet/gcnet_r101-d8_4xb4-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_160k_ade20k/gcnet_r101-d8_512x512_160k_ade20k_20200615_225406-615528d7.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_160k_ade20k/gcnet_r101-d8_512x512_160k_ade20k_20200615_225406.log.json) |
-
-### Pascal VOC 2012 + Aug
-
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                       | download                                                                                                                                                                                                                                                                                                                               |
-| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ---------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| GCNet  | R-50-D8  | 512x512   |   20000 | 5.8      | 23.35          | 76.42 |         77.51 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/gcnet/gcnet_r50-d8_4xb4-20k_voc12aug-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_20k_voc12aug/gcnet_r50-d8_512x512_20k_voc12aug_20200617_165701-3cbfdab1.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_20k_voc12aug/gcnet_r50-d8_512x512_20k_voc12aug_20200617_165701.log.json)     |
-| GCNet  | R-101-D8 | 512x512   |   20000 | 9.2      | 14.80          | 77.41 |         78.56 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/gcnet/gcnet_r101-d8_4xb4-20k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_20k_voc12aug/gcnet_r101-d8_512x512_20k_voc12aug_20200617_165713-6c720aa9.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_20k_voc12aug/gcnet_r101-d8_512x512_20k_voc12aug_20200617_165713.log.json) |
-| GCNet  | R-50-D8  | 512x512   |   40000 | -        | -              | 76.24 |         77.63 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/gcnet/gcnet_r50-d8_4xb4-40k_voc12aug-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_40k_voc12aug/gcnet_r50-d8_512x512_40k_voc12aug_20200613_195105-9797336d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_40k_voc12aug/gcnet_r50-d8_512x512_40k_voc12aug_20200613_195105.log.json)     |
-| GCNet  | R-101-D8 | 512x512   |   40000 | -        | -              | 77.84 |         78.59 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/gcnet/gcnet_r101-d8_4xb4-40k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_40k_voc12aug/gcnet_r101-d8_512x512_40k_voc12aug_20200613_185806-1e38208d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_40k_voc12aug/gcnet_r101-d8_512x512_40k_voc12aug_20200613_185806.log.json) |
diff --git a/configs/gcnet/gcnet.yml b/configs/gcnet/gcnet.yml
deleted file mode 100644
index dfd8cf56c4..0000000000
--- a/configs/gcnet/gcnet.yml
+++ /dev/null
@@ -1,305 +0,0 @@
-Collections:
-- Name: GCNet
-  Metadata:
-    Training Data:
-    - Cityscapes
-    - ADE20K
-    - Pascal VOC 2012 + Aug
-  Paper:
-    URL: https://arxiv.org/abs/1904.11492
-    Title: 'GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond'
-  README: configs/gcnet/README.md
-  Code:
-    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/gc_head.py#L10
-    Version: v0.17.0
-  Converted From:
-    Code: https://github.com/xvjiarui/GCNet
-Models:
-- Name: gcnet_r50-d8_4xb2-40k_cityscapes-512x1024
-  In Collection: GCNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,1024)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 254.45
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 5.8
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 77.69
-      mIoU(ms+flip): 78.56
-  Config: configs/gcnet/gcnet_r50-d8_4xb2-40k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x1024_40k_cityscapes/gcnet_r50-d8_512x1024_40k_cityscapes_20200618_074436-4b0fd17b.pth
-- Name: gcnet_r101-d8_4xb2-40k_cityscapes-512x1024
-  In Collection: GCNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,1024)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 383.14
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 9.2
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 78.28
-      mIoU(ms+flip): 79.34
-  Config: configs/gcnet/gcnet_r101-d8_4xb2-40k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x1024_40k_cityscapes/gcnet_r101-d8_512x1024_40k_cityscapes_20200618_074436-5e62567f.pth
-- Name: gcnet_r50-d8_4xb2-40k_cityscapes-769x769
-  In Collection: GCNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (769,769)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 598.8
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (769,769)
-    Training Memory (GB): 6.5
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 78.12
-      mIoU(ms+flip): 80.09
-  Config: configs/gcnet/gcnet_r50-d8_4xb2-40k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_769x769_40k_cityscapes/gcnet_r50-d8_769x769_40k_cityscapes_20200618_182814-a26f4471.pth
-- Name: gcnet_r101-d8_4xb2-40k_cityscapes-769x769
-  In Collection: GCNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (769,769)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 884.96
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (769,769)
-    Training Memory (GB): 10.5
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 78.95
-      mIoU(ms+flip): 80.71
-  Config: configs/gcnet/gcnet_r101-d8_4xb2-40k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_769x769_40k_cityscapes/gcnet_r101-d8_769x769_40k_cityscapes_20200619_092550-ca4f0a84.pth
-- Name: gcnet_r50-d8_4xb2-80k_cityscapes-512x1024
-  In Collection: GCNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,1024)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 78.48
-      mIoU(ms+flip): 80.01
-  Config: configs/gcnet/gcnet_r50-d8_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x1024_80k_cityscapes/gcnet_r50-d8_512x1024_80k_cityscapes_20200618_074450-ef8f069b.pth
-- Name: gcnet_r101-d8_4xb2-80k_cityscapes-512x1024
-  In Collection: GCNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,1024)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 79.03
-      mIoU(ms+flip): 79.84
-  Config: configs/gcnet/gcnet_r101-d8_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x1024_80k_cityscapes/gcnet_r101-d8_512x1024_80k_cityscapes_20200618_074450-778ebf69.pth
-- Name: gcnet_r50-d8_4xb2-80k_cityscapes-769x769
-  In Collection: GCNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (769,769)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 78.68
-      mIoU(ms+flip): 80.66
-  Config: configs/gcnet/gcnet_r50-d8_4xb2-80k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_769x769_80k_cityscapes/gcnet_r50-d8_769x769_80k_cityscapes_20200619_092516-4839565b.pth
-- Name: gcnet_r101-d8_4xb2-80k_cityscapes-769x769
-  In Collection: GCNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (769,769)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 79.18
-      mIoU(ms+flip): 80.71
-  Config: configs/gcnet/gcnet_r101-d8_4xb2-80k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_769x769_80k_cityscapes/gcnet_r101-d8_769x769_80k_cityscapes_20200619_092628-8e043423.pth
-- Name: gcnet_r50-d8_4xb4-80k_ade20k-512x512
-  In Collection: GCNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 42.77
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 8.5
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 41.47
-      mIoU(ms+flip): 42.85
-  Config: configs/gcnet/gcnet_r50-d8_4xb4-80k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_80k_ade20k/gcnet_r50-d8_512x512_80k_ade20k_20200614_185146-91a6da41.pth
-- Name: gcnet_r101-d8_4xb4-80k_ade20k-512x512
-  In Collection: GCNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 65.79
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 12.0
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 42.82
-      mIoU(ms+flip): 44.54
-  Config: configs/gcnet/gcnet_r101-d8_4xb4-80k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_80k_ade20k/gcnet_r101-d8_512x512_80k_ade20k_20200615_020811-c3fcb6dd.pth
-- Name: gcnet_r50-d8_4xb4-160k_ade20k-512x512
-  In Collection: GCNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 160000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 42.37
-      mIoU(ms+flip): 43.52
-  Config: configs/gcnet/gcnet_r50-d8_4xb4-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_160k_ade20k/gcnet_r50-d8_512x512_160k_ade20k_20200615_224122-d95f3e1f.pth
-- Name: gcnet_r101-d8_4xb4-160k_ade20k-512x512
-  In Collection: GCNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 160000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 43.69
-      mIoU(ms+flip): 45.21
-  Config: configs/gcnet/gcnet_r101-d8_4xb4-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_160k_ade20k/gcnet_r101-d8_512x512_160k_ade20k_20200615_225406-615528d7.pth
-- Name: gcnet_r50-d8_4xb4-20k_voc12aug-512x512
-  In Collection: GCNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 20000
-    inference time (ms/im):
-    - value: 42.83
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 5.8
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal VOC 2012 + Aug
-    Metrics:
-      mIoU: 76.42
-      mIoU(ms+flip): 77.51
-  Config: configs/gcnet/gcnet_r50-d8_4xb4-20k_voc12aug-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_20k_voc12aug/gcnet_r50-d8_512x512_20k_voc12aug_20200617_165701-3cbfdab1.pth
-- Name: gcnet_r101-d8_4xb4-20k_voc12aug-512x512
-  In Collection: GCNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 20000
-    inference time (ms/im):
-    - value: 67.57
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 9.2
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal VOC 2012 + Aug
-    Metrics:
-      mIoU: 77.41
-      mIoU(ms+flip): 78.56
-  Config: configs/gcnet/gcnet_r101-d8_4xb4-20k_voc12aug-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_20k_voc12aug/gcnet_r101-d8_512x512_20k_voc12aug_20200617_165713-6c720aa9.pth
-- Name: gcnet_r50-d8_4xb4-40k_voc12aug-512x512
-  In Collection: GCNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 40000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal VOC 2012 + Aug
-    Metrics:
-      mIoU: 76.24
-      mIoU(ms+flip): 77.63
-  Config: configs/gcnet/gcnet_r50-d8_4xb4-40k_voc12aug-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_40k_voc12aug/gcnet_r50-d8_512x512_40k_voc12aug_20200613_195105-9797336d.pth
-- Name: gcnet_r101-d8_4xb4-40k_voc12aug-512x512
-  In Collection: GCNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 40000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal VOC 2012 + Aug
-    Metrics:
-      mIoU: 77.84
-      mIoU(ms+flip): 78.59
-  Config: configs/gcnet/gcnet_r101-d8_4xb4-40k_voc12aug-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_40k_voc12aug/gcnet_r101-d8_512x512_40k_voc12aug_20200613_185806-1e38208d.pth
diff --git a/configs/gcnet/metafile.yaml b/configs/gcnet/metafile.yaml
new file mode 100644
index 0000000000..1f3c4623a0
--- /dev/null
+++ b/configs/gcnet/metafile.yaml
@@ -0,0 +1,391 @@
+Collections:
+- Name: GCNet
+  License: Apache License 2.0
+  Metadata:
+    Training Data:
+    - Cityscapes
+    - ADE20K
+    - Pascal VOC 2012 + Aug
+  Paper:
+    Title: 'GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond'
+    URL: https://arxiv.org/abs/1904.11492
+  README: configs/gcnet/README.md
+  Frameworks:
+  - PyTorch
+Models:
+- Name: gcnet_r50-d8_4xb2-40k_cityscapes-512x1024
+  In Collection: GCNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.69
+      mIoU(ms+flip): 78.56
+  Config: configs/gcnet/gcnet_r50-d8_4xb2-40k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - GCNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 5.8
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x1024_40k_cityscapes/gcnet_r50-d8_512x1024_40k_cityscapes_20200618_074436-4b0fd17b.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x1024_40k_cityscapes/gcnet_r50-d8_512x1024_40k_cityscapes_20200618_074436.log.json
+  Paper:
+    Title: 'GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond'
+    URL: https://arxiv.org/abs/1904.11492
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/gc_head.py#L10
+  Framework: PyTorch
+- Name: gcnet_r101-d8_4xb2-40k_cityscapes-512x1024
+  In Collection: GCNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.28
+      mIoU(ms+flip): 79.34
+  Config: configs/gcnet/gcnet_r101-d8_4xb2-40k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - GCNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 9.2
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x1024_40k_cityscapes/gcnet_r101-d8_512x1024_40k_cityscapes_20200618_074436-5e62567f.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x1024_40k_cityscapes/gcnet_r101-d8_512x1024_40k_cityscapes_20200618_074436.log.json
+  Paper:
+    Title: 'GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond'
+    URL: https://arxiv.org/abs/1904.11492
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/gc_head.py#L10
+  Framework: PyTorch
+- Name: gcnet_r50-d8_4xb2-40k_cityscapes-769x769
+  In Collection: GCNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.12
+      mIoU(ms+flip): 80.09
+  Config: configs/gcnet/gcnet_r50-d8_4xb2-40k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - GCNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 6.5
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_769x769_40k_cityscapes/gcnet_r50-d8_769x769_40k_cityscapes_20200618_182814-a26f4471.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_769x769_40k_cityscapes/gcnet_r50-d8_769x769_40k_cityscapes_20200618_182814.log.json
+  Paper:
+    Title: 'GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond'
+    URL: https://arxiv.org/abs/1904.11492
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/gc_head.py#L10
+  Framework: PyTorch
+- Name: gcnet_r101-d8_4xb2-40k_cityscapes-769x769
+  In Collection: GCNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.95
+      mIoU(ms+flip): 80.71
+  Config: configs/gcnet/gcnet_r101-d8_4xb2-40k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - GCNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 10.5
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_769x769_40k_cityscapes/gcnet_r101-d8_769x769_40k_cityscapes_20200619_092550-ca4f0a84.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_769x769_40k_cityscapes/gcnet_r101-d8_769x769_40k_cityscapes_20200619_092550.log.json
+  Paper:
+    Title: 'GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond'
+    URL: https://arxiv.org/abs/1904.11492
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/gc_head.py#L10
+  Framework: PyTorch
+- Name: gcnet_r50-d8_4xb2-80k_cityscapes-512x1024
+  In Collection: GCNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.48
+      mIoU(ms+flip): 80.01
+  Config: configs/gcnet/gcnet_r50-d8_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - GCNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x1024_80k_cityscapes/gcnet_r50-d8_512x1024_80k_cityscapes_20200618_074450-ef8f069b.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x1024_80k_cityscapes/gcnet_r50-d8_512x1024_80k_cityscapes_20200618_074450.log.json
+  Paper:
+    Title: 'GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond'
+    URL: https://arxiv.org/abs/1904.11492
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/gc_head.py#L10
+  Framework: PyTorch
+- Name: gcnet_r101-d8_4xb2-80k_cityscapes-512x1024
+  In Collection: GCNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.03
+      mIoU(ms+flip): 79.84
+  Config: configs/gcnet/gcnet_r101-d8_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - GCNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x1024_80k_cityscapes/gcnet_r101-d8_512x1024_80k_cityscapes_20200618_074450-778ebf69.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x1024_80k_cityscapes/gcnet_r101-d8_512x1024_80k_cityscapes_20200618_074450.log.json
+  Paper:
+    Title: 'GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond'
+    URL: https://arxiv.org/abs/1904.11492
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/gc_head.py#L10
+  Framework: PyTorch
+- Name: gcnet_r50-d8_4xb2-80k_cityscapes-769x769
+  In Collection: GCNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.68
+      mIoU(ms+flip): 80.66
+  Config: configs/gcnet/gcnet_r50-d8_4xb2-80k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - GCNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_769x769_80k_cityscapes/gcnet_r50-d8_769x769_80k_cityscapes_20200619_092516-4839565b.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_769x769_80k_cityscapes/gcnet_r50-d8_769x769_80k_cityscapes_20200619_092516.log.json
+  Paper:
+    Title: 'GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond'
+    URL: https://arxiv.org/abs/1904.11492
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/gc_head.py#L10
+  Framework: PyTorch
+- Name: gcnet_r101-d8_4xb2-80k_cityscapes-769x769
+  In Collection: GCNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.18
+      mIoU(ms+flip): 80.71
+  Config: configs/gcnet/gcnet_r101-d8_4xb2-80k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - GCNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_769x769_80k_cityscapes/gcnet_r101-d8_769x769_80k_cityscapes_20200619_092628-8e043423.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_769x769_80k_cityscapes/gcnet_r101-d8_769x769_80k_cityscapes_20200619_092628.log.json
+  Paper:
+    Title: 'GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond'
+    URL: https://arxiv.org/abs/1904.11492
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/gc_head.py#L10
+  Framework: PyTorch
+- Name: gcnet_r50-d8_4xb4-80k_ade20k-512x512
+  In Collection: GCNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 41.47
+      mIoU(ms+flip): 42.85
+  Config: configs/gcnet/gcnet_r50-d8_4xb4-80k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - GCNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 8.5
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_80k_ade20k/gcnet_r50-d8_512x512_80k_ade20k_20200614_185146-91a6da41.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_80k_ade20k/gcnet_r50-d8_512x512_80k_ade20k_20200614_185146.log.json
+  Paper:
+    Title: 'GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond'
+    URL: https://arxiv.org/abs/1904.11492
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/gc_head.py#L10
+  Framework: PyTorch
+- Name: gcnet_r101-d8_4xb4-80k_ade20k-512x512
+  In Collection: GCNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 42.82
+      mIoU(ms+flip): 44.54
+  Config: configs/gcnet/gcnet_r101-d8_4xb4-80k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - GCNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 12.0
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_80k_ade20k/gcnet_r101-d8_512x512_80k_ade20k_20200615_020811-c3fcb6dd.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_80k_ade20k/gcnet_r101-d8_512x512_80k_ade20k_20200615_020811.log.json
+  Paper:
+    Title: 'GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond'
+    URL: https://arxiv.org/abs/1904.11492
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/gc_head.py#L10
+  Framework: PyTorch
+- Name: gcnet_r50-d8_4xb4-160k_ade20k-512x512
+  In Collection: GCNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 42.37
+      mIoU(ms+flip): 43.52
+  Config: configs/gcnet/gcnet_r50-d8_4xb4-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - GCNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_160k_ade20k/gcnet_r50-d8_512x512_160k_ade20k_20200615_224122-d95f3e1f.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_160k_ade20k/gcnet_r50-d8_512x512_160k_ade20k_20200615_224122.log.json
+  Paper:
+    Title: 'GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond'
+    URL: https://arxiv.org/abs/1904.11492
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/gc_head.py#L10
+  Framework: PyTorch
+- Name: gcnet_r101-d8_4xb4-160k_ade20k-512x512
+  In Collection: GCNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 43.69
+      mIoU(ms+flip): 45.21
+  Config: configs/gcnet/gcnet_r101-d8_4xb4-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - GCNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_160k_ade20k/gcnet_r101-d8_512x512_160k_ade20k_20200615_225406-615528d7.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_160k_ade20k/gcnet_r101-d8_512x512_160k_ade20k_20200615_225406.log.json
+  Paper:
+    Title: 'GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond'
+    URL: https://arxiv.org/abs/1904.11492
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/gc_head.py#L10
+  Framework: PyTorch
+- Name: gcnet_r50-d8_4xb4-20k_voc12aug-512x512
+  In Collection: GCNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 76.42
+      mIoU(ms+flip): 77.51
+  Config: configs/gcnet/gcnet_r50-d8_4xb4-20k_voc12aug-512x512.py
+  Metadata:
+    Training Data: Pascal VOC 2012 + Aug
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - GCNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 5.8
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_20k_voc12aug/gcnet_r50-d8_512x512_20k_voc12aug_20200617_165701-3cbfdab1.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_20k_voc12aug/gcnet_r50-d8_512x512_20k_voc12aug_20200617_165701.log.json
+  Paper:
+    Title: 'GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond'
+    URL: https://arxiv.org/abs/1904.11492
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/gc_head.py#L10
+  Framework: PyTorch
+- Name: gcnet_r101-d8_4xb4-20k_voc12aug-512x512
+  In Collection: GCNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 77.41
+      mIoU(ms+flip): 78.56
+  Config: configs/gcnet/gcnet_r101-d8_4xb4-20k_voc12aug-512x512.py
+  Metadata:
+    Training Data: Pascal VOC 2012 + Aug
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - GCNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 9.2
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_20k_voc12aug/gcnet_r101-d8_512x512_20k_voc12aug_20200617_165713-6c720aa9.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_20k_voc12aug/gcnet_r101-d8_512x512_20k_voc12aug_20200617_165713.log.json
+  Paper:
+    Title: 'GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond'
+    URL: https://arxiv.org/abs/1904.11492
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/gc_head.py#L10
+  Framework: PyTorch
+- Name: gcnet_r50-d8_4xb4-40k_voc12aug-512x512
+  In Collection: GCNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 76.24
+      mIoU(ms+flip): 77.63
+  Config: configs/gcnet/gcnet_r50-d8_4xb4-40k_voc12aug-512x512.py
+  Metadata:
+    Training Data: Pascal VOC 2012 + Aug
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - GCNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_40k_voc12aug/gcnet_r50-d8_512x512_40k_voc12aug_20200613_195105-9797336d.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_40k_voc12aug/gcnet_r50-d8_512x512_40k_voc12aug_20200613_195105.log.json
+  Paper:
+    Title: 'GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond'
+    URL: https://arxiv.org/abs/1904.11492
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/gc_head.py#L10
+  Framework: PyTorch
+- Name: gcnet_r101-d8_4xb4-40k_voc12aug-512x512
+  In Collection: GCNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 77.84
+      mIoU(ms+flip): 78.59
+  Config: configs/gcnet/gcnet_r101-d8_4xb4-40k_voc12aug-512x512.py
+  Metadata:
+    Training Data: Pascal VOC 2012 + Aug
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - GCNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_40k_voc12aug/gcnet_r101-d8_512x512_40k_voc12aug_20200613_185806-1e38208d.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_40k_voc12aug/gcnet_r101-d8_512x512_40k_voc12aug_20200613_185806.log.json
+  Paper:
+    Title: 'GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond'
+    URL: https://arxiv.org/abs/1904.11492
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/gc_head.py#L10
+  Framework: PyTorch
diff --git a/configs/hrnet/README.md b/configs/hrnet/README.md
index f85683b63f..38a6bd0e76 100644
--- a/configs/hrnet/README.md
+++ b/configs/hrnet/README.md
@@ -1,6 +1,6 @@
 # HRNet
 
-[Deep High-Resolution Representation Learning for Human Pose Estimation](https://arxiv.org/abs/1908.07919)
+> [Deep High-Resolution Representation Learning for Human Pose Estimation](https://arxiv.org/abs/1908.07919)
 
 ## Introduction
 
@@ -22,101 +22,101 @@ High-resolution representations are essential for position-sensitive vision prob
 <img src="https://user-images.githubusercontent.com/24582831/142901680-64c285bc-669f-4924-b054-46a2f07c5427.png" width="80%"/>
 </div>
 
-## Citation
-
-```bibtext
-@inproceedings{SunXLW19,
-  title={Deep High-Resolution Representation Learning for Human Pose Estimation},
-  author={Ke Sun and Bin Xiao and Dong Liu and Jingdong Wang},
-  booktitle={CVPR},
-  year={2019}
-}
-```
-
 ## Results and models
 
 ### Cityscapes
 
-| Method | Backbone           | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                       | download                                                                                                                                                                                                                                                                                                                               |
-| ------ | ------------------ | --------- | ------: | -------- | -------------- | ----: | ------------: | ---------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| FCN    | HRNetV2p-W18-Small | 512x1024  |   40000 | 1.7      | 23.74          | 73.86 |         75.91 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr18s_4xb2-40k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_40k_cityscapes/fcn_hr18s_512x1024_40k_cityscapes_20200601_014216-93db27d0.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_40k_cityscapes/fcn_hr18s_512x1024_40k_cityscapes_20200601_014216.log.json)     |
-| FCN    | HRNetV2p-W18       | 512x1024  |   40000 | 2.9      | 12.97          | 77.19 |         78.92 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr18_4xb2-40k_cityscapes-512x1024.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_40k_cityscapes/fcn_hr18_512x1024_40k_cityscapes_20200601_014216-f196fb4e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_40k_cityscapes/fcn_hr18_512x1024_40k_cityscapes_20200601_014216.log.json)         |
-| FCN    | HRNetV2p-W48       | 512x1024  |   40000 | 6.2      | 6.42           | 78.48 |         79.69 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr48_4xb2-40k_cityscapes-512x1024.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_40k_cityscapes/fcn_hr48_512x1024_40k_cityscapes_20200601_014240-a989b146.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_40k_cityscapes/fcn_hr48_512x1024_40k_cityscapes_20200601_014240.log.json)         |
-| FCN    | HRNetV2p-W18-Small | 512x1024  |   80000 | -        | -              | 75.31 |         77.48 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr18s_4xb2-80k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_80k_cityscapes/fcn_hr18s_512x1024_80k_cityscapes_20200601_202700-1462b75d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_80k_cityscapes/fcn_hr18s_512x1024_80k_cityscapes_20200601_202700.log.json)     |
-| FCN    | HRNetV2p-W18       | 512x1024  |   80000 | -        | -              | 78.65 |         80.35 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr18_4xb2-80k_cityscapes-512x1024.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_80k_cityscapes/fcn_hr18_512x1024_80k_cityscapes_20200601_223255-4e7b345e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_80k_cityscapes/fcn_hr18_512x1024_80k_cityscapes_20200601_223255.log.json)         |
-| FCN    | HRNetV2p-W48       | 512x1024  |   80000 | -        | -              | 79.93 |         80.72 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr48_4xb2-80k_cityscapes-512x1024.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_80k_cityscapes/fcn_hr48_512x1024_80k_cityscapes_20200601_202606-58ea95d6.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_80k_cityscapes/fcn_hr48_512x1024_80k_cityscapes_20200601_202606.log.json)         |
-| FCN    | HRNetV2p-W18-Small | 512x1024  |  160000 | -        | -              | 76.31 |         78.31 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr18s_4xb2-160k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_160k_cityscapes/fcn_hr18s_512x1024_160k_cityscapes_20200602_190901-4a0797ea.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_160k_cityscapes/fcn_hr18s_512x1024_160k_cityscapes_20200602_190901.log.json) |
-| FCN    | HRNetV2p-W18       | 512x1024  |  160000 | -        | -              | 78.80 |         80.74 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr18_4xb2-160k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_160k_cityscapes/fcn_hr18_512x1024_160k_cityscapes_20200602_190822-221e4a4f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_160k_cityscapes/fcn_hr18_512x1024_160k_cityscapes_20200602_190822.log.json)     |
-| FCN    | HRNetV2p-W48       | 512x1024  |  160000 | -        | -              | 80.65 |         81.92 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr48_4xb2-160k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_160k_cityscapes/fcn_hr48_512x1024_160k_cityscapes_20200602_190946-59b7973e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_160k_cityscapes/fcn_hr48_512x1024_160k_cityscapes_20200602_190946.log.json)     |
+| Method | Backbone           | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                       | download                                                                                                                                                                                                                                                                                                                               |
+| ------ | ------------------ | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ---------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| FCN    | HRNetV2p-W18-Small | 512x1024  |   40000 | 1.7      | 23.74          | V100   | 73.86 |         75.91 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr18s_4xb2-40k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_40k_cityscapes/fcn_hr18s_512x1024_40k_cityscapes_20200601_014216-93db27d0.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_40k_cityscapes/fcn_hr18s_512x1024_40k_cityscapes_20200601_014216.log.json)     |
+| FCN    | HRNetV2p-W18       | 512x1024  |   40000 | 2.9      | 12.97          | V100   | 77.19 |         78.92 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr18_4xb2-40k_cityscapes-512x1024.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_40k_cityscapes/fcn_hr18_512x1024_40k_cityscapes_20200601_014216-f196fb4e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_40k_cityscapes/fcn_hr18_512x1024_40k_cityscapes_20200601_014216.log.json)         |
+| FCN    | HRNetV2p-W48       | 512x1024  |   40000 | 6.2      | 6.42           | V100   | 78.48 |         79.69 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr48_4xb2-40k_cityscapes-512x1024.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_40k_cityscapes/fcn_hr48_512x1024_40k_cityscapes_20200601_014240-a989b146.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_40k_cityscapes/fcn_hr48_512x1024_40k_cityscapes_20200601_014240.log.json)         |
+| FCN    | HRNetV2p-W18-Small | 512x1024  |   80000 | -        | -              | V100   | 75.31 |         77.48 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr18s_4xb2-80k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_80k_cityscapes/fcn_hr18s_512x1024_80k_cityscapes_20200601_202700-1462b75d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_80k_cityscapes/fcn_hr18s_512x1024_80k_cityscapes_20200601_202700.log.json)     |
+| FCN    | HRNetV2p-W18       | 512x1024  |   80000 | -        | -              | V100   | 78.65 |         80.35 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr18_4xb2-80k_cityscapes-512x1024.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_80k_cityscapes/fcn_hr18_512x1024_80k_cityscapes_20200601_223255-4e7b345e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_80k_cityscapes/fcn_hr18_512x1024_80k_cityscapes_20200601_223255.log.json)         |
+| FCN    | HRNetV2p-W48       | 512x1024  |   80000 | -        | -              | V100   | 79.93 |         80.72 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr48_4xb2-80k_cityscapes-512x1024.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_80k_cityscapes/fcn_hr48_512x1024_80k_cityscapes_20200601_202606-58ea95d6.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_80k_cityscapes/fcn_hr48_512x1024_80k_cityscapes_20200601_202606.log.json)         |
+| FCN    | HRNetV2p-W18-Small | 512x1024  |  160000 | -        | -              | V100   | 76.31 |         78.31 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr18s_4xb2-160k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_160k_cityscapes/fcn_hr18s_512x1024_160k_cityscapes_20200602_190901-4a0797ea.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_160k_cityscapes/fcn_hr18s_512x1024_160k_cityscapes_20200602_190901.log.json) |
+| FCN    | HRNetV2p-W18       | 512x1024  |  160000 | -        | -              | V100   | 78.80 |         80.74 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr18_4xb2-160k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_160k_cityscapes/fcn_hr18_512x1024_160k_cityscapes_20200602_190822-221e4a4f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_160k_cityscapes/fcn_hr18_512x1024_160k_cityscapes_20200602_190822.log.json)     |
+| FCN    | HRNetV2p-W48       | 512x1024  |  160000 | -        | -              | V100   | 80.65 |         81.92 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr48_4xb2-160k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_160k_cityscapes/fcn_hr48_512x1024_160k_cityscapes_20200602_190946-59b7973e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_160k_cityscapes/fcn_hr48_512x1024_160k_cityscapes_20200602_190946.log.json)     |
 
 ### ADE20K
 
-| Method | Backbone           | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                  | download                                                                                                                                                                                                                                                                                                           |
-| ------ | ------------------ | --------- | ------: | -------- | -------------- | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| FCN    | HRNetV2p-W18-Small | 512x512   |   80000 | 3.8      | 38.66          | 31.38 |         32.45 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr18s_4xb4-80k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_ade20k/fcn_hr18s_512x512_80k_ade20k_20200614_144345-77fc814a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_ade20k/fcn_hr18s_512x512_80k_ade20k_20200614_144345.log.json)     |
-| FCN    | HRNetV2p-W18       | 512x512   |   80000 | 4.9      | 22.57          | 36.27 |         37.28 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr18_4xb4-80k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_ade20k/fcn_hr18_512x512_80k_ade20k_20210827_114910-6c9382c0.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_ade20k/fcn_hr18_512x512_80k_ade20k_20210827_114910.log.json)         |
-| FCN    | HRNetV2p-W48       | 512x512   |   80000 | 8.2      | 21.23          | 41.90 |         43.27 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr48_4xb4-80k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_ade20k/fcn_hr48_512x512_80k_ade20k_20200614_193946-7ba5258d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_ade20k/fcn_hr48_512x512_80k_ade20k_20200614_193946.log.json)         |
-| FCN    | HRNetV2p-W18-Small | 512x512   |  160000 | -        | -              | 33.07 |         34.56 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr18s_4xb4-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_160k_ade20k/fcn_hr18s_512x512_160k_ade20k_20210829_174739-f1e7c2e7.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_160k_ade20k/fcn_hr18s_512x512_160k_ade20k_20210829_174739.log.json) |
-| FCN    | HRNetV2p-W18       | 512x512   |  160000 | -        | -              | 36.79 |         38.58 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr18_4xb4-160k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_160k_ade20k/fcn_hr18_512x512_160k_ade20k_20200614_214426-ca961836.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_160k_ade20k/fcn_hr18_512x512_160k_ade20k_20200614_214426.log.json)     |
-| FCN    | HRNetV2p-W48       | 512x512   |  160000 | -        | -              | 42.02 |         43.86 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr48_4xb4-160k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_160k_ade20k/fcn_hr48_512x512_160k_ade20k_20200614_214407-a52fc02c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_160k_ade20k/fcn_hr48_512x512_160k_ade20k_20200614_214407.log.json)     |
+| Method | Backbone           | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                  | download                                                                                                                                                                                                                                                                                                           |
+| ------ | ------------------ | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| FCN    | HRNetV2p-W18-Small | 512x512   |   80000 | 3.8      | 38.66          | V100   | 31.38 |         32.45 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr18s_4xb4-80k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_ade20k/fcn_hr18s_512x512_80k_ade20k_20200614_144345-77fc814a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_ade20k/fcn_hr18s_512x512_80k_ade20k_20200614_144345.log.json)     |
+| FCN    | HRNetV2p-W18       | 512x512   |   80000 | 4.9      | 22.57          | V100   | 36.27 |         37.28 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr18_4xb4-80k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_ade20k/fcn_hr18_512x512_80k_ade20k_20210827_114910-6c9382c0.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_ade20k/fcn_hr18_512x512_80k_ade20k_20210827_114910.log.json)         |
+| FCN    | HRNetV2p-W48       | 512x512   |   80000 | 8.2      | 21.23          | V100   | 41.90 |         43.27 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr48_4xb4-80k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_ade20k/fcn_hr48_512x512_80k_ade20k_20200614_193946-7ba5258d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_ade20k/fcn_hr48_512x512_80k_ade20k_20200614_193946.log.json)         |
+| FCN    | HRNetV2p-W18-Small | 512x512   |  160000 | -        | -              | V100   | 33.07 |         34.56 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr18s_4xb4-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_160k_ade20k/fcn_hr18s_512x512_160k_ade20k_20210829_174739-f1e7c2e7.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_160k_ade20k/fcn_hr18s_512x512_160k_ade20k_20210829_174739.log.json) |
+| FCN    | HRNetV2p-W18       | 512x512   |  160000 | -        | -              | V100   | 36.79 |         38.58 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr18_4xb4-160k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_160k_ade20k/fcn_hr18_512x512_160k_ade20k_20200614_214426-ca961836.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_160k_ade20k/fcn_hr18_512x512_160k_ade20k_20200614_214426.log.json)     |
+| FCN    | HRNetV2p-W48       | 512x512   |  160000 | -        | -              | V100   | 42.02 |         43.86 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr48_4xb4-160k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_160k_ade20k/fcn_hr48_512x512_160k_ade20k_20200614_214407-a52fc02c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_160k_ade20k/fcn_hr48_512x512_160k_ade20k_20200614_214407.log.json)     |
 
 ### Pascal VOC 2012 + Aug
 
-| Method | Backbone           | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                   | download                                                                                                                                                                                                                                                                                                               |
-| ------ | ------------------ | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| FCN    | HRNetV2p-W18-Small | 512x512   |   20000 | 1.8      | 43.36          |  65.5 |         68.89 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr18s_4xb4-20k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_20k_voc12aug/fcn_hr18s_512x512_20k_voc12aug_20210829_174910-0aceadb4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_20k_voc12aug/fcn_hr18s_512x512_20k_voc12aug_20210829_174910.log.json) |
-| FCN    | HRNetV2p-W18       | 512x512   |   20000 | 2.9      | 23.48          | 72.30 |         74.71 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr18_4xb4-20k_voc12aug-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_20k_voc12aug/fcn_hr18_512x512_20k_voc12aug_20200617_224503-488d45f7.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_20k_voc12aug/fcn_hr18_512x512_20k_voc12aug_20200617_224503.log.json)     |
-| FCN    | HRNetV2p-W48       | 512x512   |   20000 | 6.2      | 22.05          | 75.87 |         78.58 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr48_4xb4-20k_voc12aug-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_20k_voc12aug/fcn_hr48_512x512_20k_voc12aug_20200617_224419-89de05cd.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_20k_voc12aug/fcn_hr48_512x512_20k_voc12aug_20200617_224419.log.json)     |
-| FCN    | HRNetV2p-W18-Small | 512x512   |   40000 | -        | -              | 66.61 |         70.00 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr18s_4xb4-40k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_40k_voc12aug/fcn_hr18s_512x512_40k_voc12aug_20200614_000648-4f8d6e7f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_40k_voc12aug/fcn_hr18s_512x512_40k_voc12aug_20200614_000648.log.json) |
-| FCN    | HRNetV2p-W18       | 512x512   |   40000 | -        | -              | 72.90 |         75.59 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr18_4xb4-40k_voc12aug-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_40k_voc12aug/fcn_hr18_512x512_40k_voc12aug_20200613_224401-1b4b76cd.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_40k_voc12aug/fcn_hr18_512x512_40k_voc12aug_20200613_224401.log.json)     |
-| FCN    | HRNetV2p-W48       | 512x512   |   40000 | -        | -              | 76.24 |         78.49 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr48_4xb4-40k_voc12aug-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_40k_voc12aug/fcn_hr48_512x512_40k_voc12aug_20200613_222111-1b0f18bc.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_40k_voc12aug/fcn_hr48_512x512_40k_voc12aug_20200613_222111.log.json)     |
+| Method | Backbone           | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                   | download                                                                                                                                                                                                                                                                                                               |
+| ------ | ------------------ | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| FCN    | HRNetV2p-W18-Small | 512x512   |   20000 | 1.8      | 43.36          | V100   |  65.5 |         68.89 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr18s_4xb4-20k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_20k_voc12aug/fcn_hr18s_512x512_20k_voc12aug_20210829_174910-0aceadb4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_20k_voc12aug/fcn_hr18s_512x512_20k_voc12aug_20210829_174910.log.json) |
+| FCN    | HRNetV2p-W18       | 512x512   |   20000 | 2.9      | 23.48          | V100   | 72.30 |         74.71 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr18_4xb4-20k_voc12aug-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_20k_voc12aug/fcn_hr18_512x512_20k_voc12aug_20200617_224503-488d45f7.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_20k_voc12aug/fcn_hr18_512x512_20k_voc12aug_20200617_224503.log.json)     |
+| FCN    | HRNetV2p-W48       | 512x512   |   20000 | 6.2      | 22.05          | V100   | 75.87 |         78.58 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr48_4xb4-20k_voc12aug-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_20k_voc12aug/fcn_hr48_512x512_20k_voc12aug_20200617_224419-89de05cd.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_20k_voc12aug/fcn_hr48_512x512_20k_voc12aug_20200617_224419.log.json)     |
+| FCN    | HRNetV2p-W18-Small | 512x512   |   40000 | -        | -              | V100   | 66.61 |         70.00 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr18s_4xb4-40k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_40k_voc12aug/fcn_hr18s_512x512_40k_voc12aug_20200614_000648-4f8d6e7f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_40k_voc12aug/fcn_hr18s_512x512_40k_voc12aug_20200614_000648.log.json) |
+| FCN    | HRNetV2p-W18       | 512x512   |   40000 | -        | -              | V100   | 72.90 |         75.59 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr18_4xb4-40k_voc12aug-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_40k_voc12aug/fcn_hr18_512x512_40k_voc12aug_20200613_224401-1b4b76cd.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_40k_voc12aug/fcn_hr18_512x512_40k_voc12aug_20200613_224401.log.json)     |
+| FCN    | HRNetV2p-W48       | 512x512   |   40000 | -        | -              | V100   | 76.24 |         78.49 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr48_4xb4-40k_voc12aug-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_40k_voc12aug/fcn_hr48_512x512_40k_voc12aug_20200613_222111-1b0f18bc.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_40k_voc12aug/fcn_hr48_512x512_40k_voc12aug_20200613_222111.log.json)     |
 
 ### Pascal Context
 
-| Method | Backbone     | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                        | download                                                                                                                                                                                                                                                                                                                                   |
-| ------ | ------------ | --------- | ------: | -------- | -------------- | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| FCN    | HRNetV2p-W48 | 480x480   |   40000 | 6.1      | 8.86           | 45.14 |         47.42 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr48_4xb4-40k_pascal-context-480x480.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_40k_pascal_context/fcn_hr48_480x480_40k_pascal_context_20200911_164852-667d00b0.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_40k_pascal_context/fcn_hr48_480x480_40k_pascal_context-20200911_164852.log.json) |
-| FCN    | HRNetV2p-W48 | 480x480   |   80000 | -        | -              | 45.84 |         47.84 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr48_4xb4-80k_pascal-context-480x480.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_80k_pascal_context/fcn_hr48_480x480_80k_pascal_context_20200911_155322-847a6711.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_80k_pascal_context/fcn_hr48_480x480_80k_pascal_context-20200911_155322.log.json) |
+| Method | Backbone     | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                        | download                                                                                                                                                                                                                                                                                                                                   |
+| ------ | ------------ | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| FCN    | HRNetV2p-W48 | 480x480   |   40000 | 6.1      | 8.86           | V100   | 45.14 |         47.42 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr48_4xb4-40k_pascal-context-480x480.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_40k_pascal_context/fcn_hr48_480x480_40k_pascal_context_20200911_164852-667d00b0.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_40k_pascal_context/fcn_hr48_480x480_40k_pascal_context-20200911_164852.log.json) |
+| FCN    | HRNetV2p-W48 | 480x480   |   80000 | -        | -              | V100   | 45.84 |         47.84 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr48_4xb4-80k_pascal-context-480x480.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_80k_pascal_context/fcn_hr48_480x480_80k_pascal_context_20200911_155322-847a6711.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_80k_pascal_context/fcn_hr48_480x480_80k_pascal_context-20200911_155322.log.json) |
 
 ### Pascal Context 59
 
-| Method | Backbone     | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                           | download                                                                                                                                                                                                                                                                                                                                               |
-| ------ | ------------ | --------- | ------: | -------- | -------------- | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| FCN    | HRNetV2p-W48 | 480x480   |   40000 | -        | -              | 50.33 |         52.83 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr48_4xb4-40k_pascal-context-59-480x480.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_40k_pascal_context_59/fcn_hr48_480x480_40k_pascal_context_59_20210410_122738-b808b8b2.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_40k_pascal_context_59/fcn_hr48_480x480_40k_pascal_context_59-20210410_122738.log.json) |
-| FCN    | HRNetV2p-W48 | 480x480   |   80000 | -        | -              | 51.12 |         53.56 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr48_4xb4-80k_pascal-context-59-480x480.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_80k_pascal_context_59/fcn_hr48_480x480_80k_pascal_context_59_20210411_003240-3ae7081e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_80k_pascal_context_59/fcn_hr48_480x480_80k_pascal_context_59-20210411_003240.log.json) |
+| Method | Backbone     | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                           | download                                                                                                                                                                                                                                                                                                                                               |
+| ------ | ------------ | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| FCN    | HRNetV2p-W48 | 480x480   |   40000 | -        | -              | V100   | 50.33 |         52.83 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr48_4xb4-40k_pascal-context-59-480x480.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_40k_pascal_context_59/fcn_hr48_480x480_40k_pascal_context_59_20210410_122738-b808b8b2.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_40k_pascal_context_59/fcn_hr48_480x480_40k_pascal_context_59-20210410_122738.log.json) |
+| FCN    | HRNetV2p-W48 | 480x480   |   80000 | -        | -              | V100   | 51.12 |         53.56 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr48_4xb4-80k_pascal-context-59-480x480.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_80k_pascal_context_59/fcn_hr48_480x480_80k_pascal_context_59_20210411_003240-3ae7081e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_80k_pascal_context_59/fcn_hr48_480x480_80k_pascal_context_59-20210411_003240.log.json) |
 
 ### LoveDA
 
-| Method | Backbone           | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                  | download                                                                                                                                                                                                                                                                                                       |
-| ------ | ------------------ | --------- | ------: | -------- | -------------- | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| FCN    | HRNetV2p-W18-Small | 512x512   |   80000 | 1.59     | 24.87          | 49.28 |         49.42 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr18s_4xb4-80k_loveda-512x512.pyy) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_loveda/fcn_hr18s_512x512_80k_loveda_20211210_203228-60a86a7a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_loveda/fcn_hr18s_512x512_80k_loveda_20211210_203228.log.json) |
-| FCN    | HRNetV2p-W18       | 512x512   |   80000 | 2.76     | 12.92          | 50.81 |         50.95 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr18_4xb4-80k_loveda-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_loveda/fcn_hr18_512x512_80k_loveda_20211210_203952-93d9c3b3.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_loveda/fcn_hr18_512x512_80k_loveda_20211210_203952.log.json)     |
-| FCN    | HRNetV2p-W48       | 512x512   |   80000 | 6.20     | 9.61           | 51.42 |         51.64 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr48_4xb4-80k_loveda-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_loveda/fcn_hr48_512x512_80k_loveda_20211211_044756-67072f55.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_loveda/fcn_hr48_512x512_80k_loveda_20211211_044756.log.json)     |
+| Method | Backbone           | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                  | download                                                                                                                                                                                                                                                                                                       |
+| ------ | ------------------ | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| FCN    | HRNetV2p-W18-Small | 512x512   |   80000 | 1.59     | 24.87          | V100   | 49.28 |         49.42 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr18s_4xb4-80k_loveda-512x512.pyy) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_loveda/fcn_hr18s_512x512_80k_loveda_20211210_203228-60a86a7a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_loveda/fcn_hr18s_512x512_80k_loveda_20211210_203228.log.json) |
+| FCN    | HRNetV2p-W18       | 512x512   |   80000 | 2.76     | 12.92          | V100   | 50.81 |         50.95 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr18_4xb4-80k_loveda-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_loveda/fcn_hr18_512x512_80k_loveda_20211210_203952-93d9c3b3.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_loveda/fcn_hr18_512x512_80k_loveda_20211210_203952.log.json)     |
+| FCN    | HRNetV2p-W48       | 512x512   |   80000 | 6.20     | 9.61           | V100   | 51.42 |         51.64 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr48_4xb4-80k_loveda-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_loveda/fcn_hr48_512x512_80k_loveda_20211211_044756-67072f55.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_loveda/fcn_hr48_512x512_80k_loveda_20211211_044756.log.json)     |
 
 ### Potsdam
 
-| Method | Backbone           | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                  | download                                                                                                                                                                                                                                                                                                           |
-| ------ | ------------------ | --------- | ------: | -------- | -------------- | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| FCN    | HRNetV2p-W18-Small | 512x512   |   80000 | 1.58     | 36.00          | 77.64 |          78.8 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr18s_4xb4-80k_potsdam-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_potsdam/fcn_hr18s_512x512_80k_potsdam_20211218_205517-ba32af63.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_potsdam/fcn_hr18s_512x512_80k_potsdam_20211218_205517.log.json) |
-| FCN    | HRNetV2p-W18       | 512x512   |   80000 | 2.76     | 19.25          | 78.26 |         79.24 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr18_4xb4-80k_potsdam-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_potsdam/fcn_hr18_512x512_80k_potsdam_20211218_205517-5d0387ad.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_potsdam/fcn_hr18_512x512_80k_potsdam_20211218_205517.log.json)     |
-| FCN    | HRNetV2p-W48       | 512x512   |   80000 | 6.20     | 16.42          | 78.39 |         79.34 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr48_4xb4-80k_potsdam-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_potsdam/fcn_hr48_512x512_80k_potsdam_20211219_020601-97434c78.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_potsdam/fcn_hr48_512x512_80k_potsdam_20211219_020601.log.json)     |
+| Method | Backbone           | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                  | download                                                                                                                                                                                                                                                                                                           |
+| ------ | ------------------ | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| FCN    | HRNetV2p-W18-Small | 512x512   |   80000 | 1.58     | 36.00          | V100   | 77.64 |          78.8 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr18s_4xb4-80k_potsdam-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_potsdam/fcn_hr18s_512x512_80k_potsdam_20211218_205517-ba32af63.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_potsdam/fcn_hr18s_512x512_80k_potsdam_20211218_205517.log.json) |
+| FCN    | HRNetV2p-W18       | 512x512   |   80000 | 2.76     | 19.25          | V100   | 78.26 |         79.24 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr18_4xb4-80k_potsdam-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_potsdam/fcn_hr18_512x512_80k_potsdam_20211218_205517-5d0387ad.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_potsdam/fcn_hr18_512x512_80k_potsdam_20211218_205517.log.json)     |
+| FCN    | HRNetV2p-W48       | 512x512   |   80000 | 6.20     | 16.42          | V100   | 78.39 |         79.34 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr48_4xb4-80k_potsdam-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_potsdam/fcn_hr48_512x512_80k_potsdam_20211219_020601-97434c78.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_potsdam/fcn_hr48_512x512_80k_potsdam_20211219_020601.log.json)     |
 
 ### Vaihingen
 
-| Method | Backbone           | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                    | download                                                                                                                                                                                                                                                                                                                                   |
-| ------ | ------------------ | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| FCN    | HRNetV2p-W18-Small | 512x512   |   80000 | 1.58     | 38.11          | 71.81 |          73.1 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr18s_4xb4-80k_vaihingen-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_4x4_512x512_80k_vaihingen/fcn_hr18s_4x4_512x512_80k_vaihingen_20211231_230909-b23aae02.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_4x4_512x512_80k_vaihingen/fcn_hr18s_4x4_512x512_80k_vaihingen_20211231_230909.log.json) |
-| FCN    | HRNetV2p-W18       | 512x512   |   80000 | 2.76     | 19.55          | 72.57 |         74.09 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr18_4xb4-80k_vaihingen-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_4x4_512x512_80k_vaihingen/fcn_hr18_4x4_512x512_80k_vaihingen_20211231_231216-2ec3ae8a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_4x4_512x512_80k_vaihingen/fcn_hr18_4x4_512x512_80k_vaihingen_20211231_231216.log.json)     |
-| FCN    | HRNetV2p-W48       | 512x512   |   80000 | 6.20     | 17.25          | 72.50 |         73.52 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr48_4xb4-80k_vaihingen-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_4x4_512x512_80k_vaihingen/fcn_hr48_4x4_512x512_80k_vaihingen_20211231_231244-7133cb22.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_4x4_512x512_80k_vaihingen/fcn_hr48_4x4_512x512_80k_vaihingen_20211231_231244.log.json)     |
+| Method | Backbone           | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                    | download                                                                                                                                                                                                                                                                                                                                   |
+| ------ | ------------------ | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| FCN    | HRNetV2p-W18-Small | 512x512   |   80000 | 1.58     | 38.11          | V100   | 71.81 |          73.1 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr18s_4xb4-80k_vaihingen-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_4x4_512x512_80k_vaihingen/fcn_hr18s_4x4_512x512_80k_vaihingen_20211231_230909-b23aae02.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_4x4_512x512_80k_vaihingen/fcn_hr18s_4x4_512x512_80k_vaihingen_20211231_230909.log.json) |
+| FCN    | HRNetV2p-W18       | 512x512   |   80000 | 2.76     | 19.55          | V100   | 72.57 |         74.09 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr18_4xb4-80k_vaihingen-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_4x4_512x512_80k_vaihingen/fcn_hr18_4x4_512x512_80k_vaihingen_20211231_231216-2ec3ae8a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_4x4_512x512_80k_vaihingen/fcn_hr18_4x4_512x512_80k_vaihingen_20211231_231216.log.json)     |
+| FCN    | HRNetV2p-W48       | 512x512   |   80000 | 6.20     | 17.25          | V100   | 72.50 |         73.52 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr48_4xb4-80k_vaihingen-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_4x4_512x512_80k_vaihingen/fcn_hr48_4x4_512x512_80k_vaihingen_20211231_231244-7133cb22.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_4x4_512x512_80k_vaihingen/fcn_hr48_4x4_512x512_80k_vaihingen_20211231_231244.log.json)     |
 
 ### iSAID
 
-| Method | Backbone           | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                | download                                                                                                                                                                                                                                                                                                                   |
-| ------ | ------------------ | --------- | ------: | -------- | -------------- | ----: | ------------: | --------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| FCN    | HRNetV2p-W18-Small | 896x896   |   80000 | 4.95     | 13.84          | 62.30 |         62.97 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr18s_4xb4-80k_isaid-896x896.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_4x4_896x896_80k_isaid/fcn_hr18s_4x4_896x896_80k_isaid_20220118_001603-3cc0769b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_4x4_896x896_80k_isaid/fcn_hr18s_4x4_896x896_80k_isaid_20220118_001603.log.json) |
-| FCN    | HRNetV2p-W18       | 896x896   |   80000 | 8.30     | 7.71           | 65.06 |         65.60 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr18_4xb4-80k_isaid-896x896.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_4x4_896x896_80k_isaid/fcn_hr18_4x4_896x896_80k_isaid_20220110_182230-49bf752e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_4x4_896x896_80k_isaid/fcn_hr18_4x4_896x896_80k_isaid_20220110_182230.log.json)     |
-| FCN    | HRNetV2p-W48       | 896x896   |   80000 | 16.89    | 7.34           | 67.80 |         68.53 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr48_4xb4-80k_isaid-896x896.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_4x4_896x896_80k_isaid/fcn_hr48_4x4_896x896_80k_isaid_20220114_174643-547fc420.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_4x4_896x896_80k_isaid/fcn_hr48_4x4_896x896_80k_isaid_20220114_174643.log.json)     |
+| Method | Backbone           | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                | download                                                                                                                                                                                                                                                                                                                   |
+| ------ | ------------------ | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | --------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| FCN    | HRNetV2p-W18-Small | 896x896   |   80000 | 4.95     | 13.84          | V100   | 62.30 |         62.97 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr18s_4xb4-80k_isaid-896x896.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_4x4_896x896_80k_isaid/fcn_hr18s_4x4_896x896_80k_isaid_20220118_001603-3cc0769b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_4x4_896x896_80k_isaid/fcn_hr18s_4x4_896x896_80k_isaid_20220118_001603.log.json) |
+| FCN    | HRNetV2p-W18       | 896x896   |   80000 | 8.30     | 7.71           | V100   | 65.06 |         65.60 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr18_4xb4-80k_isaid-896x896.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_4x4_896x896_80k_isaid/fcn_hr18_4x4_896x896_80k_isaid_20220110_182230-49bf752e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_4x4_896x896_80k_isaid/fcn_hr18_4x4_896x896_80k_isaid_20220110_182230.log.json)     |
+| FCN    | HRNetV2p-W48       | 896x896   |   80000 | 16.89    | 7.34           | V100   | 67.80 |         68.53 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/hrnet/fcn_hr48_4xb4-80k_isaid-896x896.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_4x4_896x896_80k_isaid/fcn_hr48_4x4_896x896_80k_isaid_20220114_174643-547fc420.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_4x4_896x896_80k_isaid/fcn_hr48_4x4_896x896_80k_isaid_20220114_174643.log.json)     |
 
 Note:
 
 - `896x896` is the Crop Size of iSAID dataset, which is followed by the implementation of [PointFlow: Flowing Semantics Through Points for Aerial Image Segmentation](https://arxiv.org/pdf/2103.06564.pdf)
+
+## Citation
+
+```bibtext
+@inproceedings{SunXLW19,
+  title={Deep High-Resolution Representation Learning for Human Pose Estimation},
+  author={Ke Sun and Bin Xiao and Dong Liu and Jingdong Wang},
+  booktitle={CVPR},
+  year={2019}
+}
+```
diff --git a/configs/hrnet/hrnet.yml b/configs/hrnet/hrnet.yml
deleted file mode 100644
index 77f556e17a..0000000000
--- a/configs/hrnet/hrnet.yml
+++ /dev/null
@@ -1,695 +0,0 @@
-Models:
-- Name: fcn_hr18s_4xb2-40k_cityscapes-512x1024
-  In Collection: FCN
-  Metadata:
-    backbone: HRNetV2p-W18-Small
-    crop size: (512,1024)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 42.12
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 1.7
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 73.86
-      mIoU(ms+flip): 75.91
-  Config: configs/hrnet/fcn_hr18s_4xb2-40k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_40k_cityscapes/fcn_hr18s_512x1024_40k_cityscapes_20200601_014216-93db27d0.pth
-- Name: fcn_hr18_4xb2-40k_cityscapes-512x1024
-  In Collection: FCN
-  Metadata:
-    backbone: HRNetV2p-W18
-    crop size: (512,1024)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 77.1
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 2.9
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 77.19
-      mIoU(ms+flip): 78.92
-  Config: configs/hrnet/fcn_hr18_4xb2-40k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_40k_cityscapes/fcn_hr18_512x1024_40k_cityscapes_20200601_014216-f196fb4e.pth
-- Name: fcn_hr48_4xb2-40k_cityscapes-512x1024
-  In Collection: FCN
-  Metadata:
-    backbone: HRNetV2p-W48
-    crop size: (512,1024)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 155.76
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 6.2
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 78.48
-      mIoU(ms+flip): 79.69
-  Config: configs/hrnet/fcn_hr48_4xb2-40k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_40k_cityscapes/fcn_hr48_512x1024_40k_cityscapes_20200601_014240-a989b146.pth
-- Name: fcn_hr18s_4xb2-80k_cityscapes-512x1024
-  In Collection: FCN
-  Metadata:
-    backbone: HRNetV2p-W18-Small
-    crop size: (512,1024)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 75.31
-      mIoU(ms+flip): 77.48
-  Config: configs/hrnet/fcn_hr18s_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_80k_cityscapes/fcn_hr18s_512x1024_80k_cityscapes_20200601_202700-1462b75d.pth
-- Name: fcn_hr18_4xb2-80k_cityscapes-512x1024
-  In Collection: FCN
-  Metadata:
-    backbone: HRNetV2p-W18
-    crop size: (512,1024)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 78.65
-      mIoU(ms+flip): 80.35
-  Config: configs/hrnet/fcn_hr18_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_80k_cityscapes/fcn_hr18_512x1024_80k_cityscapes_20200601_223255-4e7b345e.pth
-- Name: fcn_hr48_4xb2-80k_cityscapes-512x1024
-  In Collection: FCN
-  Metadata:
-    backbone: HRNetV2p-W48
-    crop size: (512,1024)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 79.93
-      mIoU(ms+flip): 80.72
-  Config: configs/hrnet/fcn_hr48_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_80k_cityscapes/fcn_hr48_512x1024_80k_cityscapes_20200601_202606-58ea95d6.pth
-- Name: fcn_hr18s_4xb2-160k_cityscapes-512x1024
-  In Collection: FCN
-  Metadata:
-    backbone: HRNetV2p-W18-Small
-    crop size: (512,1024)
-    lr schd: 160000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 76.31
-      mIoU(ms+flip): 78.31
-  Config: configs/hrnet/fcn_hr18s_4xb2-160k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_160k_cityscapes/fcn_hr18s_512x1024_160k_cityscapes_20200602_190901-4a0797ea.pth
-- Name: fcn_hr18_4xb2-160k_cityscapes-512x1024
-  In Collection: FCN
-  Metadata:
-    backbone: HRNetV2p-W18
-    crop size: (512,1024)
-    lr schd: 160000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 78.8
-      mIoU(ms+flip): 80.74
-  Config: configs/hrnet/fcn_hr18_4xb2-160k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_160k_cityscapes/fcn_hr18_512x1024_160k_cityscapes_20200602_190822-221e4a4f.pth
-- Name: fcn_hr48_4xb2-160k_cityscapes-512x1024
-  In Collection: FCN
-  Metadata:
-    backbone: HRNetV2p-W48
-    crop size: (512,1024)
-    lr schd: 160000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 80.65
-      mIoU(ms+flip): 81.92
-  Config: configs/hrnet/fcn_hr48_4xb2-160k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_160k_cityscapes/fcn_hr48_512x1024_160k_cityscapes_20200602_190946-59b7973e.pth
-- Name: fcn_hr18s_4xb4-80k_ade20k-512x512
-  In Collection: FCN
-  Metadata:
-    backbone: HRNetV2p-W18-Small
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 25.87
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 3.8
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 31.38
-      mIoU(ms+flip): 32.45
-  Config: configs/hrnet/fcn_hr18s_4xb4-80k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_ade20k/fcn_hr18s_512x512_80k_ade20k_20200614_144345-77fc814a.pth
-- Name: fcn_hr18_4xb4-80k_ade20k-512x512
-  In Collection: FCN
-  Metadata:
-    backbone: HRNetV2p-W18
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 44.31
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 4.9
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 36.27
-      mIoU(ms+flip): 37.28
-  Config: configs/hrnet/fcn_hr18_4xb4-80k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_ade20k/fcn_hr18_512x512_80k_ade20k_20210827_114910-6c9382c0.pth
-- Name: fcn_hr48_4xb4-80k_ade20k-512x512
-  In Collection: FCN
-  Metadata:
-    backbone: HRNetV2p-W48
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 47.1
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 8.2
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 41.9
-      mIoU(ms+flip): 43.27
-  Config: configs/hrnet/fcn_hr48_4xb4-80k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_ade20k/fcn_hr48_512x512_80k_ade20k_20200614_193946-7ba5258d.pth
-- Name: fcn_hr18s_4xb4-160k_ade20k-512x512
-  In Collection: FCN
-  Metadata:
-    backbone: HRNetV2p-W18-Small
-    crop size: (512,512)
-    lr schd: 160000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 33.07
-      mIoU(ms+flip): 34.56
-  Config: configs/hrnet/fcn_hr18s_4xb4-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_160k_ade20k/fcn_hr18s_512x512_160k_ade20k_20210829_174739-f1e7c2e7.pth
-- Name: fcn_hr18_4xb4-160k_ade20k-512x512
-  In Collection: FCN
-  Metadata:
-    backbone: HRNetV2p-W18
-    crop size: (512,512)
-    lr schd: 160000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 36.79
-      mIoU(ms+flip): 38.58
-  Config: configs/hrnet/fcn_hr18_4xb4-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_160k_ade20k/fcn_hr18_512x512_160k_ade20k_20200614_214426-ca961836.pth
-- Name: fcn_hr48_4xb4-160k_ade20k-512x512
-  In Collection: FCN
-  Metadata:
-    backbone: HRNetV2p-W48
-    crop size: (512,512)
-    lr schd: 160000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 42.02
-      mIoU(ms+flip): 43.86
-  Config: configs/hrnet/fcn_hr48_4xb4-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_160k_ade20k/fcn_hr48_512x512_160k_ade20k_20200614_214407-a52fc02c.pth
-- Name: fcn_hr18s_4xb4-20k_voc12aug-512x512
-  In Collection: FCN
-  Metadata:
-    backbone: HRNetV2p-W18-Small
-    crop size: (512,512)
-    lr schd: 20000
-    inference time (ms/im):
-    - value: 23.06
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 1.8
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal VOC 2012 + Aug
-    Metrics:
-      mIoU: 65.5
-      mIoU(ms+flip): 68.89
-  Config: configs/hrnet/fcn_hr18s_4xb4-20k_voc12aug-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_20k_voc12aug/fcn_hr18s_512x512_20k_voc12aug_20210829_174910-0aceadb4.pth
-- Name: fcn_hr18_4xb4-20k_voc12aug-512x512
-  In Collection: FCN
-  Metadata:
-    backbone: HRNetV2p-W18
-    crop size: (512,512)
-    lr schd: 20000
-    inference time (ms/im):
-    - value: 42.59
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 2.9
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal VOC 2012 + Aug
-    Metrics:
-      mIoU: 72.3
-      mIoU(ms+flip): 74.71
-  Config: configs/hrnet/fcn_hr18_4xb4-20k_voc12aug-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_20k_voc12aug/fcn_hr18_512x512_20k_voc12aug_20200617_224503-488d45f7.pth
-- Name: fcn_hr48_4xb4-20k_voc12aug-512x512
-  In Collection: FCN
-  Metadata:
-    backbone: HRNetV2p-W48
-    crop size: (512,512)
-    lr schd: 20000
-    inference time (ms/im):
-    - value: 45.35
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 6.2
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal VOC 2012 + Aug
-    Metrics:
-      mIoU: 75.87
-      mIoU(ms+flip): 78.58
-  Config: configs/hrnet/fcn_hr48_4xb4-20k_voc12aug-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_20k_voc12aug/fcn_hr48_512x512_20k_voc12aug_20200617_224419-89de05cd.pth
-- Name: fcn_hr18s_4xb4-40k_voc12aug-512x512
-  In Collection: FCN
-  Metadata:
-    backbone: HRNetV2p-W18-Small
-    crop size: (512,512)
-    lr schd: 40000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal VOC 2012 + Aug
-    Metrics:
-      mIoU: 66.61
-      mIoU(ms+flip): 70.0
-  Config: configs/hrnet/fcn_hr18s_4xb4-40k_voc12aug-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_40k_voc12aug/fcn_hr18s_512x512_40k_voc12aug_20200614_000648-4f8d6e7f.pth
-- Name: fcn_hr18_4xb4-40k_voc12aug-512x512
-  In Collection: FCN
-  Metadata:
-    backbone: HRNetV2p-W18
-    crop size: (512,512)
-    lr schd: 40000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal VOC 2012 + Aug
-    Metrics:
-      mIoU: 72.9
-      mIoU(ms+flip): 75.59
-  Config: configs/hrnet/fcn_hr18_4xb4-40k_voc12aug-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_40k_voc12aug/fcn_hr18_512x512_40k_voc12aug_20200613_224401-1b4b76cd.pth
-- Name: fcn_hr48_4xb4-40k_voc12aug-512x512
-  In Collection: FCN
-  Metadata:
-    backbone: HRNetV2p-W48
-    crop size: (512,512)
-    lr schd: 40000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal VOC 2012 + Aug
-    Metrics:
-      mIoU: 76.24
-      mIoU(ms+flip): 78.49
-  Config: configs/hrnet/fcn_hr48_4xb4-40k_voc12aug-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_40k_voc12aug/fcn_hr48_512x512_40k_voc12aug_20200613_222111-1b0f18bc.pth
-- Name: fcn_hr48_4xb4-40k_pascal-context-480x480
-  In Collection: FCN
-  Metadata:
-    backbone: HRNetV2p-W48
-    crop size: (480,480)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 112.87
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (480,480)
-    Training Memory (GB): 6.1
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal Context
-    Metrics:
-      mIoU: 45.14
-      mIoU(ms+flip): 47.42
-  Config: configs/hrnet/fcn_hr48_4xb4-40k_pascal-context-480x480.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_40k_pascal_context/fcn_hr48_480x480_40k_pascal_context_20200911_164852-667d00b0.pth
-- Name: fcn_hr48_4xb4-80k_pascal-context-480x480
-  In Collection: FCN
-  Metadata:
-    backbone: HRNetV2p-W48
-    crop size: (480,480)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal Context
-    Metrics:
-      mIoU: 45.84
-      mIoU(ms+flip): 47.84
-  Config: configs/hrnet/fcn_hr48_4xb4-80k_pascal-context-480x480.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_80k_pascal_context/fcn_hr48_480x480_80k_pascal_context_20200911_155322-847a6711.pth
-- Name: fcn_hr48_4xb4-40k_pascal-context-59-480x480
-  In Collection: FCN
-  Metadata:
-    backbone: HRNetV2p-W48
-    crop size: (480,480)
-    lr schd: 40000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal Context 59
-    Metrics:
-      mIoU: 50.33
-      mIoU(ms+flip): 52.83
-  Config: configs/hrnet/fcn_hr48_4xb4-40k_pascal-context-59-480x480.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_40k_pascal_context_59/fcn_hr48_480x480_40k_pascal_context_59_20210410_122738-b808b8b2.pth
-- Name: fcn_hr48_4xb4-80k_pascal-context-59-480x480
-  In Collection: FCN
-  Metadata:
-    backbone: HRNetV2p-W48
-    crop size: (480,480)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal Context 59
-    Metrics:
-      mIoU: 51.12
-      mIoU(ms+flip): 53.56
-  Config: configs/hrnet/fcn_hr48_4xb4-80k_pascal-context-59-480x480.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_80k_pascal_context_59/fcn_hr48_480x480_80k_pascal_context_59_20210411_003240-3ae7081e.pth
-- Name: fcn_hr18s_4xb4-80k_loveda-512x512
-  In Collection: FCN
-  Metadata:
-    backbone: HRNetV2p-W18-Small
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 40.21
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 1.59
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: LoveDA
-    Metrics:
-      mIoU: 49.28
-      mIoU(ms+flip): 49.42
-  Config: configs/hrnet/fcn_hr18s_4xb4-80k_loveda-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_loveda/fcn_hr18s_512x512_80k_loveda_20211210_203228-60a86a7a.pth
-- Name: fcn_hr18_4xb4-80k_loveda-512x512
-  In Collection: FCN
-  Metadata:
-    backbone: HRNetV2p-W18
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 77.4
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 2.76
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: LoveDA
-    Metrics:
-      mIoU: 50.81
-      mIoU(ms+flip): 50.95
-  Config: configs/hrnet/fcn_hr18_4xb4-80k_loveda-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_loveda/fcn_hr18_512x512_80k_loveda_20211210_203952-93d9c3b3.pth
-- Name: fcn_hr48_4xb4-80k_loveda-512x512
-  In Collection: FCN
-  Metadata:
-    backbone: HRNetV2p-W48
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 104.06
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 6.2
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: LoveDA
-    Metrics:
-      mIoU: 51.42
-      mIoU(ms+flip): 51.64
-  Config: configs/hrnet/fcn_hr48_4xb4-80k_loveda-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_loveda/fcn_hr48_512x512_80k_loveda_20211211_044756-67072f55.pth
-- Name: fcn_hr18s_4xb4-80k_potsdam-512x512
-  In Collection: FCN
-  Metadata:
-    backbone: HRNetV2p-W18-Small
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 27.78
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 1.58
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Potsdam
-    Metrics:
-      mIoU: 77.64
-      mIoU(ms+flip): 78.8
-  Config: configs/hrnet/fcn_hr18s_4xb4-80k_potsdam-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_potsdam/fcn_hr18s_512x512_80k_potsdam_20211218_205517-ba32af63.pth
-- Name: fcn_hr18_4xb4-80k_potsdam-512x512
-  In Collection: FCN
-  Metadata:
-    backbone: HRNetV2p-W18
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 51.95
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 2.76
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Potsdam
-    Metrics:
-      mIoU: 78.26
-      mIoU(ms+flip): 79.24
-  Config: configs/hrnet/fcn_hr18_4xb4-80k_potsdam-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_potsdam/fcn_hr18_512x512_80k_potsdam_20211218_205517-5d0387ad.pth
-- Name: fcn_hr48_4xb4-80k_potsdam-512x512
-  In Collection: FCN
-  Metadata:
-    backbone: HRNetV2p-W48
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 60.9
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 6.2
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Potsdam
-    Metrics:
-      mIoU: 78.39
-      mIoU(ms+flip): 79.34
-  Config: configs/hrnet/fcn_hr48_4xb4-80k_potsdam-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_potsdam/fcn_hr48_512x512_80k_potsdam_20211219_020601-97434c78.pth
-- Name: fcn_hr18s_4xb4-80k_vaihingen-512x512
-  In Collection: FCN
-  Metadata:
-    backbone: HRNetV2p-W18-Small
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 26.24
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 1.58
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Vaihingen
-    Metrics:
-      mIoU: 71.81
-      mIoU(ms+flip): 73.1
-  Config: configs/hrnet/fcn_hr18s_4xb4-80k_vaihingen-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_4x4_512x512_80k_vaihingen/fcn_hr18s_4x4_512x512_80k_vaihingen_20211231_230909-b23aae02.pth
-- Name: fcn_hr18_4xb4-80k_vaihingen-512x512
-  In Collection: FCN
-  Metadata:
-    backbone: HRNetV2p-W18
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 51.15
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 2.76
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Vaihingen
-    Metrics:
-      mIoU: 72.57
-      mIoU(ms+flip): 74.09
-  Config: configs/hrnet/fcn_hr18_4xb4-80k_vaihingen-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_4x4_512x512_80k_vaihingen/fcn_hr18_4x4_512x512_80k_vaihingen_20211231_231216-2ec3ae8a.pth
-- Name: fcn_hr48_4xb4-80k_vaihingen-512x512
-  In Collection: FCN
-  Metadata:
-    backbone: HRNetV2p-W48
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 57.97
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 6.2
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Vaihingen
-    Metrics:
-      mIoU: 72.5
-      mIoU(ms+flip): 73.52
-  Config: configs/hrnet/fcn_hr48_4xb4-80k_vaihingen-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_4x4_512x512_80k_vaihingen/fcn_hr48_4x4_512x512_80k_vaihingen_20211231_231244-7133cb22.pth
-- Name: fcn_hr18s_4xb4-80k_isaid-896x896
-  In Collection: FCN
-  Metadata:
-    backbone: HRNetV2p-W18-Small
-    crop size: (896,896)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 72.25
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (896,896)
-    Training Memory (GB): 4.95
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: iSAID
-    Metrics:
-      mIoU: 62.3
-      mIoU(ms+flip): 62.97
-  Config: configs/hrnet/fcn_hr18s_4xb4-80k_isaid-896x896.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_4x4_896x896_80k_isaid/fcn_hr18s_4x4_896x896_80k_isaid_20220118_001603-3cc0769b.pth
-- Name: fcn_hr18_4xb4-80k_isaid-896x896
-  In Collection: FCN
-  Metadata:
-    backbone: HRNetV2p-W18
-    crop size: (896,896)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 129.7
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (896,896)
-    Training Memory (GB): 8.3
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: iSAID
-    Metrics:
-      mIoU: 65.06
-      mIoU(ms+flip): 65.6
-  Config: configs/hrnet/fcn_hr18_4xb4-80k_isaid-896x896.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_4x4_896x896_80k_isaid/fcn_hr18_4x4_896x896_80k_isaid_20220110_182230-49bf752e.pth
-- Name: fcn_hr48_4xb4-80k_isaid-896x896
-  In Collection: FCN
-  Metadata:
-    backbone: HRNetV2p-W48
-    crop size: (896,896)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 136.24
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (896,896)
-    Training Memory (GB): 16.89
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: iSAID
-    Metrics:
-      mIoU: 67.8
-      mIoU(ms+flip): 68.53
-  Config: configs/hrnet/fcn_hr48_4xb4-80k_isaid-896x896.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_4x4_896x896_80k_isaid/fcn_hr48_4x4_896x896_80k_isaid_20220114_174643-547fc420.pth
diff --git a/configs/hrnet/metafile.yaml b/configs/hrnet/metafile.yaml
new file mode 100644
index 0000000000..11c30165a5
--- /dev/null
+++ b/configs/hrnet/metafile.yaml
@@ -0,0 +1,874 @@
+Models:
+- Name: fcn_hr18s_4xb2-40k_cityscapes-512x1024
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 73.86
+      mIoU(ms+flip): 75.91
+  Config: configs/hrnet/fcn_hr18s_4xb2-40k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - HRNetV2p-W18-Small
+    - FCN
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 1.7
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_40k_cityscapes/fcn_hr18s_512x1024_40k_cityscapes_20200601_014216-93db27d0.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_40k_cityscapes/fcn_hr18s_512x1024_40k_cityscapes_20200601_014216.log.json
+  Paper:
+    Title: Deep High-Resolution Representation Learning for Human Pose Estimation
+    URL: https://arxiv.org/abs/1908.07919
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
+  Framework: PyTorch
+- Name: fcn_hr18_4xb2-40k_cityscapes-512x1024
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.19
+      mIoU(ms+flip): 78.92
+  Config: configs/hrnet/fcn_hr18_4xb2-40k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - HRNetV2p-W18
+    - FCN
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 2.9
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_40k_cityscapes/fcn_hr18_512x1024_40k_cityscapes_20200601_014216-f196fb4e.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_40k_cityscapes/fcn_hr18_512x1024_40k_cityscapes_20200601_014216.log.json
+  Paper:
+    Title: Deep High-Resolution Representation Learning for Human Pose Estimation
+    URL: https://arxiv.org/abs/1908.07919
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
+  Framework: PyTorch
+- Name: fcn_hr48_4xb2-40k_cityscapes-512x1024
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.48
+      mIoU(ms+flip): 79.69
+  Config: configs/hrnet/fcn_hr48_4xb2-40k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - HRNetV2p-W48
+    - FCN
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 6.2
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_40k_cityscapes/fcn_hr48_512x1024_40k_cityscapes_20200601_014240-a989b146.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_40k_cityscapes/fcn_hr48_512x1024_40k_cityscapes_20200601_014240.log.json
+  Paper:
+    Title: Deep High-Resolution Representation Learning for Human Pose Estimation
+    URL: https://arxiv.org/abs/1908.07919
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
+  Framework: PyTorch
+- Name: fcn_hr18s_4xb2-80k_cityscapes-512x1024
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 75.31
+      mIoU(ms+flip): 77.48
+  Config: configs/hrnet/fcn_hr18s_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - HRNetV2p-W18-Small
+    - FCN
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_80k_cityscapes/fcn_hr18s_512x1024_80k_cityscapes_20200601_202700-1462b75d.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_80k_cityscapes/fcn_hr18s_512x1024_80k_cityscapes_20200601_202700.log.json
+  Paper:
+    Title: Deep High-Resolution Representation Learning for Human Pose Estimation
+    URL: https://arxiv.org/abs/1908.07919
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
+  Framework: PyTorch
+- Name: fcn_hr18_4xb2-80k_cityscapes-512x1024
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.65
+      mIoU(ms+flip): 80.35
+  Config: configs/hrnet/fcn_hr18_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - HRNetV2p-W18
+    - FCN
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_80k_cityscapes/fcn_hr18_512x1024_80k_cityscapes_20200601_223255-4e7b345e.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_80k_cityscapes/fcn_hr18_512x1024_80k_cityscapes_20200601_223255.log.json
+  Paper:
+    Title: Deep High-Resolution Representation Learning for Human Pose Estimation
+    URL: https://arxiv.org/abs/1908.07919
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
+  Framework: PyTorch
+- Name: fcn_hr48_4xb2-80k_cityscapes-512x1024
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.93
+      mIoU(ms+flip): 80.72
+  Config: configs/hrnet/fcn_hr48_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - HRNetV2p-W48
+    - FCN
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_80k_cityscapes/fcn_hr48_512x1024_80k_cityscapes_20200601_202606-58ea95d6.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_80k_cityscapes/fcn_hr48_512x1024_80k_cityscapes_20200601_202606.log.json
+  Paper:
+    Title: Deep High-Resolution Representation Learning for Human Pose Estimation
+    URL: https://arxiv.org/abs/1908.07919
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
+  Framework: PyTorch
+- Name: fcn_hr18s_4xb2-160k_cityscapes-512x1024
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 76.31
+      mIoU(ms+flip): 78.31
+  Config: configs/hrnet/fcn_hr18s_4xb2-160k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - HRNetV2p-W18-Small
+    - FCN
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_160k_cityscapes/fcn_hr18s_512x1024_160k_cityscapes_20200602_190901-4a0797ea.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_160k_cityscapes/fcn_hr18s_512x1024_160k_cityscapes_20200602_190901.log.json
+  Paper:
+    Title: Deep High-Resolution Representation Learning for Human Pose Estimation
+    URL: https://arxiv.org/abs/1908.07919
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
+  Framework: PyTorch
+- Name: fcn_hr18_4xb2-160k_cityscapes-512x1024
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.8
+      mIoU(ms+flip): 80.74
+  Config: configs/hrnet/fcn_hr18_4xb2-160k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - HRNetV2p-W18
+    - FCN
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_160k_cityscapes/fcn_hr18_512x1024_160k_cityscapes_20200602_190822-221e4a4f.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_160k_cityscapes/fcn_hr18_512x1024_160k_cityscapes_20200602_190822.log.json
+  Paper:
+    Title: Deep High-Resolution Representation Learning for Human Pose Estimation
+    URL: https://arxiv.org/abs/1908.07919
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
+  Framework: PyTorch
+- Name: fcn_hr48_4xb2-160k_cityscapes-512x1024
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 80.65
+      mIoU(ms+flip): 81.92
+  Config: configs/hrnet/fcn_hr48_4xb2-160k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - HRNetV2p-W48
+    - FCN
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_160k_cityscapes/fcn_hr48_512x1024_160k_cityscapes_20200602_190946-59b7973e.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_160k_cityscapes/fcn_hr48_512x1024_160k_cityscapes_20200602_190946.log.json
+  Paper:
+    Title: Deep High-Resolution Representation Learning for Human Pose Estimation
+    URL: https://arxiv.org/abs/1908.07919
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
+  Framework: PyTorch
+- Name: fcn_hr18s_4xb4-80k_ade20k-512x512
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 31.38
+      mIoU(ms+flip): 32.45
+  Config: configs/hrnet/fcn_hr18s_4xb4-80k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - HRNetV2p-W18-Small
+    - FCN
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 3.8
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_ade20k/fcn_hr18s_512x512_80k_ade20k_20200614_144345-77fc814a.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_ade20k/fcn_hr18s_512x512_80k_ade20k_20200614_144345.log.json
+  Paper:
+    Title: Deep High-Resolution Representation Learning for Human Pose Estimation
+    URL: https://arxiv.org/abs/1908.07919
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
+  Framework: PyTorch
+- Name: fcn_hr18_4xb4-80k_ade20k-512x512
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 36.27
+      mIoU(ms+flip): 37.28
+  Config: configs/hrnet/fcn_hr18_4xb4-80k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - HRNetV2p-W18
+    - FCN
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 4.9
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_ade20k/fcn_hr18_512x512_80k_ade20k_20210827_114910-6c9382c0.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_ade20k/fcn_hr18_512x512_80k_ade20k_20210827_114910.log.json
+  Paper:
+    Title: Deep High-Resolution Representation Learning for Human Pose Estimation
+    URL: https://arxiv.org/abs/1908.07919
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
+  Framework: PyTorch
+- Name: fcn_hr48_4xb4-80k_ade20k-512x512
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 41.9
+      mIoU(ms+flip): 43.27
+  Config: configs/hrnet/fcn_hr48_4xb4-80k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - HRNetV2p-W48
+    - FCN
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 8.2
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_ade20k/fcn_hr48_512x512_80k_ade20k_20200614_193946-7ba5258d.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_ade20k/fcn_hr48_512x512_80k_ade20k_20200614_193946.log.json
+  Paper:
+    Title: Deep High-Resolution Representation Learning for Human Pose Estimation
+    URL: https://arxiv.org/abs/1908.07919
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
+  Framework: PyTorch
+- Name: fcn_hr18s_4xb4-160k_ade20k-512x512
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 33.07
+      mIoU(ms+flip): 34.56
+  Config: configs/hrnet/fcn_hr18s_4xb4-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - HRNetV2p-W18-Small
+    - FCN
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_160k_ade20k/fcn_hr18s_512x512_160k_ade20k_20210829_174739-f1e7c2e7.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_160k_ade20k/fcn_hr18s_512x512_160k_ade20k_20210829_174739.log.json
+  Paper:
+    Title: Deep High-Resolution Representation Learning for Human Pose Estimation
+    URL: https://arxiv.org/abs/1908.07919
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
+  Framework: PyTorch
+- Name: fcn_hr18_4xb4-160k_ade20k-512x512
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 36.79
+      mIoU(ms+flip): 38.58
+  Config: configs/hrnet/fcn_hr18_4xb4-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - HRNetV2p-W18
+    - FCN
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_160k_ade20k/fcn_hr18_512x512_160k_ade20k_20200614_214426-ca961836.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_160k_ade20k/fcn_hr18_512x512_160k_ade20k_20200614_214426.log.json
+  Paper:
+    Title: Deep High-Resolution Representation Learning for Human Pose Estimation
+    URL: https://arxiv.org/abs/1908.07919
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
+  Framework: PyTorch
+- Name: fcn_hr48_4xb4-160k_ade20k-512x512
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 42.02
+      mIoU(ms+flip): 43.86
+  Config: configs/hrnet/fcn_hr48_4xb4-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - HRNetV2p-W48
+    - FCN
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_160k_ade20k/fcn_hr48_512x512_160k_ade20k_20200614_214407-a52fc02c.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_160k_ade20k/fcn_hr48_512x512_160k_ade20k_20200614_214407.log.json
+  Paper:
+    Title: Deep High-Resolution Representation Learning for Human Pose Estimation
+    URL: https://arxiv.org/abs/1908.07919
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
+  Framework: PyTorch
+- Name: fcn_hr18s_4xb4-20k_voc12aug-512x512
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 65.5
+      mIoU(ms+flip): 68.89
+  Config: configs/hrnet/fcn_hr18s_4xb4-20k_voc12aug-512x512.py
+  Metadata:
+    Training Data: Pascal VOC 2012 + Aug
+    Batch Size: 16
+    Architecture:
+    - HRNetV2p-W18-Small
+    - FCN
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 1.8
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_20k_voc12aug/fcn_hr18s_512x512_20k_voc12aug_20210829_174910-0aceadb4.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_20k_voc12aug/fcn_hr18s_512x512_20k_voc12aug_20210829_174910.log.json
+  Paper:
+    Title: Deep High-Resolution Representation Learning for Human Pose Estimation
+    URL: https://arxiv.org/abs/1908.07919
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
+  Framework: PyTorch
+- Name: fcn_hr18_4xb4-20k_voc12aug-512x512
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 72.3
+      mIoU(ms+flip): 74.71
+  Config: configs/hrnet/fcn_hr18_4xb4-20k_voc12aug-512x512.py
+  Metadata:
+    Training Data: Pascal VOC 2012 + Aug
+    Batch Size: 16
+    Architecture:
+    - HRNetV2p-W18
+    - FCN
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 2.9
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_20k_voc12aug/fcn_hr18_512x512_20k_voc12aug_20200617_224503-488d45f7.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_20k_voc12aug/fcn_hr18_512x512_20k_voc12aug_20200617_224503.log.json
+  Paper:
+    Title: Deep High-Resolution Representation Learning for Human Pose Estimation
+    URL: https://arxiv.org/abs/1908.07919
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
+  Framework: PyTorch
+- Name: fcn_hr48_4xb4-20k_voc12aug-512x512
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 75.87
+      mIoU(ms+flip): 78.58
+  Config: configs/hrnet/fcn_hr48_4xb4-20k_voc12aug-512x512.py
+  Metadata:
+    Training Data: Pascal VOC 2012 + Aug
+    Batch Size: 16
+    Architecture:
+    - HRNetV2p-W48
+    - FCN
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 6.2
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_20k_voc12aug/fcn_hr48_512x512_20k_voc12aug_20200617_224419-89de05cd.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_20k_voc12aug/fcn_hr48_512x512_20k_voc12aug_20200617_224419.log.json
+  Paper:
+    Title: Deep High-Resolution Representation Learning for Human Pose Estimation
+    URL: https://arxiv.org/abs/1908.07919
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
+  Framework: PyTorch
+- Name: fcn_hr18s_4xb4-40k_voc12aug-512x512
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 66.61
+      mIoU(ms+flip): 70.0
+  Config: configs/hrnet/fcn_hr18s_4xb4-40k_voc12aug-512x512.py
+  Metadata:
+    Training Data: Pascal VOC 2012 + Aug
+    Batch Size: 16
+    Architecture:
+    - HRNetV2p-W18-Small
+    - FCN
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_40k_voc12aug/fcn_hr18s_512x512_40k_voc12aug_20200614_000648-4f8d6e7f.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_40k_voc12aug/fcn_hr18s_512x512_40k_voc12aug_20200614_000648.log.json
+  Paper:
+    Title: Deep High-Resolution Representation Learning for Human Pose Estimation
+    URL: https://arxiv.org/abs/1908.07919
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
+  Framework: PyTorch
+- Name: fcn_hr18_4xb4-40k_voc12aug-512x512
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 72.9
+      mIoU(ms+flip): 75.59
+  Config: configs/hrnet/fcn_hr18_4xb4-40k_voc12aug-512x512.py
+  Metadata:
+    Training Data: Pascal VOC 2012 + Aug
+    Batch Size: 16
+    Architecture:
+    - HRNetV2p-W18
+    - FCN
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_40k_voc12aug/fcn_hr18_512x512_40k_voc12aug_20200613_224401-1b4b76cd.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_40k_voc12aug/fcn_hr18_512x512_40k_voc12aug_20200613_224401.log.json
+  Paper:
+    Title: Deep High-Resolution Representation Learning for Human Pose Estimation
+    URL: https://arxiv.org/abs/1908.07919
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
+  Framework: PyTorch
+- Name: fcn_hr48_4xb4-40k_voc12aug-512x512
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 76.24
+      mIoU(ms+flip): 78.49
+  Config: configs/hrnet/fcn_hr48_4xb4-40k_voc12aug-512x512.py
+  Metadata:
+    Training Data: Pascal VOC 2012 + Aug
+    Batch Size: 16
+    Architecture:
+    - HRNetV2p-W48
+    - FCN
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_40k_voc12aug/fcn_hr48_512x512_40k_voc12aug_20200613_222111-1b0f18bc.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_40k_voc12aug/fcn_hr48_512x512_40k_voc12aug_20200613_222111.log.json
+  Paper:
+    Title: Deep High-Resolution Representation Learning for Human Pose Estimation
+    URL: https://arxiv.org/abs/1908.07919
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
+  Framework: PyTorch
+- Name: fcn_hr48_4xb4-40k_pascal-context-480x480
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal Context
+    Metrics:
+      mIoU: 45.14
+      mIoU(ms+flip): 47.42
+  Config: configs/hrnet/fcn_hr48_4xb4-40k_pascal-context-480x480.py
+  Metadata:
+    Training Data: Pascal Context
+    Batch Size: 16
+    Architecture:
+    - HRNetV2p-W48
+    - FCN
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 6.1
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_40k_pascal_context/fcn_hr48_480x480_40k_pascal_context_20200911_164852-667d00b0.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_40k_pascal_context/fcn_hr48_480x480_40k_pascal_context-20200911_164852.log.json
+  Paper:
+    Title: Deep High-Resolution Representation Learning for Human Pose Estimation
+    URL: https://arxiv.org/abs/1908.07919
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
+  Framework: PyTorch
+- Name: fcn_hr48_4xb4-80k_pascal-context-480x480
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal Context
+    Metrics:
+      mIoU: 45.84
+      mIoU(ms+flip): 47.84
+  Config: configs/hrnet/fcn_hr48_4xb4-80k_pascal-context-480x480.py
+  Metadata:
+    Training Data: Pascal Context
+    Batch Size: 16
+    Architecture:
+    - HRNetV2p-W48
+    - FCN
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_80k_pascal_context/fcn_hr48_480x480_80k_pascal_context_20200911_155322-847a6711.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_80k_pascal_context/fcn_hr48_480x480_80k_pascal_context-20200911_155322.log.json
+  Paper:
+    Title: Deep High-Resolution Representation Learning for Human Pose Estimation
+    URL: https://arxiv.org/abs/1908.07919
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
+  Framework: PyTorch
+- Name: fcn_hr48_4xb4-40k_pascal-context-59-480x480
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal Context 59
+    Metrics:
+      mIoU: 50.33
+      mIoU(ms+flip): 52.83
+  Config: configs/hrnet/fcn_hr48_4xb4-40k_pascal-context-59-480x480.py
+  Metadata:
+    Training Data: Pascal Context 59
+    Batch Size: 16
+    Architecture:
+    - HRNetV2p-W48
+    - FCN
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_40k_pascal_context_59/fcn_hr48_480x480_40k_pascal_context_59_20210410_122738-b808b8b2.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_40k_pascal_context_59/fcn_hr48_480x480_40k_pascal_context_59-20210410_122738.log.json
+  Paper:
+    Title: Deep High-Resolution Representation Learning for Human Pose Estimation
+    URL: https://arxiv.org/abs/1908.07919
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
+  Framework: PyTorch
+- Name: fcn_hr48_4xb4-80k_pascal-context-59-480x480
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal Context 59
+    Metrics:
+      mIoU: 51.12
+      mIoU(ms+flip): 53.56
+  Config: configs/hrnet/fcn_hr48_4xb4-80k_pascal-context-59-480x480.py
+  Metadata:
+    Training Data: Pascal Context 59
+    Batch Size: 16
+    Architecture:
+    - HRNetV2p-W48
+    - FCN
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_80k_pascal_context_59/fcn_hr48_480x480_80k_pascal_context_59_20210411_003240-3ae7081e.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_80k_pascal_context_59/fcn_hr48_480x480_80k_pascal_context_59-20210411_003240.log.json
+  Paper:
+    Title: Deep High-Resolution Representation Learning for Human Pose Estimation
+    URL: https://arxiv.org/abs/1908.07919
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
+  Framework: PyTorch
+- Name: fcn_hr18s_4xb4-80k_loveda-512x512
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: LoveDA
+    Metrics:
+      mIoU: 49.28
+      mIoU(ms+flip): 49.42
+  Config: configs/hrnet/fcn_hr18s_4xb4-80k_loveda-512x512.py
+  Metadata:
+    Training Data: LoveDA
+    Batch Size: 16
+    Architecture:
+    - HRNetV2p-W18-Small
+    - FCN
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 1.59
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_loveda/fcn_hr18s_512x512_80k_loveda_20211210_203228-60a86a7a.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_loveda/fcn_hr18s_512x512_80k_loveda_20211210_203228.log.json
+  Paper:
+    Title: Deep High-Resolution Representation Learning for Human Pose Estimation
+    URL: https://arxiv.org/abs/1908.07919
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
+  Framework: PyTorch
+- Name: fcn_hr18_4xb4-80k_loveda-512x512
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: LoveDA
+    Metrics:
+      mIoU: 50.81
+      mIoU(ms+flip): 50.95
+  Config: configs/hrnet/fcn_hr18_4xb4-80k_loveda-512x512.py
+  Metadata:
+    Training Data: LoveDA
+    Batch Size: 16
+    Architecture:
+    - HRNetV2p-W18
+    - FCN
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 2.76
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_loveda/fcn_hr18_512x512_80k_loveda_20211210_203952-93d9c3b3.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_loveda/fcn_hr18_512x512_80k_loveda_20211210_203952.log.json
+  Paper:
+    Title: Deep High-Resolution Representation Learning for Human Pose Estimation
+    URL: https://arxiv.org/abs/1908.07919
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
+  Framework: PyTorch
+- Name: fcn_hr48_4xb4-80k_loveda-512x512
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: LoveDA
+    Metrics:
+      mIoU: 51.42
+      mIoU(ms+flip): 51.64
+  Config: configs/hrnet/fcn_hr48_4xb4-80k_loveda-512x512.py
+  Metadata:
+    Training Data: LoveDA
+    Batch Size: 16
+    Architecture:
+    - HRNetV2p-W48
+    - FCN
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 6.2
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_loveda/fcn_hr48_512x512_80k_loveda_20211211_044756-67072f55.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_loveda/fcn_hr48_512x512_80k_loveda_20211211_044756.log.json
+  Paper:
+    Title: Deep High-Resolution Representation Learning for Human Pose Estimation
+    URL: https://arxiv.org/abs/1908.07919
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
+  Framework: PyTorch
+- Name: fcn_hr18s_4xb4-80k_potsdam-512x512
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Potsdam
+    Metrics:
+      mIoU: 77.64
+      mIoU(ms+flip): 78.8
+  Config: configs/hrnet/fcn_hr18s_4xb4-80k_potsdam-512x512.py
+  Metadata:
+    Training Data: Potsdam
+    Batch Size: 16
+    Architecture:
+    - HRNetV2p-W18-Small
+    - FCN
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 1.58
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_potsdam/fcn_hr18s_512x512_80k_potsdam_20211218_205517-ba32af63.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_potsdam/fcn_hr18s_512x512_80k_potsdam_20211218_205517.log.json
+  Paper:
+    Title: Deep High-Resolution Representation Learning for Human Pose Estimation
+    URL: https://arxiv.org/abs/1908.07919
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
+  Framework: PyTorch
+- Name: fcn_hr18_4xb4-80k_potsdam-512x512
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Potsdam
+    Metrics:
+      mIoU: 78.26
+      mIoU(ms+flip): 79.24
+  Config: configs/hrnet/fcn_hr18_4xb4-80k_potsdam-512x512.py
+  Metadata:
+    Training Data: Potsdam
+    Batch Size: 16
+    Architecture:
+    - HRNetV2p-W18
+    - FCN
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 2.76
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_potsdam/fcn_hr18_512x512_80k_potsdam_20211218_205517-5d0387ad.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_potsdam/fcn_hr18_512x512_80k_potsdam_20211218_205517.log.json
+  Paper:
+    Title: Deep High-Resolution Representation Learning for Human Pose Estimation
+    URL: https://arxiv.org/abs/1908.07919
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
+  Framework: PyTorch
+- Name: fcn_hr48_4xb4-80k_potsdam-512x512
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Potsdam
+    Metrics:
+      mIoU: 78.39
+      mIoU(ms+flip): 79.34
+  Config: configs/hrnet/fcn_hr48_4xb4-80k_potsdam-512x512.py
+  Metadata:
+    Training Data: Potsdam
+    Batch Size: 16
+    Architecture:
+    - HRNetV2p-W48
+    - FCN
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 6.2
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_potsdam/fcn_hr48_512x512_80k_potsdam_20211219_020601-97434c78.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_potsdam/fcn_hr48_512x512_80k_potsdam_20211219_020601.log.json
+  Paper:
+    Title: Deep High-Resolution Representation Learning for Human Pose Estimation
+    URL: https://arxiv.org/abs/1908.07919
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
+  Framework: PyTorch
+- Name: fcn_hr18s_4xb4-80k_vaihingen-512x512
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Vaihingen
+    Metrics:
+      mIoU: 71.81
+      mIoU(ms+flip): 73.1
+  Config: configs/hrnet/fcn_hr18s_4xb4-80k_vaihingen-512x512.py
+  Metadata:
+    Training Data: Vaihingen
+    Batch Size: 16
+    Architecture:
+    - HRNetV2p-W18-Small
+    - FCN
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 1.58
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_4x4_512x512_80k_vaihingen/fcn_hr18s_4x4_512x512_80k_vaihingen_20211231_230909-b23aae02.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_4x4_512x512_80k_vaihingen/fcn_hr18s_4x4_512x512_80k_vaihingen_20211231_230909.log.json
+  Paper:
+    Title: Deep High-Resolution Representation Learning for Human Pose Estimation
+    URL: https://arxiv.org/abs/1908.07919
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
+  Framework: PyTorch
+- Name: fcn_hr18_4xb4-80k_vaihingen-512x512
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Vaihingen
+    Metrics:
+      mIoU: 72.57
+      mIoU(ms+flip): 74.09
+  Config: configs/hrnet/fcn_hr18_4xb4-80k_vaihingen-512x512.py
+  Metadata:
+    Training Data: Vaihingen
+    Batch Size: 16
+    Architecture:
+    - HRNetV2p-W18
+    - FCN
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 2.76
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_4x4_512x512_80k_vaihingen/fcn_hr18_4x4_512x512_80k_vaihingen_20211231_231216-2ec3ae8a.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_4x4_512x512_80k_vaihingen/fcn_hr18_4x4_512x512_80k_vaihingen_20211231_231216.log.json
+  Paper:
+    Title: Deep High-Resolution Representation Learning for Human Pose Estimation
+    URL: https://arxiv.org/abs/1908.07919
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
+  Framework: PyTorch
+- Name: fcn_hr48_4xb4-80k_vaihingen-512x512
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Vaihingen
+    Metrics:
+      mIoU: 72.5
+      mIoU(ms+flip): 73.52
+  Config: configs/hrnet/fcn_hr48_4xb4-80k_vaihingen-512x512.py
+  Metadata:
+    Training Data: Vaihingen
+    Batch Size: 16
+    Architecture:
+    - HRNetV2p-W48
+    - FCN
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 6.2
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_4x4_512x512_80k_vaihingen/fcn_hr48_4x4_512x512_80k_vaihingen_20211231_231244-7133cb22.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_4x4_512x512_80k_vaihingen/fcn_hr48_4x4_512x512_80k_vaihingen_20211231_231244.log.json
+  Paper:
+    Title: Deep High-Resolution Representation Learning for Human Pose Estimation
+    URL: https://arxiv.org/abs/1908.07919
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
+  Framework: PyTorch
+- Name: fcn_hr18s_4xb4-80k_isaid-896x896
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: iSAID
+    Metrics:
+      mIoU: 62.3
+      mIoU(ms+flip): 62.97
+  Config: configs/hrnet/fcn_hr18s_4xb4-80k_isaid-896x896.py
+  Metadata:
+    Training Data: iSAID
+    Batch Size: 16
+    Architecture:
+    - HRNetV2p-W18-Small
+    - FCN
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 4.95
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_4x4_896x896_80k_isaid/fcn_hr18s_4x4_896x896_80k_isaid_20220118_001603-3cc0769b.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_4x4_896x896_80k_isaid/fcn_hr18s_4x4_896x896_80k_isaid_20220118_001603.log.json
+  Paper:
+    Title: Deep High-Resolution Representation Learning for Human Pose Estimation
+    URL: https://arxiv.org/abs/1908.07919
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
+  Framework: PyTorch
+- Name: fcn_hr18_4xb4-80k_isaid-896x896
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: iSAID
+    Metrics:
+      mIoU: 65.06
+      mIoU(ms+flip): 65.6
+  Config: configs/hrnet/fcn_hr18_4xb4-80k_isaid-896x896.py
+  Metadata:
+    Training Data: iSAID
+    Batch Size: 16
+    Architecture:
+    - HRNetV2p-W18
+    - FCN
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 8.3
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_4x4_896x896_80k_isaid/fcn_hr18_4x4_896x896_80k_isaid_20220110_182230-49bf752e.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_4x4_896x896_80k_isaid/fcn_hr18_4x4_896x896_80k_isaid_20220110_182230.log.json
+  Paper:
+    Title: Deep High-Resolution Representation Learning for Human Pose Estimation
+    URL: https://arxiv.org/abs/1908.07919
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
+  Framework: PyTorch
+- Name: fcn_hr48_4xb4-80k_isaid-896x896
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: iSAID
+    Metrics:
+      mIoU: 67.8
+      mIoU(ms+flip): 68.53
+  Config: configs/hrnet/fcn_hr48_4xb4-80k_isaid-896x896.py
+  Metadata:
+    Training Data: iSAID
+    Batch Size: 16
+    Architecture:
+    - HRNetV2p-W48
+    - FCN
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 16.89
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_4x4_896x896_80k_isaid/fcn_hr48_4x4_896x896_80k_isaid_20220114_174643-547fc420.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_4x4_896x896_80k_isaid/fcn_hr48_4x4_896x896_80k_isaid_20220114_174643.log.json
+  Paper:
+    Title: Deep High-Resolution Representation Learning for Human Pose Estimation
+    URL: https://arxiv.org/abs/1908.07919
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/hrnet.py#L218
+  Framework: PyTorch
diff --git a/configs/icnet/README.md b/configs/icnet/README.md
index 134f53b29f..2b58c2eed8 100644
--- a/configs/icnet/README.md
+++ b/configs/icnet/README.md
@@ -1,6 +1,6 @@
 # ICNet
 
-[ICNet for Real-time Semantic Segmentation on High-resolution Images](https://arxiv.org/abs/1704.08545)
+> [ICNet for Real-time Semantic Segmentation on High-resolution Images](https://arxiv.org/abs/1704.08545)
 
 ## Introduction
 
@@ -22,6 +22,27 @@ We focus on the challenging task of real-time semantic segmentation in this pape
 <img src="https://user-images.githubusercontent.com/24582831/142901772-4570455d-7b27-44ae-a690-47dd9fde8445.png" width="70%"/>
 </div>
 
+## Results and models
+
+### Cityscapes
+
+| Method           | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                                   | download                                                                                                                                                                                                                                                                                                                                                                               |
+| ---------------- | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ---------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| ICNet            | R-18-D8  | 832x832   |   80000 | 1.70     | 27.12          | V100   | 68.14 |         70.16 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/icnet/icnet_r18-d8_4xb2-80k_cityscapes-832x832.py)            | [model](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_832x832_80k_cityscapes/icnet_r18-d8_832x832_80k_cityscapes_20210925_225521-2e36638d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_832x832_80k_cityscapes/icnet_r18-d8_832x832_80k_cityscapes_20210925_225521.log.json)                                             |
+| ICNet            | R-18-D8  | 832x832   |  160000 | -        | -              | V100   | 71.64 |         74.18 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/icnet/icnet_r18-d8_4xb2-160k_cityscapes-832x832.py)           | [model](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_832x832_160k_cityscapes/icnet_r18-d8_832x832_160k_cityscapes_20210925_230153-2c6eb6e0.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_832x832_160k_cityscapes/icnet_r18-d8_832x832_160k_cityscapes_20210925_230153.log.json)                                         |
+| ICNet (in1k-pre) | R-18-D8  | 832x832   |   80000 | -        | -              | V100   | 72.51 |         74.78 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/icnet/icnet_r18-d8-in1k-pre_4xb2-80k_cityscapes-832x832.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_in1k-pre_832x832_80k_cityscapes/icnet_r18-d8_in1k-pre_832x832_80k_cityscapes_20210925_230354-1cbe3022.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_in1k-pre_832x832_80k_cityscapes/icnet_r18-d8_in1k-pre_832x832_80k_cityscapes_20210925_230354.log.json)         |
+| ICNet (in1k-pre) | R-18-D8  | 832x832   |  160000 | -        | -              | V100   | 74.43 |         76.72 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/icnet/icnet_r18-d8-in1k-pre_4xb2-160k_cityscapes-832x832.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_in1k-pre_832x832_160k_cityscapes/icnet_r18-d8_in1k-pre_832x832_160k_cityscapes_20210926_052702-619c8ae1.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_in1k-pre_832x832_160k_cityscapes/icnet_r18-d8_in1k-pre_832x832_160k_cityscapes_20210926_052702.log.json)     |
+| ICNet            | R-50-D8  | 832x832   |   80000 | 2.53     | 20.08          | V100   | 68.91 |         69.72 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/icnet/icnet_r50-d8_4xb2-80k_cityscapes-832x832.py)            | [model](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_832x832_80k_cityscapes/icnet_r50-d8_832x832_80k_cityscapes_20210926_044625-c6407341.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_832x832_80k_cityscapes/icnet_r50-d8_832x832_80k_cityscapes_20210926_044625.log.json)                                             |
+| ICNet            | R-50-D8  | 832x832   |  160000 | -        | -              | V100   | 73.82 |         75.67 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/icnet/icnet_r50-d8_4xb2-160k_cityscapes-832x832.py)           | [model](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_832x832_160k_cityscapes/icnet_r50-d8_832x832_160k_cityscapes_20210925_232612-a95f0d4e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_832x832_160k_cityscapes/icnet_r50-d8_832x832_160k_cityscapes_20210925_232612.log.json)                                         |
+| ICNet (in1k-pre) | R-50-D8  | 832x832   |   80000 | -        | -              | V100   | 74.58 |         76.41 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/icnet/icnet_r50-d8-in1k-pre_4xb2-80k_cityscapes-832x832.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_in1k-pre_832x832_80k_cityscapes/icnet_r50-d8_in1k-pre_832x832_80k_cityscapes_20210926_032943-1743dc7b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_in1k-pre_832x832_80k_cityscapes/icnet_r50-d8_in1k-pre_832x832_80k_cityscapes_20210926_032943.log.json)         |
+| ICNet (in1k-pre) | R-50-D8  | 832x832   |  160000 | -        | -              | V100   | 76.29 |         78.09 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/icnet/icnet_r50-d8-in1k-pre_4xb2-160k_cityscapes-832x832.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_in1k-pre_832x832_160k_cityscapes/icnet_r50-d8_in1k-pre_832x832_160k_cityscapes_20210926_042715-ce310aea.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_in1k-pre_832x832_160k_cityscapes/icnet_r50-d8_in1k-pre_832x832_160k_cityscapes_20210926_042715.log.json)     |
+| ICNet            | R-101-D8 | 832x832   |   80000 | 3.08     | 16.95          | V100   | 70.28 |         71.95 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/icnet/icnet_r101-d8_4xb2-80k_cityscapes-832x832.py)           | [model](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_832x832_80k_cityscapes/icnet_r101-d8_832x832_80k_cityscapes_20210926_072447-b52f936e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_832x832_80k_cityscapes/icnet_r101-d8_832x832_80k_cityscapes_20210926_072447.log.json)                                         |
+| ICNet            | R-101-D8 | 832x832   |  160000 | -        | -              | V100   | 73.80 |         76.10 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/icnet/icnet_r101-d8_4xb2-160k_cityscapes-832x832.py)          | [model](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_832x832_160k_cityscapes/icnet_r101-d8_832x832_160k_cityscapes_20210926_092350-3a1ebf1a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_832x832_160k_cityscapes/icnet_r101-d8_832x832_160k_cityscapes_20210926_092350.log.json)                                     |
+| ICNet (in1k-pre) | R-101-D8 | 832x832   |   80000 | -        | -              | V100   | 75.57 |         77.86 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/icnet/icnet_r101-d8-in1k-pre_4xb2-80k_cityscapes-832x832.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_in1k-pre_832x832_80k_cityscapes/icnet_r101-d8_in1k-pre_832x832_80k_cityscapes_20210926_020414-7ceb12c5.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_in1k-pre_832x832_80k_cityscapes/icnet_r101-d8_in1k-pre_832x832_80k_cityscapes_20210926_020414.log.json)     |
+| ICNet (in1k-pre) | R-101-D8 | 832x832   |  160000 | -        | -              | V100   | 76.15 |         77.98 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/icnet/icnet_r101-d8-in1k-pre_4xb2-160k_cityscapes-832x832.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_in1k-pre_832x832_160k_cityscapes/icnet_r101-d8_in1k-pre_832x832_160k_cityscapes_20210925_232612-9484ae8a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_in1k-pre_832x832_160k_cityscapes/icnet_r101-d8_in1k-pre_832x832_160k_cityscapes_20210925_232612.log.json) |
+
+Note: `in1k-pre` means pretrained model is used.
+
 ## Citation
 
 ```bibtext
@@ -33,24 +54,3 @@ We focus on the challenging task of real-time semantic segmentation in this pape
   year={2018}
 }
 ```
-
-## Results and models
-
-### Cityscapes
-
-| Method           | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                                   | download                                                                                                                                                                                                                                                                                                                                                                               |
-| ---------------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ---------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| ICNet            | R-18-D8  | 832x832   |   80000 | 1.70     | 27.12          | 68.14 |         70.16 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/icnet/icnet_r18-d8_4xb2-80k_cityscapes-832x832.py)            | [model](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_832x832_80k_cityscapes/icnet_r18-d8_832x832_80k_cityscapes_20210925_225521-2e36638d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_832x832_80k_cityscapes/icnet_r18-d8_832x832_80k_cityscapes_20210925_225521.log.json)                                             |
-| ICNet            | R-18-D8  | 832x832   |  160000 | -        | -              | 71.64 |         74.18 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/icnet/icnet_r18-d8_4xb2-160k_cityscapes-832x832.py)           | [model](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_832x832_160k_cityscapes/icnet_r18-d8_832x832_160k_cityscapes_20210925_230153-2c6eb6e0.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_832x832_160k_cityscapes/icnet_r18-d8_832x832_160k_cityscapes_20210925_230153.log.json)                                         |
-| ICNet (in1k-pre) | R-18-D8  | 832x832   |   80000 | -        | -              | 72.51 |         74.78 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/icnet/icnet_r18-d8-in1k-pre_4xb2-80k_cityscapes-832x832.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_in1k-pre_832x832_80k_cityscapes/icnet_r18-d8_in1k-pre_832x832_80k_cityscapes_20210925_230354-1cbe3022.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_in1k-pre_832x832_80k_cityscapes/icnet_r18-d8_in1k-pre_832x832_80k_cityscapes_20210925_230354.log.json)         |
-| ICNet (in1k-pre) | R-18-D8  | 832x832   |  160000 | -        | -              | 74.43 |         76.72 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/icnet/icnet_r18-d8-in1k-pre_4xb2-160k_cityscapes-832x832.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_in1k-pre_832x832_160k_cityscapes/icnet_r18-d8_in1k-pre_832x832_160k_cityscapes_20210926_052702-619c8ae1.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_in1k-pre_832x832_160k_cityscapes/icnet_r18-d8_in1k-pre_832x832_160k_cityscapes_20210926_052702.log.json)     |
-| ICNet            | R-50-D8  | 832x832   |   80000 | 2.53     | 20.08          | 68.91 |         69.72 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/icnet/icnet_r50-d8_4xb2-80k_cityscapes-832x832.py)            | [model](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_832x832_80k_cityscapes/icnet_r50-d8_832x832_80k_cityscapes_20210926_044625-c6407341.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_832x832_80k_cityscapes/icnet_r50-d8_832x832_80k_cityscapes_20210926_044625.log.json)                                             |
-| ICNet            | R-50-D8  | 832x832   |  160000 | -        | -              | 73.82 |         75.67 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/icnet/icnet_r50-d8_4xb2-160k_cityscapes-832x832.py)           | [model](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_832x832_160k_cityscapes/icnet_r50-d8_832x832_160k_cityscapes_20210925_232612-a95f0d4e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_832x832_160k_cityscapes/icnet_r50-d8_832x832_160k_cityscapes_20210925_232612.log.json)                                         |
-| ICNet (in1k-pre) | R-50-D8  | 832x832   |   80000 | -        | -              | 74.58 |         76.41 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/icnet/icnet_r50-d8-in1k-pre_4xb2-80k_cityscapes-832x832.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_in1k-pre_832x832_80k_cityscapes/icnet_r50-d8_in1k-pre_832x832_80k_cityscapes_20210926_032943-1743dc7b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_in1k-pre_832x832_80k_cityscapes/icnet_r50-d8_in1k-pre_832x832_80k_cityscapes_20210926_032943.log.json)         |
-| ICNet (in1k-pre) | R-50-D8  | 832x832   |  160000 | -        | -              | 76.29 |         78.09 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/icnet/icnet_r50-d8-in1k-pre_4xb2-160k_cityscapes-832x832.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_in1k-pre_832x832_160k_cityscapes/icnet_r50-d8_in1k-pre_832x832_160k_cityscapes_20210926_042715-ce310aea.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_in1k-pre_832x832_160k_cityscapes/icnet_r50-d8_in1k-pre_832x832_160k_cityscapes_20210926_042715.log.json)     |
-| ICNet            | R-101-D8 | 832x832   |   80000 | 3.08     | 16.95          | 70.28 |         71.95 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/icnet/icnet_r101-d8_4xb2-80k_cityscapes-832x832.py)           | [model](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_832x832_80k_cityscapes/icnet_r101-d8_832x832_80k_cityscapes_20210926_072447-b52f936e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_832x832_80k_cityscapes/icnet_r101-d8_832x832_80k_cityscapes_20210926_072447.log.json)                                         |
-| ICNet            | R-101-D8 | 832x832   |  160000 | -        | -              | 73.80 |         76.10 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/icnet/icnet_r101-d8_4xb2-160k_cityscapes-832x832.py)          | [model](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_832x832_160k_cityscapes/icnet_r101-d8_832x832_160k_cityscapes_20210926_092350-3a1ebf1a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_832x832_160k_cityscapes/icnet_r101-d8_832x832_160k_cityscapes_20210926_092350.log.json)                                     |
-| ICNet (in1k-pre) | R-101-D8 | 832x832   |   80000 | -        | -              | 75.57 |         77.86 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/icnet/icnet_r101-d8-in1k-pre_4xb2-80k_cityscapes-832x832.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_in1k-pre_832x832_80k_cityscapes/icnet_r101-d8_in1k-pre_832x832_80k_cityscapes_20210926_020414-7ceb12c5.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_in1k-pre_832x832_80k_cityscapes/icnet_r101-d8_in1k-pre_832x832_80k_cityscapes_20210926_020414.log.json)     |
-| ICNet (in1k-pre) | R-101-D8 | 832x832   |  160000 | -        | -              | 76.15 |         77.98 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/icnet/icnet_r101-d8-in1k-pre_4xb2-160k_cityscapes-832x832.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_in1k-pre_832x832_160k_cityscapes/icnet_r101-d8_in1k-pre_832x832_160k_cityscapes_20210925_232612-9484ae8a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_in1k-pre_832x832_160k_cityscapes/icnet_r101-d8_in1k-pre_832x832_160k_cityscapes_20210925_232612.log.json) |
-
-Note: `in1k-pre` means pretrained model is used.
diff --git a/configs/icnet/icnet.yml b/configs/icnet/icnet.yml
deleted file mode 100644
index 5ded544726..0000000000
--- a/configs/icnet/icnet.yml
+++ /dev/null
@@ -1,207 +0,0 @@
-Collections:
-- Name: ICNet
-  Metadata:
-    Training Data:
-    - Cityscapes
-  Paper:
-    URL: https://arxiv.org/abs/1704.08545
-    Title: ICNet for Real-time Semantic Segmentation on High-resolution Images
-  README: configs/icnet/README.md
-  Code:
-    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/ic_neck.py#L77
-    Version: v0.18.0
-  Converted From:
-    Code: https://github.com/hszhao/ICNet
-Models:
-- Name: icnet_r18-d8_4xb2-80k_cityscapes-832x832
-  In Collection: ICNet
-  Metadata:
-    backbone: R-18-D8
-    crop size: (832,832)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 36.87
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (832,832)
-    Training Memory (GB): 1.7
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 68.14
-      mIoU(ms+flip): 70.16
-  Config: configs/icnet/icnet_r18-d8_4xb2-80k_cityscapes-832x832.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_832x832_80k_cityscapes/icnet_r18-d8_832x832_80k_cityscapes_20210925_225521-2e36638d.pth
-- Name: icnet_r18-d8_4xb2-160k_cityscapes-832x832
-  In Collection: ICNet
-  Metadata:
-    backbone: R-18-D8
-    crop size: (832,832)
-    lr schd: 160000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 71.64
-      mIoU(ms+flip): 74.18
-  Config: configs/icnet/icnet_r18-d8_4xb2-160k_cityscapes-832x832.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_832x832_160k_cityscapes/icnet_r18-d8_832x832_160k_cityscapes_20210925_230153-2c6eb6e0.pth
-- Name: icnet_r18-d8-in1k-pre_4xb2-80k_cityscapes-832x832
-  In Collection: ICNet
-  Metadata:
-    backbone: R-18-D8
-    crop size: (832,832)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 72.51
-      mIoU(ms+flip): 74.78
-  Config: configs/icnet/icnet_r18-d8-in1k-pre_4xb2-80k_cityscapes-832x832.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_in1k-pre_832x832_80k_cityscapes/icnet_r18-d8_in1k-pre_832x832_80k_cityscapes_20210925_230354-1cbe3022.pth
-- Name: icnet_r18-d8-in1k-pre_4xb2-160k_cityscapes-832x832
-  In Collection: ICNet
-  Metadata:
-    backbone: R-18-D8
-    crop size: (832,832)
-    lr schd: 160000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 74.43
-      mIoU(ms+flip): 76.72
-  Config: configs/icnet/icnet_r18-d8-in1k-pre_4xb2-160k_cityscapes-832x832.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_in1k-pre_832x832_160k_cityscapes/icnet_r18-d8_in1k-pre_832x832_160k_cityscapes_20210926_052702-619c8ae1.pth
-- Name: icnet_r50-d8_4xb2-80k_cityscapes-832x832
-  In Collection: ICNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (832,832)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 49.8
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (832,832)
-    Training Memory (GB): 2.53
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 68.91
-      mIoU(ms+flip): 69.72
-  Config: configs/icnet/icnet_r50-d8_4xb2-80k_cityscapes-832x832.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_832x832_80k_cityscapes/icnet_r50-d8_832x832_80k_cityscapes_20210926_044625-c6407341.pth
-- Name: icnet_r50-d8_4xb2-160k_cityscapes-832x832
-  In Collection: ICNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (832,832)
-    lr schd: 160000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 73.82
-      mIoU(ms+flip): 75.67
-  Config: configs/icnet/icnet_r50-d8_4xb2-160k_cityscapes-832x832.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_832x832_160k_cityscapes/icnet_r50-d8_832x832_160k_cityscapes_20210925_232612-a95f0d4e.pth
-- Name: icnet_r50-d8-in1k-pre_4xb2-80k_cityscapes-832x832
-  In Collection: ICNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (832,832)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 74.58
-      mIoU(ms+flip): 76.41
-  Config: configs/icnet/icnet_r50-d8-in1k-pre_4xb2-80k_cityscapes-832x832.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_in1k-pre_832x832_80k_cityscapes/icnet_r50-d8_in1k-pre_832x832_80k_cityscapes_20210926_032943-1743dc7b.pth
-- Name: icnet_r50-d8-in1k-pre_4xb2-160k_cityscapes-832x832
-  In Collection: ICNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (832,832)
-    lr schd: 160000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 76.29
-      mIoU(ms+flip): 78.09
-  Config: configs/icnet/icnet_r50-d8-in1k-pre_4xb2-160k_cityscapes-832x832.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_in1k-pre_832x832_160k_cityscapes/icnet_r50-d8_in1k-pre_832x832_160k_cityscapes_20210926_042715-ce310aea.pth
-- Name: icnet_r101-d8_4xb2-80k_cityscapes-832x832
-  In Collection: ICNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (832,832)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 59.0
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (832,832)
-    Training Memory (GB): 3.08
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 70.28
-      mIoU(ms+flip): 71.95
-  Config: configs/icnet/icnet_r101-d8_4xb2-80k_cityscapes-832x832.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_832x832_80k_cityscapes/icnet_r101-d8_832x832_80k_cityscapes_20210926_072447-b52f936e.pth
-- Name: icnet_r101-d8_4xb2-160k_cityscapes-832x832
-  In Collection: ICNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (832,832)
-    lr schd: 160000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 73.8
-      mIoU(ms+flip): 76.1
-  Config: configs/icnet/icnet_r101-d8_4xb2-160k_cityscapes-832x832.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_832x832_160k_cityscapes/icnet_r101-d8_832x832_160k_cityscapes_20210926_092350-3a1ebf1a.pth
-- Name: icnet_r101-d8-in1k-pre_4xb2-80k_cityscapes-832x832
-  In Collection: ICNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (832,832)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 75.57
-      mIoU(ms+flip): 77.86
-  Config: configs/icnet/icnet_r101-d8-in1k-pre_4xb2-80k_cityscapes-832x832.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_in1k-pre_832x832_80k_cityscapes/icnet_r101-d8_in1k-pre_832x832_80k_cityscapes_20210926_020414-7ceb12c5.pth
-- Name: icnet_r101-d8-in1k-pre_4xb2-160k_cityscapes-832x832
-  In Collection: ICNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (832,832)
-    lr schd: 160000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 76.15
-      mIoU(ms+flip): 77.98
-  Config: configs/icnet/icnet_r101-d8-in1k-pre_4xb2-160k_cityscapes-832x832.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_in1k-pre_832x832_160k_cityscapes/icnet_r101-d8_in1k-pre_832x832_160k_cityscapes_20210925_232612-9484ae8a.pth
diff --git a/configs/icnet/metafile.yaml b/configs/icnet/metafile.yaml
new file mode 100644
index 0000000000..1d843ee4b6
--- /dev/null
+++ b/configs/icnet/metafile.yaml
@@ -0,0 +1,298 @@
+Collections:
+- Name: ICNet
+  License: Apache License 2.0
+  Metadata:
+    Training Data:
+    - Cityscapes
+  Paper:
+    Title: ICNet for Real-time Semantic Segmentation on High-resolution Images
+    URL: https://arxiv.org/abs/1704.08545
+  README: configs/icnet/README.md
+  Frameworks:
+  - PyTorch
+Models:
+- Name: icnet_r18-d8_4xb2-80k_cityscapes-832x832
+  In Collection: ICNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 68.14
+      mIoU(ms+flip): 70.16
+  Config: configs/icnet/icnet_r18-d8_4xb2-80k_cityscapes-832x832.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-18-D8
+    - ICNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 1.7
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_832x832_80k_cityscapes/icnet_r18-d8_832x832_80k_cityscapes_20210925_225521-2e36638d.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_832x832_80k_cityscapes/icnet_r18-d8_832x832_80k_cityscapes_20210925_225521.log.json
+  Paper:
+    Title: ICNet for Real-time Semantic Segmentation on High-resolution Images
+    URL: https://arxiv.org/abs/1704.08545
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/ic_neck.py#L77
+  Framework: PyTorch
+- Name: icnet_r18-d8_4xb2-160k_cityscapes-832x832
+  In Collection: ICNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 71.64
+      mIoU(ms+flip): 74.18
+  Config: configs/icnet/icnet_r18-d8_4xb2-160k_cityscapes-832x832.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-18-D8
+    - ICNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_832x832_160k_cityscapes/icnet_r18-d8_832x832_160k_cityscapes_20210925_230153-2c6eb6e0.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_832x832_160k_cityscapes/icnet_r18-d8_832x832_160k_cityscapes_20210925_230153.log.json
+  Paper:
+    Title: ICNet for Real-time Semantic Segmentation on High-resolution Images
+    URL: https://arxiv.org/abs/1704.08545
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/ic_neck.py#L77
+  Framework: PyTorch
+- Name: icnet_r18-d8-in1k-pre_4xb2-80k_cityscapes-832x832
+  In Collection: ICNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 72.51
+      mIoU(ms+flip): 74.78
+  Config: configs/icnet/icnet_r18-d8-in1k-pre_4xb2-80k_cityscapes-832x832.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-18-D8
+    - ICNet
+    - (in1k-pre)
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_in1k-pre_832x832_80k_cityscapes/icnet_r18-d8_in1k-pre_832x832_80k_cityscapes_20210925_230354-1cbe3022.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_in1k-pre_832x832_80k_cityscapes/icnet_r18-d8_in1k-pre_832x832_80k_cityscapes_20210925_230354.log.json
+  Paper:
+    Title: ICNet for Real-time Semantic Segmentation on High-resolution Images
+    URL: https://arxiv.org/abs/1704.08545
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/ic_neck.py#L77
+  Framework: PyTorch
+- Name: icnet_r18-d8-in1k-pre_4xb2-160k_cityscapes-832x832
+  In Collection: ICNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 74.43
+      mIoU(ms+flip): 76.72
+  Config: configs/icnet/icnet_r18-d8-in1k-pre_4xb2-160k_cityscapes-832x832.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-18-D8
+    - ICNet
+    - (in1k-pre)
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_in1k-pre_832x832_160k_cityscapes/icnet_r18-d8_in1k-pre_832x832_160k_cityscapes_20210926_052702-619c8ae1.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_in1k-pre_832x832_160k_cityscapes/icnet_r18-d8_in1k-pre_832x832_160k_cityscapes_20210926_052702.log.json
+  Paper:
+    Title: ICNet for Real-time Semantic Segmentation on High-resolution Images
+    URL: https://arxiv.org/abs/1704.08545
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/ic_neck.py#L77
+  Framework: PyTorch
+- Name: icnet_r50-d8_4xb2-80k_cityscapes-832x832
+  In Collection: ICNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 68.91
+      mIoU(ms+flip): 69.72
+  Config: configs/icnet/icnet_r50-d8_4xb2-80k_cityscapes-832x832.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - ICNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 2.53
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_832x832_80k_cityscapes/icnet_r50-d8_832x832_80k_cityscapes_20210926_044625-c6407341.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_832x832_80k_cityscapes/icnet_r50-d8_832x832_80k_cityscapes_20210926_044625.log.json
+  Paper:
+    Title: ICNet for Real-time Semantic Segmentation on High-resolution Images
+    URL: https://arxiv.org/abs/1704.08545
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/ic_neck.py#L77
+  Framework: PyTorch
+- Name: icnet_r50-d8_4xb2-160k_cityscapes-832x832
+  In Collection: ICNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 73.82
+      mIoU(ms+flip): 75.67
+  Config: configs/icnet/icnet_r50-d8_4xb2-160k_cityscapes-832x832.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - ICNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_832x832_160k_cityscapes/icnet_r50-d8_832x832_160k_cityscapes_20210925_232612-a95f0d4e.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_832x832_160k_cityscapes/icnet_r50-d8_832x832_160k_cityscapes_20210925_232612.log.json
+  Paper:
+    Title: ICNet for Real-time Semantic Segmentation on High-resolution Images
+    URL: https://arxiv.org/abs/1704.08545
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/ic_neck.py#L77
+  Framework: PyTorch
+- Name: icnet_r50-d8-in1k-pre_4xb2-80k_cityscapes-832x832
+  In Collection: ICNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 74.58
+      mIoU(ms+flip): 76.41
+  Config: configs/icnet/icnet_r50-d8-in1k-pre_4xb2-80k_cityscapes-832x832.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - ICNet
+    - (in1k-pre)
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_in1k-pre_832x832_80k_cityscapes/icnet_r50-d8_in1k-pre_832x832_80k_cityscapes_20210926_032943-1743dc7b.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_in1k-pre_832x832_80k_cityscapes/icnet_r50-d8_in1k-pre_832x832_80k_cityscapes_20210926_032943.log.json
+  Paper:
+    Title: ICNet for Real-time Semantic Segmentation on High-resolution Images
+    URL: https://arxiv.org/abs/1704.08545
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/ic_neck.py#L77
+  Framework: PyTorch
+- Name: icnet_r50-d8-in1k-pre_4xb2-160k_cityscapes-832x832
+  In Collection: ICNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 76.29
+      mIoU(ms+flip): 78.09
+  Config: configs/icnet/icnet_r50-d8-in1k-pre_4xb2-160k_cityscapes-832x832.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - ICNet
+    - (in1k-pre)
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_in1k-pre_832x832_160k_cityscapes/icnet_r50-d8_in1k-pre_832x832_160k_cityscapes_20210926_042715-ce310aea.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_in1k-pre_832x832_160k_cityscapes/icnet_r50-d8_in1k-pre_832x832_160k_cityscapes_20210926_042715.log.json
+  Paper:
+    Title: ICNet for Real-time Semantic Segmentation on High-resolution Images
+    URL: https://arxiv.org/abs/1704.08545
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/ic_neck.py#L77
+  Framework: PyTorch
+- Name: icnet_r101-d8_4xb2-80k_cityscapes-832x832
+  In Collection: ICNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 70.28
+      mIoU(ms+flip): 71.95
+  Config: configs/icnet/icnet_r101-d8_4xb2-80k_cityscapes-832x832.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - ICNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 3.08
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_832x832_80k_cityscapes/icnet_r101-d8_832x832_80k_cityscapes_20210926_072447-b52f936e.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_832x832_80k_cityscapes/icnet_r101-d8_832x832_80k_cityscapes_20210926_072447.log.json
+  Paper:
+    Title: ICNet for Real-time Semantic Segmentation on High-resolution Images
+    URL: https://arxiv.org/abs/1704.08545
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/ic_neck.py#L77
+  Framework: PyTorch
+- Name: icnet_r101-d8_4xb2-160k_cityscapes-832x832
+  In Collection: ICNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 73.8
+      mIoU(ms+flip): 76.1
+  Config: configs/icnet/icnet_r101-d8_4xb2-160k_cityscapes-832x832.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - ICNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_832x832_160k_cityscapes/icnet_r101-d8_832x832_160k_cityscapes_20210926_092350-3a1ebf1a.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_832x832_160k_cityscapes/icnet_r101-d8_832x832_160k_cityscapes_20210926_092350.log.json
+  Paper:
+    Title: ICNet for Real-time Semantic Segmentation on High-resolution Images
+    URL: https://arxiv.org/abs/1704.08545
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/ic_neck.py#L77
+  Framework: PyTorch
+- Name: icnet_r101-d8-in1k-pre_4xb2-80k_cityscapes-832x832
+  In Collection: ICNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 75.57
+      mIoU(ms+flip): 77.86
+  Config: configs/icnet/icnet_r101-d8-in1k-pre_4xb2-80k_cityscapes-832x832.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - ICNet
+    - (in1k-pre)
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_in1k-pre_832x832_80k_cityscapes/icnet_r101-d8_in1k-pre_832x832_80k_cityscapes_20210926_020414-7ceb12c5.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_in1k-pre_832x832_80k_cityscapes/icnet_r101-d8_in1k-pre_832x832_80k_cityscapes_20210926_020414.log.json
+  Paper:
+    Title: ICNet for Real-time Semantic Segmentation on High-resolution Images
+    URL: https://arxiv.org/abs/1704.08545
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/ic_neck.py#L77
+  Framework: PyTorch
+- Name: icnet_r101-d8-in1k-pre_4xb2-160k_cityscapes-832x832
+  In Collection: ICNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 76.15
+      mIoU(ms+flip): 77.98
+  Config: configs/icnet/icnet_r101-d8-in1k-pre_4xb2-160k_cityscapes-832x832.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - ICNet
+    - (in1k-pre)
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_in1k-pre_832x832_160k_cityscapes/icnet_r101-d8_in1k-pre_832x832_160k_cityscapes_20210925_232612-9484ae8a.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_in1k-pre_832x832_160k_cityscapes/icnet_r101-d8_in1k-pre_832x832_160k_cityscapes_20210925_232612.log.json
+  Paper:
+    Title: ICNet for Real-time Semantic Segmentation on High-resolution Images
+    URL: https://arxiv.org/abs/1704.08545
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/ic_neck.py#L77
+  Framework: PyTorch
diff --git a/configs/isanet/README.md b/configs/isanet/README.md
index db93dae234..da9f0104c5 100644
--- a/configs/isanet/README.md
+++ b/configs/isanet/README.md
@@ -1,6 +1,6 @@
 # ISANet
 
-[Interlaced Sparse Self-Attention for Semantic Segmentation](https://arxiv.org/abs/1907.12273)
+> [Interlaced Sparse Self-Attention for Semantic Segmentation](https://arxiv.org/abs/1907.12273)
 
 ## Introduction
 
@@ -22,6 +22,39 @@ In this paper, we present a so-called interlaced sparse self-attention approach
 <img src="https://user-images.githubusercontent.com/24582831/142901868-03d80da4-b9c0-4df9-8509-5f684ba9dadc.png" width="80%"/>
 </div>
 
+## Results and models
+
+### Cityscapes
+
+| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device | mIoU  | mIoU(ms+flip) |                                                                                                                            config | download                                                                                                                                                                                                                                                                                                                                                 |
+| ------ | -------- | --------- | ------- | -------: | -------------- | ------ | ----- | ------------: | --------------------------------------------------------------------------------------------------------------------------------: | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| ISANet | R-50-D8  | 512x1024  | 40000   |    5.869 | 2.91           | V100   | 78.49 |         79.44 |  [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/isanet/isanet_r50-d8_4xb2-40k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x1024_40k_cityscapes/isanet_r50-d8_512x1024_40k_cityscapes_20210901_054739-981bd763.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x1024_40k_cityscapes/isanet_r50-d8_512x1024_40k_cityscapes_20210901_054739.log.json)     |
+| ISANet | R-50-D8  | 512x1024  | 80000   |    5.869 | 2.91           | V100   | 78.68 |         80.25 |  [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/isanet/isanet_r50-d8_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x1024_80k_cityscapes/isanet_r50-d8_512x1024_80k_cityscapes_20210901_074202-89384497.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x1024_80k_cityscapes/isanet_r50-d8_512x1024_80k_cityscapes_20210901_074202.log.json)     |
+| ISANet | R-50-D8  | 769x769   | 40000   |    6.759 | 1.54           | V100   | 78.70 |         80.28 |   [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/isanet/isanet_r50-d8_4xb2-40k_cityscapes-769x769.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_769x769_40k_cityscapes/isanet_r50-d8_769x769_40k_cityscapes_20210903_050200-4ae7e65b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_769x769_40k_cityscapes/isanet_r50-d8_769x769_40k_cityscapes_20210903_050200.log.json)         |
+| ISANet | R-50-D8  | 769x769   | 80000   |    6.759 | 1.54           | V100   | 79.29 |         80.53 |   [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/isanet/isanet_r50-d8_4xb2-80k_cityscapes-769x769.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_769x769_80k_cityscapes/isanet_r50-d8_769x769_80k_cityscapes_20210903_101126-99b54519.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_769x769_80k_cityscapes/isanet_r50-d8_769x769_80k_cityscapes_20210903_101126.log.json)         |
+| ISANet | R-101-D8 | 512x1024  | 40000   |    9.425 | 2.35           | V100   | 79.58 |         81.05 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/isanet/isanet_r101-d8_4xb2-40k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x1024_40k_cityscapes/isanet_r101-d8_512x1024_40k_cityscapes_20210901_145553-293e6bd6.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x1024_40k_cityscapes/isanet_r101-d8_512x1024_40k_cityscapes_20210901_145553.log.json) |
+| ISANet | R-101-D8 | 512x1024  | 80000   |    9.425 | 2.35           | V100   | 80.32 |         81.58 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/isanet/isanet_r101-d8_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x1024_80k_cityscapes/isanet_r101-d8_512x1024_80k_cityscapes_20210901_145243-5b99c9b2.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x1024_80k_cityscapes/isanet_r101-d8_512x1024_80k_cityscapes_20210901_145243.log.json) |
+| ISANet | R-101-D8 | 769x769   | 40000   |   10.815 | 0.92           | V100   | 79.68 |         80.95 |  [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/isanet/isanet_r101-d8_4xb2-40k_cityscapes-769x769.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_769x769_40k_cityscapes/isanet_r101-d8_769x769_40k_cityscapes_20210903_111320-509e7224.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_769x769_40k_cityscapes/isanet_r101-d8_769x769_40k_cityscapes_20210903_111320.log.json)     |
+| ISANet | R-101-D8 | 769x769   | 80000   |   10.815 | 0.92           | V100   | 80.61 |         81.59 |  [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/isanet/isanet_r101-d8_4xb2-80k_cityscapes-769x769.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_769x769_80k_cityscapes/isanet_r101-d8_769x769_80k_cityscapes_20210903_111319-24f71dfa.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_769x769_80k_cityscapes/isanet_r101-d8_769x769_80k_cityscapes_20210903_111319.log.json)     |
+
+### ADE20K
+
+| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device | mIoU  | mIoU(ms+flip) |                                                                                                                        config | download                                                                                                                                                                                                                                                                                                                                 |
+| ------ | -------- | --------- | ------- | -------: | -------------- | ------ | ----- | ------------: | ----------------------------------------------------------------------------------------------------------------------------: | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| ISANet | R-50-D8  | 512x512   | 80000   |      9.0 | 22.55          | V100   | 41.12 |         42.35 |   [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/isanet/isanet_r50-d8_4xb4-80k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x512_80k_ade20k/isanet_r50-d8_512x512_80k_ade20k_20210903_124557-6ed83a0c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x512_80k_ade20k/isanet_r50-d8_512x512_80k_ade20k_20210903_124557.log.json)         |
+| ISANet | R-50-D8  | 512x512   | 160000  |      9.0 | 22.55          | V100   | 42.59 |         43.07 |  [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/isanet/isanet_r50-d8_4xb4-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x512_160k_ade20k/isanet_r50-d8_512x512_160k_ade20k_20210903_104850-f752d0a3.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x512_160k_ade20k/isanet_r50-d8_512x512_160k_ade20k_20210903_104850.log.json)     |
+| ISANet | R-101-D8 | 512x512   | 80000   |   12.562 | 10.56          | V100   | 43.51 |         44.38 |  [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/isanet/isanet_r101-d8_4xb4-80k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x512_80k_ade20k/isanet_r101-d8_512x512_80k_ade20k_20210903_162056-68b235c2.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x512_80k_ade20k/isanet_r101-d8_512x512_80k_ade20k_20210903_162056.log.json)     |
+| ISANet | R-101-D8 | 512x512   | 160000  |   12.562 | 10.56          | V100   | 43.80 |          45.4 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/isanet/isanet_r101-d8_4xb4-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x512_160k_ade20k/isanet_r101-d8_512x512_160k_ade20k_20210903_211431-a7879dcd.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x512_160k_ade20k/isanet_r101-d8_512x512_160k_ade20k_20210903_211431.log.json) |
+
+### Pascal VOC 2012 + Aug
+
+| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device | mIoU  | mIoU(ms+flip) |                                                                                                                         config | download                                                                                                                                                                                                                                                                                                                                     |
+| ------ | -------- | --------- | ------- | -------: | -------------- | ------ | ----- | ------------: | -----------------------------------------------------------------------------------------------------------------------------: | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| ISANet | R-50-D8  | 512x512   | 20000   |      5.9 | 23.08          | V100   | 76.78 |         77.79 |  [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/isanet/isanet_r50-d8_4xb4-20k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x512_20k_voc12aug/isanet_r50-d8_512x512_20k_voc12aug_20210901_164838-79d59b80.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x512_20k_voc12aug/isanet_r50-d8_512x512_20k_voc12aug_20210901_164838.log.json)     |
+| ISANet | R-50-D8  | 512x512   | 40000   |      5.9 | 23.08          | V100   | 76.20 |         77.22 |  [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/isanet/isanet_r50-d8_4xb4-40k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x512_40k_voc12aug/isanet_r50-d8_512x512_40k_voc12aug_20210901_151349-7d08a54e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x512_40k_voc12aug/isanet_r50-d8_512x512_40k_voc12aug_20210901_151349.log.json)     |
+| ISANet | R-101-D8 | 512x512   | 20000   |    9.465 | 7.42           | V100   | 78.46 |         79.16 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/isanet/isanet_r101-d8_4xb4-20k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x512_20k_voc12aug/isanet_r101-d8_512x512_20k_voc12aug_20210901_115805-3ccbf355.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x512_20k_voc12aug/isanet_r101-d8_512x512_20k_voc12aug_20210901_115805.log.json) |
+| ISANet | R-101-D8 | 512x512   | 40000   |    9.465 | 7.42           | V100   | 78.12 |         79.04 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/isanet/isanet_r101-d8_4xb4-40k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x512_40k_voc12aug/isanet_r101-d8_512x512_40k_voc12aug_20210901_145814-bc71233b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x512_40k_voc12aug/isanet_r101-d8_512x512_40k_voc12aug_20210901_145814.log.json) |
+
 ## Citation
 
 ```bibetex
@@ -45,36 +78,3 @@ The technical report above is also presented at:
   publisher={Springer}
 }
 ```
-
-## Results and models
-
-### Cityscapes
-
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU  | mIoU(ms+flip) |                                                                                                                            config | download                                                                                                                                                                                                                                                                                                                                                 |
-| ------ | -------- | --------- | ------- | -------: | -------------- | ----- | ------------: | --------------------------------------------------------------------------------------------------------------------------------: | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| ISANet | R-50-D8  | 512x1024  | 40000   |    5.869 | 2.91           | 78.49 |         79.44 |  [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/isanet/isanet_r50-d8_4xb2-40k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x1024_40k_cityscapes/isanet_r50-d8_512x1024_40k_cityscapes_20210901_054739-981bd763.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x1024_40k_cityscapes/isanet_r50-d8_512x1024_40k_cityscapes_20210901_054739.log.json)     |
-| ISANet | R-50-D8  | 512x1024  | 80000   |    5.869 | 2.91           | 78.68 |         80.25 |  [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/isanet/isanet_r50-d8_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x1024_80k_cityscapes/isanet_r50-d8_512x1024_80k_cityscapes_20210901_074202-89384497.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x1024_80k_cityscapes/isanet_r50-d8_512x1024_80k_cityscapes_20210901_074202.log.json)     |
-| ISANet | R-50-D8  | 769x769   | 40000   |    6.759 | 1.54           | 78.70 |         80.28 |   [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/isanet/isanet_r50-d8_4xb2-40k_cityscapes-769x769.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_769x769_40k_cityscapes/isanet_r50-d8_769x769_40k_cityscapes_20210903_050200-4ae7e65b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_769x769_40k_cityscapes/isanet_r50-d8_769x769_40k_cityscapes_20210903_050200.log.json)         |
-| ISANet | R-50-D8  | 769x769   | 80000   |    6.759 | 1.54           | 79.29 |         80.53 |   [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/isanet/isanet_r50-d8_4xb2-80k_cityscapes-769x769.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_769x769_80k_cityscapes/isanet_r50-d8_769x769_80k_cityscapes_20210903_101126-99b54519.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_769x769_80k_cityscapes/isanet_r50-d8_769x769_80k_cityscapes_20210903_101126.log.json)         |
-| ISANet | R-101-D8 | 512x1024  | 40000   |    9.425 | 2.35           | 79.58 |         81.05 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/isanet/isanet_r101-d8_4xb2-40k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x1024_40k_cityscapes/isanet_r101-d8_512x1024_40k_cityscapes_20210901_145553-293e6bd6.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x1024_40k_cityscapes/isanet_r101-d8_512x1024_40k_cityscapes_20210901_145553.log.json) |
-| ISANet | R-101-D8 | 512x1024  | 80000   |    9.425 | 2.35           | 80.32 |         81.58 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/isanet/isanet_r101-d8_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x1024_80k_cityscapes/isanet_r101-d8_512x1024_80k_cityscapes_20210901_145243-5b99c9b2.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x1024_80k_cityscapes/isanet_r101-d8_512x1024_80k_cityscapes_20210901_145243.log.json) |
-| ISANet | R-101-D8 | 769x769   | 40000   |   10.815 | 0.92           | 79.68 |         80.95 |  [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/isanet/isanet_r101-d8_4xb2-40k_cityscapes-769x769.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_769x769_40k_cityscapes/isanet_r101-d8_769x769_40k_cityscapes_20210903_111320-509e7224.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_769x769_40k_cityscapes/isanet_r101-d8_769x769_40k_cityscapes_20210903_111320.log.json)     |
-| ISANet | R-101-D8 | 769x769   | 80000   |   10.815 | 0.92           | 80.61 |         81.59 |  [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/isanet/isanet_r101-d8_4xb2-80k_cityscapes-769x769.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_769x769_80k_cityscapes/isanet_r101-d8_769x769_80k_cityscapes_20210903_111319-24f71dfa.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_769x769_80k_cityscapes/isanet_r101-d8_769x769_80k_cityscapes_20210903_111319.log.json)     |
-
-### ADE20K
-
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU  | mIoU(ms+flip) |                                                                                                                        config | download                                                                                                                                                                                                                                                                                                                                 |
-| ------ | -------- | --------- | ------- | -------: | -------------- | ----- | ------------: | ----------------------------------------------------------------------------------------------------------------------------: | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| ISANet | R-50-D8  | 512x512   | 80000   |      9.0 | 22.55          | 41.12 |         42.35 |   [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/isanet/isanet_r50-d8_4xb4-80k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x512_80k_ade20k/isanet_r50-d8_512x512_80k_ade20k_20210903_124557-6ed83a0c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x512_80k_ade20k/isanet_r50-d8_512x512_80k_ade20k_20210903_124557.log.json)         |
-| ISANet | R-50-D8  | 512x512   | 160000  |      9.0 | 22.55          | 42.59 |         43.07 |  [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/isanet/isanet_r50-d8_4xb4-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x512_160k_ade20k/isanet_r50-d8_512x512_160k_ade20k_20210903_104850-f752d0a3.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x512_160k_ade20k/isanet_r50-d8_512x512_160k_ade20k_20210903_104850.log.json)     |
-| ISANet | R-101-D8 | 512x512   | 80000   |   12.562 | 10.56          | 43.51 |         44.38 |  [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/isanet/isanet_r101-d8_4xb4-80k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x512_80k_ade20k/isanet_r101-d8_512x512_80k_ade20k_20210903_162056-68b235c2.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x512_80k_ade20k/isanet_r101-d8_512x512_80k_ade20k_20210903_162056.log.json)     |
-| ISANet | R-101-D8 | 512x512   | 160000  |   12.562 | 10.56          | 43.80 |          45.4 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/isanet/isanet_r101-d8_4xb4-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x512_160k_ade20k/isanet_r101-d8_512x512_160k_ade20k_20210903_211431-a7879dcd.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x512_160k_ade20k/isanet_r101-d8_512x512_160k_ade20k_20210903_211431.log.json) |
-
-### Pascal VOC 2012 + Aug
-
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU  | mIoU(ms+flip) |                                                                                                                         config | download                                                                                                                                                                                                                                                                                                                                     |
-| ------ | -------- | --------- | ------- | -------: | -------------- | ----- | ------------: | -----------------------------------------------------------------------------------------------------------------------------: | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| ISANet | R-50-D8  | 512x512   | 20000   |      5.9 | 23.08          | 76.78 |         77.79 |  [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/isanet/isanet_r50-d8_4xb4-20k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x512_20k_voc12aug/isanet_r50-d8_512x512_20k_voc12aug_20210901_164838-79d59b80.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x512_20k_voc12aug/isanet_r50-d8_512x512_20k_voc12aug_20210901_164838.log.json)     |
-| ISANet | R-50-D8  | 512x512   | 40000   |      5.9 | 23.08          | 76.20 |         77.22 |  [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/isanet/isanet_r50-d8_4xb4-40k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x512_40k_voc12aug/isanet_r50-d8_512x512_40k_voc12aug_20210901_151349-7d08a54e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x512_40k_voc12aug/isanet_r50-d8_512x512_40k_voc12aug_20210901_151349.log.json)     |
-| ISANet | R-101-D8 | 512x512   | 20000   |    9.465 | 7.42           | 78.46 |         79.16 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/isanet/isanet_r101-d8_4xb4-20k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x512_20k_voc12aug/isanet_r101-d8_512x512_20k_voc12aug_20210901_115805-3ccbf355.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x512_20k_voc12aug/isanet_r101-d8_512x512_20k_voc12aug_20210901_115805.log.json) |
-| ISANet | R-101-D8 | 512x512   | 40000   |    9.465 | 7.42           | 78.12 |         79.04 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/isanet/isanet_r101-d8_4xb4-40k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x512_40k_voc12aug/isanet_r101-d8_512x512_40k_voc12aug_20210901_145814-bc71233b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x512_40k_voc12aug/isanet_r101-d8_512x512_40k_voc12aug_20210901_145814.log.json) |
diff --git a/configs/isanet/isanet.yml b/configs/isanet/isanet.yml
deleted file mode 100644
index 405b3c1231..0000000000
--- a/configs/isanet/isanet.yml
+++ /dev/null
@@ -1,369 +0,0 @@
-Collections:
-- Name: ISANet
-  Metadata:
-    Training Data:
-    - Cityscapes
-    - ADE20K
-    - Pascal VOC 2012 + Aug
-  Paper:
-    URL: https://arxiv.org/abs/1907.12273
-    Title: Interlaced Sparse Self-Attention for Semantic Segmentation
-  README: configs/isanet/README.md
-  Code:
-    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/decode_heads/isa_head.py#L58
-    Version: v0.18.0
-  Converted From:
-    Code: https://github.com/openseg-group/openseg.pytorch
-Models:
-- Name: isanet_r50-d8_4xb2-40k_cityscapes-512x1024
-  In Collection: ISANet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,1024)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 343.64
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 5.869
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 78.49
-      mIoU(ms+flip): 79.44
-  Config: configs/isanet/isanet_r50-d8_4xb2-40k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x1024_40k_cityscapes/isanet_r50-d8_512x1024_40k_cityscapes_20210901_054739-981bd763.pth
-- Name: isanet_r50-d8_4xb2-80k_cityscapes-512x1024
-  In Collection: ISANet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,1024)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 343.64
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 5.869
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 78.68
-      mIoU(ms+flip): 80.25
-  Config: configs/isanet/isanet_r50-d8_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x1024_80k_cityscapes/isanet_r50-d8_512x1024_80k_cityscapes_20210901_074202-89384497.pth
-- Name: isanet_r50-d8_4xb2-40k_cityscapes-769x769
-  In Collection: ISANet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (769,769)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 649.35
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (769,769)
-    Training Memory (GB): 6.759
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 78.7
-      mIoU(ms+flip): 80.28
-  Config: configs/isanet/isanet_r50-d8_4xb2-40k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_769x769_40k_cityscapes/isanet_r50-d8_769x769_40k_cityscapes_20210903_050200-4ae7e65b.pth
-- Name: isanet_r50-d8_4xb2-80k_cityscapes-769x769
-  In Collection: ISANet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (769,769)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 649.35
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (769,769)
-    Training Memory (GB): 6.759
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 79.29
-      mIoU(ms+flip): 80.53
-  Config: configs/isanet/isanet_r50-d8_4xb2-80k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_769x769_80k_cityscapes/isanet_r50-d8_769x769_80k_cityscapes_20210903_101126-99b54519.pth
-- Name: isanet_r101-d8_4xb2-40k_cityscapes-512x1024
-  In Collection: ISANet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,1024)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 425.53
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 9.425
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 79.58
-      mIoU(ms+flip): 81.05
-  Config: configs/isanet/isanet_r101-d8_4xb2-40k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x1024_40k_cityscapes/isanet_r101-d8_512x1024_40k_cityscapes_20210901_145553-293e6bd6.pth
-- Name: isanet_r101-d8_4xb2-80k_cityscapes-512x1024
-  In Collection: ISANet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,1024)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 425.53
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 9.425
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 80.32
-      mIoU(ms+flip): 81.58
-  Config: configs/isanet/isanet_r101-d8_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x1024_80k_cityscapes/isanet_r101-d8_512x1024_80k_cityscapes_20210901_145243-5b99c9b2.pth
-- Name: isanet_r101-d8_4xb2-40k_cityscapes-769x769
-  In Collection: ISANet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (769,769)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 1086.96
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (769,769)
-    Training Memory (GB): 10.815
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 79.68
-      mIoU(ms+flip): 80.95
-  Config: configs/isanet/isanet_r101-d8_4xb2-40k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_769x769_40k_cityscapes/isanet_r101-d8_769x769_40k_cityscapes_20210903_111320-509e7224.pth
-- Name: isanet_r101-d8_4xb2-80k_cityscapes-769x769
-  In Collection: ISANet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (769,769)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 1086.96
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (769,769)
-    Training Memory (GB): 10.815
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 80.61
-      mIoU(ms+flip): 81.59
-  Config: configs/isanet/isanet_r101-d8_4xb2-80k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_769x769_80k_cityscapes/isanet_r101-d8_769x769_80k_cityscapes_20210903_111319-24f71dfa.pth
-- Name: isanet_r50-d8_4xb4-80k_ade20k-512x512
-  In Collection: ISANet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 44.35
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 9.0
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 41.12
-      mIoU(ms+flip): 42.35
-  Config: configs/isanet/isanet_r50-d8_4xb4-80k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x512_80k_ade20k/isanet_r50-d8_512x512_80k_ade20k_20210903_124557-6ed83a0c.pth
-- Name: isanet_r50-d8_4xb4-160k_ade20k-512x512
-  In Collection: ISANet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 44.35
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 9.0
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 42.59
-      mIoU(ms+flip): 43.07
-  Config: configs/isanet/isanet_r50-d8_4xb4-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x512_160k_ade20k/isanet_r50-d8_512x512_160k_ade20k_20210903_104850-f752d0a3.pth
-- Name: isanet_r101-d8_4xb4-80k_ade20k-512x512
-  In Collection: ISANet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 94.7
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 12.562
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 43.51
-      mIoU(ms+flip): 44.38
-  Config: configs/isanet/isanet_r101-d8_4xb4-80k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x512_80k_ade20k/isanet_r101-d8_512x512_80k_ade20k_20210903_162056-68b235c2.pth
-- Name: isanet_r101-d8_4xb4-160k_ade20k-512x512
-  In Collection: ISANet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 94.7
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 12.562
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 43.8
-      mIoU(ms+flip): 45.4
-  Config: configs/isanet/isanet_r101-d8_4xb4-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x512_160k_ade20k/isanet_r101-d8_512x512_160k_ade20k_20210903_211431-a7879dcd.pth
-- Name: isanet_r50-d8_4xb4-20k_voc12aug-512x512
-  In Collection: ISANet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 20000
-    inference time (ms/im):
-    - value: 43.33
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 5.9
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal VOC 2012 + Aug
-    Metrics:
-      mIoU: 76.78
-      mIoU(ms+flip): 77.79
-  Config: configs/isanet/isanet_r50-d8_4xb4-20k_voc12aug-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x512_20k_voc12aug/isanet_r50-d8_512x512_20k_voc12aug_20210901_164838-79d59b80.pth
-- Name: isanet_r50-d8_4xb4-40k_voc12aug-512x512
-  In Collection: ISANet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 43.33
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 5.9
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal VOC 2012 + Aug
-    Metrics:
-      mIoU: 76.2
-      mIoU(ms+flip): 77.22
-  Config: configs/isanet/isanet_r50-d8_4xb4-40k_voc12aug-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x512_40k_voc12aug/isanet_r50-d8_512x512_40k_voc12aug_20210901_151349-7d08a54e.pth
-- Name: isanet_r101-d8_4xb4-20k_voc12aug-512x512
-  In Collection: ISANet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 20000
-    inference time (ms/im):
-    - value: 134.77
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 9.465
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal VOC 2012 + Aug
-    Metrics:
-      mIoU: 78.46
-      mIoU(ms+flip): 79.16
-  Config: configs/isanet/isanet_r101-d8_4xb4-20k_voc12aug-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x512_20k_voc12aug/isanet_r101-d8_512x512_20k_voc12aug_20210901_115805-3ccbf355.pth
-- Name: isanet_r101-d8_4xb4-40k_voc12aug-512x512
-  In Collection: ISANet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 134.77
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 9.465
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal VOC 2012 + Aug
-    Metrics:
-      mIoU: 78.12
-      mIoU(ms+flip): 79.04
-  Config: configs/isanet/isanet_r101-d8_4xb4-40k_voc12aug-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x512_40k_voc12aug/isanet_r101-d8_512x512_40k_voc12aug_20210901_145814-bc71233b.pth
diff --git a/configs/isanet/metafile.yaml b/configs/isanet/metafile.yaml
new file mode 100644
index 0000000000..ad394eabb2
--- /dev/null
+++ b/configs/isanet/metafile.yaml
@@ -0,0 +1,399 @@
+Collections:
+- Name: ISANet
+  License: Apache License 2.0
+  Metadata:
+    Training Data:
+    - Cityscapes
+    - ADE20K
+    - Pascal VOC 2012 + Aug
+  Paper:
+    Title: Interlaced Sparse Self-Attention for Semantic Segmentation
+    URL: https://arxiv.org/abs/1907.12273
+  README: configs/isanet/README.md
+  Frameworks:
+  - PyTorch
+Models:
+- Name: isanet_r50-d8_4xb2-40k_cityscapes-512x1024
+  In Collection: ISANet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.49
+      mIoU(ms+flip): 79.44
+  Config: configs/isanet/isanet_r50-d8_4xb2-40k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - ISANet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 5.869
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x1024_40k_cityscapes/isanet_r50-d8_512x1024_40k_cityscapes_20210901_054739-981bd763.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x1024_40k_cityscapes/isanet_r50-d8_512x1024_40k_cityscapes_20210901_054739.log.json
+  Paper:
+    Title: Interlaced Sparse Self-Attention for Semantic Segmentation
+    URL: https://arxiv.org/abs/1907.12273
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/decode_heads/isa_head.py#L58
+  Framework: PyTorch
+- Name: isanet_r50-d8_4xb2-80k_cityscapes-512x1024
+  In Collection: ISANet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.68
+      mIoU(ms+flip): 80.25
+  Config: configs/isanet/isanet_r50-d8_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - ISANet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 5.869
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x1024_80k_cityscapes/isanet_r50-d8_512x1024_80k_cityscapes_20210901_074202-89384497.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x1024_80k_cityscapes/isanet_r50-d8_512x1024_80k_cityscapes_20210901_074202.log.json
+  Paper:
+    Title: Interlaced Sparse Self-Attention for Semantic Segmentation
+    URL: https://arxiv.org/abs/1907.12273
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/decode_heads/isa_head.py#L58
+  Framework: PyTorch
+- Name: isanet_r50-d8_4xb2-40k_cityscapes-769x769
+  In Collection: ISANet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.7
+      mIoU(ms+flip): 80.28
+  Config: configs/isanet/isanet_r50-d8_4xb2-40k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - ISANet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 6.759
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_769x769_40k_cityscapes/isanet_r50-d8_769x769_40k_cityscapes_20210903_050200-4ae7e65b.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_769x769_40k_cityscapes/isanet_r50-d8_769x769_40k_cityscapes_20210903_050200.log.json
+  Paper:
+    Title: Interlaced Sparse Self-Attention for Semantic Segmentation
+    URL: https://arxiv.org/abs/1907.12273
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/decode_heads/isa_head.py#L58
+  Framework: PyTorch
+- Name: isanet_r50-d8_4xb2-80k_cityscapes-769x769
+  In Collection: ISANet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.29
+      mIoU(ms+flip): 80.53
+  Config: configs/isanet/isanet_r50-d8_4xb2-80k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - ISANet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 6.759
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_769x769_80k_cityscapes/isanet_r50-d8_769x769_80k_cityscapes_20210903_101126-99b54519.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_769x769_80k_cityscapes/isanet_r50-d8_769x769_80k_cityscapes_20210903_101126.log.json
+  Paper:
+    Title: Interlaced Sparse Self-Attention for Semantic Segmentation
+    URL: https://arxiv.org/abs/1907.12273
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/decode_heads/isa_head.py#L58
+  Framework: PyTorch
+- Name: isanet_r101-d8_4xb2-40k_cityscapes-512x1024
+  In Collection: ISANet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.58
+      mIoU(ms+flip): 81.05
+  Config: configs/isanet/isanet_r101-d8_4xb2-40k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - ISANet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 9.425
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x1024_40k_cityscapes/isanet_r101-d8_512x1024_40k_cityscapes_20210901_145553-293e6bd6.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x1024_40k_cityscapes/isanet_r101-d8_512x1024_40k_cityscapes_20210901_145553.log.json
+  Paper:
+    Title: Interlaced Sparse Self-Attention for Semantic Segmentation
+    URL: https://arxiv.org/abs/1907.12273
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/decode_heads/isa_head.py#L58
+  Framework: PyTorch
+- Name: isanet_r101-d8_4xb2-80k_cityscapes-512x1024
+  In Collection: ISANet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 80.32
+      mIoU(ms+flip): 81.58
+  Config: configs/isanet/isanet_r101-d8_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - ISANet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 9.425
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x1024_80k_cityscapes/isanet_r101-d8_512x1024_80k_cityscapes_20210901_145243-5b99c9b2.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x1024_80k_cityscapes/isanet_r101-d8_512x1024_80k_cityscapes_20210901_145243.log.json
+  Paper:
+    Title: Interlaced Sparse Self-Attention for Semantic Segmentation
+    URL: https://arxiv.org/abs/1907.12273
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/decode_heads/isa_head.py#L58
+  Framework: PyTorch
+- Name: isanet_r101-d8_4xb2-40k_cityscapes-769x769
+  In Collection: ISANet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.68
+      mIoU(ms+flip): 80.95
+  Config: configs/isanet/isanet_r101-d8_4xb2-40k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - ISANet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 10.815
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_769x769_40k_cityscapes/isanet_r101-d8_769x769_40k_cityscapes_20210903_111320-509e7224.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_769x769_40k_cityscapes/isanet_r101-d8_769x769_40k_cityscapes_20210903_111320.log.json
+  Paper:
+    Title: Interlaced Sparse Self-Attention for Semantic Segmentation
+    URL: https://arxiv.org/abs/1907.12273
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/decode_heads/isa_head.py#L58
+  Framework: PyTorch
+- Name: isanet_r101-d8_4xb2-80k_cityscapes-769x769
+  In Collection: ISANet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 80.61
+      mIoU(ms+flip): 81.59
+  Config: configs/isanet/isanet_r101-d8_4xb2-80k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - ISANet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 10.815
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_769x769_80k_cityscapes/isanet_r101-d8_769x769_80k_cityscapes_20210903_111319-24f71dfa.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_769x769_80k_cityscapes/isanet_r101-d8_769x769_80k_cityscapes_20210903_111319.log.json
+  Paper:
+    Title: Interlaced Sparse Self-Attention for Semantic Segmentation
+    URL: https://arxiv.org/abs/1907.12273
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/decode_heads/isa_head.py#L58
+  Framework: PyTorch
+- Name: isanet_r50-d8_4xb4-80k_ade20k-512x512
+  In Collection: ISANet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 41.12
+      mIoU(ms+flip): 42.35
+  Config: configs/isanet/isanet_r50-d8_4xb4-80k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - ISANet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 9.0
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x512_80k_ade20k/isanet_r50-d8_512x512_80k_ade20k_20210903_124557-6ed83a0c.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x512_80k_ade20k/isanet_r50-d8_512x512_80k_ade20k_20210903_124557.log.json
+  Paper:
+    Title: Interlaced Sparse Self-Attention for Semantic Segmentation
+    URL: https://arxiv.org/abs/1907.12273
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/decode_heads/isa_head.py#L58
+  Framework: PyTorch
+- Name: isanet_r50-d8_4xb4-160k_ade20k-512x512
+  In Collection: ISANet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 42.59
+      mIoU(ms+flip): 43.07
+  Config: configs/isanet/isanet_r50-d8_4xb4-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - ISANet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 9.0
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x512_160k_ade20k/isanet_r50-d8_512x512_160k_ade20k_20210903_104850-f752d0a3.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x512_160k_ade20k/isanet_r50-d8_512x512_160k_ade20k_20210903_104850.log.json
+  Paper:
+    Title: Interlaced Sparse Self-Attention for Semantic Segmentation
+    URL: https://arxiv.org/abs/1907.12273
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/decode_heads/isa_head.py#L58
+  Framework: PyTorch
+- Name: isanet_r101-d8_4xb4-80k_ade20k-512x512
+  In Collection: ISANet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 43.51
+      mIoU(ms+flip): 44.38
+  Config: configs/isanet/isanet_r101-d8_4xb4-80k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - ISANet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 12.562
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x512_80k_ade20k/isanet_r101-d8_512x512_80k_ade20k_20210903_162056-68b235c2.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x512_80k_ade20k/isanet_r101-d8_512x512_80k_ade20k_20210903_162056.log.json
+  Paper:
+    Title: Interlaced Sparse Self-Attention for Semantic Segmentation
+    URL: https://arxiv.org/abs/1907.12273
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/decode_heads/isa_head.py#L58
+  Framework: PyTorch
+- Name: isanet_r101-d8_4xb4-160k_ade20k-512x512
+  In Collection: ISANet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 43.8
+      mIoU(ms+flip): 45.4
+  Config: configs/isanet/isanet_r101-d8_4xb4-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - ISANet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 12.562
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x512_160k_ade20k/isanet_r101-d8_512x512_160k_ade20k_20210903_211431-a7879dcd.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x512_160k_ade20k/isanet_r101-d8_512x512_160k_ade20k_20210903_211431.log.json
+  Paper:
+    Title: Interlaced Sparse Self-Attention for Semantic Segmentation
+    URL: https://arxiv.org/abs/1907.12273
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/decode_heads/isa_head.py#L58
+  Framework: PyTorch
+- Name: isanet_r50-d8_4xb4-20k_voc12aug-512x512
+  In Collection: ISANet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 76.78
+      mIoU(ms+flip): 77.79
+  Config: configs/isanet/isanet_r50-d8_4xb4-20k_voc12aug-512x512.py
+  Metadata:
+    Training Data: Pascal VOC 2012 + Aug
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - ISANet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 5.9
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x512_20k_voc12aug/isanet_r50-d8_512x512_20k_voc12aug_20210901_164838-79d59b80.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x512_20k_voc12aug/isanet_r50-d8_512x512_20k_voc12aug_20210901_164838.log.json
+  Paper:
+    Title: Interlaced Sparse Self-Attention for Semantic Segmentation
+    URL: https://arxiv.org/abs/1907.12273
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/decode_heads/isa_head.py#L58
+  Framework: PyTorch
+- Name: isanet_r50-d8_4xb4-40k_voc12aug-512x512
+  In Collection: ISANet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 76.2
+      mIoU(ms+flip): 77.22
+  Config: configs/isanet/isanet_r50-d8_4xb4-40k_voc12aug-512x512.py
+  Metadata:
+    Training Data: Pascal VOC 2012 + Aug
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - ISANet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 5.9
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x512_40k_voc12aug/isanet_r50-d8_512x512_40k_voc12aug_20210901_151349-7d08a54e.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x512_40k_voc12aug/isanet_r50-d8_512x512_40k_voc12aug_20210901_151349.log.json
+  Paper:
+    Title: Interlaced Sparse Self-Attention for Semantic Segmentation
+    URL: https://arxiv.org/abs/1907.12273
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/decode_heads/isa_head.py#L58
+  Framework: PyTorch
+- Name: isanet_r101-d8_4xb4-20k_voc12aug-512x512
+  In Collection: ISANet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 78.46
+      mIoU(ms+flip): 79.16
+  Config: configs/isanet/isanet_r101-d8_4xb4-20k_voc12aug-512x512.py
+  Metadata:
+    Training Data: Pascal VOC 2012 + Aug
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - ISANet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 9.465
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x512_20k_voc12aug/isanet_r101-d8_512x512_20k_voc12aug_20210901_115805-3ccbf355.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x512_20k_voc12aug/isanet_r101-d8_512x512_20k_voc12aug_20210901_115805.log.json
+  Paper:
+    Title: Interlaced Sparse Self-Attention for Semantic Segmentation
+    URL: https://arxiv.org/abs/1907.12273
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/decode_heads/isa_head.py#L58
+  Framework: PyTorch
+- Name: isanet_r101-d8_4xb4-40k_voc12aug-512x512
+  In Collection: ISANet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 78.12
+      mIoU(ms+flip): 79.04
+  Config: configs/isanet/isanet_r101-d8_4xb4-40k_voc12aug-512x512.py
+  Metadata:
+    Training Data: Pascal VOC 2012 + Aug
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - ISANet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 9.465
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x512_40k_voc12aug/isanet_r101-d8_512x512_40k_voc12aug_20210901_145814-bc71233b.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x512_40k_voc12aug/isanet_r101-d8_512x512_40k_voc12aug_20210901_145814.log.json
+  Paper:
+    Title: Interlaced Sparse Self-Attention for Semantic Segmentation
+    URL: https://arxiv.org/abs/1907.12273
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/decode_heads/isa_head.py#L58
+  Framework: PyTorch
diff --git a/configs/knet/README.md b/configs/knet/README.md
index ed5bc06257..070c7256da 100644
--- a/configs/knet/README.md
+++ b/configs/knet/README.md
@@ -1,6 +1,6 @@
 # K-Net
 
-[K-Net: Towards Unified Image Segmentation](https://arxiv.org/abs/2106.14855)
+> [K-Net: Towards Unified Image Segmentation](https://arxiv.org/abs/2106.14855)
 
 ## Introduction
 
@@ -22,29 +22,31 @@ Semantic, instance, and panoptic segmentations have been addressed using differe
 <img src="https://user-images.githubusercontent.com/24582831/157008300-9f40905c-b8e8-4a2a-9593-c1177fa35b2c.png" width="90%"/>
 </div>
 
-```bibtex
-@inproceedings{zhang2021knet,
-    title={{K-Net: Towards} Unified Image Segmentation},
-    author={Wenwei Zhang and Jiangmiao Pang and Kai Chen and Chen Change Loy},
-    year={2021},
-    booktitle={NeurIPS},
-}
-```
-
 ## Results and models
 
 ### ADE20K
 
-| Method           | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU  | mIoU(ms+flip) | config                                                                                                                                     | download                                                                                                                                                                                                                                                                                                                                                                                                         |
-| ---------------- | -------- | --------- | ------- | -------- | -------------- | ----- | ------------- | ------------------------------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| KNet + FCN       | R-50-D8  | 512x512   | 80000   | 7.01     | 19.24          | 43.60 | 45.12         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/knet/knet-s3_r50-d8_fcn_8xb2-adamw-80k_ade20k-512x512.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_fcn_r50-d8_8x2_512x512_adamw_80k_ade20k/knet_s3_fcn_r50-d8_8x2_512x512_adamw_80k_ade20k_20220228_043751-abcab920.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_fcn_r50-d8_8x2_512x512_adamw_80k_ade20k/knet_s3_fcn_r50-d8_8x2_512x512_adamw_80k_ade20k_20220228_043751.log.json)                         |
-| KNet + PSPNet    | R-50-D8  | 512x512   | 80000   | 6.98     | 20.04          | 44.18 | 45.58         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/knet/knet-s3_r50-d8_pspnet_8xb2-adamw-80k_ade20k-512x512.py)    | [model](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_pspnet_r50-d8_8x2_512x512_adamw_80k_ade20k/knet_s3_pspnet_r50-d8_8x2_512x512_adamw_80k_ade20k_20220228_054634-d2c72240.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_pspnet_r50-d8_8x2_512x512_adamw_80k_ade20k/knet_s3_pspnet_r50-d8_8x2_512x512_adamw_80k_ade20k_20220228_054634.log.json)             |
-| KNet + DeepLabV3 | R-50-D8  | 512x512   | 80000   | 7.42     | 12.10          | 45.06 | 46.11         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/knet/knet-s3_r50-d8_deeplabv3_8xb2-adamw-80k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_deeplabv3_r50-d8_8x2_512x512_adamw_80k_ade20k/knet_s3_deeplabv3_r50-d8_8x2_512x512_adamw_80k_ade20k_20220228_041642-00c8fbeb.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_deeplabv3_r50-d8_8x2_512x512_adamw_80k_ade20k/knet_s3_deeplabv3_r50-d8_8x2_512x512_adamw_80k_ade20k_20220228_041642.log.json) |
-| KNet + UperNet   | R-50-D8  | 512x512   | 80000   | 7.34     | 17.11          | 43.45 | 44.07         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/knet/knet-s3_r50-d8_upernet_8xb2-adamw-80k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_r50-d8_8x2_512x512_adamw_80k_ade20k/knet_s3_upernet_r50-d8_8x2_512x512_adamw_80k_ade20k_20220304_125657-215753b0.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_r50-d8_8x2_512x512_adamw_80k_ade20k/knet_s3_upernet_r50-d8_8x2_512x512_adamw_80k_ade20k_20220304_125657.log.json)         |
-| KNet + UperNet   | Swin-T   | 512x512   | 80000   | 7.57     | 15.56          | 45.84 | 46.27         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/knet/knet-s3_swin-t_upernet_8xb2-adamw-80k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_swin-t_8x2_512x512_adamw_80k_ade20k/knet_s3_upernet_swin-t_8x2_512x512_adamw_80k_ade20k_20220303_133059-7545e1dc.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_swin-t_8x2_512x512_adamw_80k_ade20k/knet_s3_upernet_swin-t_8x2_512x512_adamw_80k_ade20k_20220303_133059.log.json)         |
-| KNet + UperNet   | Swin-L   | 512x512   | 80000   | 13.5     | 8.29           | 52.05 | 53.24         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/knet/knet-s3_swin-l_upernet_8xb2-adamw-80k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_swin-l_8x2_512x512_adamw_80k_ade20k/knet_s3_upernet_swin-l_8x2_512x512_adamw_80k_ade20k_20220303_154559-d8da9a90.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_swin-l_8x2_512x512_adamw_80k_ade20k/knet_s3_upernet_swin-l_8x2_512x512_adamw_80k_ade20k_20220303_154559.log.json)         |
-| KNet + UperNet   | Swin-L   | 640x640   | 80000   | 13.54    | 8.29           | 52.21 | 53.34         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/knet/knet-s3_swin-l_upernet_8xb2-adamw-80k_ade20k-640x640.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k/knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k_20220301_220747-8787fc71.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k/knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k_20220301_220747.log.json)         |
+| Method           | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device | mIoU  | mIoU(ms+flip) | config                                                                                                                                     | download                                                                                                                                                                                                                                                                                                                                                                                                         |
+| ---------------- | -------- | --------- | ------- | -------- | -------------- | ------ | ----- | ------------- | ------------------------------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| KNet + FCN       | R-50-D8  | 512x512   | 80000   | 7.01     | 19.24          | V100   | 43.60 | 45.12         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/knet/knet-s3_r50-d8_fcn_8xb2-adamw-80k_ade20k-512x512.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_fcn_r50-d8_8x2_512x512_adamw_80k_ade20k/knet_s3_fcn_r50-d8_8x2_512x512_adamw_80k_ade20k_20220228_043751-abcab920.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_fcn_r50-d8_8x2_512x512_adamw_80k_ade20k/knet_s3_fcn_r50-d8_8x2_512x512_adamw_80k_ade20k_20220228_043751.log.json)                         |
+| KNet + PSPNet    | R-50-D8  | 512x512   | 80000   | 6.98     | 20.04          | V100   | 44.18 | 45.58         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/knet/knet-s3_r50-d8_pspnet_8xb2-adamw-80k_ade20k-512x512.py)    | [model](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_pspnet_r50-d8_8x2_512x512_adamw_80k_ade20k/knet_s3_pspnet_r50-d8_8x2_512x512_adamw_80k_ade20k_20220228_054634-d2c72240.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_pspnet_r50-d8_8x2_512x512_adamw_80k_ade20k/knet_s3_pspnet_r50-d8_8x2_512x512_adamw_80k_ade20k_20220228_054634.log.json)             |
+| KNet + DeepLabV3 | R-50-D8  | 512x512   | 80000   | 7.42     | 12.10          | V100   | 45.06 | 46.11         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/knet/knet-s3_r50-d8_deeplabv3_8xb2-adamw-80k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_deeplabv3_r50-d8_8x2_512x512_adamw_80k_ade20k/knet_s3_deeplabv3_r50-d8_8x2_512x512_adamw_80k_ade20k_20220228_041642-00c8fbeb.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_deeplabv3_r50-d8_8x2_512x512_adamw_80k_ade20k/knet_s3_deeplabv3_r50-d8_8x2_512x512_adamw_80k_ade20k_20220228_041642.log.json) |
+| KNet + UperNet   | R-50-D8  | 512x512   | 80000   | 7.34     | 17.11          | V100   | 43.45 | 44.07         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/knet/knet-s3_r50-d8_upernet_8xb2-adamw-80k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_r50-d8_8x2_512x512_adamw_80k_ade20k/knet_s3_upernet_r50-d8_8x2_512x512_adamw_80k_ade20k_20220304_125657-215753b0.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_r50-d8_8x2_512x512_adamw_80k_ade20k/knet_s3_upernet_r50-d8_8x2_512x512_adamw_80k_ade20k_20220304_125657.log.json)         |
+| KNet + UperNet   | Swin-T   | 512x512   | 80000   | 7.57     | 15.56          | V100   | 45.84 | 46.27         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/knet/knet-s3_swin-t_upernet_8xb2-adamw-80k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_swin-t_8x2_512x512_adamw_80k_ade20k/knet_s3_upernet_swin-t_8x2_512x512_adamw_80k_ade20k_20220303_133059-7545e1dc.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_swin-t_8x2_512x512_adamw_80k_ade20k/knet_s3_upernet_swin-t_8x2_512x512_adamw_80k_ade20k_20220303_133059.log.json)         |
+| KNet + UperNet   | Swin-L   | 512x512   | 80000   | 13.5     | 8.29           | V100   | 52.05 | 53.24         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/knet/knet-s3_swin-l_upernet_8xb2-adamw-80k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_swin-l_8x2_512x512_adamw_80k_ade20k/knet_s3_upernet_swin-l_8x2_512x512_adamw_80k_ade20k_20220303_154559-d8da9a90.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_swin-l_8x2_512x512_adamw_80k_ade20k/knet_s3_upernet_swin-l_8x2_512x512_adamw_80k_ade20k_20220303_154559.log.json)         |
+| KNet + UperNet   | Swin-L   | 640x640   | 80000   | 13.54    | 8.29           | V100   | 52.21 | 53.34         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/knet/knet-s3_swin-l_upernet_8xb2-adamw-80k_ade20k-640x640.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k/knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k_20220301_220747-8787fc71.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k/knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k_20220301_220747.log.json)         |
 
 Note:
 
 - All experiments of K-Net are implemented with 8 V100 (32G) GPUs with 2 samplers per GPU.
+
+# Citation
+
+```bibtex
+@inproceedings{zhang2021knet,
+    title={{K-Net: Towards} Unified Image Segmentation},
+    author={Wenwei Zhang and Jiangmiao Pang and Kai Chen and Chen Change Loy},
+    year={2021},
+    booktitle={NeurIPS},
+}
+```
diff --git a/configs/knet/knet.yml b/configs/knet/knet.yml
deleted file mode 100644
index 1c98e4703c..0000000000
--- a/configs/knet/knet.yml
+++ /dev/null
@@ -1,169 +0,0 @@
-Collections:
-- Name: KNet
-  Metadata:
-    Training Data:
-    - ADE20K
-  Paper:
-    URL: https://arxiv.org/abs/2106.14855
-    Title: 'K-Net: Towards Unified Image Segmentation'
-  README: configs/knet/README.md
-  Code:
-    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.23.0/mmseg/models/decode_heads/knet_head.py#L392
-    Version: v0.23.0
-  Converted From:
-    Code: https://github.com/ZwwWayne/K-Net/
-Models:
-- Name: knet-s3_r50-d8_fcn_8xb2-adamw-80k_ade20k-512x512
-  In Collection: KNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 51.98
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 7.01
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 43.6
-      mIoU(ms+flip): 45.12
-  Config: configs/knet/knet-s3_r50-d8_fcn_8xb2-adamw-80k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_fcn_r50-d8_8x2_512x512_adamw_80k_ade20k/knet_s3_fcn_r50-d8_8x2_512x512_adamw_80k_ade20k_20220228_043751-abcab920.pth
-- Name: knet-s3_r50-d8_pspnet_8xb2-adamw-80k_ade20k-512x512
-  In Collection: KNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 49.9
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 6.98
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 44.18
-      mIoU(ms+flip): 45.58
-  Config: configs/knet/knet-s3_r50-d8_pspnet_8xb2-adamw-80k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_pspnet_r50-d8_8x2_512x512_adamw_80k_ade20k/knet_s3_pspnet_r50-d8_8x2_512x512_adamw_80k_ade20k_20220228_054634-d2c72240.pth
-- Name: knet-s3_r50-d8_deeplabv3_8xb2-adamw-80k_ade20k-512x512
-  In Collection: KNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 82.64
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 7.42
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 45.06
-      mIoU(ms+flip): 46.11
-  Config: configs/knet/knet-s3_r50-d8_deeplabv3_8xb2-adamw-80k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_deeplabv3_r50-d8_8x2_512x512_adamw_80k_ade20k/knet_s3_deeplabv3_r50-d8_8x2_512x512_adamw_80k_ade20k_20220228_041642-00c8fbeb.pth
-- Name: knet-s3_r50-d8_upernet_8xb2-adamw-80k_ade20k-512x512
-  In Collection: KNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 58.45
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 7.34
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 43.45
-      mIoU(ms+flip): 44.07
-  Config: configs/knet/knet-s3_r50-d8_upernet_8xb2-adamw-80k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_r50-d8_8x2_512x512_adamw_80k_ade20k/knet_s3_upernet_r50-d8_8x2_512x512_adamw_80k_ade20k_20220304_125657-215753b0.pth
-- Name: knet-s3_swin-t_upernet_8xb2-adamw-80k_ade20k-512x512
-  In Collection: KNet
-  Metadata:
-    backbone: Swin-T
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 64.27
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 7.57
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 45.84
-      mIoU(ms+flip): 46.27
-  Config: configs/knet/knet-s3_swin-t_upernet_8xb2-adamw-80k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_swin-t_8x2_512x512_adamw_80k_ade20k/knet_s3_upernet_swin-t_8x2_512x512_adamw_80k_ade20k_20220303_133059-7545e1dc.pth
-- Name: knet-s3_swin-l_upernet_8xb2-adamw-80k_ade20k-512x512
-  In Collection: KNet
-  Metadata:
-    backbone: Swin-L
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 120.63
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 13.5
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 52.05
-      mIoU(ms+flip): 53.24
-  Config: configs/knet/knet-s3_swin-l_upernet_8xb2-adamw-80k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_swin-l_8x2_512x512_adamw_80k_ade20k/knet_s3_upernet_swin-l_8x2_512x512_adamw_80k_ade20k_20220303_154559-d8da9a90.pth
-- Name: knet-s3_swin-l_upernet_8xb2-adamw-80k_ade20k-640x640
-  In Collection: KNet
-  Metadata:
-    backbone: Swin-L
-    crop size: (640,640)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 120.63
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (640,640)
-    Training Memory (GB): 13.54
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 52.21
-      mIoU(ms+flip): 53.34
-  Config: configs/knet/knet-s3_swin-l_upernet_8xb2-adamw-80k_ade20k-640x640.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k/knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k_20220301_220747-8787fc71.pth
diff --git a/configs/knet/metafile.yaml b/configs/knet/metafile.yaml
new file mode 100644
index 0000000000..0f4ab79609
--- /dev/null
+++ b/configs/knet/metafile.yaml
@@ -0,0 +1,188 @@
+Collections:
+- Name: KNet
+  License: Apache License 2.0
+  Metadata:
+    Training Data:
+    - ADE20K
+  Paper:
+    Title: 'K-Net: Towards Unified Image Segmentation'
+    URL: https://arxiv.org/abs/2106.14855
+  README: configs/knet/README.md
+  Frameworks:
+  - PyTorch
+Models:
+- Name: knet-s3_r50-d8_fcn_8xb2-adamw-80k_ade20k-512x512
+  In Collection: KNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 43.6
+      mIoU(ms+flip): 45.12
+  Config: configs/knet/knet-s3_r50-d8_fcn_8xb2-adamw-80k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - KNet
+    - FCN
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 7.01
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_fcn_r50-d8_8x2_512x512_adamw_80k_ade20k/knet_s3_fcn_r50-d8_8x2_512x512_adamw_80k_ade20k_20220228_043751-abcab920.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_fcn_r50-d8_8x2_512x512_adamw_80k_ade20k/knet_s3_fcn_r50-d8_8x2_512x512_adamw_80k_ade20k_20220228_043751.log.json
+  Paper:
+    Title: 'K-Net: Towards Unified Image Segmentation'
+    URL: https://arxiv.org/abs/2106.14855
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.23.0/mmseg/models/decode_heads/knet_head.py#L392
+  Framework: PyTorch
+- Name: knet-s3_r50-d8_pspnet_8xb2-adamw-80k_ade20k-512x512
+  In Collection: KNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 44.18
+      mIoU(ms+flip): 45.58
+  Config: configs/knet/knet-s3_r50-d8_pspnet_8xb2-adamw-80k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - KNet
+    - PSPNet
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 6.98
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_pspnet_r50-d8_8x2_512x512_adamw_80k_ade20k/knet_s3_pspnet_r50-d8_8x2_512x512_adamw_80k_ade20k_20220228_054634-d2c72240.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_pspnet_r50-d8_8x2_512x512_adamw_80k_ade20k/knet_s3_pspnet_r50-d8_8x2_512x512_adamw_80k_ade20k_20220228_054634.log.json
+  Paper:
+    Title: 'K-Net: Towards Unified Image Segmentation'
+    URL: https://arxiv.org/abs/2106.14855
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.23.0/mmseg/models/decode_heads/knet_head.py#L392
+  Framework: PyTorch
+- Name: knet-s3_r50-d8_deeplabv3_8xb2-adamw-80k_ade20k-512x512
+  In Collection: KNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 45.06
+      mIoU(ms+flip): 46.11
+  Config: configs/knet/knet-s3_r50-d8_deeplabv3_8xb2-adamw-80k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - KNet
+    - DeepLabV3
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 7.42
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_deeplabv3_r50-d8_8x2_512x512_adamw_80k_ade20k/knet_s3_deeplabv3_r50-d8_8x2_512x512_adamw_80k_ade20k_20220228_041642-00c8fbeb.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_deeplabv3_r50-d8_8x2_512x512_adamw_80k_ade20k/knet_s3_deeplabv3_r50-d8_8x2_512x512_adamw_80k_ade20k_20220228_041642.log.json
+  Paper:
+    Title: 'K-Net: Towards Unified Image Segmentation'
+    URL: https://arxiv.org/abs/2106.14855
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.23.0/mmseg/models/decode_heads/knet_head.py#L392
+  Framework: PyTorch
+- Name: knet-s3_r50-d8_upernet_8xb2-adamw-80k_ade20k-512x512
+  In Collection: KNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 43.45
+      mIoU(ms+flip): 44.07
+  Config: configs/knet/knet-s3_r50-d8_upernet_8xb2-adamw-80k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - KNet
+    - UperNet
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 7.34
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_r50-d8_8x2_512x512_adamw_80k_ade20k/knet_s3_upernet_r50-d8_8x2_512x512_adamw_80k_ade20k_20220304_125657-215753b0.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_r50-d8_8x2_512x512_adamw_80k_ade20k/knet_s3_upernet_r50-d8_8x2_512x512_adamw_80k_ade20k_20220304_125657.log.json
+  Paper:
+    Title: 'K-Net: Towards Unified Image Segmentation'
+    URL: https://arxiv.org/abs/2106.14855
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.23.0/mmseg/models/decode_heads/knet_head.py#L392
+  Framework: PyTorch
+- Name: knet-s3_swin-t_upernet_8xb2-adamw-80k_ade20k-512x512
+  In Collection: KNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 45.84
+      mIoU(ms+flip): 46.27
+  Config: configs/knet/knet-s3_swin-t_upernet_8xb2-adamw-80k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - Swin-T
+    - KNet
+    - UperNet
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 7.57
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_swin-t_8x2_512x512_adamw_80k_ade20k/knet_s3_upernet_swin-t_8x2_512x512_adamw_80k_ade20k_20220303_133059-7545e1dc.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_swin-t_8x2_512x512_adamw_80k_ade20k/knet_s3_upernet_swin-t_8x2_512x512_adamw_80k_ade20k_20220303_133059.log.json
+  Paper:
+    Title: 'K-Net: Towards Unified Image Segmentation'
+    URL: https://arxiv.org/abs/2106.14855
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.23.0/mmseg/models/decode_heads/knet_head.py#L392
+  Framework: PyTorch
+- Name: knet-s3_swin-l_upernet_8xb2-adamw-80k_ade20k-512x512
+  In Collection: KNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 52.05
+      mIoU(ms+flip): 53.24
+  Config: configs/knet/knet-s3_swin-l_upernet_8xb2-adamw-80k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - Swin-L
+    - KNet
+    - UperNet
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 13.5
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_swin-l_8x2_512x512_adamw_80k_ade20k/knet_s3_upernet_swin-l_8x2_512x512_adamw_80k_ade20k_20220303_154559-d8da9a90.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_swin-l_8x2_512x512_adamw_80k_ade20k/knet_s3_upernet_swin-l_8x2_512x512_adamw_80k_ade20k_20220303_154559.log.json
+  Paper:
+    Title: 'K-Net: Towards Unified Image Segmentation'
+    URL: https://arxiv.org/abs/2106.14855
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.23.0/mmseg/models/decode_heads/knet_head.py#L392
+  Framework: PyTorch
+- Name: knet-s3_swin-l_upernet_8xb2-adamw-80k_ade20k-640x640
+  In Collection: KNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 52.21
+      mIoU(ms+flip): 53.34
+  Config: configs/knet/knet-s3_swin-l_upernet_8xb2-adamw-80k_ade20k-640x640.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - Swin-L
+    - KNet
+    - UperNet
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 13.54
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k/knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k_20220301_220747-8787fc71.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k/knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k_20220301_220747.log.json
+  Paper:
+    Title: 'K-Net: Towards Unified Image Segmentation'
+    URL: https://arxiv.org/abs/2106.14855
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.23.0/mmseg/models/decode_heads/knet_head.py#L392
+  Framework: PyTorch
diff --git a/configs/mae/README.md b/configs/mae/README.md
index 330749732e..94ade08823 100644
--- a/configs/mae/README.md
+++ b/configs/mae/README.md
@@ -1,6 +1,6 @@
 # MAE
 
-[Masked Autoencoders Are Scalable Vision Learners](https://arxiv.org/abs/2111.06377)
+> [Masked Autoencoders Are Scalable Vision Learners](https://arxiv.org/abs/2111.06377)
 
 ## Introduction
 
@@ -22,17 +22,6 @@ This paper shows that masked autoencoders (MAE) are scalable self-supervised lea
 <img src="https://user-images.githubusercontent.com/24582831/165456416-1cba54bf-b1b5-4bdf-ad86-d6390de7f342.png" width="70%"/>
 </div>
 
-## Citation
-
-```bibtex
-@article{he2021masked,
-  title={Masked autoencoders are scalable vision learners},
-  author={He, Kaiming and Chen, Xinlei and Xie, Saining and Li, Yanghao and Doll{\'a}r, Piotr and Girshick, Ross},
-  journal={arXiv preprint arXiv:2111.06377},
-  year={2021}
-}
-```
-
 ## Usage
 
 To use other repositories' pre-trained models, it is necessary to convert keys.
@@ -77,6 +66,17 @@ upernet_mae-base_fp16_8x2_512x512_160k_ade20k_20220426_174752-f92a2975.pth $GPUS
 
 ### ADE20K
 
-| Method  | Backbone | Crop Size | pretrain    | pretrain img size | Batch Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU  | mIoU(ms+flip) | config                                                                                                                           | download                                                                                                                                                                                                                                                                                                                                                                       |
-| ------- | -------- | --------- | ----------- | ----------------- | ---------- | ------- | -------- | -------------- | ----- | ------------: | -------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| UPerNet | ViT-B    | 512x512   | ImageNet-1K | 224x224           | 16         | 160000  | 9.96     | 7.14           | 48.13 |         48.70 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mae/upernet_mae-base_fp16_8x2_512x512_160k_ade20k/upernet_mae-base_fp16_8x2_512x512_160k_ade20k_20220426_174752-f92a2975.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mae/upernet_mae-base_fp16_8x2_512x512_160k_ade20k/upernet_mae-base_fp16_8x2_512x512_160k_ade20k_20220426_174752.log.json) |
+| Method  | Backbone | Crop Size | pretrain    | pretrain img size | Batch Size | Lr schd | Mem (GB) | Inf time (fps) | Device | mIoU  | mIoU(ms+flip) | config                                                                                                                           | download                                                                                                                                                                                                                                                                                                                                                                       |
+| ------- | -------- | --------- | ----------- | ----------------- | ---------- | ------- | -------- | -------------- | ------ | ----- | ------------: | -------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| UPerNet | ViT-B    | 512x512   | ImageNet-1K | 224x224           | 16         | 160000  | 9.96     | 7.14           | V100   | 48.13 |         48.70 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mae/upernet_mae-base_fp16_8x2_512x512_160k_ade20k/upernet_mae-base_fp16_8x2_512x512_160k_ade20k_20220426_174752-f92a2975.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mae/upernet_mae-base_fp16_8x2_512x512_160k_ade20k/upernet_mae-base_fp16_8x2_512x512_160k_ade20k_20220426_174752.log.json) |
+
+## Citation
+
+```bibtex
+@article{he2021masked,
+  title={Masked autoencoders are scalable vision learners},
+  author={He, Kaiming and Chen, Xinlei and Xie, Saining and Li, Yanghao and Doll{\'a}r, Piotr and Girshick, Ross},
+  journal={arXiv preprint arXiv:2111.06377},
+  year={2021}
+}
+```
diff --git a/configs/mae/mae.yml b/configs/mae/mae.yml
deleted file mode 100644
index 72b2cc7f12..0000000000
--- a/configs/mae/mae.yml
+++ /dev/null
@@ -1,23 +0,0 @@
-Models:
-- Name: mae-base_upernet_8xb2-amp-160k_ade20k-512x512
-  In Collection: UPerNet
-  Metadata:
-    backbone: ViT-B
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 140.06
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: AMP
-      resolution: (512,512)
-    Training Memory (GB): 9.96
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 48.13
-      mIoU(ms+flip): 48.7
-  Config: configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mae/upernet_mae-base_fp16_8x2_512x512_160k_ade20k/upernet_mae-base_fp16_8x2_512x512_160k_ade20k_20220426_174752-f92a2975.pth
diff --git a/configs/mae/metafile.yaml b/configs/mae/metafile.yaml
new file mode 100644
index 0000000000..567eafe131
--- /dev/null
+++ b/configs/mae/metafile.yaml
@@ -0,0 +1,25 @@
+Models:
+- Name: mae-base_upernet_8xb2-amp-160k_ade20k-512x512
+  In Collection: UPerNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 48.13
+      mIoU(ms+flip): 48.7
+  Config: configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - ViT-B
+    - UPerNet
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 9.96
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mae/upernet_mae-base_fp16_8x2_512x512_160k_ade20k/upernet_mae-base_fp16_8x2_512x512_160k_ade20k_20220426_174752-f92a2975.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/mae/upernet_mae-base_fp16_8x2_512x512_160k_ade20k/upernet_mae-base_fp16_8x2_512x512_160k_ade20k_20220426_174752.log.json
+  Paper:
+    Title: Masked Autoencoders Are Scalable Vision Learners
+    URL: https://arxiv.org/abs/2111.06377
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.24.0/mmseg/models/backbones/mae.py#L46
+  Framework: PyTorch
diff --git a/configs/mask2former/README.md b/configs/mask2former/README.md
index 1861fec3b1..02639bef58 100644
--- a/configs/mask2former/README.md
+++ b/configs/mask2former/README.md
@@ -1,6 +1,6 @@
 # Mask2Former
 
-[Masked-attention Mask Transformer for Universal Image Segmentation](https://arxiv.org/abs/2112.01527)
+> [Masked-attention Mask Transformer for Universal Image Segmentation](https://arxiv.org/abs/2112.01527)
 
 ## Introduction
 
@@ -16,21 +16,6 @@
 
 Image segmentation is about grouping pixels with different semantics, e.g., category or instance membership, where each choice of semantics defines a task. While only the semantics of each task differ, current research focuses on designing specialized architectures for each task. We present Masked-attention Mask Transformer (Mask2Former), a new architecture capable of addressing any image segmentation task (panoptic, instance or semantic). Its key components include masked attention, which extracts localized features by constraining cross-attention within predicted mask regions. In addition to reducing the research effort by at least three times, it outperforms the best specialized architectures by a significant margin on four popular datasets. Most notably, Mask2Former sets a new state-of-the-art for panoptic segmentation (57.8 PQ on COCO), instance segmentation (50.1 AP on COCO) and semantic segmentation (57.7 mIoU on ADE20K).
 
-```bibtex
-@inproceedings{cheng2021mask2former,
-  title={Masked-attention Mask Transformer for Universal Image Segmentation},
-  author={Bowen Cheng and Ishan Misra and Alexander G. Schwing and Alexander Kirillov and Rohit Girdhar},
-  journal={CVPR},
-  year={2022}
-}
-@inproceedings{cheng2021maskformer,
-  title={Per-Pixel Classification is Not All You Need for Semantic Segmentation},
-  author={Bowen Cheng and Alexander G. Schwing and Alexander Kirillov},
-  journal={NeurIPS},
-  year={2021}
-}
-```
-
 ### Usage
 
 - Mask2Former model needs to install [MMDetection](https://github.com/open-mmlab/mmdetection) first.
@@ -43,26 +28,26 @@ pip install "mmdet>=3.0.0rc4"
 
 ### Cityscapes
 
-| Method      | Backbone       | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU  | mIoU(ms+flip) |                                                                                                                                                       config | download                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |
-| ----------- | -------------- | --------- | ------- | -------: | -------------- | ----- | ------------: | -----------------------------------------------------------------------------------------------------------------------------------------------------------: | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| Mask2Former | R-50-D32       | 512x1024  | 90000   |     5806 | 9.17           | 80.44 |             - |                      [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mask2former/mask2former_r50_8xb2-90k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-90k_cityscapes-512x1024/mask2former_r50_8xb2-90k_cityscapes-512x1024_20221202_140802-ffd9d750.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-90k_cityscapes-512x1024/mask2former_r50_8xb2-90k_cityscapes-512x1024_20221202_140802.json)                                                                                      |
-| Mask2Former | R-101-D32      | 512x1024  | 90000   |     6971 | 7.11           | 80.80 |             - |                     [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mask2former/mask2former_r101_8xb2-90k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r101_8xb2-90k_cityscapes-512x1024/mask2former_r101_8xb2-90k_cityscapes-512x1024_20221130_031628-43e68666.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r101_8xb2-90k_cityscapes-512x1024/mask2former_r101_8xb2-90k_cityscapes-512x1024_20221130_031628.json))                                                                                 |
-| Mask2Former | Swin-T         | 512x1024  | 90000   |     6511 | 7.18           | 81.71 |             - |                   [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mask2former/mask2former_swin-t_8xb2-90k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-t_8xb2-90k_cityscapes-512x1024/mask2former_swin-t_8xb2-90k_cityscapes-512x1024_20221127_144501-36c59341.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-t_8xb2-90k_cityscapes-512x1024/mask2former_swin-t_8xb2-90k_cityscapes-512x1024_20221127_144501.json))                                                                         |
-| Mask2Former | Swin-S         | 512x1024  | 90000   |     8282 | 5.57           | 82.57 |             - |                   [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mask2former/mask2former_swin-s_8xb2-90k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-s_8xb2-90k_cityscapes-512x1024/mask2former_swin-s_8xb2-90k_cityscapes-512x1024_20221127_143802-9ab177f6.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-s_8xb2-90k_cityscapes-512x1024/mask2former_swin-s_8xb2-90k_cityscapes-512x1024_20221127_143802.json))                                                                         |
-| Mask2Former | Swin-B (in22k) | 512x1024  | 90000   |    11152 | 4.32           | 83.52 |             - | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mask2former/mask2former_swin-b-in22k-384x384-pre_8xb2-90k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-b-in22k-384x384-pre_8xb2-90k_cityscapes-512x1024/mask2former_swin-b-in22k-384x384-pre_8xb2-90k_cityscapes-512x1024_20221203_045030-9a86a225.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-b-in22k-384x384-pre_8xb2-90k_cityscapes-512x1024/mask2former_swin-b-in22k-384x384-pre_8xb2-90k_cityscapes-512x1024_20221203_045030.json)) |
-| Mask2Former | Swin-L (in22k) | 512x1024  | 90000   |    16207 | 2.86           | 83.65 |             - | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mask2former/mask2former_swin-l-in22k-384x384-pre_8xb2-90k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-l-in22k-384x384-pre_8xb2-90k_cityscapes-512x1024/mask2former_swin-l-in22k-384x384-pre_8xb2-90k_cityscapes-512x1024_20221202_141901-28ad20f1.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-l-in22k-384x384-pre_8xb2-90k_cityscapes-512x1024/mask2former_swin-l-in22k-384x384-pre_8xb2-90k_cityscapes-512x1024_20221202_141901.json)) |
+| Method      | Backbone       | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device | mIoU  | mIoU(ms+flip) |                                                                                                                                                       config | download                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |
+| ----------- | -------------- | --------- | ------- | -------: | -------------- | ------ | ----- | ------------: | -----------------------------------------------------------------------------------------------------------------------------------------------------------: | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| Mask2Former | R-50-D32       | 512x1024  | 90000   |     5.67 | 9.17           | A100   | 80.44 |             - |                      [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mask2former/mask2former_r50_8xb2-90k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-90k_cityscapes-512x1024/mask2former_r50_8xb2-90k_cityscapes-512x1024_20221202_140802-ffd9d750.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-90k_cityscapes-512x1024/mask2former_r50_8xb2-90k_cityscapes-512x1024_20221202_140802.json)                                                                                      |
+| Mask2Former | R-101-D32      | 512x1024  | 90000   |     6.81 | 7.11           | A100   | 80.80 |             - |                     [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mask2former/mask2former_r101_8xb2-90k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r101_8xb2-90k_cityscapes-512x1024/mask2former_r101_8xb2-90k_cityscapes-512x1024_20221130_031628-43e68666.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r101_8xb2-90k_cityscapes-512x1024/mask2former_r101_8xb2-90k_cityscapes-512x1024_20221130_031628.json))                                                                                 |
+| Mask2Former | Swin-T         | 512x1024  | 90000   |     6.36 | 7.18           | A100   | 81.71 |             - |                   [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mask2former/mask2former_swin-t_8xb2-90k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-t_8xb2-90k_cityscapes-512x1024/mask2former_swin-t_8xb2-90k_cityscapes-512x1024_20221127_144501-36c59341.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-t_8xb2-90k_cityscapes-512x1024/mask2former_swin-t_8xb2-90k_cityscapes-512x1024_20221127_144501.json))                                                                         |
+| Mask2Former | Swin-S         | 512x1024  | 90000   |     8.09 | 5.57           | A100   | 82.57 |             - |                   [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mask2former/mask2former_swin-s_8xb2-90k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-s_8xb2-90k_cityscapes-512x1024/mask2former_swin-s_8xb2-90k_cityscapes-512x1024_20221127_143802-9ab177f6.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-s_8xb2-90k_cityscapes-512x1024/mask2former_swin-s_8xb2-90k_cityscapes-512x1024_20221127_143802.json))                                                                         |
+| Mask2Former | Swin-B (in22k) | 512x1024  | 90000   |    10.89 | 4.32           | A100   | 83.52 |             - | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mask2former/mask2former_swin-b-in22k-384x384-pre_8xb2-90k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-b-in22k-384x384-pre_8xb2-90k_cityscapes-512x1024/mask2former_swin-b-in22k-384x384-pre_8xb2-90k_cityscapes-512x1024_20221203_045030-9a86a225.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-b-in22k-384x384-pre_8xb2-90k_cityscapes-512x1024/mask2former_swin-b-in22k-384x384-pre_8xb2-90k_cityscapes-512x1024_20221203_045030.json)) |
+| Mask2Former | Swin-L (in22k) | 512x1024  | 90000   |    15.83 | 2.86           | A100   | 83.65 |             - | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mask2former/mask2former_swin-l-in22k-384x384-pre_8xb2-90k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-l-in22k-384x384-pre_8xb2-90k_cityscapes-512x1024/mask2former_swin-l-in22k-384x384-pre_8xb2-90k_cityscapes-512x1024_20221202_141901-28ad20f1.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-l-in22k-384x384-pre_8xb2-90k_cityscapes-512x1024/mask2former_swin-l-in22k-384x384-pre_8xb2-90k_cityscapes-512x1024_20221202_141901.json)) |
 
 ### ADE20K
 
-| Method      | Backbone       | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU  | mIoU(ms+flip) |                                                                                                                                                   config | download                                                                                                                                                                                                                                                                                                                                                                                                                                                    |
-| ----------- | -------------- | --------- | ------- | -------: | -------------- | ----- | ------------: | -------------------------------------------------------------------------------------------------------------------------------------------------------: | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| Mask2Former | R-50-D32       | 512x512   | 160000  |     3385 | 26.59          | 47.87 |             - |                      [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-2d1f55f1.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055.json))                                                                                     |
-| Mask2Former | R-101-D32      | 512x512   | 160000  |     4190 | 22.97          | 48.60 |             - |                     [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mask2former/mask2former_r101_8xb2-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r101_8xb2-160k_ade20k-512x512/mask2former_r101_8xb2-160k_ade20k-512x512_20221203_233905-b7135890.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r101_8xb2-160k_ade20k-512x512/mask2former_r101_8xb2-160k_ade20k-512x512_20221203_233905.json))                                                                                 |
-| Mask2Former | Swin-T         | 512x512   | 160000  |     3826 | 23.82          | 48.66 |             - |                   [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mask2former/mask2former_swin-t_8xb2-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-t_8xb2-160k_ade20k-512x512/mask2former_swin-t_8xb2-160k_ade20k-512x512_20221203_234230-7d64e5dd.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-t_8xb2-160k_ade20k-512x512/mask2former_swin-t_8xb2-160k_ade20k-512x512_20221203_234230.json))                                                                         |
-| Mask2Former | Swin-S         | 512x512   | 160000  |     5034 | 19.69          | 51.24 |             - |                   [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mask2former/mask2former_swin-s_8xb2-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-s_8xb2-160k_ade20k-512x512/mask2former_swin-s_8xb2-160k_ade20k-512x512_20221204_143905-e715144e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-s_8xb2-160k_ade20k-512x512/mask2former_swin-s_8xb2-160k_ade20k-512x512_20221204_143905.json))                                                                         |
-| Mask2Former | Swin-B         | 640x640   | 160000  |     5795 | 12.48          | 52.44 |             - |  [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mask2former/mask2former_swin-b-in1k-384x384-pre_8xb2-160k_ade20k-640x640.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-b-in1k-384x384-pre_8xb2-160k_ade20k-640x640/mask2former_swin-b-in1k-384x384-pre_8xb2-160k_ade20k-640x640_20221129_125118-a4a086d2.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-b-in1k-384x384-pre_8xb2-160k_ade20k-640x640/mask2former_swin-b-in1k-384x384-pre_8xb2-160k_ade20k-640x640_20221129_125118.json))     |
-| Mask2Former | Swin-B (in22k) | 640x640   | 160000  |     5795 | 12.43          | 53.90 |             - | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mask2former/mask2former_swin-b-in22k-384x384-pre_8xb2-160k_ade20k-640x640.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-b-in22k-384x384-pre_8xb2-160k_ade20k-640x640/mask2former_swin-b-in22k-384x384-pre_8xb2-160k_ade20k-640x640_20221203_235230-7ec0f569.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-b-in22k-384x384-pre_8xb2-160k_ade20k-640x640/mask2former_swin-b-in22k-384x384-pre_8xb2-160k_ade20k-640x640_20221203_235230.json)) |
-| Mask2Former | Swin-L (in22k) | 640x640   | 160000  |     9077 | 8.81           | 56.01 |             - | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mask2former/mask2former_swin-l-in22k-384x384-pre_8xb2-160k_ade20k-640x640.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-l-in22k-384x384-pre_8xb2-160k_ade20k-640x640/mask2former_swin-l-in22k-384x384-pre_8xb2-160k_ade20k-640x640_20221203_235933-7120c214.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-l-in22k-384x384-pre_8xb2-160k_ade20k-640x640/mask2former_swin-l-in22k-384x384-pre_8xb2-160k_ade20k-640x640_20221203_235933.json)) |
+| Method      | Backbone       | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device | mIoU  | mIoU(ms+flip) |                                                                                                                                                   config | download                                                                                                                                                                                                                                                                                                                                                                                                                                                    |
+| ----------- | -------------- | --------- | ------- | -------: | -------------- | ------ | ----- | ------------: | -------------------------------------------------------------------------------------------------------------------------------------------------------: | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| Mask2Former | R-50-D32       | 512x512   | 160000  |     3.31 | 26.59          | A100   | 47.87 |             - |                      [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-2d1f55f1.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055.json))                                                                                     |
+| Mask2Former | R-101-D32      | 512x512   | 160000  |     4.09 | 22.97          | A100   | 48.60 |             - |                     [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mask2former/mask2former_r101_8xb2-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r101_8xb2-160k_ade20k-512x512/mask2former_r101_8xb2-160k_ade20k-512x512_20221203_233905-b7135890.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r101_8xb2-160k_ade20k-512x512/mask2former_r101_8xb2-160k_ade20k-512x512_20221203_233905.json))                                                                                 |
+| Mask2Former | Swin-T         | 512x512   | 160000  |     3826 | 23.82          | A100   | 48.66 |             - |                   [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mask2former/mask2former_swin-t_8xb2-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-t_8xb2-160k_ade20k-512x512/mask2former_swin-t_8xb2-160k_ade20k-512x512_20221203_234230-7d64e5dd.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-t_8xb2-160k_ade20k-512x512/mask2former_swin-t_8xb2-160k_ade20k-512x512_20221203_234230.json))                                                                         |
+| Mask2Former | Swin-S         | 512x512   | 160000  |     3.74 | 19.69          | A100   | 51.24 |             - |                   [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mask2former/mask2former_swin-s_8xb2-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-s_8xb2-160k_ade20k-512x512/mask2former_swin-s_8xb2-160k_ade20k-512x512_20221204_143905-e715144e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-s_8xb2-160k_ade20k-512x512/mask2former_swin-s_8xb2-160k_ade20k-512x512_20221204_143905.json))                                                                         |
+| Mask2Former | Swin-B         | 640x640   | 160000  |     5.66 | 12.48          | A100   | 52.44 |             - |  [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mask2former/mask2former_swin-b-in1k-384x384-pre_8xb2-160k_ade20k-640x640.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-b-in1k-384x384-pre_8xb2-160k_ade20k-640x640/mask2former_swin-b-in1k-384x384-pre_8xb2-160k_ade20k-640x640_20221129_125118-a4a086d2.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-b-in1k-384x384-pre_8xb2-160k_ade20k-640x640/mask2former_swin-b-in1k-384x384-pre_8xb2-160k_ade20k-640x640_20221129_125118.json))     |
+| Mask2Former | Swin-B (in22k) | 640x640   | 160000  |     5.66 | 12.43          | A100   | 53.90 |             - | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mask2former/mask2former_swin-b-in22k-384x384-pre_8xb2-160k_ade20k-640x640.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-b-in22k-384x384-pre_8xb2-160k_ade20k-640x640/mask2former_swin-b-in22k-384x384-pre_8xb2-160k_ade20k-640x640_20221203_235230-7ec0f569.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-b-in22k-384x384-pre_8xb2-160k_ade20k-640x640/mask2former_swin-b-in22k-384x384-pre_8xb2-160k_ade20k-640x640_20221203_235230.json)) |
+| Mask2Former | Swin-L (in22k) | 640x640   | 160000  |     8.86 | 8.81           | A100   | 56.01 |             - | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mask2former/mask2former_swin-l-in22k-384x384-pre_8xb2-160k_ade20k-640x640.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-l-in22k-384x384-pre_8xb2-160k_ade20k-640x640/mask2former_swin-l-in22k-384x384-pre_8xb2-160k_ade20k-640x640_20221203_235933-7120c214.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-l-in22k-384x384-pre_8xb2-160k_ade20k-640x640/mask2former_swin-l-in22k-384x384-pre_8xb2-160k_ade20k-640x640_20221203_235933.json)) |
 
 Note:
 
@@ -70,3 +55,20 @@ Note:
 - As mentioned at [the official repo](https://github.com/facebookresearch/Mask2Former/issues/5), the results of Mask2Former are relatively not stable, the result of Mask2Former(swin-s) on ADE20K dataset in the table is the medium result obtained by training 5 times following the suggestion of the author.
 - The ResNet backbones utilized in MaskFormer models are standard `ResNet` rather than `ResNetV1c`.
 - Test time augmentation is not supported in MMSegmentation 1.x version yet, we would add "ms+flip" results as soon as possible.
+
+## Citation
+
+```bibtex
+@inproceedings{cheng2021mask2former,
+  title={Masked-attention Mask Transformer for Universal Image Segmentation},
+  author={Bowen Cheng and Ishan Misra and Alexander G. Schwing and Alexander Kirillov and Rohit Girdhar},
+  journal={CVPR},
+  year={2022}
+}
+@inproceedings{cheng2021maskformer,
+  title={Per-Pixel Classification is Not All You Need for Semantic Segmentation},
+  author={Bowen Cheng and Alexander G. Schwing and Alexander Kirillov},
+  journal={NeurIPS},
+  year={2021}
+}
+```
diff --git a/configs/mask2former/mask2former.yml b/configs/mask2former/mask2former.yml
deleted file mode 100644
index 4e33766c70..0000000000
--- a/configs/mask2former/mask2former.yml
+++ /dev/null
@@ -1,290 +0,0 @@
-Collections:
-- Name: Mask2Former
-  Metadata:
-    Training Data:
-    - Usage
-    - Cityscapes
-    - ADE20K
-  Paper:
-    URL: https://arxiv.org/abs/2112.01527
-    Title: Masked-attention Mask Transformer for Universal Image Segmentation
-  README: configs/mask2former/README.md
-  Code:
-    URL: https://github.com/open-mmlab/mmdetection/blob/3.x/mmdet/models/dense_heads/mask2former_head.py
-    Version: 3.x
-  Converted From:
-    Code: https://github.com/facebookresearch/Mask2Former
-Models:
-- Name: mask2former_r50_8xb2-90k_cityscapes-512x1024
-  In Collection: Mask2Former
-  Metadata:
-    backbone: R-50-D32
-    crop size: (512,1024)
-    lr schd: 90000
-    inference time (ms/im):
-    - value: 109.05
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 5806.0
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 80.44
-  Config: configs/mask2former/mask2former_r50_8xb2-90k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-90k_cityscapes-512x1024/mask2former_r50_8xb2-90k_cityscapes-512x1024_20221202_140802-ffd9d750.pth
-- Name: mask2former_r101_8xb2-90k_cityscapes-512x1024
-  In Collection: Mask2Former
-  Metadata:
-    backbone: R-101-D32
-    crop size: (512,1024)
-    lr schd: 90000
-    inference time (ms/im):
-    - value: 140.65
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 6971.0
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 80.8
-  Config: configs/mask2former/mask2former_r101_8xb2-90k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r101_8xb2-90k_cityscapes-512x1024/mask2former_r101_8xb2-90k_cityscapes-512x1024_20221130_031628-43e68666.pth
-- Name: mask2former_swin-t_8xb2-90k_cityscapes-512x1024
-  In Collection: Mask2Former
-  Metadata:
-    backbone: Swin-T
-    crop size: (512,1024)
-    lr schd: 90000
-    inference time (ms/im):
-    - value: 139.28
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 6511.0
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 81.71
-  Config: configs/mask2former/mask2former_swin-t_8xb2-90k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-t_8xb2-90k_cityscapes-512x1024/mask2former_swin-t_8xb2-90k_cityscapes-512x1024_20221127_144501-36c59341.pth
-- Name: mask2former_swin-s_8xb2-90k_cityscapes-512x1024
-  In Collection: Mask2Former
-  Metadata:
-    backbone: Swin-S
-    crop size: (512,1024)
-    lr schd: 90000
-    inference time (ms/im):
-    - value: 179.53
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 8282.0
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 82.57
-  Config: configs/mask2former/mask2former_swin-s_8xb2-90k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-s_8xb2-90k_cityscapes-512x1024/mask2former_swin-s_8xb2-90k_cityscapes-512x1024_20221127_143802-9ab177f6.pth
-- Name: mask2former_swin-b-in22k-384x384-pre_8xb2-90k_cityscapes-512x1024
-  In Collection: Mask2Former
-  Metadata:
-    backbone: Swin-B (in22k)
-    crop size: (512,1024)
-    lr schd: 90000
-    inference time (ms/im):
-    - value: 231.48
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 11152.0
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 83.52
-  Config: configs/mask2former/mask2former_swin-b-in22k-384x384-pre_8xb2-90k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-b-in22k-384x384-pre_8xb2-90k_cityscapes-512x1024/mask2former_swin-b-in22k-384x384-pre_8xb2-90k_cityscapes-512x1024_20221203_045030-9a86a225.pth
-- Name: mask2former_swin-l-in22k-384x384-pre_8xb2-90k_cityscapes-512x1024
-  In Collection: Mask2Former
-  Metadata:
-    backbone: Swin-L (in22k)
-    crop size: (512,1024)
-    lr schd: 90000
-    inference time (ms/im):
-    - value: 349.65
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 16207.0
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 83.65
-  Config: configs/mask2former/mask2former_swin-l-in22k-384x384-pre_8xb2-90k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-l-in22k-384x384-pre_8xb2-90k_cityscapes-512x1024/mask2former_swin-l-in22k-384x384-pre_8xb2-90k_cityscapes-512x1024_20221202_141901-28ad20f1.pth
-- Name: mask2former_r50_8xb2-160k_ade20k-512x512
-  In Collection: Mask2Former
-  Metadata:
-    backbone: R-50-D32
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 37.61
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 3385.0
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 47.87
-  Config: configs/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-2d1f55f1.pth
-- Name: mask2former_r101_8xb2-160k_ade20k-512x512
-  In Collection: Mask2Former
-  Metadata:
-    backbone: R-101-D32
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 43.54
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 4190.0
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 48.6
-  Config: configs/mask2former/mask2former_r101_8xb2-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r101_8xb2-160k_ade20k-512x512/mask2former_r101_8xb2-160k_ade20k-512x512_20221203_233905-b7135890.pth
-- Name: mask2former_swin-t_8xb2-160k_ade20k-512x512
-  In Collection: Mask2Former
-  Metadata:
-    backbone: Swin-T
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 41.98
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 3826.0
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 48.66
-  Config: configs/mask2former/mask2former_swin-t_8xb2-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-t_8xb2-160k_ade20k-512x512/mask2former_swin-t_8xb2-160k_ade20k-512x512_20221203_234230-7d64e5dd.pth
-- Name: mask2former_swin-s_8xb2-160k_ade20k-512x512
-  In Collection: Mask2Former
-  Metadata:
-    backbone: Swin-S
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 50.79
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 5034.0
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 51.24
-  Config: configs/mask2former/mask2former_swin-s_8xb2-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-s_8xb2-160k_ade20k-512x512/mask2former_swin-s_8xb2-160k_ade20k-512x512_20221204_143905-e715144e.pth
-- Name: mask2former_swin-b-in1k-384x384-pre_8xb2-160k_ade20k-640x640
-  In Collection: Mask2Former
-  Metadata:
-    backbone: Swin-B
-    crop size: (640,640)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 80.13
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (640,640)
-    Training Memory (GB): 5795.0
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 52.44
-  Config: configs/mask2former/mask2former_swin-b-in1k-384x384-pre_8xb2-160k_ade20k-640x640.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-b-in1k-384x384-pre_8xb2-160k_ade20k-640x640/mask2former_swin-b-in1k-384x384-pre_8xb2-160k_ade20k-640x640_20221129_125118-a4a086d2.pth
-- Name: mask2former_swin-b-in22k-384x384-pre_8xb2-160k_ade20k-640x640
-  In Collection: Mask2Former
-  Metadata:
-    backbone: Swin-B (in22k)
-    crop size: (640,640)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 80.45
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (640,640)
-    Training Memory (GB): 5795.0
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 53.9
-  Config: configs/mask2former/mask2former_swin-b-in22k-384x384-pre_8xb2-160k_ade20k-640x640.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-b-in22k-384x384-pre_8xb2-160k_ade20k-640x640/mask2former_swin-b-in22k-384x384-pre_8xb2-160k_ade20k-640x640_20221203_235230-7ec0f569.pth
-- Name: mask2former_swin-l-in22k-384x384-pre_8xb2-160k_ade20k-640x640
-  In Collection: Mask2Former
-  Metadata:
-    backbone: Swin-L (in22k)
-    crop size: (640,640)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 113.51
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (640,640)
-    Training Memory (GB): 9077.0
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 56.01
-  Config: configs/mask2former/mask2former_swin-l-in22k-384x384-pre_8xb2-160k_ade20k-640x640.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-l-in22k-384x384-pre_8xb2-160k_ade20k-640x640/mask2former_swin-l-in22k-384x384-pre_8xb2-160k_ade20k-640x640_20221203_235933-7120c214.pth
diff --git a/configs/mask2former/metafile.yaml b/configs/mask2former/metafile.yaml
new file mode 100644
index 0000000000..090c95e7cf
--- /dev/null
+++ b/configs/mask2former/metafile.yaml
@@ -0,0 +1,314 @@
+Collections:
+- Name: Mask2Former
+  License: Apache License 2.0
+  Metadata:
+    Training Data:
+    - Usage
+    - Cityscapes
+    - ADE20K
+  Paper:
+    Title: Masked-attention Mask Transformer for Universal Image Segmentation
+    URL: https://arxiv.org/abs/2112.01527
+  README: configs/mask2former/README.md
+  Frameworks:
+  - PyTorch
+Models:
+- Name: mask2former_r50_8xb2-90k_cityscapes-512x1024
+  In Collection: Mask2Former
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 80.44
+  Config: configs/mask2former/mask2former_r50_8xb2-90k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 16
+    Architecture:
+    - R-50-D32
+    - Mask2Former
+    Training Resources: 8x A100 GPUS
+    Memory (GB): 5.67
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-90k_cityscapes-512x1024/mask2former_r50_8xb2-90k_cityscapes-512x1024_20221202_140802-ffd9d750.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-90k_cityscapes-512x1024/mask2former_r50_8xb2-90k_cityscapes-512x1024_20221202_140802.json
+  Paper:
+    Title: Masked-attention Mask Transformer for Universal Image Segmentation
+    URL: https://arxiv.org/abs/2112.01527
+  Code: https://github.com/open-mmlab/mmdetection/blob/3.x/mmdet/models/dense_heads/mask2former_head.py
+  Framework: PyTorch
+- Name: mask2former_r101_8xb2-90k_cityscapes-512x1024
+  In Collection: Mask2Former
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 80.8
+  Config: configs/mask2former/mask2former_r101_8xb2-90k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 16
+    Architecture:
+    - R-101-D32
+    - Mask2Former
+    Training Resources: 8x A100 GPUS
+    Memory (GB): 6.81
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r101_8xb2-90k_cityscapes-512x1024/mask2former_r101_8xb2-90k_cityscapes-512x1024_20221130_031628-43e68666.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r101_8xb2-90k_cityscapes-512x1024/mask2former_r101_8xb2-90k_cityscapes-512x1024_20221130_031628.json
+  Paper:
+    Title: Masked-attention Mask Transformer for Universal Image Segmentation
+    URL: https://arxiv.org/abs/2112.01527
+  Code: https://github.com/open-mmlab/mmdetection/blob/3.x/mmdet/models/dense_heads/mask2former_head.py
+  Framework: PyTorch
+- Name: mask2former_swin-t_8xb2-90k_cityscapes-512x1024
+  In Collection: Mask2Former
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 81.71
+  Config: configs/mask2former/mask2former_swin-t_8xb2-90k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 16
+    Architecture:
+    - Swin-T
+    - Mask2Former
+    Training Resources: 8x A100 GPUS
+    Memory (GB): 6.36
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-t_8xb2-90k_cityscapes-512x1024/mask2former_swin-t_8xb2-90k_cityscapes-512x1024_20221127_144501-36c59341.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-t_8xb2-90k_cityscapes-512x1024/mask2former_swin-t_8xb2-90k_cityscapes-512x1024_20221127_144501.json
+  Paper:
+    Title: Masked-attention Mask Transformer for Universal Image Segmentation
+    URL: https://arxiv.org/abs/2112.01527
+  Code: https://github.com/open-mmlab/mmdetection/blob/3.x/mmdet/models/dense_heads/mask2former_head.py
+  Framework: PyTorch
+- Name: mask2former_swin-s_8xb2-90k_cityscapes-512x1024
+  In Collection: Mask2Former
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 82.57
+  Config: configs/mask2former/mask2former_swin-s_8xb2-90k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 16
+    Architecture:
+    - Swin-S
+    - Mask2Former
+    Training Resources: 8x A100 GPUS
+    Memory (GB): 8.09
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-s_8xb2-90k_cityscapes-512x1024/mask2former_swin-s_8xb2-90k_cityscapes-512x1024_20221127_143802-9ab177f6.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-s_8xb2-90k_cityscapes-512x1024/mask2former_swin-s_8xb2-90k_cityscapes-512x1024_20221127_143802.json
+  Paper:
+    Title: Masked-attention Mask Transformer for Universal Image Segmentation
+    URL: https://arxiv.org/abs/2112.01527
+  Code: https://github.com/open-mmlab/mmdetection/blob/3.x/mmdet/models/dense_heads/mask2former_head.py
+  Framework: PyTorch
+- Name: mask2former_swin-b-in22k-384x384-pre_8xb2-90k_cityscapes-512x1024
+  In Collection: Mask2Former
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 83.52
+  Config: configs/mask2former/mask2former_swin-b-in22k-384x384-pre_8xb2-90k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 16
+    Architecture:
+    - Swin-B
+    - Mask2Former
+    Training Resources: 8x A100 GPUS
+    Memory (GB): 10.89
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-b-in22k-384x384-pre_8xb2-90k_cityscapes-512x1024/mask2former_swin-b-in22k-384x384-pre_8xb2-90k_cityscapes-512x1024_20221203_045030-9a86a225.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-b-in22k-384x384-pre_8xb2-90k_cityscapes-512x1024/mask2former_swin-b-in22k-384x384-pre_8xb2-90k_cityscapes-512x1024_20221203_045030.json
+  Paper:
+    Title: Masked-attention Mask Transformer for Universal Image Segmentation
+    URL: https://arxiv.org/abs/2112.01527
+  Code: https://github.com/open-mmlab/mmdetection/blob/3.x/mmdet/models/dense_heads/mask2former_head.py
+  Framework: PyTorch
+- Name: mask2former_swin-l-in22k-384x384-pre_8xb2-90k_cityscapes-512x1024
+  In Collection: Mask2Former
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 83.65
+  Config: configs/mask2former/mask2former_swin-l-in22k-384x384-pre_8xb2-90k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 16
+    Architecture:
+    - Swin-L
+    - Mask2Former
+    Training Resources: 8x A100 GPUS
+    Memory (GB): 15.83
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-l-in22k-384x384-pre_8xb2-90k_cityscapes-512x1024/mask2former_swin-l-in22k-384x384-pre_8xb2-90k_cityscapes-512x1024_20221202_141901-28ad20f1.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-l-in22k-384x384-pre_8xb2-90k_cityscapes-512x1024/mask2former_swin-l-in22k-384x384-pre_8xb2-90k_cityscapes-512x1024_20221202_141901.json
+  Paper:
+    Title: Masked-attention Mask Transformer for Universal Image Segmentation
+    URL: https://arxiv.org/abs/2112.01527
+  Code: https://github.com/open-mmlab/mmdetection/blob/3.x/mmdet/models/dense_heads/mask2former_head.py
+  Framework: PyTorch
+- Name: mask2former_r50_8xb2-160k_ade20k-512x512
+  In Collection: Mask2Former
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 47.87
+  Config: configs/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-50-D32
+    - Mask2Former
+    Training Resources: 8x A100 GPUS
+    Memory (GB): 3.31
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-2d1f55f1.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055.json
+  Paper:
+    Title: Masked-attention Mask Transformer for Universal Image Segmentation
+    URL: https://arxiv.org/abs/2112.01527
+  Code: https://github.com/open-mmlab/mmdetection/blob/3.x/mmdet/models/dense_heads/mask2former_head.py
+  Framework: PyTorch
+- Name: mask2former_r101_8xb2-160k_ade20k-512x512
+  In Collection: Mask2Former
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 48.6
+  Config: configs/mask2former/mask2former_r101_8xb2-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-101-D32
+    - Mask2Former
+    Training Resources: 8x A100 GPUS
+    Memory (GB): 4.09
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r101_8xb2-160k_ade20k-512x512/mask2former_r101_8xb2-160k_ade20k-512x512_20221203_233905-b7135890.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r101_8xb2-160k_ade20k-512x512/mask2former_r101_8xb2-160k_ade20k-512x512_20221203_233905.json
+  Paper:
+    Title: Masked-attention Mask Transformer for Universal Image Segmentation
+    URL: https://arxiv.org/abs/2112.01527
+  Code: https://github.com/open-mmlab/mmdetection/blob/3.x/mmdet/models/dense_heads/mask2former_head.py
+  Framework: PyTorch
+- Name: mask2former_swin-t_8xb2-160k_ade20k-512x512
+  In Collection: Mask2Former
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 48.66
+  Config: configs/mask2former/mask2former_swin-t_8xb2-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - Swin-T
+    - Mask2Former
+    Training Resources: 8x A100 GPUS
+    Memory (GB): 3826.0
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-t_8xb2-160k_ade20k-512x512/mask2former_swin-t_8xb2-160k_ade20k-512x512_20221203_234230-7d64e5dd.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-t_8xb2-160k_ade20k-512x512/mask2former_swin-t_8xb2-160k_ade20k-512x512_20221203_234230.json
+  Paper:
+    Title: Masked-attention Mask Transformer for Universal Image Segmentation
+    URL: https://arxiv.org/abs/2112.01527
+  Code: https://github.com/open-mmlab/mmdetection/blob/3.x/mmdet/models/dense_heads/mask2former_head.py
+  Framework: PyTorch
+- Name: mask2former_swin-s_8xb2-160k_ade20k-512x512
+  In Collection: Mask2Former
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 51.24
+  Config: configs/mask2former/mask2former_swin-s_8xb2-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - Swin-S
+    - Mask2Former
+    Training Resources: 8x A100 GPUS
+    Memory (GB): 3.74
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-s_8xb2-160k_ade20k-512x512/mask2former_swin-s_8xb2-160k_ade20k-512x512_20221204_143905-e715144e.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-s_8xb2-160k_ade20k-512x512/mask2former_swin-s_8xb2-160k_ade20k-512x512_20221204_143905.json
+  Paper:
+    Title: Masked-attention Mask Transformer for Universal Image Segmentation
+    URL: https://arxiv.org/abs/2112.01527
+  Code: https://github.com/open-mmlab/mmdetection/blob/3.x/mmdet/models/dense_heads/mask2former_head.py
+  Framework: PyTorch
+- Name: mask2former_swin-b-in1k-384x384-pre_8xb2-160k_ade20k-640x640
+  In Collection: Mask2Former
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 52.44
+  Config: configs/mask2former/mask2former_swin-b-in1k-384x384-pre_8xb2-160k_ade20k-640x640.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - Swin-B
+    - Mask2Former
+    Training Resources: 8x A100 GPUS
+    Memory (GB): 5.66
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-b-in1k-384x384-pre_8xb2-160k_ade20k-640x640/mask2former_swin-b-in1k-384x384-pre_8xb2-160k_ade20k-640x640_20221129_125118-a4a086d2.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-b-in1k-384x384-pre_8xb2-160k_ade20k-640x640/mask2former_swin-b-in1k-384x384-pre_8xb2-160k_ade20k-640x640_20221129_125118.json
+  Paper:
+    Title: Masked-attention Mask Transformer for Universal Image Segmentation
+    URL: https://arxiv.org/abs/2112.01527
+  Code: https://github.com/open-mmlab/mmdetection/blob/3.x/mmdet/models/dense_heads/mask2former_head.py
+  Framework: PyTorch
+- Name: mask2former_swin-b-in22k-384x384-pre_8xb2-160k_ade20k-640x640
+  In Collection: Mask2Former
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 53.9
+  Config: configs/mask2former/mask2former_swin-b-in22k-384x384-pre_8xb2-160k_ade20k-640x640.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - Swin-B
+    - Mask2Former
+    Training Resources: 8x A100 GPUS
+    Memory (GB): 5.66
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-b-in22k-384x384-pre_8xb2-160k_ade20k-640x640/mask2former_swin-b-in22k-384x384-pre_8xb2-160k_ade20k-640x640_20221203_235230-7ec0f569.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-b-in22k-384x384-pre_8xb2-160k_ade20k-640x640/mask2former_swin-b-in22k-384x384-pre_8xb2-160k_ade20k-640x640_20221203_235230.json
+  Paper:
+    Title: Masked-attention Mask Transformer for Universal Image Segmentation
+    URL: https://arxiv.org/abs/2112.01527
+  Code: https://github.com/open-mmlab/mmdetection/blob/3.x/mmdet/models/dense_heads/mask2former_head.py
+  Framework: PyTorch
+- Name: mask2former_swin-l-in22k-384x384-pre_8xb2-160k_ade20k-640x640
+  In Collection: Mask2Former
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 56.01
+  Config: configs/mask2former/mask2former_swin-l-in22k-384x384-pre_8xb2-160k_ade20k-640x640.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - Swin-L
+    - Mask2Former
+    Training Resources: 8x A100 GPUS
+    Memory (GB): 8.86
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-l-in22k-384x384-pre_8xb2-160k_ade20k-640x640/mask2former_swin-l-in22k-384x384-pre_8xb2-160k_ade20k-640x640_20221203_235933-7120c214.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_swin-l-in22k-384x384-pre_8xb2-160k_ade20k-640x640/mask2former_swin-l-in22k-384x384-pre_8xb2-160k_ade20k-640x640_20221203_235933.json
+  Paper:
+    Title: Masked-attention Mask Transformer for Universal Image Segmentation
+    URL: https://arxiv.org/abs/2112.01527
+  Code: https://github.com/open-mmlab/mmdetection/blob/3.x/mmdet/models/dense_heads/mask2former_head.py
+  Framework: PyTorch
diff --git a/configs/maskformer/README.md b/configs/maskformer/README.md
index 0248dbb63c..aa54a27088 100644
--- a/configs/maskformer/README.md
+++ b/configs/maskformer/README.md
@@ -1,6 +1,6 @@
 # MaskFormer
 
-[MaskFormer: Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://arxiv.org/abs/2107.06278)
+> [MaskFormer: Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://arxiv.org/abs/2107.06278)
 
 ## Introduction
 
@@ -22,17 +22,6 @@ Modern approaches typically formulate semantic segmentation as a per-pixel class
 <img src="https://user-images.githubusercontent.com/24582831/199215459-ea507126-aafe-4823-8eb1-ae6487509d5c.png" width="90%"/>
 </div>
 
-```bibtex
-@article{cheng2021per,
-  title={Per-pixel classification is not all you need for semantic segmentation},
-  author={Cheng, Bowen and Schwing, Alex and Kirillov, Alexander},
-  journal={Advances in Neural Information Processing Systems},
-  volume={34},
-  pages={17864--17875},
-  year={2021}
-}
-```
-
 ### Usage
 
 - MaskFormer model needs to install [MMDetection](https://github.com/open-mmlab/mmdetection) first.
@@ -45,12 +34,12 @@ pip install "mmdet>=3.0.0rc4"
 
 ### ADE20K
 
-| Method     | Backbone  | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU  | mIoU(ms+flip) | config                                                                                                                                       | download                                                                                                                                                                                                                                                                                                                                                                                                     |
-| ---------- | --------- | --------- | ------- | -------- | -------------- | ----- | ------------- | -------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| MaskFormer | R-50-D32  | 512x512   | 160000  | 3.29     | 42.20          | 44.29 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/maskformer/maskformer_r50-d32_8xb2-160k_ade20k-512x512.py)        | [model](https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_r50-d32_8xb2-160k_ade20k-512x512/maskformer_r50-d32_8xb2-160k_ade20k-512x512_20221030_182724-3a9cfe45.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_r50-d32_8xb2-160k_ade20k-512x512/maskformer_r50-d32_8xb2-160k_ade20k-512x512_20221030_182724.json)                             |
-| MaskFormer | R-101-D32 | 512x512   | 160000  | 4.12     | 34.90          | 45.11 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/maskformer/maskformer_r101-d32_8xb2-160k_ade20k-512x512.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_r101-d32_8xb2-160k_ade20k-512x512/maskformer_r101-d32_8xb2-160k_ade20k-512x512_20221031_223053-84adbfcb.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_r101-d32_8xb2-160k_ade20k-512x512/maskformer_r101-d32_8xb2-160k_ade20k-512x512_20221031_223053.json)                         |
-| MaskFormer | Swin-T    | 512x512   | 160000  | 3.73     | 40.53          | 46.69 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-f14e7ce0.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813.json) |
-| MaskFormer | Swin-S    | 512x512   | 160000  | 5.33     | 26.98          | 49.36 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/maskformer/maskformer_swin-s_upernet_8xb2-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-s_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-s_upernet_8xb2-160k_ade20k-512x512_20221115_114710-723512c7.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-s_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-s_upernet_8xb2-160k_ade20k-512x512_20221115_114710.json) |
+| Method     | Backbone  | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device | mIoU  | mIoU(ms+flip) | config                                                                                                                                       | download                                                                                                                                                                                                                                                                                                                                                                                                     |
+| ---------- | --------- | --------- | ------- | -------- | -------------- | ------ | ----- | ------------- | -------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| MaskFormer | R-50-D32  | 512x512   | 160000  | 3.29     | A100           | 42.20  | 44.29 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/maskformer/maskformer_r50-d32_8xb2-160k_ade20k-512x512.py)        | [model](https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_r50-d32_8xb2-160k_ade20k-512x512/maskformer_r50-d32_8xb2-160k_ade20k-512x512_20221030_182724-3a9cfe45.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_r50-d32_8xb2-160k_ade20k-512x512/maskformer_r50-d32_8xb2-160k_ade20k-512x512_20221030_182724.json)                             |
+| MaskFormer | R-101-D32 | 512x512   | 160000  | 4.12     | A100           | 34.90  | 45.11 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/maskformer/maskformer_r101-d32_8xb2-160k_ade20k-512x512.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_r101-d32_8xb2-160k_ade20k-512x512/maskformer_r101-d32_8xb2-160k_ade20k-512x512_20221031_223053-84adbfcb.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_r101-d32_8xb2-160k_ade20k-512x512/maskformer_r101-d32_8xb2-160k_ade20k-512x512_20221031_223053.json)                         |
+| MaskFormer | Swin-T    | 512x512   | 160000  | 3.73     | A100           | 40.53  | 46.69 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-f14e7ce0.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813.json) |
+| MaskFormer | Swin-S    | 512x512   | 160000  | 5.33     | A100           | 26.98  | 49.36 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/maskformer/maskformer_swin-s_upernet_8xb2-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-s_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-s_upernet_8xb2-160k_ade20k-512x512_20221115_114710-723512c7.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-s_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-s_upernet_8xb2-160k_ade20k-512x512_20221115_114710.json) |
 
 Note:
 
@@ -58,3 +47,16 @@ Note:
 - The results of MaskFormer are relatively not stable.  The accuracy (mIoU) of model with `R-101-D32` is from 44.7 to 46.0, and with `Swin-S` is from 49.0 to 49.8.
 - The ResNet backbones utilized in MaskFormer models are standard `ResNet` rather than `ResNetV1c`.
 - Test time augmentation is not supported in MMSegmentation 1.x version yet, we would add "ms+flip" results as soon as possible.
+
+## Citation
+
+```bibtex
+@article{cheng2021per,
+  title={Per-pixel classification is not all you need for semantic segmentation},
+  author={Cheng, Bowen and Schwing, Alex and Kirillov, Alexander},
+  journal={Advances in Neural Information Processing Systems},
+  volume={34},
+  pages={17864--17875},
+  year={2021}
+}
+```
diff --git a/configs/maskformer/maskformer.yml b/configs/maskformer/maskformer.yml
deleted file mode 100644
index b499476a50..0000000000
--- a/configs/maskformer/maskformer.yml
+++ /dev/null
@@ -1,101 +0,0 @@
-Collections:
-- Name: MaskFormer
-  Metadata:
-    Training Data:
-    - Usage
-    - ADE20K
-  Paper:
-    URL: https://arxiv.org/abs/2107.06278
-    Title: 'MaskFormer: Per-Pixel Classification is Not All You Need for Semantic
-      Segmentation'
-  README: configs/maskformer/README.md
-  Code:
-    URL: https://github.com/open-mmlab/mmdetection/blob/dev-3.x/mmdet/models/dense_heads/maskformer_head.py#L21
-    Version: dev-3.x
-  Converted From:
-    Code: https://github.com/facebookresearch/MaskFormer/
-Models:
-- Name: maskformer_r50-d32_8xb2-160k_ade20k-512x512
-  In Collection: MaskFormer
-  Metadata:
-    backbone: R-50-D32
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 23.7
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 3.29
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 44.29
-  Config: configs/maskformer/maskformer_r50-d32_8xb2-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_r50-d32_8xb2-160k_ade20k-512x512/maskformer_r50-d32_8xb2-160k_ade20k-512x512_20221030_182724-3a9cfe45.pth
-- Name: maskformer_r101-d32_8xb2-160k_ade20k-512x512
-  In Collection: MaskFormer
-  Metadata:
-    backbone: R-101-D32
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 28.65
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 4.12
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 45.11
-  Config: configs/maskformer/maskformer_r101-d32_8xb2-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_r101-d32_8xb2-160k_ade20k-512x512/maskformer_r101-d32_8xb2-160k_ade20k-512x512_20221031_223053-84adbfcb.pth
-- Name: maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512
-  In Collection: MaskFormer
-  Metadata:
-    backbone: Swin-T
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 24.67
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 3.73
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 46.69
-  Config: configs/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-f14e7ce0.pth
-- Name: maskformer_swin-s_upernet_8xb2-160k_ade20k-512x512
-  In Collection: MaskFormer
-  Metadata:
-    backbone: Swin-S
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 37.06
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 5.33
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 49.36
-  Config: configs/maskformer/maskformer_swin-s_upernet_8xb2-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-s_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-s_upernet_8xb2-160k_ade20k-512x512_20221115_114710-723512c7.pth
diff --git a/configs/maskformer/metafile.yaml b/configs/maskformer/metafile.yaml
new file mode 100644
index 0000000000..c9853e131f
--- /dev/null
+++ b/configs/maskformer/metafile.yaml
@@ -0,0 +1,111 @@
+Collections:
+- Name: MaskFormer
+  License: Apache License 2.0
+  Metadata:
+    Training Data:
+    - Usage
+    - ADE20K
+  Paper:
+    Title: 'MaskFormer: Per-Pixel Classification is Not All You Need for Semantic
+      Segmentation'
+    URL: https://arxiv.org/abs/2107.06278
+  README: configs/maskformer/README.md
+  Frameworks:
+  - PyTorch
+Models:
+- Name: maskformer_r50-d32_8xb2-160k_ade20k-512x512
+  In Collection: MaskFormer
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 44.29
+  Config: configs/maskformer/maskformer_r50-d32_8xb2-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-50-D32
+    - MaskFormer
+    Training Resources: 8x 42.20 GPUS
+    Memory (GB): 3.29
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_r50-d32_8xb2-160k_ade20k-512x512/maskformer_r50-d32_8xb2-160k_ade20k-512x512_20221030_182724-3a9cfe45.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_r50-d32_8xb2-160k_ade20k-512x512/maskformer_r50-d32_8xb2-160k_ade20k-512x512_20221030_182724.json
+  Paper:
+    Title: 'MaskFormer: Per-Pixel Classification is Not All You Need for Semantic
+      Segmentation'
+    URL: https://arxiv.org/abs/2107.06278
+  Code: https://github.com/open-mmlab/mmdetection/blob/dev-3.x/mmdet/models/dense_heads/maskformer_head.py#L21
+  Framework: PyTorch
+- Name: maskformer_r101-d32_8xb2-160k_ade20k-512x512
+  In Collection: MaskFormer
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 45.11
+  Config: configs/maskformer/maskformer_r101-d32_8xb2-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-101-D32
+    - MaskFormer
+    Training Resources: 8x 34.90 GPUS
+    Memory (GB): 4.12
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_r101-d32_8xb2-160k_ade20k-512x512/maskformer_r101-d32_8xb2-160k_ade20k-512x512_20221031_223053-84adbfcb.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_r101-d32_8xb2-160k_ade20k-512x512/maskformer_r101-d32_8xb2-160k_ade20k-512x512_20221031_223053.json
+  Paper:
+    Title: 'MaskFormer: Per-Pixel Classification is Not All You Need for Semantic
+      Segmentation'
+    URL: https://arxiv.org/abs/2107.06278
+  Code: https://github.com/open-mmlab/mmdetection/blob/dev-3.x/mmdet/models/dense_heads/maskformer_head.py#L21
+  Framework: PyTorch
+- Name: maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512
+  In Collection: MaskFormer
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 46.69
+  Config: configs/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - Swin-T
+    - MaskFormer
+    Training Resources: 8x 40.53 GPUS
+    Memory (GB): 3.73
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-f14e7ce0.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813.json
+  Paper:
+    Title: 'MaskFormer: Per-Pixel Classification is Not All You Need for Semantic
+      Segmentation'
+    URL: https://arxiv.org/abs/2107.06278
+  Code: https://github.com/open-mmlab/mmdetection/blob/dev-3.x/mmdet/models/dense_heads/maskformer_head.py#L21
+  Framework: PyTorch
+- Name: maskformer_swin-s_upernet_8xb2-160k_ade20k-512x512
+  In Collection: MaskFormer
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 49.36
+  Config: configs/maskformer/maskformer_swin-s_upernet_8xb2-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - Swin-S
+    - MaskFormer
+    Training Resources: 8x 26.98 GPUS
+    Memory (GB): 5.33
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-s_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-s_upernet_8xb2-160k_ade20k-512x512_20221115_114710-723512c7.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-s_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-s_upernet_8xb2-160k_ade20k-512x512_20221115_114710.json
+  Paper:
+    Title: 'MaskFormer: Per-Pixel Classification is Not All You Need for Semantic
+      Segmentation'
+    URL: https://arxiv.org/abs/2107.06278
+  Code: https://github.com/open-mmlab/mmdetection/blob/dev-3.x/mmdet/models/dense_heads/maskformer_head.py#L21
+  Framework: PyTorch
diff --git a/configs/mobilenet_v2/README.md b/configs/mobilenet_v2/README.md
index 30f1fe3ce2..6d8e5945cb 100644
--- a/configs/mobilenet_v2/README.md
+++ b/configs/mobilenet_v2/README.md
@@ -1,6 +1,6 @@
 # MobileNetV2
 
-[MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://arxiv.org/abs/1801.04381)
+> [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://arxiv.org/abs/1801.04381)
 
 ## Introduction
 
@@ -23,6 +23,26 @@ The MobileNetV2 architecture is based on an inverted residual structure where th
 <img src="https://user-images.githubusercontent.com/24582831/142901935-fa22700e-4b77-477f-90b9-334a4197506f.png" width="50%"/>
 </div>
 
+## Results and models
+
+### Cityscapes
+
+| Method     | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                                                 | download                                                                                                                                                                                                                                                                                                                                                                                                 |
+| ---------- | -------- | --------- | ------: | -------: | -------------- | ------ | ----: | ------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| FCN        | M-V2-D8  | 512x1024  |   80000 |      3.4 | 14.2           | A100   | 71.19 | 73.34         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mobilenet_v2/mobilenet-v2-d8_fcn_4xb2-80k_cityscapes-512x1024.py)           | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/mobilenet-v2-d8_fcn_4xb2-80k_cityscapes-512x1024/mobilenet-v2-d8_fcn_4xb2-80k_cityscapes-512x1024-20230224_185436-13fef4ea.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/mobilenet-v2-d8_fcn_4xb2-80k_cityscapes-512x1024/mobilenet-v2-d8_fcn_4xb2-80k_cityscapes-512x1024_20230224_185436.json) |
+| PSPNet     | M-V2-D8  | 512x1024  |   80000 |      3.6 | 11.2           | V100   | 70.23 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mobilenet_v2/mobilenet-v2-d8_pspnet_4xb2-80k_cityscapes-512x1024.py)        | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/pspnet_m-v2-d8_512x1024_80k_cityscapes/pspnet_m-v2-d8_512x1024_80k_cityscapes_20200825_124817-19e81d51.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/pspnet_m-v2-d8_512x1024_80k_cityscapes/pspnet_m-v2-d8_512x1024_80k_cityscapes-20200825_124817.log.json)                                     |
+| DeepLabV3  | M-V2-D8  | 512x1024  |   80000 |      3.9 | 8.4            | V100   | 73.84 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mobilenet_v2/mobilenet-v2-d8_deeplabv3_4xb2-80k_cityscapes-512x1024.py)     | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3_m-v2-d8_512x1024_80k_cityscapes/deeplabv3_m-v2-d8_512x1024_80k_cityscapes_20200825_124836-bef03590.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3_m-v2-d8_512x1024_80k_cityscapes/deeplabv3_m-v2-d8_512x1024_80k_cityscapes-20200825_124836.log.json)                         |
+| DeepLabV3+ | M-V2-D8  | 512x1024  |   80000 |      5.1 | 8.4            | V100   | 75.20 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mobilenet_v2/mobilenet-v2-d8_deeplabv3plus_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes_20200825_124836-d256dd4b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes-20200825_124836.log.json)         |
+
+### ADE20K
+
+| Method     | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                                             | download                                                                                                                                                                                                                                                                                                                                                                         |
+| ---------- | -------- | --------- | ------: | -------: | -------------- | ------ | ----: | ------------- | -------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| FCN        | M-V2-D8  | 512x512   |  160000 |      6.5 | 64.4           | V100   | 19.71 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mobilenet_v2/mobilenet-v2-d8_fcn_4xb4-160k_ade20k-512x512.py)           | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/fcn_m-v2-d8_512x512_160k_ade20k/fcn_m-v2-d8_512x512_160k_ade20k_20200825_214953-c40e1095.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/fcn_m-v2-d8_512x512_160k_ade20k/fcn_m-v2-d8_512x512_160k_ade20k-20200825_214953.log.json)                                         |
+| PSPNet     | M-V2-D8  | 512x512   |  160000 |      6.5 | 57.7           | V100   | 29.68 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mobilenet_v2/mobilenet-v2-d8_pspnet_4xb4-160k_ade20k-512x512.py)        | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/pspnet_m-v2-d8_512x512_160k_ade20k/pspnet_m-v2-d8_512x512_160k_ade20k_20200825_214953-f5942f7a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/pspnet_m-v2-d8_512x512_160k_ade20k/pspnet_m-v2-d8_512x512_160k_ade20k-20200825_214953.log.json)                             |
+| DeepLabV3  | M-V2-D8  | 512x512   |  160000 |      6.8 | 39.9           | V100   | 34.08 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mobilenet_v2/mobilenet-v2-d8_deeplabv3_4xb4-160k_ade20k-512x512.py)     | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3_m-v2-d8_512x512_160k_ade20k/deeplabv3_m-v2-d8_512x512_160k_ade20k_20200825_223255-63986343.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3_m-v2-d8_512x512_160k_ade20k/deeplabv3_m-v2-d8_512x512_160k_ade20k-20200825_223255.log.json)                 |
+| DeepLabV3+ | M-V2-D8  | 512x512   |  160000 |      8.2 | 43.1           | V100   | 34.02 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mobilenet_v2/mobilenet-v2-d8_deeplabv3plus_4xb4-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3plus_m-v2-d8_512x512_160k_ade20k/deeplabv3plus_m-v2-d8_512x512_160k_ade20k_20200825_223255-465a01d4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3plus_m-v2-d8_512x512_160k_ade20k/deeplabv3plus_m-v2-d8_512x512_160k_ade20k-20200825_223255.log.json) |
+
 ## Citation
 
 ```bibtex
@@ -34,23 +54,3 @@ The MobileNetV2 architecture is based on an inverted residual structure where th
   year={2018}
 }
 ```
-
-## Results and models
-
-### Cityscapes
-
-| Method     | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                                                 | download                                                                                                                                                                                                                                                                                                                                                                                                 |
-| ---------- | -------- | --------- | ------: | -------: | -------------- | ----: | ------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| FCN        | M-V2-D8  | 512x1024  |   80000 |      3.4 | 14.2           | 71.19 | 73.34         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mobilenet_v2/mobilenet-v2-d8_fcn_4xb2-80k_cityscapes-512x1024.py)           | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/mobilenet-v2-d8_fcn_4xb2-80k_cityscapes-512x1024/mobilenet-v2-d8_fcn_4xb2-80k_cityscapes-512x1024-20230224_185436-13fef4ea.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/mobilenet-v2-d8_fcn_4xb2-80k_cityscapes-512x1024/mobilenet-v2-d8_fcn_4xb2-80k_cityscapes-512x1024_20230224_185436.json) |
-| PSPNet     | M-V2-D8  | 512x1024  |   80000 |      3.6 | 11.2           | 70.23 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mobilenet_v2/mobilenet-v2-d8_pspnet_4xb2-80k_cityscapes-512x1024.py)        | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/pspnet_m-v2-d8_512x1024_80k_cityscapes/pspnet_m-v2-d8_512x1024_80k_cityscapes_20200825_124817-19e81d51.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/pspnet_m-v2-d8_512x1024_80k_cityscapes/pspnet_m-v2-d8_512x1024_80k_cityscapes-20200825_124817.log.json)                                     |
-| DeepLabV3  | M-V2-D8  | 512x1024  |   80000 |      3.9 | 8.4            | 73.84 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mobilenet_v2/mobilenet-v2-d8_deeplabv3_4xb2-80k_cityscapes-512x1024.py)     | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3_m-v2-d8_512x1024_80k_cityscapes/deeplabv3_m-v2-d8_512x1024_80k_cityscapes_20200825_124836-bef03590.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3_m-v2-d8_512x1024_80k_cityscapes/deeplabv3_m-v2-d8_512x1024_80k_cityscapes-20200825_124836.log.json)                         |
-| DeepLabV3+ | M-V2-D8  | 512x1024  |   80000 |      5.1 | 8.4            | 75.20 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mobilenet_v2/mobilenet-v2-d8_deeplabv3plus_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes_20200825_124836-d256dd4b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes-20200825_124836.log.json)         |
-
-### ADE20K
-
-| Method     | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                                             | download                                                                                                                                                                                                                                                                                                                                                                         |
-| ---------- | -------- | --------- | ------: | -------: | -------------- | ----: | ------------- | -------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| FCN        | M-V2-D8  | 512x512   |  160000 |      6.5 | 64.4           | 19.71 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mobilenet_v2/mobilenet-v2-d8_fcn_4xb4-160k_ade20k-512x512.py)           | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/fcn_m-v2-d8_512x512_160k_ade20k/fcn_m-v2-d8_512x512_160k_ade20k_20200825_214953-c40e1095.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/fcn_m-v2-d8_512x512_160k_ade20k/fcn_m-v2-d8_512x512_160k_ade20k-20200825_214953.log.json)                                         |
-| PSPNet     | M-V2-D8  | 512x512   |  160000 |      6.5 | 57.7           | 29.68 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mobilenet_v2/mobilenet-v2-d8_pspnet_4xb4-160k_ade20k-512x512.py)        | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/pspnet_m-v2-d8_512x512_160k_ade20k/pspnet_m-v2-d8_512x512_160k_ade20k_20200825_214953-f5942f7a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/pspnet_m-v2-d8_512x512_160k_ade20k/pspnet_m-v2-d8_512x512_160k_ade20k-20200825_214953.log.json)                             |
-| DeepLabV3  | M-V2-D8  | 512x512   |  160000 |      6.8 | 39.9           | 34.08 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mobilenet_v2/mobilenet-v2-d8_deeplabv3_4xb4-160k_ade20k-512x512.py)     | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3_m-v2-d8_512x512_160k_ade20k/deeplabv3_m-v2-d8_512x512_160k_ade20k_20200825_223255-63986343.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3_m-v2-d8_512x512_160k_ade20k/deeplabv3_m-v2-d8_512x512_160k_ade20k-20200825_223255.log.json)                 |
-| DeepLabV3+ | M-V2-D8  | 512x512   |  160000 |      8.2 | 43.1           | 34.02 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mobilenet_v2/mobilenet-v2-d8_deeplabv3plus_4xb4-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3plus_m-v2-d8_512x512_160k_ade20k/deeplabv3plus_m-v2-d8_512x512_160k_ade20k_20200825_223255-465a01d4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3plus_m-v2-d8_512x512_160k_ade20k/deeplabv3plus_m-v2-d8_512x512_160k_ade20k-20200825_223255.log.json) |
diff --git a/configs/mobilenet_v2/metafile.yaml b/configs/mobilenet_v2/metafile.yaml
new file mode 100644
index 0000000000..119c9ae7d6
--- /dev/null
+++ b/configs/mobilenet_v2/metafile.yaml
@@ -0,0 +1,186 @@
+Models:
+- Name: mobilenet-v2-d8_fcn_4xb2-80k_cityscapes-512x1024
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 71.19
+      mIoU(ms+flip): 73.34
+  Config: configs/mobilenet_v2/mobilenet-v2-d8_fcn_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - M-V2-D8
+    - FCN
+    Training Resources: 4x A100 GPUS
+    Memory (GB): 3.4
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/mobilenet-v2-d8_fcn_4xb2-80k_cityscapes-512x1024/mobilenet-v2-d8_fcn_4xb2-80k_cityscapes-512x1024-20230224_185436-13fef4ea.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/mobilenet-v2-d8_fcn_4xb2-80k_cityscapes-512x1024/mobilenet-v2-d8_fcn_4xb2-80k_cityscapes-512x1024_20230224_185436.json
+  Paper:
+    Title: 'MobileNetV2: Inverted Residuals and Linear Bottlenecks'
+    URL: https://arxiv.org/abs/1801.04381
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mobilenet_v2.py#L14
+  Framework: PyTorch
+- Name: mobilenet-v2-d8_pspnet_4xb2-80k_cityscapes-512x1024
+  In Collection: PSPNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 70.23
+  Config: configs/mobilenet_v2/mobilenet-v2-d8_pspnet_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - M-V2-D8
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 3.6
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/pspnet_m-v2-d8_512x1024_80k_cityscapes/pspnet_m-v2-d8_512x1024_80k_cityscapes_20200825_124817-19e81d51.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/pspnet_m-v2-d8_512x1024_80k_cityscapes/pspnet_m-v2-d8_512x1024_80k_cityscapes-20200825_124817.log.json
+  Paper:
+    Title: 'MobileNetV2: Inverted Residuals and Linear Bottlenecks'
+    URL: https://arxiv.org/abs/1801.04381
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mobilenet_v2.py#L14
+  Framework: PyTorch
+- Name: mobilenet-v2-d8_deeplabv3_4xb2-80k_cityscapes-512x1024
+  In Collection: DeepLabV3
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 73.84
+  Config: configs/mobilenet_v2/mobilenet-v2-d8_deeplabv3_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - M-V2-D8
+    - DeepLabV3
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 3.9
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3_m-v2-d8_512x1024_80k_cityscapes/deeplabv3_m-v2-d8_512x1024_80k_cityscapes_20200825_124836-bef03590.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3_m-v2-d8_512x1024_80k_cityscapes/deeplabv3_m-v2-d8_512x1024_80k_cityscapes-20200825_124836.log.json
+  Paper:
+    Title: 'MobileNetV2: Inverted Residuals and Linear Bottlenecks'
+    URL: https://arxiv.org/abs/1801.04381
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mobilenet_v2.py#L14
+  Framework: PyTorch
+- Name: mobilenet-v2-d8_deeplabv3plus_4xb2-80k_cityscapes-512x1024
+  In Collection: DeepLabV3+
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 75.2
+  Config: configs/mobilenet_v2/mobilenet-v2-d8_deeplabv3plus_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - M-V2-D8
+    - DeepLabV3+
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 5.1
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes_20200825_124836-d256dd4b.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes-20200825_124836.log.json
+  Paper:
+    Title: 'MobileNetV2: Inverted Residuals and Linear Bottlenecks'
+    URL: https://arxiv.org/abs/1801.04381
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mobilenet_v2.py#L14
+  Framework: PyTorch
+- Name: mobilenet-v2-d8_fcn_4xb4-160k_ade20k-512x512
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 19.71
+  Config: configs/mobilenet_v2/mobilenet-v2-d8_fcn_4xb4-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - M-V2-D8
+    - FCN
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 6.5
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/fcn_m-v2-d8_512x512_160k_ade20k/fcn_m-v2-d8_512x512_160k_ade20k_20200825_214953-c40e1095.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/fcn_m-v2-d8_512x512_160k_ade20k/fcn_m-v2-d8_512x512_160k_ade20k-20200825_214953.log.json
+  Paper:
+    Title: 'MobileNetV2: Inverted Residuals and Linear Bottlenecks'
+    URL: https://arxiv.org/abs/1801.04381
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mobilenet_v2.py#L14
+  Framework: PyTorch
+- Name: mobilenet-v2-d8_pspnet_4xb4-160k_ade20k-512x512
+  In Collection: PSPNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 29.68
+  Config: configs/mobilenet_v2/mobilenet-v2-d8_pspnet_4xb4-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - M-V2-D8
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 6.5
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/pspnet_m-v2-d8_512x512_160k_ade20k/pspnet_m-v2-d8_512x512_160k_ade20k_20200825_214953-f5942f7a.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/pspnet_m-v2-d8_512x512_160k_ade20k/pspnet_m-v2-d8_512x512_160k_ade20k-20200825_214953.log.json
+  Paper:
+    Title: 'MobileNetV2: Inverted Residuals and Linear Bottlenecks'
+    URL: https://arxiv.org/abs/1801.04381
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mobilenet_v2.py#L14
+  Framework: PyTorch
+- Name: mobilenet-v2-d8_deeplabv3_4xb4-160k_ade20k-512x512
+  In Collection: DeepLabV3
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 34.08
+  Config: configs/mobilenet_v2/mobilenet-v2-d8_deeplabv3_4xb4-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - M-V2-D8
+    - DeepLabV3
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 6.8
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3_m-v2-d8_512x512_160k_ade20k/deeplabv3_m-v2-d8_512x512_160k_ade20k_20200825_223255-63986343.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3_m-v2-d8_512x512_160k_ade20k/deeplabv3_m-v2-d8_512x512_160k_ade20k-20200825_223255.log.json
+  Paper:
+    Title: 'MobileNetV2: Inverted Residuals and Linear Bottlenecks'
+    URL: https://arxiv.org/abs/1801.04381
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mobilenet_v2.py#L14
+  Framework: PyTorch
+- Name: mobilenet-v2-d8_deeplabv3plus_4xb4-160k_ade20k-512x512
+  In Collection: DeepLabV3+
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 34.02
+  Config: configs/mobilenet_v2/mobilenet-v2-d8_deeplabv3plus_4xb4-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - M-V2-D8
+    - DeepLabV3+
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 8.2
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3plus_m-v2-d8_512x512_160k_ade20k/deeplabv3plus_m-v2-d8_512x512_160k_ade20k_20200825_223255-465a01d4.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3plus_m-v2-d8_512x512_160k_ade20k/deeplabv3plus_m-v2-d8_512x512_160k_ade20k-20200825_223255.log.json
+  Paper:
+    Title: 'MobileNetV2: Inverted Residuals and Linear Bottlenecks'
+    URL: https://arxiv.org/abs/1801.04381
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mobilenet_v2.py#L14
+  Framework: PyTorch
diff --git a/configs/mobilenet_v2/mobilenet_v2.yml b/configs/mobilenet_v2/mobilenet_v2.yml
deleted file mode 100644
index 6d87401ce8..0000000000
--- a/configs/mobilenet_v2/mobilenet_v2.yml
+++ /dev/null
@@ -1,170 +0,0 @@
-Models:
-- Name: mobilenet-v2-d8_fcn_4xb2-80k_cityscapes-512x1024
-  In Collection: FCN
-  Metadata:
-    backbone: M-V2-D8
-    crop size: (512,1024)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 70.42
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 3.4
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 71.19
-      mIoU(ms+flip): 73.34
-  Config: configs/mobilenet_v2/mobilenet-v2-d8_fcn_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/mobilenet-v2-d8_fcn_4xb2-80k_cityscapes-512x1024/mobilenet-v2-d8_fcn_4xb2-80k_cityscapes-512x1024-20230224_185436-13fef4ea.pth
-- Name: mobilenet-v2-d8_pspnet_4xb2-80k_cityscapes-512x1024
-  In Collection: PSPNet
-  Metadata:
-    backbone: M-V2-D8
-    crop size: (512,1024)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 89.29
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 3.6
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 70.23
-  Config: configs/mobilenet_v2/mobilenet-v2-d8_pspnet_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/pspnet_m-v2-d8_512x1024_80k_cityscapes/pspnet_m-v2-d8_512x1024_80k_cityscapes_20200825_124817-19e81d51.pth
-- Name: mobilenet-v2-d8_deeplabv3_4xb2-80k_cityscapes-512x1024
-  In Collection: DeepLabV3
-  Metadata:
-    backbone: M-V2-D8
-    crop size: (512,1024)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 119.05
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 3.9
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 73.84
-  Config: configs/mobilenet_v2/mobilenet-v2-d8_deeplabv3_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3_m-v2-d8_512x1024_80k_cityscapes/deeplabv3_m-v2-d8_512x1024_80k_cityscapes_20200825_124836-bef03590.pth
-- Name: mobilenet-v2-d8_deeplabv3plus_4xb2-80k_cityscapes-512x1024
-  In Collection: DeepLabV3+
-  Metadata:
-    backbone: M-V2-D8
-    crop size: (512,1024)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 119.05
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 5.1
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 75.2
-  Config: configs/mobilenet_v2/mobilenet-v2-d8_deeplabv3plus_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes_20200825_124836-d256dd4b.pth
-- Name: mobilenet-v2-d8_fcn_4xb4-160k_ade20k-512x512
-  In Collection: FCN
-  Metadata:
-    backbone: M-V2-D8
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 15.53
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 6.5
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 19.71
-  Config: configs/mobilenet_v2/mobilenet-v2-d8_fcn_4xb4-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/fcn_m-v2-d8_512x512_160k_ade20k/fcn_m-v2-d8_512x512_160k_ade20k_20200825_214953-c40e1095.pth
-- Name: mobilenet-v2-d8_pspnet_4xb4-160k_ade20k-512x512
-  In Collection: PSPNet
-  Metadata:
-    backbone: M-V2-D8
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 17.33
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 6.5
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 29.68
-  Config: configs/mobilenet_v2/mobilenet-v2-d8_pspnet_4xb4-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/pspnet_m-v2-d8_512x512_160k_ade20k/pspnet_m-v2-d8_512x512_160k_ade20k_20200825_214953-f5942f7a.pth
-- Name: mobilenet-v2-d8_deeplabv3_4xb4-160k_ade20k-512x512
-  In Collection: DeepLabV3
-  Metadata:
-    backbone: M-V2-D8
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 25.06
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 6.8
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 34.08
-  Config: configs/mobilenet_v2/mobilenet-v2-d8_deeplabv3_4xb4-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3_m-v2-d8_512x512_160k_ade20k/deeplabv3_m-v2-d8_512x512_160k_ade20k_20200825_223255-63986343.pth
-- Name: mobilenet-v2-d8_deeplabv3plus_4xb4-160k_ade20k-512x512
-  In Collection: DeepLabV3+
-  Metadata:
-    backbone: M-V2-D8
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 23.2
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 8.2
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 34.02
-  Config: configs/mobilenet_v2/mobilenet-v2-d8_deeplabv3plus_4xb4-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3plus_m-v2-d8_512x512_160k_ade20k/deeplabv3plus_m-v2-d8_512x512_160k_ade20k_20200825_223255-465a01d4.pth
diff --git a/configs/mobilenet_v3/README.md b/configs/mobilenet_v3/README.md
index c2fed06ccd..3b63db7e50 100644
--- a/configs/mobilenet_v3/README.md
+++ b/configs/mobilenet_v3/README.md
@@ -1,6 +1,6 @@
 # MobileNetV3
 
-[Searching for MobileNetV3](https://arxiv.org/abs/1905.02244)
+> [Searching for MobileNetV3](https://arxiv.org/abs/1905.02244)
 
 ## Introduction
 
@@ -24,6 +24,17 @@ We present the next generation of MobileNets based on a combination of complemen
 <img src="https://user-images.githubusercontent.com/24582831/142902036-3dc2e0c0-d475-4816-b1ac-961836b41f5c.png" width="60%"/>
 </div>
 
+## Results and models
+
+### Cityscapes
+
+| Method | Backbone           | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                                                     | download                                                                                                                                                                                                                                                                                                                                                                                                     |
+| ------ | ------------------ | --------- | ------: | -------: | -------------- | ------ | ----: | ------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| LRASPP | M-V3-D8            | 512x1024  |  320000 |      8.9 | 15.22          | V100   | 69.54 | 70.89         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mobilenet_v3/mobilenet-v3-d8_lraspp_4xb4-320k_cityscapes-512x1024.py)           | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3-d8_512x1024_320k_cityscapes/lraspp_m-v3-d8_512x1024_320k_cityscapes_20201224_220337-cfe8fb07.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3-d8_512x1024_320k_cityscapes/lraspp_m-v3-d8_512x1024_320k_cityscapes-20201224_220337.log.json)                                     |
+| LRASPP | M-V3-D8 (scratch)  | 512x1024  |  320000 |      8.9 | 14.77          | V100   | 67.87 | 69.78         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mobilenet_v3/mobilenet-v3-d8-scratch_lraspp_4xb4-320k_cityscapes-512x1024.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes/lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes_20201224_220337-9f29cd72.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes/lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes-20201224_220337.log.json)     |
+| LRASPP | M-V3s-D8           | 512x1024  |  320000 |      5.3 | 23.64          | V100   | 64.11 | 66.42         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mobilenet_v3/mobilenet-v3-d8-s_lraspp_4xb4-320k_cityscapes-512x1024.py)         | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3s-d8_512x1024_320k_cityscapes/lraspp_m-v3s-d8_512x1024_320k_cityscapes_20201224_223935-61565b34.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3s-d8_512x1024_320k_cityscapes/lraspp_m-v3s-d8_512x1024_320k_cityscapes-20201224_223935.log.json)                                 |
+| LRASPP | M-V3s-D8 (scratch) | 512x1024  |  320000 |      5.3 | 24.50          | V100   | 62.74 | 65.01         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mobilenet_v3/mobilenet-v3-d8-scratch-s_lraspp_4xb4-320k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes/lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes_20201224_223935-03daeabb.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes/lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes-20201224_223935.log.json) |
+
 ## Citation
 
 ```bibtex
@@ -37,14 +48,3 @@ We present the next generation of MobileNets based on a combination of complemen
   doi={10.1109/ICCV.2019.00140}}
 }
 ```
-
-## Results and models
-
-### Cityscapes
-
-| Method | Backbone           | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                                                     | download                                                                                                                                                                                                                                                                                                                                                                                                     |
-| ------ | ------------------ | --------- | ------: | -------: | -------------- | ----: | ------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| LRASPP | M-V3-D8            | 512x1024  |  320000 |      8.9 | 15.22          | 69.54 | 70.89         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mobilenet_v3/mobilenet-v3-d8_lraspp_4xb4-320k_cityscapes-512x1024.py)           | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3-d8_512x1024_320k_cityscapes/lraspp_m-v3-d8_512x1024_320k_cityscapes_20201224_220337-cfe8fb07.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3-d8_512x1024_320k_cityscapes/lraspp_m-v3-d8_512x1024_320k_cityscapes-20201224_220337.log.json)                                     |
-| LRASPP | M-V3-D8 (scratch)  | 512x1024  |  320000 |      8.9 | 14.77          | 67.87 | 69.78         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mobilenet_v3/mobilenet-v3-d8-scratch_lraspp_4xb4-320k_cityscapes-512x1024.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes/lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes_20201224_220337-9f29cd72.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes/lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes-20201224_220337.log.json)     |
-| LRASPP | M-V3s-D8           | 512x1024  |  320000 |      5.3 | 23.64          | 64.11 | 66.42         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mobilenet_v3/mobilenet-v3-d8-s_lraspp_4xb4-320k_cityscapes-512x1024.py)         | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3s-d8_512x1024_320k_cityscapes/lraspp_m-v3s-d8_512x1024_320k_cityscapes_20201224_223935-61565b34.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3s-d8_512x1024_320k_cityscapes/lraspp_m-v3s-d8_512x1024_320k_cityscapes-20201224_223935.log.json)                                 |
-| LRASPP | M-V3s-D8 (scratch) | 512x1024  |  320000 |      5.3 | 24.50          | 62.74 | 65.01         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/mobilenet_v3/mobilenet-v3-d8-scratch-s_lraspp_4xb4-320k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes/lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes_20201224_223935-03daeabb.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes/lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes-20201224_223935.log.json) |
diff --git a/configs/mobilenet_v3/metafile.yaml b/configs/mobilenet_v3/metafile.yaml
new file mode 100644
index 0000000000..0351d3b8e4
--- /dev/null
+++ b/configs/mobilenet_v3/metafile.yaml
@@ -0,0 +1,109 @@
+Collections:
+- Name: LRASPP
+  License: Apache License 2.0
+  Metadata:
+    Training Data:
+    - Cityscapes
+  Paper:
+    Title: Searching for MobileNetV3
+    URL: https://arxiv.org/abs/1905.02244
+  README: configs/mobilenet_v3/README.md
+  Frameworks:
+  - PyTorch
+Models:
+- Name: mobilenet-v3-d8_lraspp_4xb4-320k_cityscapes-512x1024
+  In Collection: LRASPP
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 69.54
+      mIoU(ms+flip): 70.89
+  Config: configs/mobilenet_v3/mobilenet-v3-d8_lraspp_4xb4-320k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 16
+    Architecture:
+    - M-V3-D8
+    - LRASPP
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 8.9
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3-d8_512x1024_320k_cityscapes/lraspp_m-v3-d8_512x1024_320k_cityscapes_20201224_220337-cfe8fb07.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3-d8_512x1024_320k_cityscapes/lraspp_m-v3-d8_512x1024_320k_cityscapes-20201224_220337.log.json
+  Paper:
+    Title: Searching for MobileNetV3
+    URL: https://arxiv.org/abs/1905.02244
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mobilenet_v3.py#L15
+  Framework: PyTorch
+- Name: mobilenet-v3-d8-scratch_lraspp_4xb4-320k_cityscapes-512x1024
+  In Collection: LRASPP
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 67.87
+      mIoU(ms+flip): 69.78
+  Config: configs/mobilenet_v3/mobilenet-v3-d8-scratch_lraspp_4xb4-320k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 16
+    Architecture:
+    - M-V3-D8
+    - LRASPP
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 8.9
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes/lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes_20201224_220337-9f29cd72.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes/lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes-20201224_220337.log.json
+  Paper:
+    Title: Searching for MobileNetV3
+    URL: https://arxiv.org/abs/1905.02244
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mobilenet_v3.py#L15
+  Framework: PyTorch
+- Name: mobilenet-v3-d8-s_lraspp_4xb4-320k_cityscapes-512x1024
+  In Collection: LRASPP
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 64.11
+      mIoU(ms+flip): 66.42
+  Config: configs/mobilenet_v3/mobilenet-v3-d8-s_lraspp_4xb4-320k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 16
+    Architecture:
+    - M-V3s-D8
+    - LRASPP
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 5.3
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3s-d8_512x1024_320k_cityscapes/lraspp_m-v3s-d8_512x1024_320k_cityscapes_20201224_223935-61565b34.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3s-d8_512x1024_320k_cityscapes/lraspp_m-v3s-d8_512x1024_320k_cityscapes-20201224_223935.log.json
+  Paper:
+    Title: Searching for MobileNetV3
+    URL: https://arxiv.org/abs/1905.02244
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mobilenet_v3.py#L15
+  Framework: PyTorch
+- Name: mobilenet-v3-d8-scratch-s_lraspp_4xb4-320k_cityscapes-512x1024
+  In Collection: LRASPP
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 62.74
+      mIoU(ms+flip): 65.01
+  Config: configs/mobilenet_v3/mobilenet-v3-d8-scratch-s_lraspp_4xb4-320k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 16
+    Architecture:
+    - M-V3s-D8
+    - LRASPP
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 5.3
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes/lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes_20201224_223935-03daeabb.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes/lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes-20201224_223935.log.json
+  Paper:
+    Title: Searching for MobileNetV3
+    URL: https://arxiv.org/abs/1905.02244
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mobilenet_v3.py#L15
+  Framework: PyTorch
diff --git a/configs/mobilenet_v3/mobilenet_v3.yml b/configs/mobilenet_v3/mobilenet_v3.yml
deleted file mode 100644
index 067a150cea..0000000000
--- a/configs/mobilenet_v3/mobilenet_v3.yml
+++ /dev/null
@@ -1,103 +0,0 @@
-Collections:
-- Name: LRASPP
-  Metadata:
-    Training Data:
-    - Cityscapes
-  Paper:
-    URL: https://arxiv.org/abs/1905.02244
-    Title: Searching for MobileNetV3
-  README: configs/mobilenet_v3/README.md
-  Code:
-    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mobilenet_v3.py#L15
-    Version: v0.17.0
-  Converted From:
-    Code: https://github.com/tensorflow/models/tree/master/research/deeplab
-Models:
-- Name: mobilenet-v3-d8_lraspp_4xb4-320k_cityscapes-512x1024
-  In Collection: LRASPP
-  Metadata:
-    backbone: M-V3-D8
-    crop size: (512,1024)
-    lr schd: 320000
-    inference time (ms/im):
-    - value: 65.7
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 8.9
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 69.54
-      mIoU(ms+flip): 70.89
-  Config: configs/mobilenet_v3/mobilenet-v3-d8_lraspp_4xb4-320k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3-d8_512x1024_320k_cityscapes/lraspp_m-v3-d8_512x1024_320k_cityscapes_20201224_220337-cfe8fb07.pth
-- Name: mobilenet-v3-d8-scratch_lraspp_4xb4-320k_cityscapes-512x1024
-  In Collection: LRASPP
-  Metadata:
-    backbone: M-V3-D8 (scratch)
-    crop size: (512,1024)
-    lr schd: 320000
-    inference time (ms/im):
-    - value: 67.7
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 8.9
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 67.87
-      mIoU(ms+flip): 69.78
-  Config: configs/mobilenet_v3/mobilenet-v3-d8-scratch_lraspp_4xb4-320k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes/lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes_20201224_220337-9f29cd72.pth
-- Name: mobilenet-v3-d8-s_lraspp_4xb4-320k_cityscapes-512x1024
-  In Collection: LRASPP
-  Metadata:
-    backbone: M-V3s-D8
-    crop size: (512,1024)
-    lr schd: 320000
-    inference time (ms/im):
-    - value: 42.3
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 5.3
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 64.11
-      mIoU(ms+flip): 66.42
-  Config: configs/mobilenet_v3/mobilenet-v3-d8-s_lraspp_4xb4-320k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3s-d8_512x1024_320k_cityscapes/lraspp_m-v3s-d8_512x1024_320k_cityscapes_20201224_223935-61565b34.pth
-- Name: mobilenet-v3-d8-scratch-s_lraspp_4xb4-320k_cityscapes-512x1024
-  In Collection: LRASPP
-  Metadata:
-    backbone: M-V3s-D8 (scratch)
-    crop size: (512,1024)
-    lr schd: 320000
-    inference time (ms/im):
-    - value: 40.82
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 5.3
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 62.74
-      mIoU(ms+flip): 65.01
-  Config: configs/mobilenet_v3/mobilenet-v3-d8-scratch-s_lraspp_4xb4-320k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes/lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes_20201224_223935-03daeabb.pth
diff --git a/configs/nonlocal_net/README.md b/configs/nonlocal_net/README.md
index 80d45ab589..0a0fcf3725 100644
--- a/configs/nonlocal_net/README.md
+++ b/configs/nonlocal_net/README.md
@@ -1,6 +1,6 @@
 # NonLocal Net
 
-[Non-local Neural Networks](https://arxiv.org/abs/1711.07971)
+> [Non-local Neural Networks](https://arxiv.org/abs/1711.07971)
 
 ## Introduction
 
@@ -22,6 +22,39 @@ Both convolutional and recurrent operations are building blocks that process one
 <img src="https://user-images.githubusercontent.com/24582831/142902128-17e29678-bf12-4ff4-b3d6-a39b47dfd253.png" width="50%"/>
 </div>
 
+## Results and models
+
+### Cityscapes
+
+| Method      | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                                    | download                                                                                                                                                                                                                                                                                                                                                                     |
+| ----------- | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------- | ----------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| NonLocalNet | R-50-D8  | 512x1024  |   40000 | 7.4      | 2.72           | V100   | 78.24 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/nonlocal_net/nonlocal_r50-d8_4xb2-40k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x1024_40k_cityscapes/nonlocal_r50-d8_512x1024_40k_cityscapes_20200605_210748-c75e81e3.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x1024_40k_cityscapes/nonlocal_r50-d8_512x1024_40k_cityscapes_20200605_210748.log.json)     |
+| NonLocalNet | R-101-D8 | 512x1024  |   40000 | 10.9     | 1.95           | V100   | 78.66 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/nonlocal_net/nonlocal_r101-d8_4xb2-40k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x1024_40k_cityscapes/nonlocal_r101-d8_512x1024_40k_cityscapes_20200605_210748-d63729fa.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x1024_40k_cityscapes/nonlocal_r101-d8_512x1024_40k_cityscapes_20200605_210748.log.json) |
+| NonLocalNet | R-50-D8  | 769x769   |   40000 | 8.9      | 1.52           | V100   | 78.33 | 79.92         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/nonlocal_net/nonlocal_r50-d8_4xb2-40k_cityscapes-769x769.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_769x769_40k_cityscapes/nonlocal_r50-d8_769x769_40k_cityscapes_20200530_045243-82ef6749.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_769x769_40k_cityscapes/nonlocal_r50-d8_769x769_40k_cityscapes_20200530_045243.log.json)         |
+| NonLocalNet | R-101-D8 | 769x769   |   40000 | 12.8     | 1.05           | V100   | 78.57 | 80.29         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/nonlocal_net/nonlocal_r101-d8_4xb2-40k_cityscapes-769x769.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_769x769_40k_cityscapes/nonlocal_r101-d8_769x769_40k_cityscapes_20200530_045348-8fe9a9dc.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_769x769_40k_cityscapes/nonlocal_r101-d8_769x769_40k_cityscapes_20200530_045348.log.json)     |
+| NonLocalNet | R-50-D8  | 512x1024  |   80000 | -        | -              | V100   | 78.01 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/nonlocal_net/nonlocal_r50-d8_4xb2-80k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x1024_80k_cityscapes/nonlocal_r50-d8_512x1024_80k_cityscapes_20200607_193518-d6839fae.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x1024_80k_cityscapes/nonlocal_r50-d8_512x1024_80k_cityscapes_20200607_193518.log.json)     |
+| NonLocalNet | R-101-D8 | 512x1024  |   80000 | -        | -              | V100   | 78.93 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/nonlocal_net/nonlocal_r101-d8_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x1024_80k_cityscapes/nonlocal_r101-d8_512x1024_80k_cityscapes_20200607_183411-32700183.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x1024_80k_cityscapes/nonlocal_r101-d8_512x1024_80k_cityscapes_20200607_183411.log.json) |
+| NonLocalNet | R-50-D8  | 769x769   |   80000 | -        | -              | V100   | 79.05 | 80.68         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/nonlocal_net/nonlocal_r50-d8_4xb2-80k_cityscapes-769x769.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_769x769_80k_cityscapes/nonlocal_r50-d8_769x769_80k_cityscapes_20200607_193506-1f9792f6.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_769x769_80k_cityscapes/nonlocal_r50-d8_769x769_80k_cityscapes_20200607_193506.log.json)         |
+| NonLocalNet | R-101-D8 | 769x769   |   80000 | -        | -              | V100   | 79.40 | 80.85         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/nonlocal_net/nonlocal_r101-d8_4xb2-80k_cityscapes-769x769.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_769x769_80k_cityscapes/nonlocal_r101-d8_769x769_80k_cityscapes_20200607_183428-0e1fa4f9.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_769x769_80k_cityscapes/nonlocal_r101-d8_769x769_80k_cityscapes_20200607_183428.log.json)     |
+
+### ADE20K
+
+| Method      | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                                | download                                                                                                                                                                                                                                                                                                                                                     |
+| ----------- | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| NonLocalNet | R-50-D8  | 512x512   |   80000 | 9.1      | 21.37          | V100   | 40.75 |         42.05 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/nonlocal_net/nonlocal_r50-d8_4xb4-80k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_80k_ade20k/nonlocal_r50-d8_512x512_80k_ade20k_20200615_015801-5ae0aa33.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_80k_ade20k/nonlocal_r50-d8_512x512_80k_ade20k_20200615_015801.log.json)         |
+| NonLocalNet | R-101-D8 | 512x512   |   80000 | 12.6     | 13.97          | V100   | 42.90 |         44.27 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/nonlocal_net/nonlocal_r101-d8_4xb4-80k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_80k_ade20k/nonlocal_r101-d8_512x512_80k_ade20k_20200615_015758-24105919.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_80k_ade20k/nonlocal_r101-d8_512x512_80k_ade20k_20200615_015758.log.json)     |
+| NonLocalNet | R-50-D8  | 512x512   |  160000 | -        | -              | V100   | 42.03 |         43.04 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/nonlocal_net/nonlocal_r50-d8_4xb4-160k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_160k_ade20k/nonlocal_r50-d8_512x512_160k_ade20k_20200616_005410-baef45e3.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_160k_ade20k/nonlocal_r50-d8_512x512_160k_ade20k_20200616_005410.log.json)     |
+| NonLocalNet | R-101-D8 | 512x512   |  160000 | -        | -              | V100   | 44.63 |         45.79 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/nonlocal_net/nonlocal_r101-d8_4xb4-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_160k_ade20k/nonlocal_r101-d8_512x512_160k_ade20k_20210827_221502-7881aa1a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_160k_ade20k/nonlocal_r101-d8_512x512_160k_ade20k_20210827_221502.log.json) |
+
+### Pascal VOC 2012 + Aug
+
+| Method      | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                                 | download                                                                                                                                                                                                                                                                                                                                                         |
+| ----------- | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| NonLocalNet | R-50-D8  | 512x512   |   20000 | 6.4      | 21.21          | V100   | 76.20 |         77.12 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/nonlocal_net/nonlocal_r50-d8_4xb4-20k_voc12aug-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_20k_voc12aug/nonlocal_r50-d8_512x512_20k_voc12aug_20200617_222613-07f2a57c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_20k_voc12aug/nonlocal_r50-d8_512x512_20k_voc12aug_20200617_222613.log.json)     |
+| NonLocalNet | R-101-D8 | 512x512   |   20000 | 9.8      | 14.01          | V100   | 78.15 |         78.86 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/nonlocal_net/nonlocal_r101-d8_4xb4-20k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_20k_voc12aug/nonlocal_r101-d8_512x512_20k_voc12aug_20200617_222615-948c68ab.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_20k_voc12aug/nonlocal_r101-d8_512x512_20k_voc12aug_20200617_222615.log.json) |
+| NonLocalNet | R-50-D8  | 512x512   |   40000 | -        | -              | V100   | 76.65 |         77.47 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/nonlocal_net/nonlocal_r50-d8_4xb4-40k_voc12aug-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_40k_voc12aug/nonlocal_r50-d8_512x512_40k_voc12aug_20200614_000028-0139d4a9.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_40k_voc12aug/nonlocal_r50-d8_512x512_40k_voc12aug_20200614_000028.log.json)     |
+| NonLocalNet | R-101-D8 | 512x512   |   40000 | -        | -              | V100   | 78.27 |         79.12 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/nonlocal_net/nonlocal_r101-d8_4xb4-40k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_40k_voc12aug/nonlocal_r101-d8_512x512_40k_voc12aug_20200614_000028-7e5ff470.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_40k_voc12aug/nonlocal_r101-d8_512x512_40k_voc12aug_20200614_000028.log.json) |
+
 ## Citation
 
 ```bibtex
@@ -33,36 +66,3 @@ Both convolutional and recurrent operations are building blocks that process one
   year={2018}
 }
 ```
-
-## Results and models
-
-### Cityscapes
-
-| Method      | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                                    | download                                                                                                                                                                                                                                                                                                                                                                     |
-| ----------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------- | ----------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| NonLocalNet | R-50-D8  | 512x1024  |   40000 | 7.4      | 2.72           | 78.24 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/nonlocal_net/nonlocal_r50-d8_4xb2-40k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x1024_40k_cityscapes/nonlocal_r50-d8_512x1024_40k_cityscapes_20200605_210748-c75e81e3.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x1024_40k_cityscapes/nonlocal_r50-d8_512x1024_40k_cityscapes_20200605_210748.log.json)     |
-| NonLocalNet | R-101-D8 | 512x1024  |   40000 | 10.9     | 1.95           | 78.66 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/nonlocal_net/nonlocal_r101-d8_4xb2-40k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x1024_40k_cityscapes/nonlocal_r101-d8_512x1024_40k_cityscapes_20200605_210748-d63729fa.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x1024_40k_cityscapes/nonlocal_r101-d8_512x1024_40k_cityscapes_20200605_210748.log.json) |
-| NonLocalNet | R-50-D8  | 769x769   |   40000 | 8.9      | 1.52           | 78.33 | 79.92         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/nonlocal_net/nonlocal_r50-d8_4xb2-40k_cityscapes-769x769.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_769x769_40k_cityscapes/nonlocal_r50-d8_769x769_40k_cityscapes_20200530_045243-82ef6749.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_769x769_40k_cityscapes/nonlocal_r50-d8_769x769_40k_cityscapes_20200530_045243.log.json)         |
-| NonLocalNet | R-101-D8 | 769x769   |   40000 | 12.8     | 1.05           | 78.57 | 80.29         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/nonlocal_net/nonlocal_r101-d8_4xb2-40k_cityscapes-769x769.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_769x769_40k_cityscapes/nonlocal_r101-d8_769x769_40k_cityscapes_20200530_045348-8fe9a9dc.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_769x769_40k_cityscapes/nonlocal_r101-d8_769x769_40k_cityscapes_20200530_045348.log.json)     |
-| NonLocalNet | R-50-D8  | 512x1024  |   80000 | -        | -              | 78.01 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/nonlocal_net/nonlocal_r50-d8_4xb2-80k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x1024_80k_cityscapes/nonlocal_r50-d8_512x1024_80k_cityscapes_20200607_193518-d6839fae.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x1024_80k_cityscapes/nonlocal_r50-d8_512x1024_80k_cityscapes_20200607_193518.log.json)     |
-| NonLocalNet | R-101-D8 | 512x1024  |   80000 | -        | -              | 78.93 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/nonlocal_net/nonlocal_r101-d8_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x1024_80k_cityscapes/nonlocal_r101-d8_512x1024_80k_cityscapes_20200607_183411-32700183.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x1024_80k_cityscapes/nonlocal_r101-d8_512x1024_80k_cityscapes_20200607_183411.log.json) |
-| NonLocalNet | R-50-D8  | 769x769   |   80000 | -        | -              | 79.05 | 80.68         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/nonlocal_net/nonlocal_r50-d8_4xb2-80k_cityscapes-769x769.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_769x769_80k_cityscapes/nonlocal_r50-d8_769x769_80k_cityscapes_20200607_193506-1f9792f6.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_769x769_80k_cityscapes/nonlocal_r50-d8_769x769_80k_cityscapes_20200607_193506.log.json)         |
-| NonLocalNet | R-101-D8 | 769x769   |   80000 | -        | -              | 79.40 | 80.85         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/nonlocal_net/nonlocal_r101-d8_4xb2-80k_cityscapes-769x769.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_769x769_80k_cityscapes/nonlocal_r101-d8_769x769_80k_cityscapes_20200607_183428-0e1fa4f9.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_769x769_80k_cityscapes/nonlocal_r101-d8_769x769_80k_cityscapes_20200607_183428.log.json)     |
-
-### ADE20K
-
-| Method      | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                                | download                                                                                                                                                                                                                                                                                                                                                     |
-| ----------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| NonLocalNet | R-50-D8  | 512x512   |   80000 | 9.1      | 21.37          | 40.75 |         42.05 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/nonlocal_net/nonlocal_r50-d8_4xb4-80k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_80k_ade20k/nonlocal_r50-d8_512x512_80k_ade20k_20200615_015801-5ae0aa33.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_80k_ade20k/nonlocal_r50-d8_512x512_80k_ade20k_20200615_015801.log.json)         |
-| NonLocalNet | R-101-D8 | 512x512   |   80000 | 12.6     | 13.97          | 42.90 |         44.27 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/nonlocal_net/nonlocal_r101-d8_4xb4-80k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_80k_ade20k/nonlocal_r101-d8_512x512_80k_ade20k_20200615_015758-24105919.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_80k_ade20k/nonlocal_r101-d8_512x512_80k_ade20k_20200615_015758.log.json)     |
-| NonLocalNet | R-50-D8  | 512x512   |  160000 | -        | -              | 42.03 |         43.04 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/nonlocal_net/nonlocal_r50-d8_4xb4-160k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_160k_ade20k/nonlocal_r50-d8_512x512_160k_ade20k_20200616_005410-baef45e3.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_160k_ade20k/nonlocal_r50-d8_512x512_160k_ade20k_20200616_005410.log.json)     |
-| NonLocalNet | R-101-D8 | 512x512   |  160000 | -        | -              | 44.63 |         45.79 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/nonlocal_net/nonlocal_r101-d8_4xb4-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_160k_ade20k/nonlocal_r101-d8_512x512_160k_ade20k_20210827_221502-7881aa1a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_160k_ade20k/nonlocal_r101-d8_512x512_160k_ade20k_20210827_221502.log.json) |
-
-### Pascal VOC 2012 + Aug
-
-| Method      | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                                 | download                                                                                                                                                                                                                                                                                                                                                         |
-| ----------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| NonLocalNet | R-50-D8  | 512x512   |   20000 | 6.4      | 21.21          | 76.20 |         77.12 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/nonlocal_net/nonlocal_r50-d8_4xb4-20k_voc12aug-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_20k_voc12aug/nonlocal_r50-d8_512x512_20k_voc12aug_20200617_222613-07f2a57c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_20k_voc12aug/nonlocal_r50-d8_512x512_20k_voc12aug_20200617_222613.log.json)     |
-| NonLocalNet | R-101-D8 | 512x512   |   20000 | 9.8      | 14.01          | 78.15 |         78.86 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/nonlocal_net/nonlocal_r101-d8_4xb4-20k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_20k_voc12aug/nonlocal_r101-d8_512x512_20k_voc12aug_20200617_222615-948c68ab.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_20k_voc12aug/nonlocal_r101-d8_512x512_20k_voc12aug_20200617_222615.log.json) |
-| NonLocalNet | R-50-D8  | 512x512   |   40000 | -        | -              | 76.65 |         77.47 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/nonlocal_net/nonlocal_r50-d8_4xb4-40k_voc12aug-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_40k_voc12aug/nonlocal_r50-d8_512x512_40k_voc12aug_20200614_000028-0139d4a9.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_40k_voc12aug/nonlocal_r50-d8_512x512_40k_voc12aug_20200614_000028.log.json)     |
-| NonLocalNet | R-101-D8 | 512x512   |   40000 | -        | -              | 78.27 |         79.12 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/nonlocal_net/nonlocal_r101-d8_4xb4-40k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_40k_voc12aug/nonlocal_r101-d8_512x512_40k_voc12aug_20200614_000028-7e5ff470.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_40k_voc12aug/nonlocal_r101-d8_512x512_40k_voc12aug_20200614_000028.log.json) |
diff --git a/configs/nonlocal_net/metafile.yaml b/configs/nonlocal_net/metafile.yaml
new file mode 100644
index 0000000000..69bd72570b
--- /dev/null
+++ b/configs/nonlocal_net/metafile.yaml
@@ -0,0 +1,387 @@
+Collections:
+- Name: NonLocalNet
+  License: Apache License 2.0
+  Metadata:
+    Training Data:
+    - Cityscapes
+    - ADE20K
+    - Pascal VOC 2012 + Aug
+  Paper:
+    Title: Non-local Neural Networks
+    URL: https://arxiv.org/abs/1711.07971
+  README: configs/nonlocal_net/README.md
+  Frameworks:
+  - PyTorch
+Models:
+- Name: nonlocal_r50-d8_4xb2-40k_cityscapes-512x1024
+  In Collection: NonLocalNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.24
+  Config: configs/nonlocal_net/nonlocal_r50-d8_4xb2-40k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - NonLocalNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 7.4
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x1024_40k_cityscapes/nonlocal_r50-d8_512x1024_40k_cityscapes_20200605_210748-c75e81e3.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x1024_40k_cityscapes/nonlocal_r50-d8_512x1024_40k_cityscapes_20200605_210748.log.json
+  Paper:
+    Title: Non-local Neural Networks
+    URL: https://arxiv.org/abs/1711.07971
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/nl_head.py#L10
+  Framework: PyTorch
+- Name: nonlocal_r101-d8_4xb2-40k_cityscapes-512x1024
+  In Collection: NonLocalNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.66
+  Config: configs/nonlocal_net/nonlocal_r101-d8_4xb2-40k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - NonLocalNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 10.9
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x1024_40k_cityscapes/nonlocal_r101-d8_512x1024_40k_cityscapes_20200605_210748-d63729fa.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x1024_40k_cityscapes/nonlocal_r101-d8_512x1024_40k_cityscapes_20200605_210748.log.json
+  Paper:
+    Title: Non-local Neural Networks
+    URL: https://arxiv.org/abs/1711.07971
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/nl_head.py#L10
+  Framework: PyTorch
+- Name: nonlocal_r50-d8_4xb2-40k_cityscapes-769x769
+  In Collection: NonLocalNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.33
+      mIoU(ms+flip): 79.92
+  Config: configs/nonlocal_net/nonlocal_r50-d8_4xb2-40k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - NonLocalNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 8.9
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_769x769_40k_cityscapes/nonlocal_r50-d8_769x769_40k_cityscapes_20200530_045243-82ef6749.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_769x769_40k_cityscapes/nonlocal_r50-d8_769x769_40k_cityscapes_20200530_045243.log.json
+  Paper:
+    Title: Non-local Neural Networks
+    URL: https://arxiv.org/abs/1711.07971
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/nl_head.py#L10
+  Framework: PyTorch
+- Name: nonlocal_r101-d8_4xb2-40k_cityscapes-769x769
+  In Collection: NonLocalNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.57
+      mIoU(ms+flip): 80.29
+  Config: configs/nonlocal_net/nonlocal_r101-d8_4xb2-40k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - NonLocalNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 12.8
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_769x769_40k_cityscapes/nonlocal_r101-d8_769x769_40k_cityscapes_20200530_045348-8fe9a9dc.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_769x769_40k_cityscapes/nonlocal_r101-d8_769x769_40k_cityscapes_20200530_045348.log.json
+  Paper:
+    Title: Non-local Neural Networks
+    URL: https://arxiv.org/abs/1711.07971
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/nl_head.py#L10
+  Framework: PyTorch
+- Name: nonlocal_r50-d8_4xb2-80k_cityscapes-512x1024
+  In Collection: NonLocalNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.01
+  Config: configs/nonlocal_net/nonlocal_r50-d8_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - NonLocalNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x1024_80k_cityscapes/nonlocal_r50-d8_512x1024_80k_cityscapes_20200607_193518-d6839fae.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x1024_80k_cityscapes/nonlocal_r50-d8_512x1024_80k_cityscapes_20200607_193518.log.json
+  Paper:
+    Title: Non-local Neural Networks
+    URL: https://arxiv.org/abs/1711.07971
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/nl_head.py#L10
+  Framework: PyTorch
+- Name: nonlocal_r101-d8_4xb2-80k_cityscapes-512x1024
+  In Collection: NonLocalNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.93
+  Config: configs/nonlocal_net/nonlocal_r101-d8_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - NonLocalNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x1024_80k_cityscapes/nonlocal_r101-d8_512x1024_80k_cityscapes_20200607_183411-32700183.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x1024_80k_cityscapes/nonlocal_r101-d8_512x1024_80k_cityscapes_20200607_183411.log.json
+  Paper:
+    Title: Non-local Neural Networks
+    URL: https://arxiv.org/abs/1711.07971
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/nl_head.py#L10
+  Framework: PyTorch
+- Name: nonlocal_r50-d8_4xb2-80k_cityscapes-769x769
+  In Collection: NonLocalNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.05
+      mIoU(ms+flip): 80.68
+  Config: configs/nonlocal_net/nonlocal_r50-d8_4xb2-80k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - NonLocalNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_769x769_80k_cityscapes/nonlocal_r50-d8_769x769_80k_cityscapes_20200607_193506-1f9792f6.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_769x769_80k_cityscapes/nonlocal_r50-d8_769x769_80k_cityscapes_20200607_193506.log.json
+  Paper:
+    Title: Non-local Neural Networks
+    URL: https://arxiv.org/abs/1711.07971
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/nl_head.py#L10
+  Framework: PyTorch
+- Name: nonlocal_r101-d8_4xb2-80k_cityscapes-769x769
+  In Collection: NonLocalNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.4
+      mIoU(ms+flip): 80.85
+  Config: configs/nonlocal_net/nonlocal_r101-d8_4xb2-80k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - NonLocalNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_769x769_80k_cityscapes/nonlocal_r101-d8_769x769_80k_cityscapes_20200607_183428-0e1fa4f9.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_769x769_80k_cityscapes/nonlocal_r101-d8_769x769_80k_cityscapes_20200607_183428.log.json
+  Paper:
+    Title: Non-local Neural Networks
+    URL: https://arxiv.org/abs/1711.07971
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/nl_head.py#L10
+  Framework: PyTorch
+- Name: nonlocal_r50-d8_4xb4-80k_ade20k-512x512
+  In Collection: NonLocalNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 40.75
+      mIoU(ms+flip): 42.05
+  Config: configs/nonlocal_net/nonlocal_r50-d8_4xb4-80k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - NonLocalNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 9.1
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_80k_ade20k/nonlocal_r50-d8_512x512_80k_ade20k_20200615_015801-5ae0aa33.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_80k_ade20k/nonlocal_r50-d8_512x512_80k_ade20k_20200615_015801.log.json
+  Paper:
+    Title: Non-local Neural Networks
+    URL: https://arxiv.org/abs/1711.07971
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/nl_head.py#L10
+  Framework: PyTorch
+- Name: nonlocal_r101-d8_4xb4-80k_ade20k-512x512
+  In Collection: NonLocalNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 42.9
+      mIoU(ms+flip): 44.27
+  Config: configs/nonlocal_net/nonlocal_r101-d8_4xb4-80k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - NonLocalNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 12.6
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_80k_ade20k/nonlocal_r101-d8_512x512_80k_ade20k_20200615_015758-24105919.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_80k_ade20k/nonlocal_r101-d8_512x512_80k_ade20k_20200615_015758.log.json
+  Paper:
+    Title: Non-local Neural Networks
+    URL: https://arxiv.org/abs/1711.07971
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/nl_head.py#L10
+  Framework: PyTorch
+- Name: nonlocal_r50-d8_4xb4-160k_ade20k-512x512
+  In Collection: NonLocalNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 42.03
+      mIoU(ms+flip): 43.04
+  Config: configs/nonlocal_net/nonlocal_r50-d8_4xb4-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - NonLocalNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_160k_ade20k/nonlocal_r50-d8_512x512_160k_ade20k_20200616_005410-baef45e3.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_160k_ade20k/nonlocal_r50-d8_512x512_160k_ade20k_20200616_005410.log.json
+  Paper:
+    Title: Non-local Neural Networks
+    URL: https://arxiv.org/abs/1711.07971
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/nl_head.py#L10
+  Framework: PyTorch
+- Name: nonlocal_r101-d8_4xb4-160k_ade20k-512x512
+  In Collection: NonLocalNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 44.63
+      mIoU(ms+flip): 45.79
+  Config: configs/nonlocal_net/nonlocal_r101-d8_4xb4-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - NonLocalNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_160k_ade20k/nonlocal_r101-d8_512x512_160k_ade20k_20210827_221502-7881aa1a.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_160k_ade20k/nonlocal_r101-d8_512x512_160k_ade20k_20210827_221502.log.json
+  Paper:
+    Title: Non-local Neural Networks
+    URL: https://arxiv.org/abs/1711.07971
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/nl_head.py#L10
+  Framework: PyTorch
+- Name: nonlocal_r50-d8_4xb4-20k_voc12aug-512x512
+  In Collection: NonLocalNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 76.2
+      mIoU(ms+flip): 77.12
+  Config: configs/nonlocal_net/nonlocal_r50-d8_4xb4-20k_voc12aug-512x512.py
+  Metadata:
+    Training Data: Pascal VOC 2012 + Aug
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - NonLocalNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 6.4
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_20k_voc12aug/nonlocal_r50-d8_512x512_20k_voc12aug_20200617_222613-07f2a57c.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_20k_voc12aug/nonlocal_r50-d8_512x512_20k_voc12aug_20200617_222613.log.json
+  Paper:
+    Title: Non-local Neural Networks
+    URL: https://arxiv.org/abs/1711.07971
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/nl_head.py#L10
+  Framework: PyTorch
+- Name: nonlocal_r101-d8_4xb4-20k_voc12aug-512x512
+  In Collection: NonLocalNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 78.15
+      mIoU(ms+flip): 78.86
+  Config: configs/nonlocal_net/nonlocal_r101-d8_4xb4-20k_voc12aug-512x512.py
+  Metadata:
+    Training Data: Pascal VOC 2012 + Aug
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - NonLocalNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 9.8
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_20k_voc12aug/nonlocal_r101-d8_512x512_20k_voc12aug_20200617_222615-948c68ab.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_20k_voc12aug/nonlocal_r101-d8_512x512_20k_voc12aug_20200617_222615.log.json
+  Paper:
+    Title: Non-local Neural Networks
+    URL: https://arxiv.org/abs/1711.07971
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/nl_head.py#L10
+  Framework: PyTorch
+- Name: nonlocal_r50-d8_4xb4-40k_voc12aug-512x512
+  In Collection: NonLocalNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 76.65
+      mIoU(ms+flip): 77.47
+  Config: configs/nonlocal_net/nonlocal_r50-d8_4xb4-40k_voc12aug-512x512.py
+  Metadata:
+    Training Data: Pascal VOC 2012 + Aug
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - NonLocalNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_40k_voc12aug/nonlocal_r50-d8_512x512_40k_voc12aug_20200614_000028-0139d4a9.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_40k_voc12aug/nonlocal_r50-d8_512x512_40k_voc12aug_20200614_000028.log.json
+  Paper:
+    Title: Non-local Neural Networks
+    URL: https://arxiv.org/abs/1711.07971
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/nl_head.py#L10
+  Framework: PyTorch
+- Name: nonlocal_r101-d8_4xb4-40k_voc12aug-512x512
+  In Collection: NonLocalNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 78.27
+      mIoU(ms+flip): 79.12
+  Config: configs/nonlocal_net/nonlocal_r101-d8_4xb4-40k_voc12aug-512x512.py
+  Metadata:
+    Training Data: Pascal VOC 2012 + Aug
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - NonLocalNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_40k_voc12aug/nonlocal_r101-d8_512x512_40k_voc12aug_20200614_000028-7e5ff470.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_40k_voc12aug/nonlocal_r101-d8_512x512_40k_voc12aug_20200614_000028.log.json
+  Paper:
+    Title: Non-local Neural Networks
+    URL: https://arxiv.org/abs/1711.07971
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/nl_head.py#L10
+  Framework: PyTorch
diff --git a/configs/nonlocal_net/nonlocal_net.yml b/configs/nonlocal_net/nonlocal_net.yml
deleted file mode 100644
index 22f32c5abb..0000000000
--- a/configs/nonlocal_net/nonlocal_net.yml
+++ /dev/null
@@ -1,301 +0,0 @@
-Collections:
-- Name: NonLocalNet
-  Metadata:
-    Training Data:
-    - Cityscapes
-    - ADE20K
-    - Pascal VOC 2012 + Aug
-  Paper:
-    URL: https://arxiv.org/abs/1711.07971
-    Title: Non-local Neural Networks
-  README: configs/nonlocal_net/README.md
-  Code:
-    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/nl_head.py#L10
-    Version: v0.17.0
-  Converted From:
-    Code: https://github.com/facebookresearch/video-nonlocal-net
-Models:
-- Name: nonlocal_r50-d8_4xb2-40k_cityscapes-512x1024
-  In Collection: NonLocalNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,1024)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 367.65
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 7.4
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 78.24
-  Config: configs/nonlocal_net/nonlocal_r50-d8_4xb2-40k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x1024_40k_cityscapes/nonlocal_r50-d8_512x1024_40k_cityscapes_20200605_210748-c75e81e3.pth
-- Name: nonlocal_r101-d8_4xb2-40k_cityscapes-512x1024
-  In Collection: NonLocalNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,1024)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 512.82
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 10.9
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 78.66
-  Config: configs/nonlocal_net/nonlocal_r101-d8_4xb2-40k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x1024_40k_cityscapes/nonlocal_r101-d8_512x1024_40k_cityscapes_20200605_210748-d63729fa.pth
-- Name: nonlocal_r50-d8_4xb2-40k_cityscapes-769x769
-  In Collection: NonLocalNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (769,769)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 657.89
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (769,769)
-    Training Memory (GB): 8.9
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 78.33
-      mIoU(ms+flip): 79.92
-  Config: configs/nonlocal_net/nonlocal_r50-d8_4xb2-40k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_769x769_40k_cityscapes/nonlocal_r50-d8_769x769_40k_cityscapes_20200530_045243-82ef6749.pth
-- Name: nonlocal_r101-d8_4xb2-40k_cityscapes-769x769
-  In Collection: NonLocalNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (769,769)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 952.38
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (769,769)
-    Training Memory (GB): 12.8
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 78.57
-      mIoU(ms+flip): 80.29
-  Config: configs/nonlocal_net/nonlocal_r101-d8_4xb2-40k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_769x769_40k_cityscapes/nonlocal_r101-d8_769x769_40k_cityscapes_20200530_045348-8fe9a9dc.pth
-- Name: nonlocal_r50-d8_4xb2-80k_cityscapes-512x1024
-  In Collection: NonLocalNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,1024)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 78.01
-  Config: configs/nonlocal_net/nonlocal_r50-d8_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x1024_80k_cityscapes/nonlocal_r50-d8_512x1024_80k_cityscapes_20200607_193518-d6839fae.pth
-- Name: nonlocal_r101-d8_4xb2-80k_cityscapes-512x1024
-  In Collection: NonLocalNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,1024)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 78.93
-  Config: configs/nonlocal_net/nonlocal_r101-d8_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x1024_80k_cityscapes/nonlocal_r101-d8_512x1024_80k_cityscapes_20200607_183411-32700183.pth
-- Name: nonlocal_r50-d8_4xb2-80k_cityscapes-769x769
-  In Collection: NonLocalNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (769,769)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 79.05
-      mIoU(ms+flip): 80.68
-  Config: configs/nonlocal_net/nonlocal_r50-d8_4xb2-80k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_769x769_80k_cityscapes/nonlocal_r50-d8_769x769_80k_cityscapes_20200607_193506-1f9792f6.pth
-- Name: nonlocal_r101-d8_4xb2-80k_cityscapes-769x769
-  In Collection: NonLocalNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (769,769)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 79.4
-      mIoU(ms+flip): 80.85
-  Config: configs/nonlocal_net/nonlocal_r101-d8_4xb2-80k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_769x769_80k_cityscapes/nonlocal_r101-d8_769x769_80k_cityscapes_20200607_183428-0e1fa4f9.pth
-- Name: nonlocal_r50-d8_4xb4-80k_ade20k-512x512
-  In Collection: NonLocalNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 46.79
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 9.1
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 40.75
-      mIoU(ms+flip): 42.05
-  Config: configs/nonlocal_net/nonlocal_r50-d8_4xb4-80k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_80k_ade20k/nonlocal_r50-d8_512x512_80k_ade20k_20200615_015801-5ae0aa33.pth
-- Name: nonlocal_r101-d8_4xb4-80k_ade20k-512x512
-  In Collection: NonLocalNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 71.58
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 12.6
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 42.9
-      mIoU(ms+flip): 44.27
-  Config: configs/nonlocal_net/nonlocal_r101-d8_4xb4-80k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_80k_ade20k/nonlocal_r101-d8_512x512_80k_ade20k_20200615_015758-24105919.pth
-- Name: nonlocal_r50-d8_4xb4-160k_ade20k-512x512
-  In Collection: NonLocalNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 160000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 42.03
-      mIoU(ms+flip): 43.04
-  Config: configs/nonlocal_net/nonlocal_r50-d8_4xb4-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_160k_ade20k/nonlocal_r50-d8_512x512_160k_ade20k_20200616_005410-baef45e3.pth
-- Name: nonlocal_r101-d8_4xb4-160k_ade20k-512x512
-  In Collection: NonLocalNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 160000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 44.63
-      mIoU(ms+flip): 45.79
-  Config: configs/nonlocal_net/nonlocal_r101-d8_4xb4-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_160k_ade20k/nonlocal_r101-d8_512x512_160k_ade20k_20210827_221502-7881aa1a.pth
-- Name: nonlocal_r50-d8_4xb4-20k_voc12aug-512x512
-  In Collection: NonLocalNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 20000
-    inference time (ms/im):
-    - value: 47.15
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 6.4
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal VOC 2012 + Aug
-    Metrics:
-      mIoU: 76.2
-      mIoU(ms+flip): 77.12
-  Config: configs/nonlocal_net/nonlocal_r50-d8_4xb4-20k_voc12aug-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_20k_voc12aug/nonlocal_r50-d8_512x512_20k_voc12aug_20200617_222613-07f2a57c.pth
-- Name: nonlocal_r101-d8_4xb4-20k_voc12aug-512x512
-  In Collection: NonLocalNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 20000
-    inference time (ms/im):
-    - value: 71.38
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 9.8
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal VOC 2012 + Aug
-    Metrics:
-      mIoU: 78.15
-      mIoU(ms+flip): 78.86
-  Config: configs/nonlocal_net/nonlocal_r101-d8_4xb4-20k_voc12aug-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_20k_voc12aug/nonlocal_r101-d8_512x512_20k_voc12aug_20200617_222615-948c68ab.pth
-- Name: nonlocal_r50-d8_4xb4-40k_voc12aug-512x512
-  In Collection: NonLocalNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 40000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal VOC 2012 + Aug
-    Metrics:
-      mIoU: 76.65
-      mIoU(ms+flip): 77.47
-  Config: configs/nonlocal_net/nonlocal_r50-d8_4xb4-40k_voc12aug-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_40k_voc12aug/nonlocal_r50-d8_512x512_40k_voc12aug_20200614_000028-0139d4a9.pth
-- Name: nonlocal_r101-d8_4xb4-40k_voc12aug-512x512
-  In Collection: NonLocalNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 40000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal VOC 2012 + Aug
-    Metrics:
-      mIoU: 78.27
-      mIoU(ms+flip): 79.12
-  Config: configs/nonlocal_net/nonlocal_r101-d8_4xb4-40k_voc12aug-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_40k_voc12aug/nonlocal_r101-d8_512x512_40k_voc12aug_20200614_000028-7e5ff470.pth
diff --git a/configs/ocrnet/README.md b/configs/ocrnet/README.md
index 4bd9c7d0b0..5b078f59c2 100644
--- a/configs/ocrnet/README.md
+++ b/configs/ocrnet/README.md
@@ -1,6 +1,6 @@
 # OCRNet
 
-[Object-Contextual Representations for Semantic Segmentation](https://arxiv.org/abs/1909.11065)
+> [Object-Contextual Representations for Semantic Segmentation](https://arxiv.org/abs/1909.11065)
 
 ## Introduction
 
@@ -22,6 +22,54 @@ In this paper, we address the problem of semantic segmentation and focus on the
 <img src="https://user-images.githubusercontent.com/24582831/142902197-b06b1e04-57ab-44ac-adc8-cea6695bb236.png" width="70%"/>
 </div>
 
+## Results and models
+
+### Cityscapes
+
+#### HRNet backbone
+
+| Method | Backbone           | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                           | download                                                                                                                                                                                                                                                                                                                                                         |
+| ------ | ------------------ | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| OCRNet | HRNetV2p-W18-Small | 512x1024  |   40000 | 3.5      | 10.45          | A100   | 76.61 |         78.01 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ocrnet/ocrnet_hr18s_4xb2-40k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_4xb2-40k_cityscapes-512x1024/ocrnet_hr18s_4xb2-40k_cityscapes-512x1024_20230227_145026-6c052a14.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_4xb2-40k_cityscapes-512x1024/ocrnet_hr18s_4xb2-40k_cityscapes-512x1024_20230227_145026.json) |
+| OCRNet | HRNetV2p-W18       | 512x1024  |   40000 | 4.7      | 7.50           | V100   | 77.72 |         79.49 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ocrnet/ocrnet_hr18_4xb2-40k_cityscapes-512x1024.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_40k_cityscapes/ocrnet_hr18_512x1024_40k_cityscapes_20200601_033320-401c5bdd.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_40k_cityscapes/ocrnet_hr18_512x1024_40k_cityscapes_20200601_033320.log.json)                     |
+| OCRNet | HRNetV2p-W48       | 512x1024  |   40000 | 8        | 4.22           | V100   | 80.58 |         81.79 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ocrnet/ocrnet_hr48_4xb2-40k_cityscapes-512x1024.pyy)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_40k_cityscapes/ocrnet_hr48_512x1024_40k_cityscapes_20200601_033336-55b32491.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_40k_cityscapes/ocrnet_hr48_512x1024_40k_cityscapes_20200601_033336.log.json)                     |
+| OCRNet | HRNetV2p-W18-Small | 512x1024  |   80000 | -        | -              | V100   | 77.16 |         78.66 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ocrnet/ocrnet_hr18s_4xb2-80k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x1024_80k_cityscapes/ocrnet_hr18s_512x1024_80k_cityscapes_20200601_222735-55979e63.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x1024_80k_cityscapes/ocrnet_hr18s_512x1024_80k_cityscapes_20200601_222735.log.json)                 |
+| OCRNet | HRNetV2p-W18       | 512x1024  |   80000 | -        | -              | V100   | 78.57 |         80.46 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ocrnet/ocrnet_hr18_4xb2-80k_cityscapes-512x1024.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_80k_cityscapes/ocrnet_hr18_512x1024_80k_cityscapes_20200614_230521-c2e1dd4a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_80k_cityscapes/ocrnet_hr18_512x1024_80k_cityscapes_20200614_230521.log.json)                     |
+| OCRNet | HRNetV2p-W48       | 512x1024  |   80000 | -        | -              | V100   | 80.70 |         81.87 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ocrnet/ocrnet_hr48_4xb2-80k_cityscapes-512x1024.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_80k_cityscapes/ocrnet_hr48_512x1024_80k_cityscapes_20200601_222752-9076bcdf.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_80k_cityscapes/ocrnet_hr48_512x1024_80k_cityscapes_20200601_222752.log.json)                     |
+| OCRNet | HRNetV2p-W18-Small | 512x1024  |  160000 | -        | -              | V100   | 78.45 |         79.97 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ocrnet/ocrnet_hr18s_4xb2-160k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x1024_160k_cityscapes/ocrnet_hr18s_512x1024_160k_cityscapes_20200602_191005-f4a7af28.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x1024_160k_cityscapes/ocrnet_hr18s_512x1024_160k_cityscapes_20200602_191005.log.json)             |
+| OCRNet | HRNetV2p-W18       | 512x1024  |  160000 | -        | -              | V100   | 79.47 |         80.91 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ocrnet/ocrnet_hr18_4xb2-160k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_160k_cityscapes/ocrnet_hr18_512x1024_160k_cityscapes_20200602_191001-b9172d0c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_160k_cityscapes/ocrnet_hr18_512x1024_160k_cityscapes_20200602_191001.log.json)                 |
+| OCRNet | HRNetV2p-W48       | 512x1024  |  160000 | -        | -              | V100   | 81.35 |         82.70 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ocrnet/ocrnet_hr48_4xb2-160k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_160k_cityscapes/ocrnet_hr48_512x1024_160k_cityscapes_20200602_191037-dfbf1b0c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_160k_cityscapes/ocrnet_hr48_512x1024_160k_cityscapes_20200602_191037.log.json)                 |
+
+#### ResNet backbone
+
+| Method | Backbone | Crop Size | Batch Size | Lr schd | Mem (GB) | Inf time (fps) | Device | mIoU  | mIoU(ms+flip) | config                                                                                                                            | download                                                                                                                                                                                                                                                                                                                                                                 |
+| ------ | -------- | --------- | ---------- | ------- | -------- | -------------- | ------ | ----- | ------------: | --------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| OCRNet | R-101-D8 | 512x1024  | 8          | 40000   | -        | -              | V100   | 80.09 |             - | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ocrnet/ocrnet_r101-d8_4xb2-40k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_r101-d8_512x1024_40k_b8_cityscapes/ocrnet_r101-d8_512x1024_40k_b8_cityscapes_20200717_110721-02ac0f13.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_r101-d8_512x1024_40k_b8_cityscapes/ocrnet_r101-d8_512x1024_40k_b8_cityscapes_20200717_110721.log.json)     |
+| OCRNet | R-101-D8 | 512x1024  | 16         | 40000   | 8.8      | 3.02           | V100   | 80.30 |             - | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ocrnet/ocrnet_r101-d8_8xb2-40k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_r101-d8_512x1024_40k_b16_cityscapes/ocrnet_r101-d8_512x1024_40k_b16_cityscapes_20200723_193726-db500f80.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_r101-d8_512x1024_40k_b16_cityscapes/ocrnet_r101-d8_512x1024_40k_b16_cityscapes_20200723_193726.log.json) |
+| OCRNet | R-101-D8 | 512x1024  | 16         | 80000   | 8.8      | 3.02           | V100   | 80.81 |             - | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ocrnet/ocrnet_r101-d8_8xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_r101-d8_512x1024_80k_b16_cityscapes/ocrnet_r101-d8_512x1024_80k_b16_cityscapes_20200723_192421-78688424.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_r101-d8_512x1024_80k_b16_cityscapes/ocrnet_r101-d8_512x1024_80k_b16_cityscapes_20200723_192421.log.json) |
+
+### ADE20K
+
+| Method | Backbone           | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                     | download                                                                                                                                                                                                                                                                                                                         |
+| ------ | ------------------ | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| OCRNet | HRNetV2p-W18-Small | 512x512   |   80000 | 6.7      | 28.98          | V100   | 35.06 |         35.80 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ocrnet/ocrnet_hr18s_4xb4-80k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_80k_ade20k/ocrnet_hr18s_512x512_80k_ade20k_20200615_055600-e80b62af.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_80k_ade20k/ocrnet_hr18s_512x512_80k_ade20k_20200615_055600.log.json)     |
+| OCRNet | HRNetV2p-W18       | 512x512   |   80000 | 7.9      | 18.93          | V100   | 37.79 |         39.16 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ocrnet/ocrnet_hr18_4xb4-80k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_80k_ade20k/ocrnet_hr18_512x512_80k_ade20k_20200615_053157-d173d83b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_80k_ade20k/ocrnet_hr18_512x512_80k_ade20k_20200615_053157.log.json)         |
+| OCRNet | HRNetV2p-W48       | 512x512   |   80000 | 11.2     | 16.99          | V100   | 43.00 |         44.30 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ocrnet/ocrnet_hr48_4xb4-80k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_80k_ade20k/ocrnet_hr48_512x512_80k_ade20k_20200615_021518-d168c2d1.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_80k_ade20k/ocrnet_hr48_512x512_80k_ade20k_20200615_021518.log.json)         |
+| OCRNet | HRNetV2p-W18-Small | 512x512   |  160000 | -        | -              | V100   | 37.19 |         38.40 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ocrnet/ocrnet_hr18s_4xb4-80k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_160k_ade20k/ocrnet_hr18s_512x512_160k_ade20k_20200615_184505-8e913058.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_160k_ade20k/ocrnet_hr18s_512x512_160k_ade20k_20200615_184505.log.json) |
+| OCRNet | HRNetV2p-W18       | 512x512   |  160000 | -        | -              | V100   | 39.32 |         40.80 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ocrnet/ocrnet_hr18_4xb4-80k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_160k_ade20k/ocrnet_hr18_512x512_160k_ade20k_20200615_200940-d8fcd9d1.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_160k_ade20k/ocrnet_hr18_512x512_160k_ade20k_20200615_200940.log.json)     |
+| OCRNet | HRNetV2p-W48       | 512x512   |  160000 | -        | -              | V100   | 43.25 |         44.88 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ocrnet/ocrnet_hr48_4xb4-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_160k_ade20k/ocrnet_hr48_512x512_160k_ade20k_20200615_184705-a073726d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_160k_ade20k/ocrnet_hr48_512x512_160k_ade20k_20200615_184705.log.json)     |
+
+### Pascal VOC 2012 + Aug
+
+| Method | Backbone           | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                       | download                                                                                                                                                                                                                                                                                                                             |
+| ------ | ------------------ | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ---------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| OCRNet | HRNetV2p-W18-Small | 512x512   |   20000 | 3.5      | 31.55          | V100   | 71.70 |         73.84 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ocrnet/ocrnet_hr18s_4xb4-20k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_20k_voc12aug/ocrnet_hr18s_512x512_20k_voc12aug_20200617_233913-02b04fcb.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_20k_voc12aug/ocrnet_hr18s_512x512_20k_voc12aug_20200617_233913.log.json) |
+| OCRNet | HRNetV2p-W18       | 512x512   |   20000 | 4.7      | 19.91          | V100   | 74.75 |         77.11 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ocrnet/ocrnet_hr18_4xb4-20k_voc12aug-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_20k_voc12aug/ocrnet_hr18_512x512_20k_voc12aug_20200617_233932-8954cbb7.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_20k_voc12aug/ocrnet_hr18_512x512_20k_voc12aug_20200617_233932.log.json)     |
+| OCRNet | HRNetV2p-W48       | 512x512   |   20000 | 8.1      | 17.83          | V100   | 77.72 |         79.87 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ocrnet/ocrnet_hr48_4xb4-20k_voc12aug-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_20k_voc12aug/ocrnet_hr48_512x512_20k_voc12aug_20200617_233932-9e82080a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_20k_voc12aug/ocrnet_hr48_512x512_20k_voc12aug_20200617_233932.log.json)     |
+| OCRNet | HRNetV2p-W18-Small | 512x512   |   40000 | -        | -              | V100   | 72.76 |         74.60 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ocrnet/ocrnet_hr18s_4xb4-40k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_40k_voc12aug/ocrnet_hr18s_512x512_40k_voc12aug_20200614_002025-42b587ac.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_40k_voc12aug/ocrnet_hr18s_512x512_40k_voc12aug_20200614_002025.log.json) |
+| OCRNet | HRNetV2p-W18       | 512x512   |   40000 | -        | -              | V100   | 74.98 |         77.40 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ocrnet/ocrnet_hr18_4xb4-40k_voc12aug-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_40k_voc12aug/ocrnet_hr18_512x512_40k_voc12aug_20200614_015958-714302be.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_40k_voc12aug/ocrnet_hr18_512x512_40k_voc12aug_20200614_015958.log.json)     |
+| OCRNet | HRNetV2p-W48       | 512x512   |   40000 | -        | -              | V100   | 77.14 |         79.71 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ocrnet/ocrnet_hr48_4xb4-40k_voc12aug-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_40k_voc12aug/ocrnet_hr48_512x512_40k_voc12aug_20200614_015958-255bc5ce.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_40k_voc12aug/ocrnet_hr48_512x512_40k_voc12aug_20200614_015958.log.json)     |
+
 ## Citation
 
 ```bibtex
@@ -39,51 +87,3 @@ In this paper, we address the problem of semantic segmentation and focus on the
   year={2020}
 }
 ```
-
-## Results and models
-
-### Cityscapes
-
-#### HRNet backbone
-
-| Method | Backbone           | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                           | download                                                                                                                                                                                                                                                                                                                                                         |
-| ------ | ------------------ | --------- | ------: | -------- | -------------- | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| OCRNet | HRNetV2p-W18-Small | 512x1024  |   40000 | 3.5      | 10.45          | 76.61 |         78.01 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ocrnet/ocrnet_hr18s_4xb2-40k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_4xb2-40k_cityscapes-512x1024/ocrnet_hr18s_4xb2-40k_cityscapes-512x1024_20230227_145026-6c052a14.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_4xb2-40k_cityscapes-512x1024/ocrnet_hr18s_4xb2-40k_cityscapes-512x1024_20230227_145026.json) |
-| OCRNet | HRNetV2p-W18       | 512x1024  |   40000 | 4.7      | 7.50           | 77.72 |         79.49 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ocrnet/ocrnet_hr18_4xb2-40k_cityscapes-512x1024.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_40k_cityscapes/ocrnet_hr18_512x1024_40k_cityscapes_20200601_033320-401c5bdd.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_40k_cityscapes/ocrnet_hr18_512x1024_40k_cityscapes_20200601_033320.log.json)                     |
-| OCRNet | HRNetV2p-W48       | 512x1024  |   40000 | 8        | 4.22           | 80.58 |         81.79 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ocrnet/ocrnet_hr48_4xb2-40k_cityscapes-512x1024.pyy)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_40k_cityscapes/ocrnet_hr48_512x1024_40k_cityscapes_20200601_033336-55b32491.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_40k_cityscapes/ocrnet_hr48_512x1024_40k_cityscapes_20200601_033336.log.json)                     |
-| OCRNet | HRNetV2p-W18-Small | 512x1024  |   80000 | -        | -              | 77.16 |         78.66 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ocrnet/ocrnet_hr18s_4xb2-80k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x1024_80k_cityscapes/ocrnet_hr18s_512x1024_80k_cityscapes_20200601_222735-55979e63.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x1024_80k_cityscapes/ocrnet_hr18s_512x1024_80k_cityscapes_20200601_222735.log.json)                 |
-| OCRNet | HRNetV2p-W18       | 512x1024  |   80000 | -        | -              | 78.57 |         80.46 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ocrnet/ocrnet_hr18_4xb2-80k_cityscapes-512x1024.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_80k_cityscapes/ocrnet_hr18_512x1024_80k_cityscapes_20200614_230521-c2e1dd4a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_80k_cityscapes/ocrnet_hr18_512x1024_80k_cityscapes_20200614_230521.log.json)                     |
-| OCRNet | HRNetV2p-W48       | 512x1024  |   80000 | -        | -              | 80.70 |         81.87 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ocrnet/ocrnet_hr48_4xb2-80k_cityscapes-512x1024.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_80k_cityscapes/ocrnet_hr48_512x1024_80k_cityscapes_20200601_222752-9076bcdf.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_80k_cityscapes/ocrnet_hr48_512x1024_80k_cityscapes_20200601_222752.log.json)                     |
-| OCRNet | HRNetV2p-W18-Small | 512x1024  |  160000 | -        | -              | 78.45 |         79.97 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ocrnet/ocrnet_hr18s_4xb2-160k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x1024_160k_cityscapes/ocrnet_hr18s_512x1024_160k_cityscapes_20200602_191005-f4a7af28.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x1024_160k_cityscapes/ocrnet_hr18s_512x1024_160k_cityscapes_20200602_191005.log.json)             |
-| OCRNet | HRNetV2p-W18       | 512x1024  |  160000 | -        | -              | 79.47 |         80.91 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ocrnet/ocrnet_hr18_4xb2-160k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_160k_cityscapes/ocrnet_hr18_512x1024_160k_cityscapes_20200602_191001-b9172d0c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_160k_cityscapes/ocrnet_hr18_512x1024_160k_cityscapes_20200602_191001.log.json)                 |
-| OCRNet | HRNetV2p-W48       | 512x1024  |  160000 | -        | -              | 81.35 |         82.70 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ocrnet/ocrnet_hr48_4xb2-160k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_160k_cityscapes/ocrnet_hr48_512x1024_160k_cityscapes_20200602_191037-dfbf1b0c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_160k_cityscapes/ocrnet_hr48_512x1024_160k_cityscapes_20200602_191037.log.json)                 |
-
-#### ResNet backbone
-
-| Method | Backbone | Crop Size | Batch Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU  | mIoU(ms+flip) | config                                                                                                                            | download                                                                                                                                                                                                                                                                                                                                                                 |
-| ------ | -------- | --------- | ---------- | ------- | -------- | -------------- | ----- | ------------: | --------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| OCRNet | R-101-D8 | 512x1024  | 8          | 40000   | -        | -              | 80.09 |             - | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ocrnet/ocrnet_r101-d8_4xb2-40k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_r101-d8_512x1024_40k_b8_cityscapes/ocrnet_r101-d8_512x1024_40k_b8_cityscapes_20200717_110721-02ac0f13.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_r101-d8_512x1024_40k_b8_cityscapes/ocrnet_r101-d8_512x1024_40k_b8_cityscapes_20200717_110721.log.json)     |
-| OCRNet | R-101-D8 | 512x1024  | 16         | 40000   | 8.8      | 3.02           | 80.30 |             - | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ocrnet/ocrnet_r101-d8_8xb2-40k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_r101-d8_512x1024_40k_b16_cityscapes/ocrnet_r101-d8_512x1024_40k_b16_cityscapes_20200723_193726-db500f80.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_r101-d8_512x1024_40k_b16_cityscapes/ocrnet_r101-d8_512x1024_40k_b16_cityscapes_20200723_193726.log.json) |
-| OCRNet | R-101-D8 | 512x1024  | 16         | 80000   | 8.8      | 3.02           | 80.81 |             - | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ocrnet/ocrnet_r101-d8_8xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_r101-d8_512x1024_80k_b16_cityscapes/ocrnet_r101-d8_512x1024_80k_b16_cityscapes_20200723_192421-78688424.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_r101-d8_512x1024_80k_b16_cityscapes/ocrnet_r101-d8_512x1024_80k_b16_cityscapes_20200723_192421.log.json) |
-
-### ADE20K
-
-| Method | Backbone           | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                     | download                                                                                                                                                                                                                                                                                                                         |
-| ------ | ------------------ | --------- | ------: | -------- | -------------- | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| OCRNet | HRNetV2p-W18-Small | 512x512   |   80000 | 6.7      | 28.98          | 35.06 |         35.80 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ocrnet/ocrnet_hr18s_4xb4-80k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_80k_ade20k/ocrnet_hr18s_512x512_80k_ade20k_20200615_055600-e80b62af.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_80k_ade20k/ocrnet_hr18s_512x512_80k_ade20k_20200615_055600.log.json)     |
-| OCRNet | HRNetV2p-W18       | 512x512   |   80000 | 7.9      | 18.93          | 37.79 |         39.16 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ocrnet/ocrnet_hr18_4xb4-80k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_80k_ade20k/ocrnet_hr18_512x512_80k_ade20k_20200615_053157-d173d83b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_80k_ade20k/ocrnet_hr18_512x512_80k_ade20k_20200615_053157.log.json)         |
-| OCRNet | HRNetV2p-W48       | 512x512   |   80000 | 11.2     | 16.99          | 43.00 |         44.30 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ocrnet/ocrnet_hr48_4xb4-80k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_80k_ade20k/ocrnet_hr48_512x512_80k_ade20k_20200615_021518-d168c2d1.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_80k_ade20k/ocrnet_hr48_512x512_80k_ade20k_20200615_021518.log.json)         |
-| OCRNet | HRNetV2p-W18-Small | 512x512   |  160000 | -        | -              | 37.19 |         38.40 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ocrnet/ocrnet_hr18s_4xb4-80k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_160k_ade20k/ocrnet_hr18s_512x512_160k_ade20k_20200615_184505-8e913058.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_160k_ade20k/ocrnet_hr18s_512x512_160k_ade20k_20200615_184505.log.json) |
-| OCRNet | HRNetV2p-W18       | 512x512   |  160000 | -        | -              | 39.32 |         40.80 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ocrnet/ocrnet_hr18_4xb4-80k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_160k_ade20k/ocrnet_hr18_512x512_160k_ade20k_20200615_200940-d8fcd9d1.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_160k_ade20k/ocrnet_hr18_512x512_160k_ade20k_20200615_200940.log.json)     |
-| OCRNet | HRNetV2p-W48       | 512x512   |  160000 | -        | -              | 43.25 |         44.88 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ocrnet/ocrnet_hr48_4xb4-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_160k_ade20k/ocrnet_hr48_512x512_160k_ade20k_20200615_184705-a073726d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_160k_ade20k/ocrnet_hr48_512x512_160k_ade20k_20200615_184705.log.json)     |
-
-### Pascal VOC 2012 + Aug
-
-| Method | Backbone           | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                       | download                                                                                                                                                                                                                                                                                                                             |
-| ------ | ------------------ | --------- | ------: | -------- | -------------- | ----: | ------------: | ---------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| OCRNet | HRNetV2p-W18-Small | 512x512   |   20000 | 3.5      | 31.55          | 71.70 |         73.84 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ocrnet/ocrnet_hr18s_4xb4-20k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_20k_voc12aug/ocrnet_hr18s_512x512_20k_voc12aug_20200617_233913-02b04fcb.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_20k_voc12aug/ocrnet_hr18s_512x512_20k_voc12aug_20200617_233913.log.json) |
-| OCRNet | HRNetV2p-W18       | 512x512   |   20000 | 4.7      | 19.91          | 74.75 |         77.11 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ocrnet/ocrnet_hr18_4xb4-20k_voc12aug-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_20k_voc12aug/ocrnet_hr18_512x512_20k_voc12aug_20200617_233932-8954cbb7.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_20k_voc12aug/ocrnet_hr18_512x512_20k_voc12aug_20200617_233932.log.json)     |
-| OCRNet | HRNetV2p-W48       | 512x512   |   20000 | 8.1      | 17.83          | 77.72 |         79.87 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ocrnet/ocrnet_hr48_4xb4-20k_voc12aug-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_20k_voc12aug/ocrnet_hr48_512x512_20k_voc12aug_20200617_233932-9e82080a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_20k_voc12aug/ocrnet_hr48_512x512_20k_voc12aug_20200617_233932.log.json)     |
-| OCRNet | HRNetV2p-W18-Small | 512x512   |   40000 | -        | -              | 72.76 |         74.60 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ocrnet/ocrnet_hr18s_4xb4-40k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_40k_voc12aug/ocrnet_hr18s_512x512_40k_voc12aug_20200614_002025-42b587ac.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_40k_voc12aug/ocrnet_hr18s_512x512_40k_voc12aug_20200614_002025.log.json) |
-| OCRNet | HRNetV2p-W18       | 512x512   |   40000 | -        | -              | 74.98 |         77.40 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ocrnet/ocrnet_hr18_4xb4-40k_voc12aug-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_40k_voc12aug/ocrnet_hr18_512x512_40k_voc12aug_20200614_015958-714302be.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_40k_voc12aug/ocrnet_hr18_512x512_40k_voc12aug_20200614_015958.log.json)     |
-| OCRNet | HRNetV2p-W48       | 512x512   |   40000 | -        | -              | 77.14 |         79.71 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/ocrnet/ocrnet_hr48_4xb4-40k_voc12aug-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_40k_voc12aug/ocrnet_hr48_512x512_40k_voc12aug_20200614_015958-255bc5ce.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_40k_voc12aug/ocrnet_hr48_512x512_40k_voc12aug_20200614_015958.log.json)     |
diff --git a/configs/ocrnet/metafile.yaml b/configs/ocrnet/metafile.yaml
new file mode 100644
index 0000000000..5467feb975
--- /dev/null
+++ b/configs/ocrnet/metafile.yaml
@@ -0,0 +1,577 @@
+Collections:
+- Name: OCRNet
+  License: Apache License 2.0
+  Metadata:
+    Training Data:
+    - Cityscapes
+    - '# HRNet backbone'
+    - '# ResNet backbone'
+    - ADE20K
+    - Pascal VOC 2012 + Aug
+  Paper:
+    Title: Object-Contextual Representations for Semantic Segmentation
+    URL: https://arxiv.org/abs/1909.11065
+  README: configs/ocrnet/README.md
+  Frameworks:
+  - PyTorch
+Models:
+- Name: ocrnet_hr18s_4xb2-40k_cityscapes-512x1024
+  In Collection: OCRNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: '# HRNet backbone'
+    Metrics:
+      mIoU: 76.61
+      mIoU(ms+flip): 78.01
+  Config: configs/ocrnet/ocrnet_hr18s_4xb2-40k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: '# HRNet backbone'
+    Batch Size: 8
+    Architecture:
+    - HRNetV2p-W18-Small
+    - OCRNet
+    Training Resources: 4x A100 GPUS
+    Memory (GB): 3.5
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_4xb2-40k_cityscapes-512x1024/ocrnet_hr18s_4xb2-40k_cityscapes-512x1024_20230227_145026-6c052a14.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_4xb2-40k_cityscapes-512x1024/ocrnet_hr18s_4xb2-40k_cityscapes-512x1024_20230227_145026.json
+  Paper:
+    Title: Object-Contextual Representations for Semantic Segmentation
+    URL: https://arxiv.org/abs/1909.11065
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ocr_head.py#L86
+  Framework: PyTorch
+- Name: ocrnet_hr18_4xb2-40k_cityscapes-512x1024
+  In Collection: OCRNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: '# HRNet backbone'
+    Metrics:
+      mIoU: 77.72
+      mIoU(ms+flip): 79.49
+  Config: configs/ocrnet/ocrnet_hr18_4xb2-40k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: '# HRNet backbone'
+    Batch Size: 8
+    Architecture:
+    - HRNetV2p-W18
+    - OCRNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 4.7
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_40k_cityscapes/ocrnet_hr18_512x1024_40k_cityscapes_20200601_033320-401c5bdd.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_40k_cityscapes/ocrnet_hr18_512x1024_40k_cityscapes_20200601_033320.log.json
+  Paper:
+    Title: Object-Contextual Representations for Semantic Segmentation
+    URL: https://arxiv.org/abs/1909.11065
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ocr_head.py#L86
+  Framework: PyTorch
+- Name: ocrnet_hr48_4xb2-40k_cityscapes-512x1024
+  In Collection: OCRNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: '# HRNet backbone'
+    Metrics:
+      mIoU: 80.58
+      mIoU(ms+flip): 81.79
+  Config: configs/ocrnet/ocrnet_hr48_4xb2-40k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: '# HRNet backbone'
+    Batch Size: 8
+    Architecture:
+    - HRNetV2p-W48
+    - OCRNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 8.0
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_40k_cityscapes/ocrnet_hr48_512x1024_40k_cityscapes_20200601_033336-55b32491.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_40k_cityscapes/ocrnet_hr48_512x1024_40k_cityscapes_20200601_033336.log.json
+  Paper:
+    Title: Object-Contextual Representations for Semantic Segmentation
+    URL: https://arxiv.org/abs/1909.11065
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ocr_head.py#L86
+  Framework: PyTorch
+- Name: ocrnet_hr18s_4xb2-80k_cityscapes-512x1024
+  In Collection: OCRNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: '# HRNet backbone'
+    Metrics:
+      mIoU: 77.16
+      mIoU(ms+flip): 78.66
+  Config: configs/ocrnet/ocrnet_hr18s_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: '# HRNet backbone'
+    Batch Size: 8
+    Architecture:
+    - HRNetV2p-W18-Small
+    - OCRNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x1024_80k_cityscapes/ocrnet_hr18s_512x1024_80k_cityscapes_20200601_222735-55979e63.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x1024_80k_cityscapes/ocrnet_hr18s_512x1024_80k_cityscapes_20200601_222735.log.json
+  Paper:
+    Title: Object-Contextual Representations for Semantic Segmentation
+    URL: https://arxiv.org/abs/1909.11065
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ocr_head.py#L86
+  Framework: PyTorch
+- Name: ocrnet_hr18_4xb2-80k_cityscapes-512x1024
+  In Collection: OCRNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: '# HRNet backbone'
+    Metrics:
+      mIoU: 78.57
+      mIoU(ms+flip): 80.46
+  Config: configs/ocrnet/ocrnet_hr18_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: '# HRNet backbone'
+    Batch Size: 8
+    Architecture:
+    - HRNetV2p-W18
+    - OCRNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_80k_cityscapes/ocrnet_hr18_512x1024_80k_cityscapes_20200614_230521-c2e1dd4a.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_80k_cityscapes/ocrnet_hr18_512x1024_80k_cityscapes_20200614_230521.log.json
+  Paper:
+    Title: Object-Contextual Representations for Semantic Segmentation
+    URL: https://arxiv.org/abs/1909.11065
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ocr_head.py#L86
+  Framework: PyTorch
+- Name: ocrnet_hr48_4xb2-80k_cityscapes-512x1024
+  In Collection: OCRNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: '# HRNet backbone'
+    Metrics:
+      mIoU: 80.7
+      mIoU(ms+flip): 81.87
+  Config: configs/ocrnet/ocrnet_hr48_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: '# HRNet backbone'
+    Batch Size: 8
+    Architecture:
+    - HRNetV2p-W48
+    - OCRNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_80k_cityscapes/ocrnet_hr48_512x1024_80k_cityscapes_20200601_222752-9076bcdf.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_80k_cityscapes/ocrnet_hr48_512x1024_80k_cityscapes_20200601_222752.log.json
+  Paper:
+    Title: Object-Contextual Representations for Semantic Segmentation
+    URL: https://arxiv.org/abs/1909.11065
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ocr_head.py#L86
+  Framework: PyTorch
+- Name: ocrnet_hr18s_4xb2-160k_cityscapes-512x1024
+  In Collection: OCRNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: '# HRNet backbone'
+    Metrics:
+      mIoU: 78.45
+      mIoU(ms+flip): 79.97
+  Config: configs/ocrnet/ocrnet_hr18s_4xb2-160k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: '# HRNet backbone'
+    Batch Size: 8
+    Architecture:
+    - HRNetV2p-W18-Small
+    - OCRNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x1024_160k_cityscapes/ocrnet_hr18s_512x1024_160k_cityscapes_20200602_191005-f4a7af28.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x1024_160k_cityscapes/ocrnet_hr18s_512x1024_160k_cityscapes_20200602_191005.log.json
+  Paper:
+    Title: Object-Contextual Representations for Semantic Segmentation
+    URL: https://arxiv.org/abs/1909.11065
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ocr_head.py#L86
+  Framework: PyTorch
+- Name: ocrnet_hr18_4xb2-160k_cityscapes-512x1024
+  In Collection: OCRNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: '# HRNet backbone'
+    Metrics:
+      mIoU: 79.47
+      mIoU(ms+flip): 80.91
+  Config: configs/ocrnet/ocrnet_hr18_4xb2-160k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: '# HRNet backbone'
+    Batch Size: 8
+    Architecture:
+    - HRNetV2p-W18
+    - OCRNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_160k_cityscapes/ocrnet_hr18_512x1024_160k_cityscapes_20200602_191001-b9172d0c.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_160k_cityscapes/ocrnet_hr18_512x1024_160k_cityscapes_20200602_191001.log.json
+  Paper:
+    Title: Object-Contextual Representations for Semantic Segmentation
+    URL: https://arxiv.org/abs/1909.11065
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ocr_head.py#L86
+  Framework: PyTorch
+- Name: ocrnet_hr48_4xb2-160k_cityscapes-512x1024
+  In Collection: OCRNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: '# HRNet backbone'
+    Metrics:
+      mIoU: 81.35
+      mIoU(ms+flip): 82.7
+  Config: configs/ocrnet/ocrnet_hr48_4xb2-160k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: '# HRNet backbone'
+    Batch Size: 8
+    Architecture:
+    - HRNetV2p-W48
+    - OCRNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_160k_cityscapes/ocrnet_hr48_512x1024_160k_cityscapes_20200602_191037-dfbf1b0c.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_160k_cityscapes/ocrnet_hr48_512x1024_160k_cityscapes_20200602_191037.log.json
+  Paper:
+    Title: Object-Contextual Representations for Semantic Segmentation
+    URL: https://arxiv.org/abs/1909.11065
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ocr_head.py#L86
+  Framework: PyTorch
+- Name: ocrnet_r101-d8_4xb2-40k_cityscapes-512x1024
+  In Collection: OCRNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: '# ResNet backbone'
+    Metrics:
+      mIoU: 80.09
+  Config: configs/ocrnet/ocrnet_r101-d8_4xb2-40k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: '# ResNet backbone'
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - OCRNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_r101-d8_512x1024_40k_b8_cityscapes/ocrnet_r101-d8_512x1024_40k_b8_cityscapes_20200717_110721-02ac0f13.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_r101-d8_512x1024_40k_b8_cityscapes/ocrnet_r101-d8_512x1024_40k_b8_cityscapes_20200717_110721.log.json
+  Paper:
+    Title: Object-Contextual Representations for Semantic Segmentation
+    URL: https://arxiv.org/abs/1909.11065
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ocr_head.py#L86
+  Framework: PyTorch
+- Name: ocrnet_r101-d8_8xb2-40k_cityscapes-512x1024
+  In Collection: OCRNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: '# ResNet backbone'
+    Metrics:
+      mIoU: 80.3
+  Config: configs/ocrnet/ocrnet_r101-d8_8xb2-40k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: '# ResNet backbone'
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - OCRNet
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 8.8
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_r101-d8_512x1024_40k_b16_cityscapes/ocrnet_r101-d8_512x1024_40k_b16_cityscapes_20200723_193726-db500f80.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_r101-d8_512x1024_40k_b16_cityscapes/ocrnet_r101-d8_512x1024_40k_b16_cityscapes_20200723_193726.log.json
+  Paper:
+    Title: Object-Contextual Representations for Semantic Segmentation
+    URL: https://arxiv.org/abs/1909.11065
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ocr_head.py#L86
+  Framework: PyTorch
+- Name: ocrnet_r101-d8_8xb2-80k_cityscapes-512x1024
+  In Collection: OCRNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: '# ResNet backbone'
+    Metrics:
+      mIoU: 80.81
+  Config: configs/ocrnet/ocrnet_r101-d8_8xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: '# ResNet backbone'
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - OCRNet
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 8.8
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_r101-d8_512x1024_80k_b16_cityscapes/ocrnet_r101-d8_512x1024_80k_b16_cityscapes_20200723_192421-78688424.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_r101-d8_512x1024_80k_b16_cityscapes/ocrnet_r101-d8_512x1024_80k_b16_cityscapes_20200723_192421.log.json
+  Paper:
+    Title: Object-Contextual Representations for Semantic Segmentation
+    URL: https://arxiv.org/abs/1909.11065
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ocr_head.py#L86
+  Framework: PyTorch
+- Name: ocrnet_hr18s_4xb4-80k_ade20k-512x512
+  In Collection: OCRNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 35.06
+      mIoU(ms+flip): 35.8
+  Config: configs/ocrnet/ocrnet_hr18s_4xb4-80k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - HRNetV2p-W18-Small
+    - OCRNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 6.7
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_80k_ade20k/ocrnet_hr18s_512x512_80k_ade20k_20200615_055600-e80b62af.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_80k_ade20k/ocrnet_hr18s_512x512_80k_ade20k_20200615_055600.log.json
+  Paper:
+    Title: Object-Contextual Representations for Semantic Segmentation
+    URL: https://arxiv.org/abs/1909.11065
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ocr_head.py#L86
+  Framework: PyTorch
+- Name: ocrnet_hr18_4xb4-80k_ade20k-512x512
+  In Collection: OCRNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 37.79
+      mIoU(ms+flip): 39.16
+  Config: configs/ocrnet/ocrnet_hr18_4xb4-80k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - HRNetV2p-W18
+    - OCRNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 7.9
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_80k_ade20k/ocrnet_hr18_512x512_80k_ade20k_20200615_053157-d173d83b.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_80k_ade20k/ocrnet_hr18_512x512_80k_ade20k_20200615_053157.log.json
+  Paper:
+    Title: Object-Contextual Representations for Semantic Segmentation
+    URL: https://arxiv.org/abs/1909.11065
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ocr_head.py#L86
+  Framework: PyTorch
+- Name: ocrnet_hr48_4xb4-80k_ade20k-512x512
+  In Collection: OCRNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 43.0
+      mIoU(ms+flip): 44.3
+  Config: configs/ocrnet/ocrnet_hr48_4xb4-80k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - HRNetV2p-W48
+    - OCRNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 11.2
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_80k_ade20k/ocrnet_hr48_512x512_80k_ade20k_20200615_021518-d168c2d1.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_80k_ade20k/ocrnet_hr48_512x512_80k_ade20k_20200615_021518.log.json
+  Paper:
+    Title: Object-Contextual Representations for Semantic Segmentation
+    URL: https://arxiv.org/abs/1909.11065
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ocr_head.py#L86
+  Framework: PyTorch
+- Name: ocrnet_hr18s_4xb4-80k_ade20k-512x512
+  In Collection: OCRNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 37.19
+      mIoU(ms+flip): 38.4
+  Config: configs/ocrnet/ocrnet_hr18s_4xb4-80k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - HRNetV2p-W18-Small
+    - OCRNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_160k_ade20k/ocrnet_hr18s_512x512_160k_ade20k_20200615_184505-8e913058.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_160k_ade20k/ocrnet_hr18s_512x512_160k_ade20k_20200615_184505.log.json
+  Paper:
+    Title: Object-Contextual Representations for Semantic Segmentation
+    URL: https://arxiv.org/abs/1909.11065
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ocr_head.py#L86
+  Framework: PyTorch
+- Name: ocrnet_hr18_4xb4-80k_ade20k-512x512
+  In Collection: OCRNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 39.32
+      mIoU(ms+flip): 40.8
+  Config: configs/ocrnet/ocrnet_hr18_4xb4-80k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - HRNetV2p-W18
+    - OCRNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_160k_ade20k/ocrnet_hr18_512x512_160k_ade20k_20200615_200940-d8fcd9d1.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_160k_ade20k/ocrnet_hr18_512x512_160k_ade20k_20200615_200940.log.json
+  Paper:
+    Title: Object-Contextual Representations for Semantic Segmentation
+    URL: https://arxiv.org/abs/1909.11065
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ocr_head.py#L86
+  Framework: PyTorch
+- Name: ocrnet_hr48_4xb4-160k_ade20k-512x512
+  In Collection: OCRNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 43.25
+      mIoU(ms+flip): 44.88
+  Config: configs/ocrnet/ocrnet_hr48_4xb4-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - HRNetV2p-W48
+    - OCRNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_160k_ade20k/ocrnet_hr48_512x512_160k_ade20k_20200615_184705-a073726d.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_160k_ade20k/ocrnet_hr48_512x512_160k_ade20k_20200615_184705.log.json
+  Paper:
+    Title: Object-Contextual Representations for Semantic Segmentation
+    URL: https://arxiv.org/abs/1909.11065
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ocr_head.py#L86
+  Framework: PyTorch
+- Name: ocrnet_hr18s_4xb4-20k_voc12aug-512x512
+  In Collection: OCRNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 71.7
+      mIoU(ms+flip): 73.84
+  Config: configs/ocrnet/ocrnet_hr18s_4xb4-20k_voc12aug-512x512.py
+  Metadata:
+    Training Data: Pascal VOC 2012 + Aug
+    Batch Size: 16
+    Architecture:
+    - HRNetV2p-W18-Small
+    - OCRNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 3.5
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_20k_voc12aug/ocrnet_hr18s_512x512_20k_voc12aug_20200617_233913-02b04fcb.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_20k_voc12aug/ocrnet_hr18s_512x512_20k_voc12aug_20200617_233913.log.json
+  Paper:
+    Title: Object-Contextual Representations for Semantic Segmentation
+    URL: https://arxiv.org/abs/1909.11065
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ocr_head.py#L86
+  Framework: PyTorch
+- Name: ocrnet_hr18_4xb4-20k_voc12aug-512x512
+  In Collection: OCRNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 74.75
+      mIoU(ms+flip): 77.11
+  Config: configs/ocrnet/ocrnet_hr18_4xb4-20k_voc12aug-512x512.py
+  Metadata:
+    Training Data: Pascal VOC 2012 + Aug
+    Batch Size: 16
+    Architecture:
+    - HRNetV2p-W18
+    - OCRNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 4.7
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_20k_voc12aug/ocrnet_hr18_512x512_20k_voc12aug_20200617_233932-8954cbb7.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_20k_voc12aug/ocrnet_hr18_512x512_20k_voc12aug_20200617_233932.log.json
+  Paper:
+    Title: Object-Contextual Representations for Semantic Segmentation
+    URL: https://arxiv.org/abs/1909.11065
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ocr_head.py#L86
+  Framework: PyTorch
+- Name: ocrnet_hr48_4xb4-20k_voc12aug-512x512
+  In Collection: OCRNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 77.72
+      mIoU(ms+flip): 79.87
+  Config: configs/ocrnet/ocrnet_hr48_4xb4-20k_voc12aug-512x512.py
+  Metadata:
+    Training Data: Pascal VOC 2012 + Aug
+    Batch Size: 16
+    Architecture:
+    - HRNetV2p-W48
+    - OCRNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 8.1
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_20k_voc12aug/ocrnet_hr48_512x512_20k_voc12aug_20200617_233932-9e82080a.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_20k_voc12aug/ocrnet_hr48_512x512_20k_voc12aug_20200617_233932.log.json
+  Paper:
+    Title: Object-Contextual Representations for Semantic Segmentation
+    URL: https://arxiv.org/abs/1909.11065
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ocr_head.py#L86
+  Framework: PyTorch
+- Name: ocrnet_hr18s_4xb4-40k_voc12aug-512x512
+  In Collection: OCRNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 72.76
+      mIoU(ms+flip): 74.6
+  Config: configs/ocrnet/ocrnet_hr18s_4xb4-40k_voc12aug-512x512.py
+  Metadata:
+    Training Data: Pascal VOC 2012 + Aug
+    Batch Size: 16
+    Architecture:
+    - HRNetV2p-W18-Small
+    - OCRNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_40k_voc12aug/ocrnet_hr18s_512x512_40k_voc12aug_20200614_002025-42b587ac.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_40k_voc12aug/ocrnet_hr18s_512x512_40k_voc12aug_20200614_002025.log.json
+  Paper:
+    Title: Object-Contextual Representations for Semantic Segmentation
+    URL: https://arxiv.org/abs/1909.11065
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ocr_head.py#L86
+  Framework: PyTorch
+- Name: ocrnet_hr18_4xb4-40k_voc12aug-512x512
+  In Collection: OCRNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 74.98
+      mIoU(ms+flip): 77.4
+  Config: configs/ocrnet/ocrnet_hr18_4xb4-40k_voc12aug-512x512.py
+  Metadata:
+    Training Data: Pascal VOC 2012 + Aug
+    Batch Size: 16
+    Architecture:
+    - HRNetV2p-W18
+    - OCRNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_40k_voc12aug/ocrnet_hr18_512x512_40k_voc12aug_20200614_015958-714302be.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_40k_voc12aug/ocrnet_hr18_512x512_40k_voc12aug_20200614_015958.log.json
+  Paper:
+    Title: Object-Contextual Representations for Semantic Segmentation
+    URL: https://arxiv.org/abs/1909.11065
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ocr_head.py#L86
+  Framework: PyTorch
+- Name: ocrnet_hr48_4xb4-40k_voc12aug-512x512
+  In Collection: OCRNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 77.14
+      mIoU(ms+flip): 79.71
+  Config: configs/ocrnet/ocrnet_hr48_4xb4-40k_voc12aug-512x512.py
+  Metadata:
+    Training Data: Pascal VOC 2012 + Aug
+    Batch Size: 16
+    Architecture:
+    - HRNetV2p-W48
+    - OCRNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_40k_voc12aug/ocrnet_hr48_512x512_40k_voc12aug_20200614_015958-255bc5ce.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_40k_voc12aug/ocrnet_hr48_512x512_40k_voc12aug_20200614_015958.log.json
+  Paper:
+    Title: Object-Contextual Representations for Semantic Segmentation
+    URL: https://arxiv.org/abs/1909.11065
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ocr_head.py#L86
+  Framework: PyTorch
diff --git a/configs/ocrnet/ocrnet.yml b/configs/ocrnet/ocrnet.yml
deleted file mode 100644
index 20002e8864..0000000000
--- a/configs/ocrnet/ocrnet.yml
+++ /dev/null
@@ -1,438 +0,0 @@
-Collections:
-- Name: OCRNet
-  Metadata:
-    Training Data:
-    - Cityscapes
-    - ADE20K
-    - Pascal VOC 2012 + Aug
-  Paper:
-    URL: https://arxiv.org/abs/1909.11065
-    Title: Object-Contextual Representations for Semantic Segmentation
-  README: configs/ocrnet/README.md
-  Code:
-    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ocr_head.py#L86
-    Version: v0.17.0
-  Converted From:
-    Code: https://github.com/openseg-group/OCNet.pytorch
-Models:
-- Name: ocrnet_hr18s_4xb2-40k_cityscapes-512x1024
-  In Collection: OCRNet
-  Metadata:
-    backbone: HRNetV2p-W18-Small
-    crop size: (512,1024)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 95.69
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 3.5
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 76.61
-      mIoU(ms+flip): 78.01
-  Config: configs/ocrnet/ocrnet_hr18s_4xb2-40k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_4xb2-40k_cityscapes-512x1024/ocrnet_hr18s_4xb2-40k_cityscapes-512x1024_20230227_145026-6c052a14.pth
-- Name: ocrnet_hr18_4xb2-40k_cityscapes-512x1024
-  In Collection: OCRNet
-  Metadata:
-    backbone: HRNetV2p-W18
-    crop size: (512,1024)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 133.33
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 4.7
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 77.72
-      mIoU(ms+flip): 79.49
-  Config: configs/ocrnet/ocrnet_hr18_4xb2-40k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_40k_cityscapes/ocrnet_hr18_512x1024_40k_cityscapes_20200601_033320-401c5bdd.pth
-- Name: ocrnet_hr48_4xb2-40k_cityscapes-512x1024
-  In Collection: OCRNet
-  Metadata:
-    backbone: HRNetV2p-W48
-    crop size: (512,1024)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 236.97
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 8.0
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 80.58
-      mIoU(ms+flip): 81.79
-  Config: configs/ocrnet/ocrnet_hr48_4xb2-40k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_40k_cityscapes/ocrnet_hr48_512x1024_40k_cityscapes_20200601_033336-55b32491.pth
-- Name: ocrnet_hr18s_4xb2-80k_cityscapes-512x1024
-  In Collection: OCRNet
-  Metadata:
-    backbone: HRNetV2p-W18-Small
-    crop size: (512,1024)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 77.16
-      mIoU(ms+flip): 78.66
-  Config: configs/ocrnet/ocrnet_hr18s_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x1024_80k_cityscapes/ocrnet_hr18s_512x1024_80k_cityscapes_20200601_222735-55979e63.pth
-- Name: ocrnet_hr18_4xb2-80k_cityscapes-512x1024
-  In Collection: OCRNet
-  Metadata:
-    backbone: HRNetV2p-W18
-    crop size: (512,1024)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 78.57
-      mIoU(ms+flip): 80.46
-  Config: configs/ocrnet/ocrnet_hr18_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_80k_cityscapes/ocrnet_hr18_512x1024_80k_cityscapes_20200614_230521-c2e1dd4a.pth
-- Name: ocrnet_hr48_4xb2-80k_cityscapes-512x1024
-  In Collection: OCRNet
-  Metadata:
-    backbone: HRNetV2p-W48
-    crop size: (512,1024)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 80.7
-      mIoU(ms+flip): 81.87
-  Config: configs/ocrnet/ocrnet_hr48_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_80k_cityscapes/ocrnet_hr48_512x1024_80k_cityscapes_20200601_222752-9076bcdf.pth
-- Name: ocrnet_hr18s_4xb2-160k_cityscapes-512x1024
-  In Collection: OCRNet
-  Metadata:
-    backbone: HRNetV2p-W18-Small
-    crop size: (512,1024)
-    lr schd: 160000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 78.45
-      mIoU(ms+flip): 79.97
-  Config: configs/ocrnet/ocrnet_hr18s_4xb2-160k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x1024_160k_cityscapes/ocrnet_hr18s_512x1024_160k_cityscapes_20200602_191005-f4a7af28.pth
-- Name: ocrnet_hr18_4xb2-160k_cityscapes-512x1024
-  In Collection: OCRNet
-  Metadata:
-    backbone: HRNetV2p-W18
-    crop size: (512,1024)
-    lr schd: 160000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 79.47
-      mIoU(ms+flip): 80.91
-  Config: configs/ocrnet/ocrnet_hr18_4xb2-160k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_160k_cityscapes/ocrnet_hr18_512x1024_160k_cityscapes_20200602_191001-b9172d0c.pth
-- Name: ocrnet_hr48_4xb2-160k_cityscapes-512x1024
-  In Collection: OCRNet
-  Metadata:
-    backbone: HRNetV2p-W48
-    crop size: (512,1024)
-    lr schd: 160000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 81.35
-      mIoU(ms+flip): 82.7
-  Config: configs/ocrnet/ocrnet_hr48_4xb2-160k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_160k_cityscapes/ocrnet_hr48_512x1024_160k_cityscapes_20200602_191037-dfbf1b0c.pth
-- Name: ocrnet_r101-d8_4xb2-40k_cityscapes-512x1024
-  In Collection: OCRNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,1024)
-    lr schd: 40000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 80.09
-  Config: configs/ocrnet/ocrnet_r101-d8_4xb2-40k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_r101-d8_512x1024_40k_b8_cityscapes/ocrnet_r101-d8_512x1024_40k_b8_cityscapes_20200717_110721-02ac0f13.pth
-- Name: ocrnet_r101-d8_8xb2-40k_cityscapes-512x1024
-  In Collection: OCRNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,1024)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 331.13
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 8.8
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 80.3
-  Config: configs/ocrnet/ocrnet_r101-d8_8xb2-40k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_r101-d8_512x1024_40k_b16_cityscapes/ocrnet_r101-d8_512x1024_40k_b16_cityscapes_20200723_193726-db500f80.pth
-- Name: ocrnet_r101-d8_8xb2-80k_cityscapes-512x1024
-  In Collection: OCRNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,1024)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 331.13
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 8.8
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 80.81
-  Config: configs/ocrnet/ocrnet_r101-d8_8xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_r101-d8_512x1024_80k_b16_cityscapes/ocrnet_r101-d8_512x1024_80k_b16_cityscapes_20200723_192421-78688424.pth
-- Name: ocrnet_hr18s_4xb4-80k_ade20k-512x512
-  In Collection: OCRNet
-  Metadata:
-    backbone: HRNetV2p-W18-Small
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 34.51
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 6.7
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 35.06
-      mIoU(ms+flip): 35.8
-  Config: configs/ocrnet/ocrnet_hr18s_4xb4-80k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_80k_ade20k/ocrnet_hr18s_512x512_80k_ade20k_20200615_055600-e80b62af.pth
-- Name: ocrnet_hr18_4xb4-80k_ade20k-512x512
-  In Collection: OCRNet
-  Metadata:
-    backbone: HRNetV2p-W18
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 52.83
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 7.9
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 37.79
-      mIoU(ms+flip): 39.16
-  Config: configs/ocrnet/ocrnet_hr18_4xb4-80k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_80k_ade20k/ocrnet_hr18_512x512_80k_ade20k_20200615_053157-d173d83b.pth
-- Name: ocrnet_hr48_4xb4-80k_ade20k-512x512
-  In Collection: OCRNet
-  Metadata:
-    backbone: HRNetV2p-W48
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 58.86
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 11.2
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 43.0
-      mIoU(ms+flip): 44.3
-  Config: configs/ocrnet/ocrnet_hr48_4xb4-80k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_80k_ade20k/ocrnet_hr48_512x512_80k_ade20k_20200615_021518-d168c2d1.pth
-- Name: ocrnet_hr18s_4xb4-80k_ade20k-512x512
-  In Collection: OCRNet
-  Metadata:
-    backbone: HRNetV2p-W18-Small
-    crop size: (512,512)
-    lr schd: 160000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 37.19
-      mIoU(ms+flip): 38.4
-  Config: configs/ocrnet/ocrnet_hr18s_4xb4-80k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_160k_ade20k/ocrnet_hr18s_512x512_160k_ade20k_20200615_184505-8e913058.pth
-- Name: ocrnet_hr18_4xb4-80k_ade20k-512x512
-  In Collection: OCRNet
-  Metadata:
-    backbone: HRNetV2p-W18
-    crop size: (512,512)
-    lr schd: 160000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 39.32
-      mIoU(ms+flip): 40.8
-  Config: configs/ocrnet/ocrnet_hr18_4xb4-80k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_160k_ade20k/ocrnet_hr18_512x512_160k_ade20k_20200615_200940-d8fcd9d1.pth
-- Name: ocrnet_hr48_4xb4-160k_ade20k-512x512
-  In Collection: OCRNet
-  Metadata:
-    backbone: HRNetV2p-W48
-    crop size: (512,512)
-    lr schd: 160000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 43.25
-      mIoU(ms+flip): 44.88
-  Config: configs/ocrnet/ocrnet_hr48_4xb4-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_160k_ade20k/ocrnet_hr48_512x512_160k_ade20k_20200615_184705-a073726d.pth
-- Name: ocrnet_hr18s_4xb4-20k_voc12aug-512x512
-  In Collection: OCRNet
-  Metadata:
-    backbone: HRNetV2p-W18-Small
-    crop size: (512,512)
-    lr schd: 20000
-    inference time (ms/im):
-    - value: 31.7
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 3.5
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal VOC 2012 + Aug
-    Metrics:
-      mIoU: 71.7
-      mIoU(ms+flip): 73.84
-  Config: configs/ocrnet/ocrnet_hr18s_4xb4-20k_voc12aug-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_20k_voc12aug/ocrnet_hr18s_512x512_20k_voc12aug_20200617_233913-02b04fcb.pth
-- Name: ocrnet_hr18_4xb4-20k_voc12aug-512x512
-  In Collection: OCRNet
-  Metadata:
-    backbone: HRNetV2p-W18
-    crop size: (512,512)
-    lr schd: 20000
-    inference time (ms/im):
-    - value: 50.23
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 4.7
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal VOC 2012 + Aug
-    Metrics:
-      mIoU: 74.75
-      mIoU(ms+flip): 77.11
-  Config: configs/ocrnet/ocrnet_hr18_4xb4-20k_voc12aug-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_20k_voc12aug/ocrnet_hr18_512x512_20k_voc12aug_20200617_233932-8954cbb7.pth
-- Name: ocrnet_hr48_4xb4-20k_voc12aug-512x512
-  In Collection: OCRNet
-  Metadata:
-    backbone: HRNetV2p-W48
-    crop size: (512,512)
-    lr schd: 20000
-    inference time (ms/im):
-    - value: 56.09
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 8.1
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal VOC 2012 + Aug
-    Metrics:
-      mIoU: 77.72
-      mIoU(ms+flip): 79.87
-  Config: configs/ocrnet/ocrnet_hr48_4xb4-20k_voc12aug-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_20k_voc12aug/ocrnet_hr48_512x512_20k_voc12aug_20200617_233932-9e82080a.pth
-- Name: ocrnet_hr18s_4xb4-40k_voc12aug-512x512
-  In Collection: OCRNet
-  Metadata:
-    backbone: HRNetV2p-W18-Small
-    crop size: (512,512)
-    lr schd: 40000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal VOC 2012 + Aug
-    Metrics:
-      mIoU: 72.76
-      mIoU(ms+flip): 74.6
-  Config: configs/ocrnet/ocrnet_hr18s_4xb4-40k_voc12aug-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_40k_voc12aug/ocrnet_hr18s_512x512_40k_voc12aug_20200614_002025-42b587ac.pth
-- Name: ocrnet_hr18_4xb4-40k_voc12aug-512x512
-  In Collection: OCRNet
-  Metadata:
-    backbone: HRNetV2p-W18
-    crop size: (512,512)
-    lr schd: 40000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal VOC 2012 + Aug
-    Metrics:
-      mIoU: 74.98
-      mIoU(ms+flip): 77.4
-  Config: configs/ocrnet/ocrnet_hr18_4xb4-40k_voc12aug-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_40k_voc12aug/ocrnet_hr18_512x512_40k_voc12aug_20200614_015958-714302be.pth
-- Name: ocrnet_hr48_4xb4-40k_voc12aug-512x512
-  In Collection: OCRNet
-  Metadata:
-    backbone: HRNetV2p-W48
-    crop size: (512,512)
-    lr schd: 40000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal VOC 2012 + Aug
-    Metrics:
-      mIoU: 77.14
-      mIoU(ms+flip): 79.71
-  Config: configs/ocrnet/ocrnet_hr48_4xb4-40k_voc12aug-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_40k_voc12aug/ocrnet_hr48_512x512_40k_voc12aug_20200614_015958-255bc5ce.pth
diff --git a/configs/pidnet/README.md b/configs/pidnet/README.md
index 545b76e8b0..c8c3f24799 100644
--- a/configs/pidnet/README.md
+++ b/configs/pidnet/README.md
@@ -26,11 +26,11 @@ Two-branch network architecture has shown its efficiency and effectiveness for r
 
 ### Cityscapes
 
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU  | mIoU(ms+flip) | config                                                                                                                        | download                                                                                                                                                                                                                                                                                                                                                 |
-| ------ | -------- | --------- | ------- | -------- | -------------- | ----- | ------------- | ----------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| PIDNet | PIDNet-S | 1024x1024 | 120000  | 3.38     | 80.82          | 78.74 | 80.87         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pidnet/pidnet-s_2xb6-120k_1024x1024-cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pidnet/pidnet-s_2xb6-120k_1024x1024-cityscapes/pidnet-s_2xb6-120k_1024x1024-cityscapes_20230302_191700-bb8e3bcc.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pidnet/pidnet-s_2xb6-120k_1024x1024-cityscapes/pidnet-s_2xb6-120k_1024x1024-cityscapes_20230302_191700.json) |
-| PIDNet | PIDNet-M | 1024x1024 | 120000  | 5.14     | 71.98          | 80.22 | 82.05         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pidnet/pidnet-m_2xb6-120k_1024x1024-cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pidnet/pidnet-m_2xb6-120k_1024x1024-cityscapes/pidnet-m_2xb6-120k_1024x1024-cityscapes_20230301_143452-f9bcdbf3.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pidnet/pidnet-m_2xb6-120k_1024x1024-cityscapes/pidnet-m_2xb6-120k_1024x1024-cityscapes_20230301_143452.json) |
-| PIDNet | PIDNet-L | 1024x1024 | 120000  | 5.83     | 60.06          | 80.89 | 82.37         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pidnet/pidnet-l_2xb6-120k_1024x1024-cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pidnet/pidnet-l_2xb6-120k_1024x1024-cityscapes/pidnet-l_2xb6-120k_1024x1024-cityscapes_20230303_114514-0783ca6b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pidnet/pidnet-l_2xb6-120k_1024x1024-cityscapes/pidnet-l_2xb6-120k_1024x1024-cityscapes_20230303_114514.json) |
+| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device | mIoU  | mIoU(ms+flip) | config                                                                                                                        | download                                                                                                                                                                                                                                                                                                                                                 |
+| ------ | -------- | --------- | ------- | -------- | -------------- | ------ | ----- | ------------- | ----------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| PIDNet | PIDNet-S | 1024x1024 | 120000  | 3.38     | 80.82          | A100   | 78.74 | 80.87         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pidnet/pidnet-s_2xb6-120k_1024x1024-cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pidnet/pidnet-s_2xb6-120k_1024x1024-cityscapes/pidnet-s_2xb6-120k_1024x1024-cityscapes_20230302_191700-bb8e3bcc.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pidnet/pidnet-s_2xb6-120k_1024x1024-cityscapes/pidnet-s_2xb6-120k_1024x1024-cityscapes_20230302_191700.json) |
+| PIDNet | PIDNet-M | 1024x1024 | 120000  | 5.14     | 71.98          | A100   | 80.22 | 82.05         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pidnet/pidnet-m_2xb6-120k_1024x1024-cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pidnet/pidnet-m_2xb6-120k_1024x1024-cityscapes/pidnet-m_2xb6-120k_1024x1024-cityscapes_20230301_143452-f9bcdbf3.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pidnet/pidnet-m_2xb6-120k_1024x1024-cityscapes/pidnet-m_2xb6-120k_1024x1024-cityscapes_20230301_143452.json) |
+| PIDNet | PIDNet-L | 1024x1024 | 120000  | 5.83     | 60.06          | A100   | 80.89 | 82.37         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pidnet/pidnet-l_2xb6-120k_1024x1024-cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pidnet/pidnet-l_2xb6-120k_1024x1024-cityscapes/pidnet-l_2xb6-120k_1024x1024-cityscapes_20230303_114514-0783ca6b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pidnet/pidnet-l_2xb6-120k_1024x1024-cityscapes/pidnet-l_2xb6-120k_1024x1024-cityscapes_20230303_114514.json) |
 
 ## Notes
 
diff --git a/configs/pidnet/metafile.yaml b/configs/pidnet/metafile.yaml
new file mode 100644
index 0000000000..9102ec51c5
--- /dev/null
+++ b/configs/pidnet/metafile.yaml
@@ -0,0 +1,85 @@
+Collections:
+- Name: PIDNet
+  License: Apache License 2.0
+  Metadata:
+    Training Data:
+    - Cityscapes
+  Paper:
+    Title: 'PIDNet: A Real-time Semantic Segmentation Network Inspired from PID Controller'
+    URL: https://arxiv.org/pdf/2206.02066.pdf
+  README: configs/pidnet/README.md
+  Frameworks:
+  - PyTorch
+Models:
+- Name: pidnet-s_2xb6-120k_1024x1024-cityscapes
+  In Collection: PIDNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.74
+      mIoU(ms+flip): 80.87
+  Config: configs/pidnet/pidnet-s_2xb6-120k_1024x1024-cityscapes.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 12
+    Architecture:
+    - PIDNet-S
+    - PIDNet
+    Training Resources: 2x A100 GPUS
+    Memory (GB): 3.38
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pidnet/pidnet-s_2xb6-120k_1024x1024-cityscapes/pidnet-s_2xb6-120k_1024x1024-cityscapes_20230302_191700-bb8e3bcc.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/pidnet/pidnet-s_2xb6-120k_1024x1024-cityscapes/pidnet-s_2xb6-120k_1024x1024-cityscapes_20230302_191700.json
+  Paper:
+    Title: 'PIDNet: A Real-time Semantic Segmentation Network Inspired from PID Controller'
+    URL: https://arxiv.org/pdf/2206.02066.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/mmseg/models/backbones/pidnet.py
+  Framework: PyTorch
+- Name: pidnet-m_2xb6-120k_1024x1024-cityscapes
+  In Collection: PIDNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 80.22
+      mIoU(ms+flip): 82.05
+  Config: configs/pidnet/pidnet-m_2xb6-120k_1024x1024-cityscapes.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 12
+    Architecture:
+    - PIDNet-M
+    - PIDNet
+    Training Resources: 2x A100 GPUS
+    Memory (GB): 5.14
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pidnet/pidnet-m_2xb6-120k_1024x1024-cityscapes/pidnet-m_2xb6-120k_1024x1024-cityscapes_20230301_143452-f9bcdbf3.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/pidnet/pidnet-m_2xb6-120k_1024x1024-cityscapes/pidnet-m_2xb6-120k_1024x1024-cityscapes_20230301_143452.json
+  Paper:
+    Title: 'PIDNet: A Real-time Semantic Segmentation Network Inspired from PID Controller'
+    URL: https://arxiv.org/pdf/2206.02066.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/mmseg/models/backbones/pidnet.py
+  Framework: PyTorch
+- Name: pidnet-l_2xb6-120k_1024x1024-cityscapes
+  In Collection: PIDNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 80.89
+      mIoU(ms+flip): 82.37
+  Config: configs/pidnet/pidnet-l_2xb6-120k_1024x1024-cityscapes.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 12
+    Architecture:
+    - PIDNet-L
+    - PIDNet
+    Training Resources: 2x A100 GPUS
+    Memory (GB): 5.83
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pidnet/pidnet-l_2xb6-120k_1024x1024-cityscapes/pidnet-l_2xb6-120k_1024x1024-cityscapes_20230303_114514-0783ca6b.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/pidnet/pidnet-l_2xb6-120k_1024x1024-cityscapes/pidnet-l_2xb6-120k_1024x1024-cityscapes_20230303_114514.json
+  Paper:
+    Title: 'PIDNet: A Real-time Semantic Segmentation Network Inspired from PID Controller'
+    URL: https://arxiv.org/pdf/2206.02066.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/mmseg/models/backbones/pidnet.py
+  Framework: PyTorch
diff --git a/configs/pidnet/pidnet.yml b/configs/pidnet/pidnet.yml
deleted file mode 100644
index 7fe818ca7c..0000000000
--- a/configs/pidnet/pidnet.yml
+++ /dev/null
@@ -1,81 +0,0 @@
-Collections:
-- Name: PIDNet
-  Metadata:
-    Training Data:
-    - Cityscapes
-  Paper:
-    URL: https://arxiv.org/pdf/2206.02066.pdf
-    Title: 'PIDNet: A Real-time Semantic Segmentation Network Inspired from PID Controller'
-  README: configs/pidnet/README.md
-  Code:
-    URL: https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/mmseg/models/backbones/pidnet.py
-    Version: dev-1.x
-  Converted From:
-    Code: https://github.com/XuJiacong/PIDNet
-Models:
-- Name: pidnet-s_2xb6-120k_1024x1024-cityscapes
-  In Collection: PIDNet
-  Metadata:
-    backbone: PIDNet-S
-    crop size: (1024,1024)
-    lr schd: 120000
-    inference time (ms/im):
-    - value: 12.37
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (1024,1024)
-    Training Memory (GB): 3.38
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 78.74
-      mIoU(ms+flip): 80.87
-  Config: configs/pidnet/pidnet-s_2xb6-120k_1024x1024-cityscapes.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pidnet/pidnet-s_2xb6-120k_1024x1024-cityscapes/pidnet-s_2xb6-120k_1024x1024-cityscapes_20230302_191700-bb8e3bcc.pth
-- Name: pidnet-m_2xb6-120k_1024x1024-cityscapes
-  In Collection: PIDNet
-  Metadata:
-    backbone: PIDNet-M
-    crop size: (1024,1024)
-    lr schd: 120000
-    inference time (ms/im):
-    - value: 13.89
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (1024,1024)
-    Training Memory (GB): 5.14
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 80.22
-      mIoU(ms+flip): 82.05
-  Config: configs/pidnet/pidnet-m_2xb6-120k_1024x1024-cityscapes.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pidnet/pidnet-m_2xb6-120k_1024x1024-cityscapes/pidnet-m_2xb6-120k_1024x1024-cityscapes_20230301_143452-f9bcdbf3.pth
-- Name: pidnet-l_2xb6-120k_1024x1024-cityscapes
-  In Collection: PIDNet
-  Metadata:
-    backbone: PIDNet-L
-    crop size: (1024,1024)
-    lr schd: 120000
-    inference time (ms/im):
-    - value: 16.65
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (1024,1024)
-    Training Memory (GB): 5.83
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 80.89
-      mIoU(ms+flip): 82.37
-  Config: configs/pidnet/pidnet-l_2xb6-120k_1024x1024-cityscapes.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pidnet/pidnet-l_2xb6-120k_1024x1024-cityscapes/pidnet-l_2xb6-120k_1024x1024-cityscapes_20230303_114514-0783ca6b.pth
diff --git a/configs/point_rend/README.md b/configs/point_rend/README.md
index 2690e7b9e6..9aab8e03ee 100644
--- a/configs/point_rend/README.md
+++ b/configs/point_rend/README.md
@@ -1,6 +1,6 @@
 # PointRend
 
-[PointRend: Image Segmentation as Rendering](https://arxiv.org/abs/1912.08193)
+> [PointRend: Image Segmentation as Rendering](https://arxiv.org/abs/1912.08193)
 
 ## Introduction
 
@@ -22,6 +22,22 @@ We present a new method for efficient high-quality image segmentation of objects
 <img src="https://user-images.githubusercontent.com/24582831/142902293-5db49cdd-4b1b-4940-9067-2acd6196c700.png" width="60%"/>
 </div>
 
+## Results and models
+
+### Cityscapes
+
+| Method    | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                                | download                                                                                                                                                                                                                                                                                                                                                         |
+| --------- | -------- | --------- | ------: | -------: | -------------- | ------ | ----: | ------------- | ------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| PointRend | R-50     | 512x1024  |   80000 |      3.1 | 8.48           | V100   | 76.47 | 78.13         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/point_rend/pointrend_r50_4xb2-80k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r50_512x1024_80k_cityscapes/pointrend_r50_512x1024_80k_cityscapes_20200711_015821-bb1ff523.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r50_512x1024_80k_cityscapes/pointrend_r50_512x1024_80k_cityscapes-20200715_214714.log.json)     |
+| PointRend | R-101    | 512x1024  |   80000 |      4.2 | 7.00           | V100   | 78.30 | 79.97         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/point_rend/pointrend_r101_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r101_512x1024_80k_cityscapes/pointrend_r101_512x1024_80k_cityscapes_20200711_170850-d0ca84be.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r101_512x1024_80k_cityscapes/pointrend_r101_512x1024_80k_cityscapes-20200715_214824.log.json) |
+
+### ADE20K
+
+| Method    | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                            | download                                                                                                                                                                                                                                                                                                                                         |
+| --------- | -------- | --------- | ------: | -------: | -------------- | ------ | ----: | ------------- | --------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| PointRend | R-50     | 512x512   |  160000 |      5.1 | 17.31          | V100   | 37.64 | 39.17         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/point_rend/pointrend_r50_4xb4-160k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r50_512x512_160k_ade20k/pointrend_r50_512x512_160k_ade20k_20200807_232644-ac3febf2.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r50_512x512_160k_ade20k/pointrend_r50_512x512_160k_ade20k-20200807_232644.log.json)     |
+| PointRend | R-101    | 512x512   |  160000 |      6.1 | 15.50          | V100   | 40.02 | 41.60         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/point_rend/pointrend_r101_4xb4-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r101_512x512_160k_ade20k/pointrend_r101_512x512_160k_ade20k_20200808_030852-8834902a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r101_512x512_160k_ade20k/pointrend_r101_512x512_160k_ade20k-20200808_030852.log.json) |
+
 ## Citation
 
 ```bibtex
@@ -33,19 +49,3 @@ We present a new method for efficient high-quality image segmentation of objects
   year={2020}
 }
 ```
-
-## Results and models
-
-### Cityscapes
-
-| Method    | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                                | download                                                                                                                                                                                                                                                                                                                                                         |
-| --------- | -------- | --------- | ------: | -------: | -------------- | ----: | ------------- | ------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| PointRend | R-50     | 512x1024  |   80000 |      3.1 | 8.48           | 76.47 | 78.13         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/point_rend/pointrend_r50_4xb2-80k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r50_512x1024_80k_cityscapes/pointrend_r50_512x1024_80k_cityscapes_20200711_015821-bb1ff523.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r50_512x1024_80k_cityscapes/pointrend_r50_512x1024_80k_cityscapes-20200715_214714.log.json)     |
-| PointRend | R-101    | 512x1024  |   80000 |      4.2 | 7.00           | 78.30 | 79.97         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/point_rend/pointrend_r101_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r101_512x1024_80k_cityscapes/pointrend_r101_512x1024_80k_cityscapes_20200711_170850-d0ca84be.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r101_512x1024_80k_cityscapes/pointrend_r101_512x1024_80k_cityscapes-20200715_214824.log.json) |
-
-### ADE20K
-
-| Method    | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                            | download                                                                                                                                                                                                                                                                                                                                         |
-| --------- | -------- | --------- | ------: | -------: | -------------- | ----: | ------------- | --------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| PointRend | R-50     | 512x512   |  160000 |      5.1 | 17.31          | 37.64 | 39.17         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/point_rend/pointrend_r50_4xb4-160k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r50_512x512_160k_ade20k/pointrend_r50_512x512_160k_ade20k_20200807_232644-ac3febf2.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r50_512x512_160k_ade20k/pointrend_r50_512x512_160k_ade20k-20200807_232644.log.json)     |
-| PointRend | R-101    | 512x512   |  160000 |      6.1 | 15.50          | 40.02 | 41.60         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/point_rend/pointrend_r101_4xb4-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r101_512x512_160k_ade20k/pointrend_r101_512x512_160k_ade20k_20200808_030852-8834902a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r101_512x512_160k_ade20k/pointrend_r101_512x512_160k_ade20k-20200808_030852.log.json) |
diff --git a/configs/point_rend/metafile.yaml b/configs/point_rend/metafile.yaml
new file mode 100644
index 0000000000..064717c9df
--- /dev/null
+++ b/configs/point_rend/metafile.yaml
@@ -0,0 +1,110 @@
+Collections:
+- Name: PointRend
+  License: Apache License 2.0
+  Metadata:
+    Training Data:
+    - Cityscapes
+    - ADE20K
+  Paper:
+    Title: 'PointRend: Image Segmentation as Rendering'
+    URL: https://arxiv.org/abs/1912.08193
+  README: configs/point_rend/README.md
+  Frameworks:
+  - PyTorch
+Models:
+- Name: pointrend_r50_4xb2-80k_cityscapes-512x1024
+  In Collection: PointRend
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 76.47
+      mIoU(ms+flip): 78.13
+  Config: configs/point_rend/pointrend_r50_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50
+    - PointRend
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 3.1
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r50_512x1024_80k_cityscapes/pointrend_r50_512x1024_80k_cityscapes_20200711_015821-bb1ff523.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r50_512x1024_80k_cityscapes/pointrend_r50_512x1024_80k_cityscapes-20200715_214714.log.json
+  Paper:
+    Title: 'PointRend: Image Segmentation as Rendering'
+    URL: https://arxiv.org/abs/1912.08193
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/point_head.py#L36
+  Framework: PyTorch
+- Name: pointrend_r101_4xb2-80k_cityscapes-512x1024
+  In Collection: PointRend
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.3
+      mIoU(ms+flip): 79.97
+  Config: configs/point_rend/pointrend_r101_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101
+    - PointRend
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 4.2
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r101_512x1024_80k_cityscapes/pointrend_r101_512x1024_80k_cityscapes_20200711_170850-d0ca84be.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r101_512x1024_80k_cityscapes/pointrend_r101_512x1024_80k_cityscapes-20200715_214824.log.json
+  Paper:
+    Title: 'PointRend: Image Segmentation as Rendering'
+    URL: https://arxiv.org/abs/1912.08193
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/point_head.py#L36
+  Framework: PyTorch
+- Name: pointrend_r50_4xb4-160k_ade20k-512x512
+  In Collection: PointRend
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 37.64
+      mIoU(ms+flip): 39.17
+  Config: configs/point_rend/pointrend_r50_4xb4-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-50
+    - PointRend
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 5.1
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r50_512x512_160k_ade20k/pointrend_r50_512x512_160k_ade20k_20200807_232644-ac3febf2.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r50_512x512_160k_ade20k/pointrend_r50_512x512_160k_ade20k-20200807_232644.log.json
+  Paper:
+    Title: 'PointRend: Image Segmentation as Rendering'
+    URL: https://arxiv.org/abs/1912.08193
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/point_head.py#L36
+  Framework: PyTorch
+- Name: pointrend_r101_4xb4-160k_ade20k-512x512
+  In Collection: PointRend
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 40.02
+      mIoU(ms+flip): 41.6
+  Config: configs/point_rend/pointrend_r101_4xb4-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-101
+    - PointRend
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 6.1
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r101_512x512_160k_ade20k/pointrend_r101_512x512_160k_ade20k_20200808_030852-8834902a.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r101_512x512_160k_ade20k/pointrend_r101_512x512_160k_ade20k-20200808_030852.log.json
+  Paper:
+    Title: 'PointRend: Image Segmentation as Rendering'
+    URL: https://arxiv.org/abs/1912.08193
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/point_head.py#L36
+  Framework: PyTorch
diff --git a/configs/point_rend/point_rend.yml b/configs/point_rend/point_rend.yml
deleted file mode 100644
index a4539081f3..0000000000
--- a/configs/point_rend/point_rend.yml
+++ /dev/null
@@ -1,104 +0,0 @@
-Collections:
-- Name: PointRend
-  Metadata:
-    Training Data:
-    - Cityscapes
-    - ADE20K
-  Paper:
-    URL: https://arxiv.org/abs/1912.08193
-    Title: 'PointRend: Image Segmentation as Rendering'
-  README: configs/point_rend/README.md
-  Code:
-    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/point_head.py#L36
-    Version: v0.17.0
-  Converted From:
-    Code: https://github.com/facebookresearch/detectron2/tree/master/projects/PointRend
-Models:
-- Name: pointrend_r50_4xb2-80k_cityscapes-512x1024
-  In Collection: PointRend
-  Metadata:
-    backbone: R-50
-    crop size: (512,1024)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 117.92
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 3.1
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 76.47
-      mIoU(ms+flip): 78.13
-  Config: configs/point_rend/pointrend_r50_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r50_512x1024_80k_cityscapes/pointrend_r50_512x1024_80k_cityscapes_20200711_015821-bb1ff523.pth
-- Name: pointrend_r101_4xb2-80k_cityscapes-512x1024
-  In Collection: PointRend
-  Metadata:
-    backbone: R-101
-    crop size: (512,1024)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 142.86
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 4.2
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 78.3
-      mIoU(ms+flip): 79.97
-  Config: configs/point_rend/pointrend_r101_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r101_512x1024_80k_cityscapes/pointrend_r101_512x1024_80k_cityscapes_20200711_170850-d0ca84be.pth
-- Name: pointrend_r50_4xb4-160k_ade20k-512x512
-  In Collection: PointRend
-  Metadata:
-    backbone: R-50
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 57.77
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 5.1
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 37.64
-      mIoU(ms+flip): 39.17
-  Config: configs/point_rend/pointrend_r50_4xb4-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r50_512x512_160k_ade20k/pointrend_r50_512x512_160k_ade20k_20200807_232644-ac3febf2.pth
-- Name: pointrend_r101_4xb4-160k_ade20k-512x512
-  In Collection: PointRend
-  Metadata:
-    backbone: R-101
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 64.52
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 6.1
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 40.02
-      mIoU(ms+flip): 41.6
-  Config: configs/point_rend/pointrend_r101_4xb4-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r101_512x512_160k_ade20k/pointrend_r101_512x512_160k_ade20k_20200808_030852-8834902a.pth
diff --git a/configs/poolformer/README.md b/configs/poolformer/README.md
index 3bdd2ba3f1..ba563a9928 100644
--- a/configs/poolformer/README.md
+++ b/configs/poolformer/README.md
@@ -1,6 +1,6 @@
 # PoolFormer
 
-[MetaFormer is Actually What You Need for Vision](https://arxiv.org/abs/2111.11418)
+> [MetaFormer is Actually What You Need for Vision](https://arxiv.org/abs/2111.11418)
 
 ## Introduction
 
@@ -48,13 +48,13 @@ pip install "mmcls>=1.0.0rc0"
 
 ### ADE20K
 
-| Method | Backbone       | Crop Size | pretrain    | Batch Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU  | mIoU(ms+flip) | mIoU\* | mIoU\*(ms+flip) | config                                                                                                                               | download                                                                                                                                                                                                                                                                                                                                                                     |
-| ------ | -------------- | --------- | ----------- | ---------- | ------- | -------- | -------------- | ----- | ------------: | ------ | --------------: | ------------------------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| FPN    | PoolFormer-S12 | 512x512   | ImageNet-1K | 32         | 40000   | 4.17     | 23.48          | 36.68 |             - | 37.07  |               - | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/poolformer/fpn_poolformer_s12_8xb4-40k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/poolformer/fpn_poolformer_s12_8x4_512x512_40k_ade20k/fpn_poolformer_s12_8x4_512x512_40k_ade20k_20220501_115154-b5aa2f49.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/poolformer/fpn_poolformer_s12_8x4_512x512_40k_ade20k/fpn_poolformer_s12_8x4_512x512_40k_ade20k_20220501_115154.log.json) |
-| FPN    | PoolFormer-S24 | 512x512   | ImageNet-1K | 32         | 40000   | 5.47     | 15.74          | 40.12 |             - | 40.36  |               - | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/poolformer/fpn_poolformer_s24_8xb4-40k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/poolformer/fpn_poolformer_s24_8x4_512x512_40k_ade20k/fpn_poolformer_s24_8x4_512x512_40k_ade20k_20220503_222049-394a7cf7.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/poolformer/fpn_poolformer_s24_8x4_512x512_40k_ade20k/fpn_poolformer_s24_8x4_512x512_40k_ade20k_20220503_222049.log.json) |
-| FPN    | PoolFormer-S36 | 512x512   | ImageNet-1K | 32         | 40000   | 6.77     | 11.34          | 41.61 |             - | 41.81  |               - | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/poolformer/fpn_poolformer_s36_8xb4-40k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/poolformer/fpn_poolformer_s36_8x4_512x512_40k_ade20k/fpn_poolformer_s36_8x4_512x512_40k_ade20k_20220501_151122-b47e607d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/poolformer/fpn_poolformer_s36_8x4_512x512_40k_ade20k/fpn_poolformer_s36_8x4_512x512_40k_ade20k_20220501_151122.log.json) |
-| FPN    | PoolFormer-M36 | 512x512   | ImageNet-1K | 32         | 40000   | 8.59     | 8.97           | 41.95 |             - | 42.35  |               - | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/poolformer/fpn_poolformer_m36_8xb4-40k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/poolformer/fpn_poolformer_m36_8x4_512x512_40k_ade20k/fpn_poolformer_m36_8x4_512x512_40k_ade20k_20220501_164230-3dc83921.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/poolformer/fpn_poolformer_m36_8x4_512x512_40k_ade20k/fpn_poolformer_m36_8x4_512x512_40k_ade20k_20220501_164230.log.json) |
-| FPN    | PoolFormer-M48 | 512x512   | ImageNet-1K | 32         | 40000   | 10.48    | 6.69           | 42.43 |             - | 42.76  |               - | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/poolformer/fpn_poolformer_m48_8xb4-40k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/poolformer/fpn_poolformer_m48_8x4_512x512_40k_ade20k/fpn_poolformer_m48_8x4_512x512_40k_ade20k_20220504_003923-64168d3b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/poolformer/fpn_poolformer_m48_8x4_512x512_40k_ade20k/fpn_poolformer_m48_8x4_512x512_40k_ade20k_20220504_003923.log.json) |
+| Method | Backbone       | Crop Size | pretrain    | Batch Size | Lr schd | Mem (GB) | Inf time (fps) | Device | mIoU  | mIoU(ms+flip) | mIoU\* | mIoU\*(ms+flip) | config                                                                                                                               | download                                                                                                                                                                                                                                                                                                                                                                     |
+| ------ | -------------- | --------- | ----------- | ---------- | ------- | -------- | -------------- | ------ | ----- | ------------: | ------ | --------------: | ------------------------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| FPN    | PoolFormer-S12 | 512x512   | ImageNet-1K | 32         | 40000   | 4.17     | 23.48          | V100   | 36.68 |             - | 37.07  |               - | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/poolformer/fpn_poolformer_s12_8xb4-40k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/poolformer/fpn_poolformer_s12_8x4_512x512_40k_ade20k/fpn_poolformer_s12_8x4_512x512_40k_ade20k_20220501_115154-b5aa2f49.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/poolformer/fpn_poolformer_s12_8x4_512x512_40k_ade20k/fpn_poolformer_s12_8x4_512x512_40k_ade20k_20220501_115154.log.json) |
+| FPN    | PoolFormer-S24 | 512x512   | ImageNet-1K | 32         | 40000   | 5.47     | 15.74          | V100   | 40.12 |             - | 40.36  |               - | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/poolformer/fpn_poolformer_s24_8xb4-40k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/poolformer/fpn_poolformer_s24_8x4_512x512_40k_ade20k/fpn_poolformer_s24_8x4_512x512_40k_ade20k_20220503_222049-394a7cf7.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/poolformer/fpn_poolformer_s24_8x4_512x512_40k_ade20k/fpn_poolformer_s24_8x4_512x512_40k_ade20k_20220503_222049.log.json) |
+| FPN    | PoolFormer-S36 | 512x512   | ImageNet-1K | 32         | 40000   | 6.77     | 11.34          | V100   | 41.61 |             - | 41.81  |               - | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/poolformer/fpn_poolformer_s36_8xb4-40k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/poolformer/fpn_poolformer_s36_8x4_512x512_40k_ade20k/fpn_poolformer_s36_8x4_512x512_40k_ade20k_20220501_151122-b47e607d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/poolformer/fpn_poolformer_s36_8x4_512x512_40k_ade20k/fpn_poolformer_s36_8x4_512x512_40k_ade20k_20220501_151122.log.json) |
+| FPN    | PoolFormer-M36 | 512x512   | ImageNet-1K | 32         | 40000   | 8.59     | 8.97           | V100   | 41.95 |             - | 42.35  |               - | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/poolformer/fpn_poolformer_m36_8xb4-40k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/poolformer/fpn_poolformer_m36_8x4_512x512_40k_ade20k/fpn_poolformer_m36_8x4_512x512_40k_ade20k_20220501_164230-3dc83921.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/poolformer/fpn_poolformer_m36_8x4_512x512_40k_ade20k/fpn_poolformer_m36_8x4_512x512_40k_ade20k_20220501_164230.log.json) |
+| FPN    | PoolFormer-M48 | 512x512   | ImageNet-1K | 32         | 40000   | 10.48    | 6.69           | V100   | 42.43 |             - | 42.76  |               - | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/poolformer/fpn_poolformer_m48_8xb4-40k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/poolformer/fpn_poolformer_m48_8x4_512x512_40k_ade20k/fpn_poolformer_m48_8x4_512x512_40k_ade20k_20220504_003923-64168d3b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/poolformer/fpn_poolformer_m48_8x4_512x512_40k_ade20k/fpn_poolformer_m48_8x4_512x512_40k_ade20k_20220504_003923.log.json) |
 
 Note:
 
diff --git a/configs/poolformer/metafile.yaml b/configs/poolformer/metafile.yaml
new file mode 100644
index 0000000000..12f402be65
--- /dev/null
+++ b/configs/poolformer/metafile.yaml
@@ -0,0 +1,116 @@
+Models:
+- Name: fpn_poolformer_s12_8xb4-40k_ade20k-512x512
+  In Collection: FPN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 36.68
+  Config: configs/poolformer/fpn_poolformer_s12_8xb4-40k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 32
+    Architecture:
+    - PoolFormer-S12
+    - FPN
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 4.17
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/poolformer/fpn_poolformer_s12_8x4_512x512_40k_ade20k/fpn_poolformer_s12_8x4_512x512_40k_ade20k_20220501_115154-b5aa2f49.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/poolformer/fpn_poolformer_s12_8x4_512x512_40k_ade20k/fpn_poolformer_s12_8x4_512x512_40k_ade20k_20220501_115154.log.json
+  Paper:
+    Title: MetaFormer is Actually What You Need for Vision
+    URL: https://arxiv.org/abs/2111.11418
+  Code: https://github.com/open-mmlab/mmclassification/blob/v0.23.0/mmcls/models/backbones/poolformer.py#L198
+  Framework: PyTorch
+- Name: fpn_poolformer_s24_8xb4-40k_ade20k-512x512
+  In Collection: FPN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 40.12
+  Config: configs/poolformer/fpn_poolformer_s24_8xb4-40k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 32
+    Architecture:
+    - PoolFormer-S24
+    - FPN
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 5.47
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/poolformer/fpn_poolformer_s24_8x4_512x512_40k_ade20k/fpn_poolformer_s24_8x4_512x512_40k_ade20k_20220503_222049-394a7cf7.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/poolformer/fpn_poolformer_s24_8x4_512x512_40k_ade20k/fpn_poolformer_s24_8x4_512x512_40k_ade20k_20220503_222049.log.json
+  Paper:
+    Title: MetaFormer is Actually What You Need for Vision
+    URL: https://arxiv.org/abs/2111.11418
+  Code: https://github.com/open-mmlab/mmclassification/blob/v0.23.0/mmcls/models/backbones/poolformer.py#L198
+  Framework: PyTorch
+- Name: fpn_poolformer_s36_8xb4-40k_ade20k-512x512
+  In Collection: FPN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 41.61
+  Config: configs/poolformer/fpn_poolformer_s36_8xb4-40k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 32
+    Architecture:
+    - PoolFormer-S36
+    - FPN
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 6.77
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/poolformer/fpn_poolformer_s36_8x4_512x512_40k_ade20k/fpn_poolformer_s36_8x4_512x512_40k_ade20k_20220501_151122-b47e607d.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/poolformer/fpn_poolformer_s36_8x4_512x512_40k_ade20k/fpn_poolformer_s36_8x4_512x512_40k_ade20k_20220501_151122.log.json
+  Paper:
+    Title: MetaFormer is Actually What You Need for Vision
+    URL: https://arxiv.org/abs/2111.11418
+  Code: https://github.com/open-mmlab/mmclassification/blob/v0.23.0/mmcls/models/backbones/poolformer.py#L198
+  Framework: PyTorch
+- Name: fpn_poolformer_m36_8xb4-40k_ade20k-512x512
+  In Collection: FPN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 41.95
+  Config: configs/poolformer/fpn_poolformer_m36_8xb4-40k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 32
+    Architecture:
+    - PoolFormer-M36
+    - FPN
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 8.59
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/poolformer/fpn_poolformer_m36_8x4_512x512_40k_ade20k/fpn_poolformer_m36_8x4_512x512_40k_ade20k_20220501_164230-3dc83921.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/poolformer/fpn_poolformer_m36_8x4_512x512_40k_ade20k/fpn_poolformer_m36_8x4_512x512_40k_ade20k_20220501_164230.log.json
+  Paper:
+    Title: MetaFormer is Actually What You Need for Vision
+    URL: https://arxiv.org/abs/2111.11418
+  Code: https://github.com/open-mmlab/mmclassification/blob/v0.23.0/mmcls/models/backbones/poolformer.py#L198
+  Framework: PyTorch
+- Name: fpn_poolformer_m48_8xb4-40k_ade20k-512x512
+  In Collection: FPN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 42.43
+  Config: configs/poolformer/fpn_poolformer_m48_8xb4-40k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 32
+    Architecture:
+    - PoolFormer-M48
+    - FPN
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 10.48
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/poolformer/fpn_poolformer_m48_8x4_512x512_40k_ade20k/fpn_poolformer_m48_8x4_512x512_40k_ade20k_20220504_003923-64168d3b.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/poolformer/fpn_poolformer_m48_8x4_512x512_40k_ade20k/fpn_poolformer_m48_8x4_512x512_40k_ade20k_20220504_003923.log.json
+  Paper:
+    Title: MetaFormer is Actually What You Need for Vision
+    URL: https://arxiv.org/abs/2111.11418
+  Code: https://github.com/open-mmlab/mmclassification/blob/v0.23.0/mmcls/models/backbones/poolformer.py#L198
+  Framework: PyTorch
diff --git a/configs/poolformer/poolformer.yml b/configs/poolformer/poolformer.yml
deleted file mode 100644
index fa5fc30125..0000000000
--- a/configs/poolformer/poolformer.yml
+++ /dev/null
@@ -1,106 +0,0 @@
-Models:
-- Name: fpn_poolformer_s12_8xb4-40k_ade20k-512x512
-  In Collection: FPN
-  Metadata:
-    backbone: PoolFormer-S12
-    crop size: (512,512)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 42.59
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 4.17
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 36.68
-  Config: configs/poolformer/fpn_poolformer_s12_8xb4-40k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/poolformer/fpn_poolformer_s12_8x4_512x512_40k_ade20k/fpn_poolformer_s12_8x4_512x512_40k_ade20k_20220501_115154-b5aa2f49.pth
-- Name: fpn_poolformer_s24_8xb4-40k_ade20k-512x512
-  In Collection: FPN
-  Metadata:
-    backbone: PoolFormer-S24
-    crop size: (512,512)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 63.53
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 5.47
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 40.12
-  Config: configs/poolformer/fpn_poolformer_s24_8xb4-40k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/poolformer/fpn_poolformer_s24_8x4_512x512_40k_ade20k/fpn_poolformer_s24_8x4_512x512_40k_ade20k_20220503_222049-394a7cf7.pth
-- Name: ''
-  In Collection: FPN
-  Metadata:
-    backbone: PoolFormer-S36
-    crop size: (512,512)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 88.18
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 6.77
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 41.61
-  Config: ''
-  Weights: ''
-- Name: fpn_poolformer_m36_8xb4-40k_ade20k-512x512
-  In Collection: FPN
-  Metadata:
-    backbone: PoolFormer-M36
-    crop size: (512,512)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 111.48
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 8.59
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 41.95
-  Config: configs/poolformer/fpn_poolformer_m36_8xb4-40k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/poolformer/fpn_poolformer_m36_8x4_512x512_40k_ade20k/fpn_poolformer_m36_8x4_512x512_40k_ade20k_20220501_164230-3dc83921.pth
-- Name: fpn_poolformer_m48_8xb4-40k_ade20k-512x512
-  In Collection: FPN
-  Metadata:
-    backbone: PoolFormer-M48
-    crop size: (512,512)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 149.48
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 10.48
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 42.43
-  Config: configs/poolformer/fpn_poolformer_m48_8xb4-40k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/poolformer/fpn_poolformer_m48_8x4_512x512_40k_ade20k/fpn_poolformer_m48_8x4_512x512_40k_ade20k_20220504_003923-64168d3b.pth
diff --git a/configs/psanet/README.md b/configs/psanet/README.md
index 7182e500a1..d657326120 100644
--- a/configs/psanet/README.md
+++ b/configs/psanet/README.md
@@ -1,6 +1,6 @@
 # PSANet
 
-[PSANet: Point-wise Spatial Attention Network for Scene Parsing](https://openaccess.thecvf.com/content_ECCV_2018/papers/Hengshuang_Zhao_PSANet_Point-wise_Spatial_ECCV_2018_paper.pdf)
+> [PSANet: Point-wise Spatial Attention Network for Scene Parsing](https://openaccess.thecvf.com/content_ECCV_2018/papers/Hengshuang_Zhao_PSANet_Point-wise_Spatial_ECCV_2018_paper.pdf)
 
 ## Introduction
 
@@ -22,6 +22,39 @@ We notice information flow in convolutional neural networksis  restricted  insid
 <img src="https://user-images.githubusercontent.com/24582831/142902367-0f29e8cb-5ac0-434b-98c4-b2af7c9c2e58.png" width="70%"/>
 </div>
 
+## Results and models
+
+### Cityscapes
+
+| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                            | download                                                                                                                                                                                                                                                                                                                                                 |
+| ------ | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | --------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| PSANet | R-50-D8  | 512x1024  |   40000 | 7        | 3.17           | V100   | 77.63 |         79.04 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/psanet/psanet_r50-d8_4xb2-40k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x1024_40k_cityscapes/psanet_r50-d8_512x1024_40k_cityscapes_20200606_103117-99fac37c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x1024_40k_cityscapes/psanet_r50-d8_512x1024_40k_cityscapes_20200606_103117.log.json)     |
+| PSANet | R-101-D8 | 512x1024  |   40000 | 10.5     | 2.20           | V100   | 79.14 |         80.19 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/psanet/psanet_r101-d8_4xb2-40k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x1024_40k_cityscapes/psanet_r101-d8_512x1024_40k_cityscapes_20200606_001418-27b9cfa7.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x1024_40k_cityscapes/psanet_r101-d8_512x1024_40k_cityscapes_20200606_001418.log.json) |
+| PSANet | R-50-D8  | 769x769   |   40000 | 7.9      | 1.40           | V100   | 77.99 |         79.64 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/psanet/psanet_r50-d8_4xb2-40k_cityscapes-769x769.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_769x769_40k_cityscapes/psanet_r50-d8_769x769_40k_cityscapes_20200530_033717-d5365506.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_769x769_40k_cityscapes/psanet_r50-d8_769x769_40k_cityscapes_20200530_033717.log.json)         |
+| PSANet | R-101-D8 | 769x769   |   40000 | 11.9     | 0.98           | V100   | 78.43 |         80.26 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/psanet/psanet_r101-d8_4xb2-40k_cityscapes-769x769.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_769x769_40k_cityscapes/psanet_r101-d8_769x769_40k_cityscapes_20200530_035107-997da1e6.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_769x769_40k_cityscapes/psanet_r101-d8_769x769_40k_cityscapes_20200530_035107.log.json)     |
+| PSANet | R-50-D8  | 512x1024  |   80000 | -        | -              | V100   | 77.24 |         78.69 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/psanet/psanet_r50-d8_4xb2-80k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x1024_80k_cityscapes/psanet_r50-d8_512x1024_80k_cityscapes_20200606_161842-ab60a24f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x1024_80k_cityscapes/psanet_r50-d8_512x1024_80k_cityscapes_20200606_161842.log.json)     |
+| PSANet | R-101-D8 | 512x1024  |   80000 | -        | -              | V100   | 79.31 |         80.53 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/psanet/psanet_r101-d8_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x1024_80k_cityscapes/psanet_r101-d8_512x1024_80k_cityscapes_20200606_161823-0f73a169.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x1024_80k_cityscapes/psanet_r101-d8_512x1024_80k_cityscapes_20200606_161823.log.json) |
+| PSANet | R-50-D8  | 769x769   |   80000 | -        | -              | V100   | 79.31 |         80.91 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/psanet/psanet_r50-d8_4xb2-80k_cityscapes-769x769.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_769x769_80k_cityscapes/psanet_r50-d8_769x769_80k_cityscapes_20200606_225134-fe42f49e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_769x769_80k_cityscapes/psanet_r50-d8_769x769_80k_cityscapes_20200606_225134.log.json)         |
+| PSANet | R-101-D8 | 769x769   |   80000 | -        | -              | V100   | 79.69 |         80.89 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/psanet/psanet_r101-d8_4xb2-80k_cityscapes-769x769.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_769x769_80k_cityscapes/psanet_r101-d8_769x769_80k_cityscapes_20200606_214550-7665827b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_769x769_80k_cityscapes/psanet_r101-d8_769x769_80k_cityscapes_20200606_214550.log.json)     |
+
+### ADE20K
+
+| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                        | download                                                                                                                                                                                                                                                                                                                                 |
+| ------ | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| PSANet | R-50-D8  | 512x512   |   80000 | 9        | 18.91          | V100   | 41.14 |         41.91 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/psanet/psanet_r50-d8_4xb4-80k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_80k_ade20k/psanet_r50-d8_512x512_80k_ade20k_20200614_144141-835e4b97.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_80k_ade20k/psanet_r50-d8_512x512_80k_ade20k_20200614_144141.log.json)         |
+| PSANet | R-101-D8 | 512x512   |   80000 | 12.5     | 13.13          | V100   | 43.80 |         44.75 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/psanet/psanet_r101-d8_4xb4-80k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_80k_ade20k/psanet_r101-d8_512x512_80k_ade20k_20200614_185117-1fab60d4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_80k_ade20k/psanet_r101-d8_512x512_80k_ade20k_20200614_185117.log.json)     |
+| PSANet | R-50-D8  | 512x512   |  160000 | -        | -              | V100   | 41.67 |         42.95 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/psanet/psanet_r50-d8_4xb4-160k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_160k_ade20k/psanet_r50-d8_512x512_160k_ade20k_20200615_161258-148077dd.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_160k_ade20k/psanet_r50-d8_512x512_160k_ade20k_20200615_161258.log.json)     |
+| PSANet | R-101-D8 | 512x512   |  160000 | -        | -              | V100   | 43.74 |         45.38 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/psanet/psanet_r101-d8_4xb4-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_160k_ade20k/psanet_r101-d8_512x512_160k_ade20k_20200615_161537-dbfa564c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_160k_ade20k/psanet_r101-d8_512x512_160k_ade20k_20200615_161537.log.json) |
+
+### Pascal VOC 2012 + Aug
+
+| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                         | download                                                                                                                                                                                                                                                                                                                                     |
+| ------ | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| PSANet | R-50-D8  | 512x512   |   20000 | 6.9      | 18.24          | V100   | 76.39 |         77.34 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/psanet/psanet_r50-d8_4xb4-20k_voc12aug-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_20k_voc12aug/psanet_r50-d8_512x512_20k_voc12aug_20200617_102413-2f1bbaa1.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_20k_voc12aug/psanet_r50-d8_512x512_20k_voc12aug_20200617_102413.log.json)     |
+| PSANet | R-101-D8 | 512x512   |   20000 | 10.4     | 12.63          | V100   | 77.91 |         79.30 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/psanet/psanet_r101-d8_4xb4-20k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_20k_voc12aug/psanet_r101-d8_512x512_20k_voc12aug_20200617_110624-946fef11.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_20k_voc12aug/psanet_r101-d8_512x512_20k_voc12aug_20200617_110624.log.json) |
+| PSANet | R-50-D8  | 512x512   |   40000 | -        | -              | V100   | 76.30 |         77.35 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/psanet/psanet_r50-d8_4xb4-40k_voc12aug-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_40k_voc12aug/psanet_r50-d8_512x512_40k_voc12aug_20200613_161946-f596afb5.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_40k_voc12aug/psanet_r50-d8_512x512_40k_voc12aug_20200613_161946.log.json)     |
+| PSANet | R-101-D8 | 512x512   |   40000 | -        | -              | V100   | 77.73 |         79.05 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/psanet/psanet_r101-d8_4xb4-40k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_40k_voc12aug/psanet_r101-d8_512x512_40k_voc12aug_20200613_161946-1f560f9e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_40k_voc12aug/psanet_r101-d8_512x512_40k_voc12aug_20200613_161946.log.json) |
+
 ## Citation
 
 ```bibtex
@@ -33,36 +66,3 @@ We notice information flow in convolutional neural networksis  restricted  insid
   year={2018}
 }
 ```
-
-## Results and models
-
-### Cityscapes
-
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                            | download                                                                                                                                                                                                                                                                                                                                                 |
-| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | --------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| PSANet | R-50-D8  | 512x1024  |   40000 | 7        | 3.17           | 77.63 |         79.04 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/psanet/psanet_r50-d8_4xb2-40k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x1024_40k_cityscapes/psanet_r50-d8_512x1024_40k_cityscapes_20200606_103117-99fac37c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x1024_40k_cityscapes/psanet_r50-d8_512x1024_40k_cityscapes_20200606_103117.log.json)     |
-| PSANet | R-101-D8 | 512x1024  |   40000 | 10.5     | 2.20           | 79.14 |         80.19 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/psanet/psanet_r101-d8_4xb2-40k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x1024_40k_cityscapes/psanet_r101-d8_512x1024_40k_cityscapes_20200606_001418-27b9cfa7.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x1024_40k_cityscapes/psanet_r101-d8_512x1024_40k_cityscapes_20200606_001418.log.json) |
-| PSANet | R-50-D8  | 769x769   |   40000 | 7.9      | 1.40           | 77.99 |         79.64 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/psanet/psanet_r50-d8_4xb2-40k_cityscapes-769x769.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_769x769_40k_cityscapes/psanet_r50-d8_769x769_40k_cityscapes_20200530_033717-d5365506.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_769x769_40k_cityscapes/psanet_r50-d8_769x769_40k_cityscapes_20200530_033717.log.json)         |
-| PSANet | R-101-D8 | 769x769   |   40000 | 11.9     | 0.98           | 78.43 |         80.26 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/psanet/psanet_r101-d8_4xb2-40k_cityscapes-769x769.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_769x769_40k_cityscapes/psanet_r101-d8_769x769_40k_cityscapes_20200530_035107-997da1e6.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_769x769_40k_cityscapes/psanet_r101-d8_769x769_40k_cityscapes_20200530_035107.log.json)     |
-| PSANet | R-50-D8  | 512x1024  |   80000 | -        | -              | 77.24 |         78.69 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/psanet/psanet_r50-d8_4xb2-80k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x1024_80k_cityscapes/psanet_r50-d8_512x1024_80k_cityscapes_20200606_161842-ab60a24f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x1024_80k_cityscapes/psanet_r50-d8_512x1024_80k_cityscapes_20200606_161842.log.json)     |
-| PSANet | R-101-D8 | 512x1024  |   80000 | -        | -              | 79.31 |         80.53 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/psanet/psanet_r101-d8_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x1024_80k_cityscapes/psanet_r101-d8_512x1024_80k_cityscapes_20200606_161823-0f73a169.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x1024_80k_cityscapes/psanet_r101-d8_512x1024_80k_cityscapes_20200606_161823.log.json) |
-| PSANet | R-50-D8  | 769x769   |   80000 | -        | -              | 79.31 |         80.91 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/psanet/psanet_r50-d8_4xb2-80k_cityscapes-769x769.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_769x769_80k_cityscapes/psanet_r50-d8_769x769_80k_cityscapes_20200606_225134-fe42f49e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_769x769_80k_cityscapes/psanet_r50-d8_769x769_80k_cityscapes_20200606_225134.log.json)         |
-| PSANet | R-101-D8 | 769x769   |   80000 | -        | -              | 79.69 |         80.89 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/psanet/psanet_r101-d8_4xb2-80k_cityscapes-769x769.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_769x769_80k_cityscapes/psanet_r101-d8_769x769_80k_cityscapes_20200606_214550-7665827b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_769x769_80k_cityscapes/psanet_r101-d8_769x769_80k_cityscapes_20200606_214550.log.json)     |
-
-### ADE20K
-
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                        | download                                                                                                                                                                                                                                                                                                                                 |
-| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| PSANet | R-50-D8  | 512x512   |   80000 | 9        | 18.91          | 41.14 |         41.91 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/psanet/psanet_r50-d8_4xb4-80k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_80k_ade20k/psanet_r50-d8_512x512_80k_ade20k_20200614_144141-835e4b97.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_80k_ade20k/psanet_r50-d8_512x512_80k_ade20k_20200614_144141.log.json)         |
-| PSANet | R-101-D8 | 512x512   |   80000 | 12.5     | 13.13          | 43.80 |         44.75 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/psanet/psanet_r101-d8_4xb4-80k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_80k_ade20k/psanet_r101-d8_512x512_80k_ade20k_20200614_185117-1fab60d4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_80k_ade20k/psanet_r101-d8_512x512_80k_ade20k_20200614_185117.log.json)     |
-| PSANet | R-50-D8  | 512x512   |  160000 | -        | -              | 41.67 |         42.95 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/psanet/psanet_r50-d8_4xb4-160k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_160k_ade20k/psanet_r50-d8_512x512_160k_ade20k_20200615_161258-148077dd.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_160k_ade20k/psanet_r50-d8_512x512_160k_ade20k_20200615_161258.log.json)     |
-| PSANet | R-101-D8 | 512x512   |  160000 | -        | -              | 43.74 |         45.38 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/psanet/psanet_r101-d8_4xb4-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_160k_ade20k/psanet_r101-d8_512x512_160k_ade20k_20200615_161537-dbfa564c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_160k_ade20k/psanet_r101-d8_512x512_160k_ade20k_20200615_161537.log.json) |
-
-### Pascal VOC 2012 + Aug
-
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                         | download                                                                                                                                                                                                                                                                                                                                     |
-| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| PSANet | R-50-D8  | 512x512   |   20000 | 6.9      | 18.24          | 76.39 |         77.34 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/psanet/psanet_r50-d8_4xb4-20k_voc12aug-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_20k_voc12aug/psanet_r50-d8_512x512_20k_voc12aug_20200617_102413-2f1bbaa1.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_20k_voc12aug/psanet_r50-d8_512x512_20k_voc12aug_20200617_102413.log.json)     |
-| PSANet | R-101-D8 | 512x512   |   20000 | 10.4     | 12.63          | 77.91 |         79.30 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/psanet/psanet_r101-d8_4xb4-20k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_20k_voc12aug/psanet_r101-d8_512x512_20k_voc12aug_20200617_110624-946fef11.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_20k_voc12aug/psanet_r101-d8_512x512_20k_voc12aug_20200617_110624.log.json) |
-| PSANet | R-50-D8  | 512x512   |   40000 | -        | -              | 76.30 |         77.35 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/psanet/psanet_r50-d8_4xb4-40k_voc12aug-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_40k_voc12aug/psanet_r50-d8_512x512_40k_voc12aug_20200613_161946-f596afb5.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_40k_voc12aug/psanet_r50-d8_512x512_40k_voc12aug_20200613_161946.log.json)     |
-| PSANet | R-101-D8 | 512x512   |   40000 | -        | -              | 77.73 |         79.05 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/psanet/psanet_r101-d8_4xb4-40k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_40k_voc12aug/psanet_r101-d8_512x512_40k_voc12aug_20200613_161946-1f560f9e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_40k_voc12aug/psanet_r101-d8_512x512_40k_voc12aug_20200613_161946.log.json) |
diff --git a/configs/psanet/metafile.yaml b/configs/psanet/metafile.yaml
new file mode 100644
index 0000000000..3fbe6f6d3e
--- /dev/null
+++ b/configs/psanet/metafile.yaml
@@ -0,0 +1,391 @@
+Collections:
+- Name: PSANet
+  License: Apache License 2.0
+  Metadata:
+    Training Data:
+    - Cityscapes
+    - ADE20K
+    - Pascal VOC 2012 + Aug
+  Paper:
+    Title: 'PSANet: Point-wise Spatial Attention Network for Scene Parsing'
+    URL: https://openaccess.thecvf.com/content_ECCV_2018/papers/Hengshuang_Zhao_PSANet_Point-wise_Spatial_ECCV_2018_paper.pdf
+  README: configs/psanet/README.md
+  Frameworks:
+  - PyTorch
+Models:
+- Name: psanet_r50-d8_4xb2-40k_cityscapes-512x1024
+  In Collection: PSANet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.63
+      mIoU(ms+flip): 79.04
+  Config: configs/psanet/psanet_r50-d8_4xb2-40k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - PSANet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 7.0
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x1024_40k_cityscapes/psanet_r50-d8_512x1024_40k_cityscapes_20200606_103117-99fac37c.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x1024_40k_cityscapes/psanet_r50-d8_512x1024_40k_cityscapes_20200606_103117.log.json
+  Paper:
+    Title: 'PSANet: Point-wise Spatial Attention Network for Scene Parsing'
+    URL: https://openaccess.thecvf.com/content_ECCV_2018/papers/Hengshuang_Zhao_PSANet_Point-wise_Spatial_ECCV_2018_paper.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psa_head.py#L18
+  Framework: PyTorch
+- Name: psanet_r101-d8_4xb2-40k_cityscapes-512x1024
+  In Collection: PSANet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.14
+      mIoU(ms+flip): 80.19
+  Config: configs/psanet/psanet_r101-d8_4xb2-40k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - PSANet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 10.5
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x1024_40k_cityscapes/psanet_r101-d8_512x1024_40k_cityscapes_20200606_001418-27b9cfa7.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x1024_40k_cityscapes/psanet_r101-d8_512x1024_40k_cityscapes_20200606_001418.log.json
+  Paper:
+    Title: 'PSANet: Point-wise Spatial Attention Network for Scene Parsing'
+    URL: https://openaccess.thecvf.com/content_ECCV_2018/papers/Hengshuang_Zhao_PSANet_Point-wise_Spatial_ECCV_2018_paper.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psa_head.py#L18
+  Framework: PyTorch
+- Name: psanet_r50-d8_4xb2-40k_cityscapes-769x769
+  In Collection: PSANet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.99
+      mIoU(ms+flip): 79.64
+  Config: configs/psanet/psanet_r50-d8_4xb2-40k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - PSANet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 7.9
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_769x769_40k_cityscapes/psanet_r50-d8_769x769_40k_cityscapes_20200530_033717-d5365506.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_769x769_40k_cityscapes/psanet_r50-d8_769x769_40k_cityscapes_20200530_033717.log.json
+  Paper:
+    Title: 'PSANet: Point-wise Spatial Attention Network for Scene Parsing'
+    URL: https://openaccess.thecvf.com/content_ECCV_2018/papers/Hengshuang_Zhao_PSANet_Point-wise_Spatial_ECCV_2018_paper.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psa_head.py#L18
+  Framework: PyTorch
+- Name: psanet_r101-d8_4xb2-40k_cityscapes-769x769
+  In Collection: PSANet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.43
+      mIoU(ms+flip): 80.26
+  Config: configs/psanet/psanet_r101-d8_4xb2-40k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - PSANet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 11.9
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_769x769_40k_cityscapes/psanet_r101-d8_769x769_40k_cityscapes_20200530_035107-997da1e6.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_769x769_40k_cityscapes/psanet_r101-d8_769x769_40k_cityscapes_20200530_035107.log.json
+  Paper:
+    Title: 'PSANet: Point-wise Spatial Attention Network for Scene Parsing'
+    URL: https://openaccess.thecvf.com/content_ECCV_2018/papers/Hengshuang_Zhao_PSANet_Point-wise_Spatial_ECCV_2018_paper.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psa_head.py#L18
+  Framework: PyTorch
+- Name: psanet_r50-d8_4xb2-80k_cityscapes-512x1024
+  In Collection: PSANet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.24
+      mIoU(ms+flip): 78.69
+  Config: configs/psanet/psanet_r50-d8_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - PSANet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x1024_80k_cityscapes/psanet_r50-d8_512x1024_80k_cityscapes_20200606_161842-ab60a24f.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x1024_80k_cityscapes/psanet_r50-d8_512x1024_80k_cityscapes_20200606_161842.log.json
+  Paper:
+    Title: 'PSANet: Point-wise Spatial Attention Network for Scene Parsing'
+    URL: https://openaccess.thecvf.com/content_ECCV_2018/papers/Hengshuang_Zhao_PSANet_Point-wise_Spatial_ECCV_2018_paper.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psa_head.py#L18
+  Framework: PyTorch
+- Name: psanet_r101-d8_4xb2-80k_cityscapes-512x1024
+  In Collection: PSANet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.31
+      mIoU(ms+flip): 80.53
+  Config: configs/psanet/psanet_r101-d8_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - PSANet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x1024_80k_cityscapes/psanet_r101-d8_512x1024_80k_cityscapes_20200606_161823-0f73a169.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x1024_80k_cityscapes/psanet_r101-d8_512x1024_80k_cityscapes_20200606_161823.log.json
+  Paper:
+    Title: 'PSANet: Point-wise Spatial Attention Network for Scene Parsing'
+    URL: https://openaccess.thecvf.com/content_ECCV_2018/papers/Hengshuang_Zhao_PSANet_Point-wise_Spatial_ECCV_2018_paper.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psa_head.py#L18
+  Framework: PyTorch
+- Name: psanet_r50-d8_4xb2-80k_cityscapes-769x769
+  In Collection: PSANet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.31
+      mIoU(ms+flip): 80.91
+  Config: configs/psanet/psanet_r50-d8_4xb2-80k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - PSANet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_769x769_80k_cityscapes/psanet_r50-d8_769x769_80k_cityscapes_20200606_225134-fe42f49e.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_769x769_80k_cityscapes/psanet_r50-d8_769x769_80k_cityscapes_20200606_225134.log.json
+  Paper:
+    Title: 'PSANet: Point-wise Spatial Attention Network for Scene Parsing'
+    URL: https://openaccess.thecvf.com/content_ECCV_2018/papers/Hengshuang_Zhao_PSANet_Point-wise_Spatial_ECCV_2018_paper.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psa_head.py#L18
+  Framework: PyTorch
+- Name: psanet_r101-d8_4xb2-80k_cityscapes-769x769
+  In Collection: PSANet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.69
+      mIoU(ms+flip): 80.89
+  Config: configs/psanet/psanet_r101-d8_4xb2-80k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - PSANet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_769x769_80k_cityscapes/psanet_r101-d8_769x769_80k_cityscapes_20200606_214550-7665827b.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_769x769_80k_cityscapes/psanet_r101-d8_769x769_80k_cityscapes_20200606_214550.log.json
+  Paper:
+    Title: 'PSANet: Point-wise Spatial Attention Network for Scene Parsing'
+    URL: https://openaccess.thecvf.com/content_ECCV_2018/papers/Hengshuang_Zhao_PSANet_Point-wise_Spatial_ECCV_2018_paper.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psa_head.py#L18
+  Framework: PyTorch
+- Name: psanet_r50-d8_4xb4-80k_ade20k-512x512
+  In Collection: PSANet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 41.14
+      mIoU(ms+flip): 41.91
+  Config: configs/psanet/psanet_r50-d8_4xb4-80k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - PSANet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 9.0
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_80k_ade20k/psanet_r50-d8_512x512_80k_ade20k_20200614_144141-835e4b97.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_80k_ade20k/psanet_r50-d8_512x512_80k_ade20k_20200614_144141.log.json
+  Paper:
+    Title: 'PSANet: Point-wise Spatial Attention Network for Scene Parsing'
+    URL: https://openaccess.thecvf.com/content_ECCV_2018/papers/Hengshuang_Zhao_PSANet_Point-wise_Spatial_ECCV_2018_paper.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psa_head.py#L18
+  Framework: PyTorch
+- Name: psanet_r101-d8_4xb4-80k_ade20k-512x512
+  In Collection: PSANet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 43.8
+      mIoU(ms+flip): 44.75
+  Config: configs/psanet/psanet_r101-d8_4xb4-80k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - PSANet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 12.5
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_80k_ade20k/psanet_r101-d8_512x512_80k_ade20k_20200614_185117-1fab60d4.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_80k_ade20k/psanet_r101-d8_512x512_80k_ade20k_20200614_185117.log.json
+  Paper:
+    Title: 'PSANet: Point-wise Spatial Attention Network for Scene Parsing'
+    URL: https://openaccess.thecvf.com/content_ECCV_2018/papers/Hengshuang_Zhao_PSANet_Point-wise_Spatial_ECCV_2018_paper.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psa_head.py#L18
+  Framework: PyTorch
+- Name: psanet_r50-d8_4xb4-160k_ade20k-512x512
+  In Collection: PSANet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 41.67
+      mIoU(ms+flip): 42.95
+  Config: configs/psanet/psanet_r50-d8_4xb4-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - PSANet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_160k_ade20k/psanet_r50-d8_512x512_160k_ade20k_20200615_161258-148077dd.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_160k_ade20k/psanet_r50-d8_512x512_160k_ade20k_20200615_161258.log.json
+  Paper:
+    Title: 'PSANet: Point-wise Spatial Attention Network for Scene Parsing'
+    URL: https://openaccess.thecvf.com/content_ECCV_2018/papers/Hengshuang_Zhao_PSANet_Point-wise_Spatial_ECCV_2018_paper.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psa_head.py#L18
+  Framework: PyTorch
+- Name: psanet_r101-d8_4xb4-160k_ade20k-512x512
+  In Collection: PSANet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 43.74
+      mIoU(ms+flip): 45.38
+  Config: configs/psanet/psanet_r101-d8_4xb4-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - PSANet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_160k_ade20k/psanet_r101-d8_512x512_160k_ade20k_20200615_161537-dbfa564c.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_160k_ade20k/psanet_r101-d8_512x512_160k_ade20k_20200615_161537.log.json
+  Paper:
+    Title: 'PSANet: Point-wise Spatial Attention Network for Scene Parsing'
+    URL: https://openaccess.thecvf.com/content_ECCV_2018/papers/Hengshuang_Zhao_PSANet_Point-wise_Spatial_ECCV_2018_paper.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psa_head.py#L18
+  Framework: PyTorch
+- Name: psanet_r50-d8_4xb4-20k_voc12aug-512x512
+  In Collection: PSANet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 76.39
+      mIoU(ms+flip): 77.34
+  Config: configs/psanet/psanet_r50-d8_4xb4-20k_voc12aug-512x512.py
+  Metadata:
+    Training Data: Pascal VOC 2012 + Aug
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - PSANet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 6.9
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_20k_voc12aug/psanet_r50-d8_512x512_20k_voc12aug_20200617_102413-2f1bbaa1.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_20k_voc12aug/psanet_r50-d8_512x512_20k_voc12aug_20200617_102413.log.json
+  Paper:
+    Title: 'PSANet: Point-wise Spatial Attention Network for Scene Parsing'
+    URL: https://openaccess.thecvf.com/content_ECCV_2018/papers/Hengshuang_Zhao_PSANet_Point-wise_Spatial_ECCV_2018_paper.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psa_head.py#L18
+  Framework: PyTorch
+- Name: psanet_r101-d8_4xb4-20k_voc12aug-512x512
+  In Collection: PSANet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 77.91
+      mIoU(ms+flip): 79.3
+  Config: configs/psanet/psanet_r101-d8_4xb4-20k_voc12aug-512x512.py
+  Metadata:
+    Training Data: Pascal VOC 2012 + Aug
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - PSANet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 10.4
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_20k_voc12aug/psanet_r101-d8_512x512_20k_voc12aug_20200617_110624-946fef11.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_20k_voc12aug/psanet_r101-d8_512x512_20k_voc12aug_20200617_110624.log.json
+  Paper:
+    Title: 'PSANet: Point-wise Spatial Attention Network for Scene Parsing'
+    URL: https://openaccess.thecvf.com/content_ECCV_2018/papers/Hengshuang_Zhao_PSANet_Point-wise_Spatial_ECCV_2018_paper.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psa_head.py#L18
+  Framework: PyTorch
+- Name: psanet_r50-d8_4xb4-40k_voc12aug-512x512
+  In Collection: PSANet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 76.3
+      mIoU(ms+flip): 77.35
+  Config: configs/psanet/psanet_r50-d8_4xb4-40k_voc12aug-512x512.py
+  Metadata:
+    Training Data: Pascal VOC 2012 + Aug
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - PSANet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_40k_voc12aug/psanet_r50-d8_512x512_40k_voc12aug_20200613_161946-f596afb5.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_40k_voc12aug/psanet_r50-d8_512x512_40k_voc12aug_20200613_161946.log.json
+  Paper:
+    Title: 'PSANet: Point-wise Spatial Attention Network for Scene Parsing'
+    URL: https://openaccess.thecvf.com/content_ECCV_2018/papers/Hengshuang_Zhao_PSANet_Point-wise_Spatial_ECCV_2018_paper.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psa_head.py#L18
+  Framework: PyTorch
+- Name: psanet_r101-d8_4xb4-40k_voc12aug-512x512
+  In Collection: PSANet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 77.73
+      mIoU(ms+flip): 79.05
+  Config: configs/psanet/psanet_r101-d8_4xb4-40k_voc12aug-512x512.py
+  Metadata:
+    Training Data: Pascal VOC 2012 + Aug
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - PSANet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_40k_voc12aug/psanet_r101-d8_512x512_40k_voc12aug_20200613_161946-1f560f9e.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_40k_voc12aug/psanet_r101-d8_512x512_40k_voc12aug_20200613_161946.log.json
+  Paper:
+    Title: 'PSANet: Point-wise Spatial Attention Network for Scene Parsing'
+    URL: https://openaccess.thecvf.com/content_ECCV_2018/papers/Hengshuang_Zhao_PSANet_Point-wise_Spatial_ECCV_2018_paper.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psa_head.py#L18
+  Framework: PyTorch
diff --git a/configs/psanet/psanet.yml b/configs/psanet/psanet.yml
deleted file mode 100644
index fca1ac1b40..0000000000
--- a/configs/psanet/psanet.yml
+++ /dev/null
@@ -1,305 +0,0 @@
-Collections:
-- Name: PSANet
-  Metadata:
-    Training Data:
-    - Cityscapes
-    - ADE20K
-    - Pascal VOC 2012 + Aug
-  Paper:
-    URL: https://openaccess.thecvf.com/content_ECCV_2018/papers/Hengshuang_Zhao_PSANet_Point-wise_Spatial_ECCV_2018_paper.pdf
-    Title: 'PSANet: Point-wise Spatial Attention Network for Scene Parsing'
-  README: configs/psanet/README.md
-  Code:
-    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psa_head.py#L18
-    Version: v0.17.0
-  Converted From:
-    Code: https://github.com/hszhao/PSANet
-Models:
-- Name: psanet_r50-d8_4xb2-40k_cityscapes-512x1024
-  In Collection: PSANet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,1024)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 315.46
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 7.0
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 77.63
-      mIoU(ms+flip): 79.04
-  Config: configs/psanet/psanet_r50-d8_4xb2-40k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x1024_40k_cityscapes/psanet_r50-d8_512x1024_40k_cityscapes_20200606_103117-99fac37c.pth
-- Name: psanet_r101-d8_4xb2-40k_cityscapes-512x1024
-  In Collection: PSANet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,1024)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 454.55
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 10.5
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 79.14
-      mIoU(ms+flip): 80.19
-  Config: configs/psanet/psanet_r101-d8_4xb2-40k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x1024_40k_cityscapes/psanet_r101-d8_512x1024_40k_cityscapes_20200606_001418-27b9cfa7.pth
-- Name: psanet_r50-d8_4xb2-40k_cityscapes-769x769
-  In Collection: PSANet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (769,769)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 714.29
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (769,769)
-    Training Memory (GB): 7.9
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 77.99
-      mIoU(ms+flip): 79.64
-  Config: configs/psanet/psanet_r50-d8_4xb2-40k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_769x769_40k_cityscapes/psanet_r50-d8_769x769_40k_cityscapes_20200530_033717-d5365506.pth
-- Name: psanet_r101-d8_4xb2-40k_cityscapes-769x769
-  In Collection: PSANet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (769,769)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 1020.41
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (769,769)
-    Training Memory (GB): 11.9
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 78.43
-      mIoU(ms+flip): 80.26
-  Config: configs/psanet/psanet_r101-d8_4xb2-40k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_769x769_40k_cityscapes/psanet_r101-d8_769x769_40k_cityscapes_20200530_035107-997da1e6.pth
-- Name: psanet_r50-d8_4xb2-80k_cityscapes-512x1024
-  In Collection: PSANet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,1024)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 77.24
-      mIoU(ms+flip): 78.69
-  Config: configs/psanet/psanet_r50-d8_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x1024_80k_cityscapes/psanet_r50-d8_512x1024_80k_cityscapes_20200606_161842-ab60a24f.pth
-- Name: psanet_r101-d8_4xb2-80k_cityscapes-512x1024
-  In Collection: PSANet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,1024)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 79.31
-      mIoU(ms+flip): 80.53
-  Config: configs/psanet/psanet_r101-d8_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x1024_80k_cityscapes/psanet_r101-d8_512x1024_80k_cityscapes_20200606_161823-0f73a169.pth
-- Name: psanet_r50-d8_4xb2-80k_cityscapes-769x769
-  In Collection: PSANet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (769,769)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 79.31
-      mIoU(ms+flip): 80.91
-  Config: configs/psanet/psanet_r50-d8_4xb2-80k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_769x769_80k_cityscapes/psanet_r50-d8_769x769_80k_cityscapes_20200606_225134-fe42f49e.pth
-- Name: psanet_r101-d8_4xb2-80k_cityscapes-769x769
-  In Collection: PSANet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (769,769)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 79.69
-      mIoU(ms+flip): 80.89
-  Config: configs/psanet/psanet_r101-d8_4xb2-80k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_769x769_80k_cityscapes/psanet_r101-d8_769x769_80k_cityscapes_20200606_214550-7665827b.pth
-- Name: psanet_r50-d8_4xb4-80k_ade20k-512x512
-  In Collection: PSANet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 52.88
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 9.0
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 41.14
-      mIoU(ms+flip): 41.91
-  Config: configs/psanet/psanet_r50-d8_4xb4-80k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_80k_ade20k/psanet_r50-d8_512x512_80k_ade20k_20200614_144141-835e4b97.pth
-- Name: psanet_r101-d8_4xb4-80k_ade20k-512x512
-  In Collection: PSANet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 76.16
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 12.5
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 43.8
-      mIoU(ms+flip): 44.75
-  Config: configs/psanet/psanet_r101-d8_4xb4-80k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_80k_ade20k/psanet_r101-d8_512x512_80k_ade20k_20200614_185117-1fab60d4.pth
-- Name: psanet_r50-d8_4xb4-160k_ade20k-512x512
-  In Collection: PSANet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 160000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 41.67
-      mIoU(ms+flip): 42.95
-  Config: configs/psanet/psanet_r50-d8_4xb4-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_160k_ade20k/psanet_r50-d8_512x512_160k_ade20k_20200615_161258-148077dd.pth
-- Name: psanet_r101-d8_4xb4-160k_ade20k-512x512
-  In Collection: PSANet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 160000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 43.74
-      mIoU(ms+flip): 45.38
-  Config: configs/psanet/psanet_r101-d8_4xb4-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_160k_ade20k/psanet_r101-d8_512x512_160k_ade20k_20200615_161537-dbfa564c.pth
-- Name: psanet_r50-d8_4xb4-20k_voc12aug-512x512
-  In Collection: PSANet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 20000
-    inference time (ms/im):
-    - value: 54.82
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 6.9
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal VOC 2012 + Aug
-    Metrics:
-      mIoU: 76.39
-      mIoU(ms+flip): 77.34
-  Config: configs/psanet/psanet_r50-d8_4xb4-20k_voc12aug-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_20k_voc12aug/psanet_r50-d8_512x512_20k_voc12aug_20200617_102413-2f1bbaa1.pth
-- Name: psanet_r101-d8_4xb4-20k_voc12aug-512x512
-  In Collection: PSANet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 20000
-    inference time (ms/im):
-    - value: 79.18
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 10.4
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal VOC 2012 + Aug
-    Metrics:
-      mIoU: 77.91
-      mIoU(ms+flip): 79.3
-  Config: configs/psanet/psanet_r101-d8_4xb4-20k_voc12aug-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_20k_voc12aug/psanet_r101-d8_512x512_20k_voc12aug_20200617_110624-946fef11.pth
-- Name: psanet_r50-d8_4xb4-40k_voc12aug-512x512
-  In Collection: PSANet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 40000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal VOC 2012 + Aug
-    Metrics:
-      mIoU: 76.3
-      mIoU(ms+flip): 77.35
-  Config: configs/psanet/psanet_r50-d8_4xb4-40k_voc12aug-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_40k_voc12aug/psanet_r50-d8_512x512_40k_voc12aug_20200613_161946-f596afb5.pth
-- Name: psanet_r101-d8_4xb4-40k_voc12aug-512x512
-  In Collection: PSANet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 40000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal VOC 2012 + Aug
-    Metrics:
-      mIoU: 77.73
-      mIoU(ms+flip): 79.05
-  Config: configs/psanet/psanet_r101-d8_4xb4-40k_voc12aug-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_40k_voc12aug/psanet_r101-d8_512x512_40k_voc12aug_20200613_161946-1f560f9e.pth
diff --git a/configs/pspnet/README.md b/configs/pspnet/README.md
index 9135091102..e1c2c112cb 100644
--- a/configs/pspnet/README.md
+++ b/configs/pspnet/README.md
@@ -1,6 +1,6 @@
 # PSPNet
 
-[Pyramid Scene Parsing Network](https://arxiv.org/abs/1612.01105)
+> [Pyramid Scene Parsing Network](https://arxiv.org/abs/1612.01105)
 
 ## Introduction
 
@@ -27,85 +27,65 @@ Scene parsing is challenging for unrestricted open vocabulary and diverse scenes
 PSPNet-R50 D8 model structure
 </div>
 
-## Citation
-
-```bibtex
-@inproceedings{zhao2017pspnet,
-  title={Pyramid Scene Parsing Network},
-  author={Zhao, Hengshuang and Shi, Jianping and Qi, Xiaojuan and Wang, Xiaogang and Jia, Jiaya},
-  booktitle={CVPR},
-  year={2017}
-}
-```
-
-```bibtex
-@article{wightman2021resnet,
-  title={Resnet strikes back: An improved training procedure in timm},
-  author={Wightman, Ross and Touvron, Hugo and J{\'e}gou, Herv{\'e}},
-  journal={arXiv preprint arXiv:2110.00476},
-  year={2021}
-}
-```
-
 ## Results and models
 
 ### Cityscapes
 
-| Method        | Backbone      | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                                      | download                                                                                                                                                                                                                                                                                                                                                                                                                             |
-| ------------- | ------------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| PSPNet        | R-50-D8       | 512x1024  |   40000 | 6.1      | 4.07           | 77.85 |         79.18 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r50-d8_4xb2-40k_cityscapes-512x1024.py)            | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338.log.json)                                                                                 |
-| PSPNet        | R-101-D8      | 512x1024  |   40000 | 9.6      | 2.68           | 78.34 |         79.74 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r101-d8_4xb2-40k_cityscapes-512x1024.py)           | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_40k_cityscapes/pspnet_r101-d8_512x1024_40k_cityscapes_20200604_232751-467e7cf4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_40k_cityscapes/pspnet_r101-d8_512x1024_40k_cityscapes_20200604_232751.log.json)                                                                             |
-| PSPNet        | R-50-D8       | 769x769   |   40000 | 6.9      | 1.76           | 78.26 |         79.88 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r50-d8_4xb2-40k_cityscapes-769x769.py)             | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_769x769_40k_cityscapes/pspnet_r50-d8_769x769_40k_cityscapes_20200606_112725-86638686.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_769x769_40k_cityscapes/pspnet_r50-d8_769x769_40k_cityscapes_20200606_112725.log.json)                                                                                     |
-| PSPNet        | R-101-D8      | 769x769   |   40000 | 10.9     | 1.15           | 79.08 |         80.28 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r101-d8_4xb2-40k_cityscapes-769x769.py)            | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_769x769_40k_cityscapes/pspnet_r101-d8_769x769_40k_cityscapes_20200606_112753-61c6f5be.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_769x769_40k_cityscapes/pspnet_r101-d8_769x769_40k_cityscapes_20200606_112753.log.json)                                                                                 |
-| PSPNet        | R-18-D8       | 512x1024  |   80000 | 1.7      | 15.71          | 74.87 |         76.04 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r18-d8_4xb2-80k_cityscapes-512x1024.py)            | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_512x1024_80k_cityscapes/pspnet_r18-d8_512x1024_80k_cityscapes_20201225_021458-09ffa746.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_512x1024_80k_cityscapes/pspnet_r18-d8_512x1024_80k_cityscapes-20201225_021458.log.json)                                                                                 |
-| PSPNet        | R-50-D8       | 512x1024  |   80000 | -        | -              | 78.55 |         79.79 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r50-d8_4xb2-80k_cityscapes-512x1024.py)            | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes/pspnet_r50-d8_512x1024_80k_cityscapes_20200606_112131-2376f12b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes/pspnet_r50-d8_512x1024_80k_cityscapes_20200606_112131.log.json)                                                                                 |
-| PSPNet        | R-50b-D8 rsb  | 512x1024  |   80000 | 6.2      | 3.82           | 78.47 |         79.45 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r50-d8-rsb_4xb2-adamw-80k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes/pspnet_r50-d8_rsb-pretrain_512x1024_adamw_80k_cityscapes_20220315_123238-588c30be.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes/pspnet_r50-d8_rsb-pretrain_512x1024_adamw_80k_cityscapes_20220315_123238.log.json)                                           |
-| PSPNet        | R-101-D8      | 512x1024  |   80000 | -        | -              | 79.76 |         81.01 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r101-d8_4xb2-80k_cityscapes-512x1024.py)           | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_80k_cityscapes/pspnet_r101-d8_512x1024_80k_cityscapes_20200606_112211-e1e1100f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_80k_cityscapes/pspnet_r101-d8_512x1024_80k_cityscapes_20200606_112211.log.json)                                                                             |
-| PSPNet (FP16) | R-101-D8      | 512x1024  |   80000 | 5.34     | 8.77           | 79.46 |             - | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r101-d8_4xb2-amp-80k_cityscapes-512x1024.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_fp16_512x1024_80k_cityscapes/pspnet_r101-d8_fp16_512x1024_80k_cityscapes_20200717_230919-a0875e5c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_fp16_512x1024_80k_cityscapes/pspnet_r101-d8_fp16_512x1024_80k_cityscapes_20200717_230919.log.json)                                                         |
-| PSPNet        | R-18-D8       | 769x769   |   80000 | 1.9      | 6.20           | 75.90 |         77.86 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r18-d8_4xb2-80k_cityscapes-769x769.py)             | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_769x769_80k_cityscapes/pspnet_r18-d8_769x769_80k_cityscapes_20201225_021458-3deefc62.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_769x769_80k_cityscapes/pspnet_r18-d8_769x769_80k_cityscapes-20201225_021458.log.json)                                                                                     |
-| PSPNet        | R-50-D8       | 769x769   |   80000 | -        | -              | 79.59 |         80.69 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r50-d8_4xb2-80k_cityscapes-769x769.py)             | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_769x769_80k_cityscapes/pspnet_r50-d8_769x769_80k_cityscapes_20200606_210121-5ccf03dd.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_769x769_80k_cityscapes/pspnet_r50-d8_769x769_80k_cityscapes_20200606_210121.log.json)                                                                                     |
-| PSPNet        | R-101-D8      | 769x769   |   80000 | -        | -              | 79.77 |         81.06 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r101-d8_4xb2-80k_cityscapes-769x769.py)            | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_769x769_80k_cityscapes/pspnet_r101-d8_769x769_80k_cityscapes_20200606_225055-dba412fa.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_769x769_80k_cityscapes/pspnet_r101-d8_769x769_80k_cityscapes_20200606_225055.log.json)                                                                                 |
-| PSPNet        | R-18b-D8      | 512x1024  |   80000 | 1.5      | 16.28          | 74.23 |         75.79 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r18b-d8_4xb2-80k_cityscapes-512x1024.py)           | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18b-d8_512x1024_80k_cityscapes/pspnet_r18b-d8_512x1024_80k_cityscapes_20201226_063116-26928a60.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18b-d8_512x1024_80k_cityscapes/pspnet_r18b-d8_512x1024_80k_cityscapes-20201226_063116.log.json)                                                                             |
-| PSPNet        | R-50b-D8      | 512x1024  |   80000 | 6.0      | 4.30           | 78.22 |         79.46 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r50b-d8_4xb2-80k_cityscapes-512x1024.py)           | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50b-d8_512x1024_80k_cityscapes/pspnet_r50b-d8_512x1024_80k_cityscapes_20201225_094315-6344287a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50b-d8_512x1024_80k_cityscapes/pspnet_r50b-d8_512x1024_80k_cityscapes-20201225_094315.log.json)                                                                             |
-| PSPNet        | R-101b-D8     | 512x1024  |   80000 | 9.5      | 2.76           | 79.69 |         80.79 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r101b-d8_4xb2-80k_cityscapes-512x1024.py)          | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101b-d8_512x1024_80k_cityscapes/pspnet_r101b-d8_512x1024_80k_cityscapes_20201226_170012-3a4d38ab.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101b-d8_512x1024_80k_cityscapes/pspnet_r101b-d8_512x1024_80k_cityscapes-20201226_170012.log.json)                                                                         |
-| PSPNet        | R-18b-D8      | 769x769   |   80000 | 1.7      | 6.41           | 74.92 |         76.90 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r18b-d8_4xb2-80k_cityscapes-769x769.py)            | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18b-d8_769x769_80k_cityscapes/pspnet_r18b-d8_769x769_80k_cityscapes_20201226_080942-bf98d186.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18b-d8_769x769_80k_cityscapes/pspnet_r18b-d8_769x769_80k_cityscapes-20201226_080942.log.json)                                                                                 |
-| PSPNet        | R-50b-D8      | 769x769   |   80000 | 6.8      | 1.88           | 78.50 |         79.96 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r50b-d8_4xb2-80k_cityscapes-769x769.py)            | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50b-d8_769x769_80k_cityscapes/pspnet_r50b-d8_769x769_80k_cityscapes_20201225_094316-4c643cf6.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50b-d8_769x769_80k_cityscapes/pspnet_r50b-d8_769x769_80k_cityscapes-20201225_094316.log.json)                                                                                 |
-| PSPNet        | R-101b-D8     | 769x769   |   80000 | 10.8     | 1.17           | 78.87 |         80.04 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r101b-d8_4xb2-80k_cityscapes-769x769.py)           | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101b-d8_769x769_80k_cityscapes/pspnet_r101b-d8_769x769_80k_cityscapes_20201226_171823-f0e7c293.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101b-d8_769x769_80k_cityscapes/pspnet_r101b-d8_769x769_80k_cityscapes-20201226_171823.log.json)                                                                             |
-| PSPNet        | R-50-D32      | 512x1024  |   80000 | 3.0      | 15.21          | 73.88 |         76.85 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r50b-d32_4xb2-80k_cityscapes-512x1024.py)          | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d32_512x1024_80k_cityscapes/pspnet_r50-d32_512x1024_80k_cityscapes_20220316_224840-9092b254.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d32_512x1024_80k_cityscapes/pspnet_r50-d32_512x1024_80k_cityscapes_20220316_224840.log.json)                                                                             |
-| PSPNet        | R-50b-D32 rsb | 512x1024  |   80000 | 3.1      | 16.08          | 74.09 |         77.18 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r50-d32_rsb_4xb2-adamw-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d32_rsb-pretrain_512x1024_adamw_80k_cityscapes/pspnet_r50-d32_rsb-pretrain_512x1024_adamw_80k_cityscapes_20220316_141229-dd9c9610.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d32_rsb-pretrain_512x1024_adamw_80k_cityscapes/pspnet_r50-d32_rsb-pretrain_512x1024_adamw_80k_cityscapes_20220316_141229.log.json) |
-| PSPNet        | R-50b-D32     | 512x1024  |   80000 | 2.9      | 15.41          | 72.61 |         75.51 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r50b-d32_4xb2-80k_cityscapes-512x1024.py)          | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50b-d32_512x1024_80k_cityscapes/pspnet_r50b-d32_512x1024_80k_cityscapes_20220311_152152-23bcaf8c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50b-d32_512x1024_80k_cityscapes/pspnet_r50b-d32_512x1024_80k_cityscapes_20220311_152152.log.json)                                                                         |
+| Method        | Backbone      | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                                      | download                                                                                                                                                                                                                                                                                                                                                                                                                             |
+| ------------- | ------------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| PSPNet        | R-50-D8       | 512x1024  |   40000 | 6.1      | 4.07           | V100   | 77.85 |         79.18 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r50-d8_4xb2-40k_cityscapes-512x1024.py)            | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338.log.json)                                                                                 |
+| PSPNet        | R-101-D8      | 512x1024  |   40000 | 9.6      | 2.68           | V100   | 78.34 |         79.74 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r101-d8_4xb2-40k_cityscapes-512x1024.py)           | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_40k_cityscapes/pspnet_r101-d8_512x1024_40k_cityscapes_20200604_232751-467e7cf4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_40k_cityscapes/pspnet_r101-d8_512x1024_40k_cityscapes_20200604_232751.log.json)                                                                             |
+| PSPNet        | R-50-D8       | 769x769   |   40000 | 6.9      | 1.76           | V100   | 78.26 |         79.88 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r50-d8_4xb2-40k_cityscapes-769x769.py)             | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_769x769_40k_cityscapes/pspnet_r50-d8_769x769_40k_cityscapes_20200606_112725-86638686.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_769x769_40k_cityscapes/pspnet_r50-d8_769x769_40k_cityscapes_20200606_112725.log.json)                                                                                     |
+| PSPNet        | R-101-D8      | 769x769   |   40000 | 10.9     | 1.15           | V100   | 79.08 |         80.28 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r101-d8_4xb2-40k_cityscapes-769x769.py)            | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_769x769_40k_cityscapes/pspnet_r101-d8_769x769_40k_cityscapes_20200606_112753-61c6f5be.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_769x769_40k_cityscapes/pspnet_r101-d8_769x769_40k_cityscapes_20200606_112753.log.json)                                                                                 |
+| PSPNet        | R-18-D8       | 512x1024  |   80000 | 1.7      | 15.71          | V100   | 74.87 |         76.04 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r18-d8_4xb2-80k_cityscapes-512x1024.py)            | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_512x1024_80k_cityscapes/pspnet_r18-d8_512x1024_80k_cityscapes_20201225_021458-09ffa746.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_512x1024_80k_cityscapes/pspnet_r18-d8_512x1024_80k_cityscapes-20201225_021458.log.json)                                                                                 |
+| PSPNet        | R-50-D8       | 512x1024  |   80000 | -        | -              | V100   | 78.55 |         79.79 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r50-d8_4xb2-80k_cityscapes-512x1024.py)            | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes/pspnet_r50-d8_512x1024_80k_cityscapes_20200606_112131-2376f12b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes/pspnet_r50-d8_512x1024_80k_cityscapes_20200606_112131.log.json)                                                                                 |
+| PSPNet        | R-50b-D8 rsb  | 512x1024  |   80000 | 6.2      | 3.82           | V100   | 78.47 |         79.45 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r50-d8-rsb_4xb2-adamw-80k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes/pspnet_r50-d8_rsb-pretrain_512x1024_adamw_80k_cityscapes_20220315_123238-588c30be.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes/pspnet_r50-d8_rsb-pretrain_512x1024_adamw_80k_cityscapes_20220315_123238.log.json)                                           |
+| PSPNet        | R-101-D8      | 512x1024  |   80000 | -        | -              | V100   | 79.76 |         81.01 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r101-d8_4xb2-80k_cityscapes-512x1024.py)           | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_80k_cityscapes/pspnet_r101-d8_512x1024_80k_cityscapes_20200606_112211-e1e1100f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_80k_cityscapes/pspnet_r101-d8_512x1024_80k_cityscapes_20200606_112211.log.json)                                                                             |
+| PSPNet (FP16) | R-101-D8      | 512x1024  |   80000 | 5.34     | 8.77           | V100   | 79.46 |             - | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r101-d8_4xb2-amp-80k_cityscapes-512x1024.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_fp16_512x1024_80k_cityscapes/pspnet_r101-d8_fp16_512x1024_80k_cityscapes_20200717_230919-a0875e5c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_fp16_512x1024_80k_cityscapes/pspnet_r101-d8_fp16_512x1024_80k_cityscapes_20200717_230919.log.json)                                                         |
+| PSPNet        | R-18-D8       | 769x769   |   80000 | 1.9      | 6.20           | V100   | 75.90 |         77.86 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r18-d8_4xb2-80k_cityscapes-769x769.py)             | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_769x769_80k_cityscapes/pspnet_r18-d8_769x769_80k_cityscapes_20201225_021458-3deefc62.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_769x769_80k_cityscapes/pspnet_r18-d8_769x769_80k_cityscapes-20201225_021458.log.json)                                                                                     |
+| PSPNet        | R-50-D8       | 769x769   |   80000 | -        | -              | V100   | 79.59 |         80.69 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r50-d8_4xb2-80k_cityscapes-769x769.py)             | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_769x769_80k_cityscapes/pspnet_r50-d8_769x769_80k_cityscapes_20200606_210121-5ccf03dd.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_769x769_80k_cityscapes/pspnet_r50-d8_769x769_80k_cityscapes_20200606_210121.log.json)                                                                                     |
+| PSPNet        | R-101-D8      | 769x769   |   80000 | -        | -              | V100   | 79.77 |         81.06 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r101-d8_4xb2-80k_cityscapes-769x769.py)            | [model](https://download.oz1z1penmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_769x769_80k_cityscapes/pspnet_r101-d8_769x769_80k_cityscapes_20200606_225055-dba412fa.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_769x769_80k_cityscapes/pspnet_r101-d8_769x769_80k_cityscapes_20200606_225055.log.json)                                                                             |
+| PSPNet        | R-18b-D8      | 512x1024  |   80000 | 1.5      | 16.28          | V100   | 74.23 |         75.79 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r18b-d8_4xb2-80k_cityscapes-512x1024.py)           | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18b-d8_512x1024_80k_cityscapes/pspnet_r18b-d8_512x1024_80k_cityscapes_20201226_063116-26928a60.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18b-d8_512x1024_80k_cityscapes/pspnet_r18b-d8_512x1024_80k_cityscapes-20201226_063116.log.json)                                                                             |
+| PSPNet        | R-50b-D8      | 512x1024  |   80000 | 6.0      | 4.30           | V100   | 78.22 |         79.46 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r50b-d8_4xb2-80k_cityscapes-512x1024.py)           | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50b-d8_512x1024_80k_cityscapes/pspnet_r50b-d8_512x1024_80k_cityscapes_20201225_094315-6344287a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50b-d8_512x1024_80k_cityscapes/pspnet_r50b-d8_512x1024_80k_cityscapes-20201225_094315.log.json)                                                                             |
+| PSPNet        | R-101b-D8     | 512x1024  |   80000 | 9.5      | 2.76           | V100   | 79.69 |         80.79 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r101b-d8_4xb2-80k_cityscapes-512x1024.py)          | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101b-d8_512x1024_80k_cityscapes/pspnet_r101b-d8_512x1024_80k_cityscapes_20201226_170012-3a4d38ab.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101b-d8_512x1024_80k_cityscapes/pspnet_r101b-d8_512x1024_80k_cityscapes-20201226_170012.log.json)                                                                         |
+| PSPNet        | R-18b-D8      | 769x769   |   80000 | 1.7      | 6.41           | V100   | 74.92 |         76.90 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r18b-d8_4xb2-80k_cityscapes-769x769.py)            | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18b-d8_769x769_80k_cityscapes/pspnet_r18b-d8_769x769_80k_cityscapes_20201226_080942-bf98d186.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18b-d8_769x769_80k_cityscapes/pspnet_r18b-d8_769x769_80k_cityscapes-20201226_080942.log.json)                                                                                 |
+| PSPNet        | R-50b-D8      | 769x769   |   80000 | 6.8      | 1.88           | V100   | 78.50 |         79.96 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r50b-d8_4xb2-80k_cityscapes-769x769.py)            | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50b-d8_769x769_80k_cityscapes/pspnet_r50b-d8_769x769_80k_cityscapes_20201225_094316-4c643cf6.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50b-d8_769x769_80k_cityscapes/pspnet_r50b-d8_769x769_80k_cityscapes-20201225_094316.log.json)                                                                                 |
+| PSPNet        | R-101b-D8     | 769x769   |   80000 | 10.8     | 1.17           | V100   | 78.87 |         80.04 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r101b-d8_4xb2-80k_cityscapes-769x769.py)           | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101b-d8_769x769_80k_cityscapes/pspnet_r101b-d8_769x769_80k_cityscapes_20201226_171823-f0e7c293.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101b-d8_769x769_80k_cityscapes/pspnet_r101b-d8_769x769_80k_cityscapes-20201226_171823.log.json)                                                                             |
+| PSPNet        | R-50-D32      | 512x1024  |   80000 | 3.0      | 15.21          | V100   | 73.88 |         76.85 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r50b-d32_4xb2-80k_cityscapes-512x1024.py)          | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d32_512x1024_80k_cityscapes/pspnet_r50-d32_512x1024_80k_cityscapes_20220316_224840-9092b254.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d32_512x1024_80k_cityscapes/pspnet_r50-d32_512x1024_80k_cityscapes_20220316_224840.log.json)                                                                             |
+| PSPNet        | R-50b-D32 rsb | 512x1024  |   80000 | 3.1      | 16.08          | V100   | 74.09 |         77.18 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r50-d32_rsb_4xb2-adamw-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d32_rsb-pretrain_512x1024_adamw_80k_cityscapes/pspnet_r50-d32_rsb-pretrain_512x1024_adamw_80k_cityscapes_20220316_141229-dd9c9610.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d32_rsb-pretrain_512x1024_adamw_80k_cityscapes/pspnet_r50-d32_rsb-pretrain_512x1024_adamw_80k_cityscapes_20220316_141229.log.json) |
+| PSPNet        | R-50b-D32     | 512x1024  |   80000 | 2.9      | 15.41          | V100   | 72.61 |         75.51 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r50b-d32_4xb2-80k_cityscapes-512x1024.py)          | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50b-d32_512x1024_80k_cityscapes/pspnet_r50b-d32_512x1024_80k_cityscapes_20220311_152152-23bcaf8c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50b-d32_512x1024_80k_cityscapes/pspnet_r50b-d32_512x1024_80k_cityscapes_20220311_152152.log.json)                                                                         |
 
 ### ADE20K
 
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                        | download                                                                                                                                                                                                                                                                                                                                 |
-| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| PSPNet | R-50-D8  | 512x512   |   80000 | 8.5      | 23.53          | 41.13 |         41.94 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r50-d8_4xb4-80k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_80k_ade20k/pspnet_r50-d8_512x512_80k_ade20k_20200615_014128-15a8b914.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_80k_ade20k/pspnet_r50-d8_512x512_80k_ade20k_20200615_014128.log.json)         |
-| PSPNet | R-101-D8 | 512x512   |   80000 | 12       | 15.30          | 43.57 |         44.35 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r101-d8_4xb4-80k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_80k_ade20k/pspnet_r101-d8_512x512_80k_ade20k_20200614_031423-b6e782f0.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_80k_ade20k/pspnet_r101-d8_512x512_80k_ade20k_20200614_031423.log.json)     |
-| PSPNet | R-50-D8  | 512x512   |  160000 | -        | -              | 42.48 |         43.44 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r50-d8_4xb4-160k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_160k_ade20k/pspnet_r50-d8_512x512_160k_ade20k_20200615_184358-1890b0bd.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_160k_ade20k/pspnet_r50-d8_512x512_160k_ade20k_20200615_184358.log.json)     |
-| PSPNet | R-101-D8 | 512x512   |  160000 | -        | -              | 44.39 |         45.35 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r101-d8_4xb4-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_160k_ade20k/pspnet_r101-d8_512x512_160k_ade20k_20200615_100650-967c316f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_160k_ade20k/pspnet_r101-d8_512x512_160k_ade20k_20200615_100650.log.json) |
+| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                        | download                                                                                                                                                                                                                                                                                                                                 |
+| ------ | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| PSPNet | R-50-D8  | 512x512   |   80000 | 8.5      | 23.53          | V100   | 41.13 |         41.94 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r50-d8_4xb4-80k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_80k_ade20k/pspnet_r50-d8_512x512_80k_ade20k_20200615_014128-15a8b914.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_80k_ade20k/pspnet_r50-d8_512x512_80k_ade20k_20200615_014128.log.json)         |
+| PSPNet | R-101-D8 | 512x512   |   80000 | 12       | 15.30          | V100   | 43.57 |         44.35 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r101-d8_4xb4-80k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_80k_ade20k/pspnet_r101-d8_512x512_80k_ade20k_20200614_031423-b6e782f0.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_80k_ade20k/pspnet_r101-d8_512x512_80k_ade20k_20200614_031423.log.json)     |
+| PSPNet | R-50-D8  | 512x512   |  160000 | -        | -              | V100   | 42.48 |         43.44 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r50-d8_4xb4-160k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_160k_ade20k/pspnet_r50-d8_512x512_160k_ade20k_20200615_184358-1890b0bd.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_160k_ade20k/pspnet_r50-d8_512x512_160k_ade20k_20200615_184358.log.json)     |
+| PSPNet | R-101-D8 | 512x512   |  160000 | -        | -              | V100   | 44.39 |         45.35 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r101-d8_4xb4-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_160k_ade20k/pspnet_r101-d8_512x512_160k_ade20k_20200615_100650-967c316f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_160k_ade20k/pspnet_r101-d8_512x512_160k_ade20k_20200615_100650.log.json) |
 
 ### Pascal VOC 2012 + Aug
 
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                         | download                                                                                                                                                                                                                                                                                                                                     |
-| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| PSPNet | R-50-D8  | 512x512   |   20000 | 6.1      | 23.59          | 76.78 |         77.61 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r50-d8_4xb4-20k_voc12aug-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_20k_voc12aug/pspnet_r50-d8_512x512_20k_voc12aug_20200617_101958-ed5dfbd9.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_20k_voc12aug/pspnet_r50-d8_512x512_20k_voc12aug_20200617_101958.log.json)     |
-| PSPNet | R-101-D8 | 512x512   |   20000 | 9.6      | 15.02          | 78.47 |         79.25 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r101-d8_4xb4-20k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_20k_voc12aug/pspnet_r101-d8_512x512_20k_voc12aug_20200617_102003-4aef3c9a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_20k_voc12aug/pspnet_r101-d8_512x512_20k_voc12aug_20200617_102003.log.json) |
-| PSPNet | R-50-D8  | 512x512   |   40000 | -        | -              | 77.29 |         78.48 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r50-d8_4xb4-40k_voc12aug-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_40k_voc12aug/pspnet_r50-d8_512x512_40k_voc12aug_20200613_161222-ae9c1b8c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_40k_voc12aug/pspnet_r50-d8_512x512_40k_voc12aug_20200613_161222.log.json)     |
-| PSPNet | R-101-D8 | 512x512   |   40000 | -        | -              | 78.52 |         79.57 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r101-d8_4xb4-40k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_40k_voc12aug/pspnet_r101-d8_512x512_40k_voc12aug_20200613_161222-bc933b18.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_40k_voc12aug/pspnet_r101-d8_512x512_40k_voc12aug_20200613_161222.log.json) |
+| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                         | download                                                                                                                                                                                                                                                                                                                                     |
+| ------ | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| PSPNet | R-50-D8  | 512x512   |   20000 | 6.1      | 23.59          | V100   | 76.78 |         77.61 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r50-d8_4xb4-20k_voc12aug-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_20k_voc12aug/pspnet_r50-d8_512x512_20k_voc12aug_20200617_101958-ed5dfbd9.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_20k_voc12aug/pspnet_r50-d8_512x512_20k_voc12aug_20200617_101958.log.json)     |
+| PSPNet | R-101-D8 | 512x512   |   20000 | 9.6      | 15.02          | V100   | 78.47 |         79.25 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r101-d8_4xb4-20k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_20k_voc12aug/pspnet_r101-d8_512x512_20k_voc12aug_20200617_102003-4aef3c9a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_20k_voc12aug/pspnet_r101-d8_512x512_20k_voc12aug_20200617_102003.log.json) |
+| PSPNet | R-50-D8  | 512x512   |   40000 | -        | -              | V100   | 77.29 |         78.48 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r50-d8_4xb4-40k_voc12aug-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_40k_voc12aug/pspnet_r50-d8_512x512_40k_voc12aug_20200613_161222-ae9c1b8c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_40k_voc12aug/pspnet_r50-d8_512x512_40k_voc12aug_20200613_161222.log.json)     |
+| PSPNet | R-101-D8 | 512x512   |   40000 | -        | -              | V100   | 78.52 |         79.57 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r101-d8_4xb4-40k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_40k_voc12aug/pspnet_r101-d8_512x512_40k_voc12aug_20200613_161222-bc933b18.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_40k_voc12aug/pspnet_r101-d8_512x512_40k_voc12aug_20200613_161222.log.json) |
 
 ### Pascal Context
 
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                               | download                                                                                                                                                                                                                                                                                                                                                             |
-| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| PSPNet | R-101-D8 | 480x480   |   40000 | 8.8      | 9.68           | 46.60 |         47.78 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r101-d8_4xb4-40k_pascal-context-480x480.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_40k_pascal_context/pspnet_r101-d8_480x480_40k_pascal_context_20200911_211210-bf0f5d7c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_40k_pascal_context/pspnet_r101-d8_480x480_40k_pascal_context-20200911_211210.log.json) |
-| PSPNet | R-101-D8 | 480x480   |   80000 | -        | -              | 46.03 |         47.15 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r101-d8_4xb4-80k_pascal-context-480x480.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_80k_pascal_context/pspnet_r101-d8_480x480_80k_pascal_context_20200911_190530-c86d6233.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_80k_pascal_context/pspnet_r101-d8_480x480_80k_pascal_context-20200911_190530.log.json) |
+| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                               | download                                                                                                                                                                                                                                                                                                                                                             |
+| ------ | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| PSPNet | R-101-D8 | 480x480   |   40000 | 8.8      | 9.68           | V100   | 46.60 |         47.78 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r101-d8_4xb4-40k_pascal-context-480x480.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_40k_pascal_context/pspnet_r101-d8_480x480_40k_pascal_context_20200911_211210-bf0f5d7c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_40k_pascal_context/pspnet_r101-d8_480x480_40k_pascal_context-20200911_211210.log.json) |
+| PSPNet | R-101-D8 | 480x480   |   80000 | -        | -              | V100   | 46.03 |         47.15 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r101-d8_4xb4-80k_pascal-context-480x480.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_80k_pascal_context/pspnet_r101-d8_480x480_80k_pascal_context_20200911_190530-c86d6233.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_80k_pascal_context/pspnet_r101-d8_480x480_80k_pascal_context-20200911_190530.log.json) |
 
 ### Pascal Context 59
 
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                                  | download                                                                                                                                                                                                                                                                                                                                                                         |
-| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | --------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| PSPNet | R-101-D8 | 480x480   |   40000 | -        | -              | 52.02 |         53.54 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r101-d8_4xb4-40k_pascal-context-59-480x480.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_40k_pascal_context_59/pspnet_r101-d8_480x480_40k_pascal_context_59_20210416_114524-86d44cd4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_40k_pascal_context_59/pspnet_r101-d8_480x480_40k_pascal_context_59-20210416_114524.log.json) |
-| PSPNet | R-101-D8 | 480x480   |   80000 | -        | -              | 52.47 |         53.99 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r101-d8_4xb4-80k_pascal-context-59-480x480.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_80k_pascal_context_59/pspnet_r101-d8_480x480_80k_pascal_context_59_20210416_114418-fa6caaa2.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_80k_pascal_context_59/pspnet_r101-d8_480x480_80k_pascal_context_59-20210416_114418.log.json) |
+| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                                  | download                                                                                                                                                                                                                                                                                                                                                                         |
+| ------ | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | --------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| PSPNet | R-101-D8 | 480x480   |   40000 | -        | -              | V100   | 52.02 |         53.54 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r101-d8_4xb4-40k_pascal-context-59-480x480.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_40k_pascal_context_59/pspnet_r101-d8_480x480_40k_pascal_context_59_20210416_114524-86d44cd4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_40k_pascal_context_59/pspnet_r101-d8_480x480_40k_pascal_context_59-20210416_114524.log.json) |
+| PSPNet | R-101-D8 | 480x480   |   80000 | -        | -              | V100   | 52.47 |         53.99 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r101-d8_4xb4-80k_pascal-context-59-480x480.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_80k_pascal_context_59/pspnet_r101-d8_480x480_80k_pascal_context_59_20210416_114418-fa6caaa2.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_80k_pascal_context_59/pspnet_r101-d8_480x480_80k_pascal_context_59-20210416_114418.log.json) |
 
 ### Dark Zurich and Nighttime Driving
 
@@ -125,54 +105,54 @@ We support evaluation results on these two datasets using models above trained o
 
 ### COCO-Stuff 10k
 
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                              | download                                                                                                                                                                                                                                                                                                                                                                         |
-| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| PSPNet | R-50-D8  | 512x512   |   20000 | 9.6      | 20.5           | 35.69 |         36.62 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r50-d8_4xb4-20k_coco-stuff10k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_20k_coco-stuff10k/pspnet_r50-d8_512x512_4x4_20k_coco-stuff10k_20210820_203258-b88df27f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_20k_coco-stuff10k/pspnet_r50-d8_512x512_4x4_20k_coco-stuff10k_20210820_203258.log.json)     |
-| PSPNet | R-101-D8 | 512x512   |   20000 | 13.2     | 11.1           | 37.26 |         38.52 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r101-d8_4xb4-20k_coco-stuff10k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_20k_coco-stuff10k/pspnet_r101-d8_512x512_4x4_20k_coco-stuff10k_20210820_232135-76aae482.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_20k_coco-stuff10k/pspnet_r101-d8_512x512_4x4_20k_coco-stuff10k_20210820_232135.log.json) |
-| PSPNet | R-50-D8  | 512x512   |   40000 | -        | -              | 36.33 |         37.24 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r50-d8_4xb4-40k_coco-stuff10k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_40k_coco-stuff10k/pspnet_r50-d8_512x512_4x4_40k_coco-stuff10k_20210821_030857-92e2902b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_40k_coco-stuff10k/pspnet_r50-d8_512x512_4x4_40k_coco-stuff10k_20210821_030857.log.json)     |
-| PSPNet | R-101-D8 | 512x512   |   40000 | -        | -              | 37.76 |         38.86 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r101-d8_4xb4-40k_coco-stuff10k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_40k_coco-stuff10k/pspnet_r101-d8_512x512_4x4_40k_coco-stuff10k_20210821_014022-831aec95.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_40k_coco-stuff10k/pspnet_r101-d8_512x512_4x4_40k_coco-stuff10k_20210821_014022.log.json) |
+| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                              | download                                                                                                                                                                                                                                                                                                                                                                         |
+| ------ | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| PSPNet | R-50-D8  | 512x512   |   20000 | 9.6      | 20.5           | V100   | 35.69 |         36.62 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r50-d8_4xb4-20k_coco-stuff10k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_20k_coco-stuff10k/pspnet_r50-d8_512x512_4x4_20k_coco-stuff10k_20210820_203258-b88df27f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_20k_coco-stuff10k/pspnet_r50-d8_512x512_4x4_20k_coco-stuff10k_20210820_203258.log.json)     |
+| PSPNet | R-101-D8 | 512x512   |   20000 | 13.2     | 11.1           | V100   | 37.26 |         38.52 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r101-d8_4xb4-20k_coco-stuff10k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_20k_coco-stuff10k/pspnet_r101-d8_512x512_4x4_20k_coco-stuff10k_20210820_232135-76aae482.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_20k_coco-stuff10k/pspnet_r101-d8_512x512_4x4_20k_coco-stuff10k_20210820_232135.log.json) |
+| PSPNet | R-50-D8  | 512x512   |   40000 | -        | -              | V100   | 36.33 |         37.24 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r50-d8_4xb4-40k_coco-stuff10k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_40k_coco-stuff10k/pspnet_r50-d8_512x512_4x4_40k_coco-stuff10k_20210821_030857-92e2902b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_40k_coco-stuff10k/pspnet_r50-d8_512x512_4x4_40k_coco-stuff10k_20210821_030857.log.json)     |
+| PSPNet | R-101-D8 | 512x512   |   40000 | -        | -              | V100   | 37.76 |         38.86 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r101-d8_4xb4-40k_coco-stuff10k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_40k_coco-stuff10k/pspnet_r101-d8_512x512_4x4_40k_coco-stuff10k_20210821_014022-831aec95.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_40k_coco-stuff10k/pspnet_r101-d8_512x512_4x4_40k_coco-stuff10k_20210821_014022.log.json) |
 
 ### COCO-Stuff 164k
 
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                                | download                                                                                                                                                                                                                                                                                                                                                                                 |
-| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| PSPNet | R-50-D8  | 512x512   |   80000 | 9.6      | 20.5           | 38.80 |         39.19 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r50-d8_4xb4-80k_coco-stuff164k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_80k_coco-stuff164k/pspnet_r50-d8_512x512_4x4_80k_coco-stuff164k_20210707_152034-0e41b2db.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_80k_coco-stuff164k/pspnet_r50-d8_512x512_4x4_80k_coco-stuff164k_20210707_152034.log.json)         |
-| PSPNet | R-101-D8 | 512x512   |   80000 | 13.2     | 11.1           | 40.34 |         40.79 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r101-d8_4xb4-80k_coco-stuff164k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_80k_coco-stuff164k/pspnet_r101-d8_512x512_4x4_80k_coco-stuff164k_20210707_152034-7eb41789.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_80k_coco-stuff164k/pspnet_r101-d8_512x512_4x4_80k_coco-stuff164k_20210707_152034.log.json)     |
-| PSPNet | R-50-D8  | 512x512   |  160000 | -        | -              | 39.64 |         39.97 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r50-d8_4xb4-160k_coco-stuff164k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_160k_coco-stuff164k/pspnet_r50-d8_512x512_4x4_160k_coco-stuff164k_20210707_152004-51276a57.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_160k_coco-stuff164k/pspnet_r50-d8_512x512_4x4_160k_coco-stuff164k_20210707_152004.log.json)     |
-| PSPNet | R-101-D8 | 512x512   |  160000 | -        | -              | 41.28 |         41.66 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r101-d8_4xb4-160k_coco-stuff164k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_160k_coco-stuff164k/pspnet_r101-d8_512x512_4x4_160k_coco-stuff164k_20210707_152004-4af9621b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_160k_coco-stuff164k/pspnet_r101-d8_512x512_4x4_160k_coco-stuff164k_20210707_152004.log.json) |
-| PSPNet | R-50-D8  | 512x512   |  320000 | -        | -              | 40.53 |         40.75 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r50-d8_4xb4-320k_coco-stuff164k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_320k_coco-stuff164k/pspnet_r50-d8_512x512_4x4_320k_coco-stuff164k_20210707_152004-be9610cc.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_320k_coco-stuff164k/pspnet_r50-d8_512x512_4x4_320k_coco-stuff164k_20210707_152004.log.json)     |
-| PSPNet | R-101-D8 | 512x512   |  320000 | -        | -              | 41.95 |         42.42 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r101-d8_4xb4-320k_coco-stuff164k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_320k_coco-stuff164k/pspnet_r101-d8_512x512_4x4_320k_coco-stuff164k_20210707_152004-72220c60.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_320k_coco-stuff164k/pspnet_r101-d8_512x512_4x4_320k_coco-stuff164k_20210707_152004.log.json) |
+| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                                | download                                                                                                                                                                                                                                                                                                                                                                                 |
+| ------ | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| PSPNet | R-50-D8  | 512x512   |   80000 | 9.6      | 20.5           | V100   | 38.80 |         39.19 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r50-d8_4xb4-80k_coco-stuff164k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_80k_coco-stuff164k/pspnet_r50-d8_512x512_4x4_80k_coco-stuff164k_20210707_152034-0e41b2db.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_80k_coco-stuff164k/pspnet_r50-d8_512x512_4x4_80k_coco-stuff164k_20210707_152034.log.json)         |
+| PSPNet | R-101-D8 | 512x512   |   80000 | 13.2     | 11.1           | V100   | 40.34 |         40.79 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r101-d8_4xb4-80k_coco-stuff164k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_80k_coco-stuff164k/pspnet_r101-d8_512x512_4x4_80k_coco-stuff164k_20210707_152034-7eb41789.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_80k_coco-stuff164k/pspnet_r101-d8_512x512_4x4_80k_coco-stuff164k_20210707_152034.log.json)     |
+| PSPNet | R-50-D8  | 512x512   |  160000 | -        | -              | V100   | 39.64 |         39.97 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r50-d8_4xb4-160k_coco-stuff164k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_160k_coco-stuff164k/pspnet_r50-d8_512x512_4x4_160k_coco-stuff164k_20210707_152004-51276a57.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_160k_coco-stuff164k/pspnet_r50-d8_512x512_4x4_160k_coco-stuff164k_20210707_152004.log.json)     |
+| PSPNet | R-101-D8 | 512x512   |  160000 | -        | -              | V100   | 41.28 |         41.66 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r101-d8_4xb4-160k_coco-stuff164k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_160k_coco-stuff164k/pspnet_r101-d8_512x512_4x4_160k_coco-stuff164k_20210707_152004-4af9621b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_160k_coco-stuff164k/pspnet_r101-d8_512x512_4x4_160k_coco-stuff164k_20210707_152004.log.json) |
+| PSPNet | R-50-D8  | 512x512   |  320000 | -        | -              | V100   | 40.53 |         40.75 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r50-d8_4xb4-320k_coco-stuff164k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_320k_coco-stuff164k/pspnet_r50-d8_512x512_4x4_320k_coco-stuff164k_20210707_152004-be9610cc.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_320k_coco-stuff164k/pspnet_r50-d8_512x512_4x4_320k_coco-stuff164k_20210707_152004.log.json)     |
+| PSPNet | R-101-D8 | 512x512   |  320000 | -        | -              | V100   | 41.95 |         42.42 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r101-d8_4xb4-320k_coco-stuff164k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_320k_coco-stuff164k/pspnet_r101-d8_512x512_4x4_320k_coco-stuff164k_20210707_152004-72220c60.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_320k_coco-stuff164k/pspnet_r101-d8_512x512_4x4_320k_coco-stuff164k_20210707_152004.log.json) |
 
 ### LoveDA
 
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                       | download                                                                                                                                                                                                                                                                                                                             |
-| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ---------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| PSPNet | R-18-D8  | 512x512   |   80000 | 1.45     | 26.87          | 48.62 |         47.57 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r18-d8_4xb4-80k_loveda-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_512x512_80k_loveda/pspnet_r18-d8_512x512_80k_loveda_20211105_052100-b97697f1.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_512x512_80k_loveda/pspnet_r18-d8_512x512_80k_loveda_20211105_052100.log.json)     |
-| PSPNet | R-50-D8  | 512x512   |   80000 | 6.14     | 6.60           | 50.46 |         50.19 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r50-d8_4xb4-80k_loveda-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_80k_loveda/pspnet_r50-d8_512x512_80k_loveda_20211104_155728-88610f9f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_80k_loveda/pspnet_r50-d8_512x512_80k_loveda_20211104_155728.log.json)     |
-| PSPNet | R-101-D8 | 512x512   |   80000 | 9.61     | 4.58           | 51.86 |         51.34 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r101-d8_4xb4-80k_loveda-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_80k_loveda/pspnet_r101-d8_512x512_80k_loveda_20211104_153212-1c06c6a8.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_80k_loveda/pspnet_r101-d8_512x512_80k_loveda_20211104_153212.log.json) |
+| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                       | download                                                                                                                                                                                                                                                                                                                             |
+| ------ | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ---------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| PSPNet | R-18-D8  | 512x512   |   80000 | 1.45     | 26.87          | V100   | 48.62 |         47.57 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r18-d8_4xb4-80k_loveda-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_512x512_80k_loveda/pspnet_r18-d8_512x512_80k_loveda_20211105_052100-b97697f1.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_512x512_80k_loveda/pspnet_r18-d8_512x512_80k_loveda_20211105_052100.log.json)     |
+| PSPNet | R-50-D8  | 512x512   |   80000 | 6.14     | 6.60           | V100   | 50.46 |         50.19 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r50-d8_4xb4-80k_loveda-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_80k_loveda/pspnet_r50-d8_512x512_80k_loveda_20211104_155728-88610f9f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_80k_loveda/pspnet_r50-d8_512x512_80k_loveda_20211104_155728.log.json)     |
+| PSPNet | R-101-D8 | 512x512   |   80000 | 9.61     | 4.58           | V100   | 51.86 |         51.34 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r101-d8_4xb4-80k_loveda-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_80k_loveda/pspnet_r101-d8_512x512_80k_loveda_20211104_153212-1c06c6a8.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_80k_loveda/pspnet_r101-d8_512x512_80k_loveda_20211104_153212.log.json) |
 
 ### Potsdam
 
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                        | download                                                                                                                                                                                                                                                                                                                                                 |
-| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| PSPNet | R-18-D8  | 512x512   |   80000 | 1.50     | 85.12          | 77.09 |         78.30 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r18-d8_4xb4-80k_potsdam-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_4x4_512x512_80k_potsdam/pspnet_r18-d8_4x4_512x512_80k_potsdam_20211220_125612-7cd046e1.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_4x4_512x512_80k_potsdam/pspnet_r18-d8_4x4_512x512_80k_potsdam_20211220_125612.log.json)     |
-| PSPNet | R-50-D8  | 512x512   |   80000 | 6.14     | 30.21          | 78.12 |         78.98 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r50-d8_4xb4-80k_potsdam-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_4x4_512x512_80k_potsdam/pspnet_r50-d8_4x4_512x512_80k_potsdam_20211219_043541-2dd5fe67.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_4x4_512x512_80k_potsdam/pspnet_r50-d8_4x4_512x512_80k_potsdam_20211219_043541.log.json)     |
-| PSPNet | R-101-D8 | 512x512   |   80000 | 9.61     | 19.40          | 78.62 |         79.47 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r101-d8_4xb4-80k_potsdam-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_4x4_512x512_80k_potsdam/pspnet_r101-d8_4x4_512x512_80k_potsdam_20211220_125612-aed036c4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_4x4_512x512_80k_potsdam/pspnet_r101-d8_4x4_512x512_80k_potsdam_20211220_125612.log.json) |
+| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                        | download                                                                                                                                                                                                                                                                                                                                                 |
+| ------ | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| PSPNet | R-18-D8  | 512x512   |   80000 | 1.50     | 85.12          | V100   | 77.09 |         78.30 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r18-d8_4xb4-80k_potsdam-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_4x4_512x512_80k_potsdam/pspnet_r18-d8_4x4_512x512_80k_potsdam_20211220_125612-7cd046e1.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_4x4_512x512_80k_potsdam/pspnet_r18-d8_4x4_512x512_80k_potsdam_20211220_125612.log.json)     |
+| PSPNet | R-50-D8  | 512x512   |   80000 | 6.14     | 30.21          | V100   | 78.12 |         78.98 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r50-d8_4xb4-80k_potsdam-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_4x4_512x512_80k_potsdam/pspnet_r50-d8_4x4_512x512_80k_potsdam_20211219_043541-2dd5fe67.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_4x4_512x512_80k_potsdam/pspnet_r50-d8_4x4_512x512_80k_potsdam_20211219_043541.log.json)     |
+| PSPNet | R-101-D8 | 512x512   |   80000 | 9.61     | 19.40          | V100   | 78.62 |         79.47 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r101-d8_4xb4-80k_potsdam-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_4x4_512x512_80k_potsdam/pspnet_r101-d8_4x4_512x512_80k_potsdam_20211220_125612-aed036c4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_4x4_512x512_80k_potsdam/pspnet_r101-d8_4x4_512x512_80k_potsdam_20211220_125612.log.json) |
 
 ### Vaihingen
 
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                          | download                                                                                                                                                                                                                                                                                                                                                         |
-| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| PSPNet | R-18-D8  | 512x512   |   80000 | 1.45     | 85.06          | 71.46 |         73.36 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r18-d8_4xb4-80k_vaihingen-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_4x4_512x512_80k_vaihingen/pspnet_r18-d8_4x4_512x512_80k_vaihingen_20211228_160355-52a8a6f6.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_4x4_512x512_80k_vaihingen/pspnet_r18-d8_4x4_512x512_80k_vaihingen_20211228_160355.log.json)     |
-| PSPNet | R-50-D8  | 512x512   |   80000 | 6.14     | 30.29          | 72.36 |         73.75 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r50-d8_4xb4-80k_vaihingen-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_4x4_512x512_80k_vaihingen/pspnet_r50-d8_4x4_512x512_80k_vaihingen_20211228_160355-382f8f5b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_4x4_512x512_80k_vaihingen/pspnet_r50-d8_4x4_512x512_80k_vaihingen_20211228_160355.log.json)     |
-| PSPNet | R-101-D8 | 512x512   |   80000 | 9.61     | 19.97          | 72.61 |         74.18 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r101-d8_4xb4-80k_vaihingen-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_4x4_512x512_80k_vaihingen/pspnet_r101-d8_4x4_512x512_80k_vaihingen_20211231_230806-8eba0a09.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_4x4_512x512_80k_vaihingen/pspnet_r101-d8_4x4_512x512_80k_vaihingen_20211231_230806.log.json) |
+| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                          | download                                                                                                                                                                                                                                                                                                                                                         |
+| ------ | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| PSPNet | R-18-D8  | 512x512   |   80000 | 1.45     | 85.06          | V100   | 71.46 |         73.36 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r18-d8_4xb4-80k_vaihingen-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_4x4_512x512_80k_vaihingen/pspnet_r18-d8_4x4_512x512_80k_vaihingen_20211228_160355-52a8a6f6.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_4x4_512x512_80k_vaihingen/pspnet_r18-d8_4x4_512x512_80k_vaihingen_20211228_160355.log.json)     |
+| PSPNet | R-50-D8  | 512x512   |   80000 | 6.14     | 30.29          | V100   | 72.36 |         73.75 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r50-d8_4xb4-80k_vaihingen-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_4x4_512x512_80k_vaihingen/pspnet_r50-d8_4x4_512x512_80k_vaihingen_20211228_160355-382f8f5b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_4x4_512x512_80k_vaihingen/pspnet_r50-d8_4x4_512x512_80k_vaihingen_20211228_160355.log.json)     |
+| PSPNet | R-101-D8 | 512x512   |   80000 | 9.61     | 19.97          | V100   | 72.61 |         74.18 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r101-d8_4xb4-80k_vaihingen-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_4x4_512x512_80k_vaihingen/pspnet_r101-d8_4x4_512x512_80k_vaihingen_20211231_230806-8eba0a09.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_4x4_512x512_80k_vaihingen/pspnet_r101-d8_4x4_512x512_80k_vaihingen_20211231_230806.log.json) |
 
 ### iSAID
 
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                     | download                                                                                                                                                                                                                                                                                                                                     |
-| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| PSPNet | R-18-D8  | 896x896   |   80000 | 4.52     | 26.91          | 60.22 |         61.25 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r18-d8_4xb4-80k_isaid-896x896.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_4x4_896x896_80k_isaid/pspnet_r18-d8_4x4_896x896_80k_isaid_20220110_180526-e84c0b6a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_4x4_896x896_80k_isaid/pspnet_r18-d8_4x4_896x896_80k_isaid_20220110_180526.log.json) |
-| PSPNet | R-50-D8  | 896x896   |   80000 | 16.58    | 8.88           | 65.36 |         66.48 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r50-d8_4xb4-80k_isaid-896x896.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_4x4_896x896_80k_isaid/pspnet_r50-d8_4x4_896x896_80k_isaid_20220110_180629-1f21dc32.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_4x4_896x896_80k_isaid/pspnet_r50-d8_4x4_896x896_80k_isaid_20220110_180629.log.json) |
+| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                     | download                                                                                                                                                                                                                                                                                                                                     |
+| ------ | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| PSPNet | R-18-D8  | 896x896   |   80000 | 4.52     | 26.91          | V100   | 60.22 |         61.25 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r18-d8_4xb4-80k_isaid-896x896.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_4x4_896x896_80k_isaid/pspnet_r18-d8_4x4_896x896_80k_isaid_20220110_180526-e84c0b6a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_4x4_896x896_80k_isaid/pspnet_r18-d8_4x4_896x896_80k_isaid_20220110_180526.log.json) |
+| PSPNet | R-50-D8  | 896x896   |   80000 | 16.58    | 8.88           | V100   | 65.36 |         66.48 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r50-d8_4xb4-80k_isaid-896x896.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_4x4_896x896_80k_isaid/pspnet_r50-d8_4x4_896x896_80k_isaid_20220110_180629-1f21dc32.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_4x4_896x896_80k_isaid/pspnet_r50-d8_4x4_896x896_80k_isaid_20220110_180629.log.json) |
 
 Note:
 
@@ -180,3 +160,23 @@ Note:
 - `896x896` is the Crop Size of iSAID dataset, which is followed by the implementation of [PointFlow: Flowing Semantics Through Points for Aerial Image Segmentation](https://arxiv.org/pdf/2103.06564.pdf)
 - `rsb` is short for 'Resnet strikes back'.
 - The `b` in `R-50b` means ResNetV1b, which is a standard ResNet backbone. In MMSegmentation, default backbone is ResNetV1c, which usually performs better in semantic segmentation task.
+
+## Citation
+
+```bibtex
+@inproceedings{zhao2017pspnet,
+  title={Pyramid Scene Parsing Network},
+  author={Zhao, Hengshuang and Shi, Jianping and Qi, Xiaojuan and Wang, Xiaogang and Jia, Jiaya},
+  booktitle={CVPR},
+  year={2017}
+}
+```
+
+```bibtex
+@article{wightman2021resnet,
+  title={Resnet strikes back: An improved training procedure in timm},
+  author={Wightman, Ross and Touvron, Hugo and J{\'e}gou, Herv{\'e}},
+  journal={arXiv preprint arXiv:2110.00476},
+  year={2021}
+}
+```
diff --git a/configs/pspnet/metafile.yaml b/configs/pspnet/metafile.yaml
new file mode 100644
index 0000000000..d00b89d5cf
--- /dev/null
+++ b/configs/pspnet/metafile.yaml
@@ -0,0 +1,1303 @@
+Collections:
+- Name: PSPNet
+  License: Apache License 2.0
+  Metadata:
+    Training Data:
+    - Cityscapes
+    - ADE20K
+    - Pascal VOC 2012 + Aug
+    - Pascal Context
+    - Pascal Context 59
+    - Dark Zurich and Nighttime Driving
+    - COCO-Stuff 10k
+    - COCO-Stuff 164k
+    - LoveDA
+    - Potsdam
+    - Vaihingen
+    - iSAID
+  Paper:
+    Title: Pyramid Scene Parsing Network
+    URL: https://arxiv.org/abs/1612.01105
+  README: configs/pspnet/README.md
+  Frameworks:
+  - PyTorch
+Models:
+- Name: pspnet_r50-d8_4xb2-40k_cityscapes-512x1024
+  In Collection: PSPNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.85
+      mIoU(ms+flip): 79.18
+  Config: configs/pspnet/pspnet_r50-d8_4xb2-40k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 6.1
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338.log.json
+  Paper:
+    Title: Pyramid Scene Parsing Network
+    URL: https://arxiv.org/abs/1612.01105
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psp_head.py#L63
+  Framework: PyTorch
+- Name: pspnet_r101-d8_4xb2-40k_cityscapes-512x1024
+  In Collection: PSPNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.34
+      mIoU(ms+flip): 79.74
+  Config: configs/pspnet/pspnet_r101-d8_4xb2-40k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 9.6
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_40k_cityscapes/pspnet_r101-d8_512x1024_40k_cityscapes_20200604_232751-467e7cf4.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_40k_cityscapes/pspnet_r101-d8_512x1024_40k_cityscapes_20200604_232751.log.json
+  Paper:
+    Title: Pyramid Scene Parsing Network
+    URL: https://arxiv.org/abs/1612.01105
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psp_head.py#L63
+  Framework: PyTorch
+- Name: pspnet_r50-d8_4xb2-40k_cityscapes-769x769
+  In Collection: PSPNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.26
+      mIoU(ms+flip): 79.88
+  Config: configs/pspnet/pspnet_r50-d8_4xb2-40k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 6.9
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_769x769_40k_cityscapes/pspnet_r50-d8_769x769_40k_cityscapes_20200606_112725-86638686.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_769x769_40k_cityscapes/pspnet_r50-d8_769x769_40k_cityscapes_20200606_112725.log.json
+  Paper:
+    Title: Pyramid Scene Parsing Network
+    URL: https://arxiv.org/abs/1612.01105
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psp_head.py#L63
+  Framework: PyTorch
+- Name: pspnet_r101-d8_4xb2-40k_cityscapes-769x769
+  In Collection: PSPNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.08
+      mIoU(ms+flip): 80.28
+  Config: configs/pspnet/pspnet_r101-d8_4xb2-40k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 10.9
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_769x769_40k_cityscapes/pspnet_r101-d8_769x769_40k_cityscapes_20200606_112753-61c6f5be.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_769x769_40k_cityscapes/pspnet_r101-d8_769x769_40k_cityscapes_20200606_112753.log.json
+  Paper:
+    Title: Pyramid Scene Parsing Network
+    URL: https://arxiv.org/abs/1612.01105
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psp_head.py#L63
+  Framework: PyTorch
+- Name: pspnet_r18-d8_4xb2-80k_cityscapes-512x1024
+  In Collection: PSPNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 74.87
+      mIoU(ms+flip): 76.04
+  Config: configs/pspnet/pspnet_r18-d8_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-18-D8
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 1.7
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_512x1024_80k_cityscapes/pspnet_r18-d8_512x1024_80k_cityscapes_20201225_021458-09ffa746.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_512x1024_80k_cityscapes/pspnet_r18-d8_512x1024_80k_cityscapes-20201225_021458.log.json
+  Paper:
+    Title: Pyramid Scene Parsing Network
+    URL: https://arxiv.org/abs/1612.01105
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psp_head.py#L63
+  Framework: PyTorch
+- Name: pspnet_r50-d8_4xb2-80k_cityscapes-512x1024
+  In Collection: PSPNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.55
+      mIoU(ms+flip): 79.79
+  Config: configs/pspnet/pspnet_r50-d8_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes/pspnet_r50-d8_512x1024_80k_cityscapes_20200606_112131-2376f12b.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes/pspnet_r50-d8_512x1024_80k_cityscapes_20200606_112131.log.json
+  Paper:
+    Title: Pyramid Scene Parsing Network
+    URL: https://arxiv.org/abs/1612.01105
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psp_head.py#L63
+  Framework: PyTorch
+- Name: pspnet_r50-d8-rsb_4xb2-adamw-80k_cityscapes-512x1024
+  In Collection: PSPNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.47
+      mIoU(ms+flip): 79.45
+  Config: configs/pspnet/pspnet_r50-d8-rsb_4xb2-adamw-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50b-D8
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 6.2
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes/pspnet_r50-d8_rsb-pretrain_512x1024_adamw_80k_cityscapes_20220315_123238-588c30be.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes/pspnet_r50-d8_rsb-pretrain_512x1024_adamw_80k_cityscapes_20220315_123238.log.json
+  Paper:
+    Title: Pyramid Scene Parsing Network
+    URL: https://arxiv.org/abs/1612.01105
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psp_head.py#L63
+  Framework: PyTorch
+- Name: pspnet_r101-d8_4xb2-80k_cityscapes-512x1024
+  In Collection: PSPNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.76
+      mIoU(ms+flip): 81.01
+  Config: configs/pspnet/pspnet_r101-d8_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_80k_cityscapes/pspnet_r101-d8_512x1024_80k_cityscapes_20200606_112211-e1e1100f.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_80k_cityscapes/pspnet_r101-d8_512x1024_80k_cityscapes_20200606_112211.log.json
+  Paper:
+    Title: Pyramid Scene Parsing Network
+    URL: https://arxiv.org/abs/1612.01105
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psp_head.py#L63
+  Framework: PyTorch
+- Name: pspnet_r101-d8_4xb2-amp-80k_cityscapes-512x1024
+  In Collection: PSPNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.46
+  Config: configs/pspnet/pspnet_r101-d8_4xb2-amp-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - PSPNet
+    - (FP16)
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 5.34
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_fp16_512x1024_80k_cityscapes/pspnet_r101-d8_fp16_512x1024_80k_cityscapes_20200717_230919-a0875e5c.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_fp16_512x1024_80k_cityscapes/pspnet_r101-d8_fp16_512x1024_80k_cityscapes_20200717_230919.log.json
+  Paper:
+    Title: Pyramid Scene Parsing Network
+    URL: https://arxiv.org/abs/1612.01105
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psp_head.py#L63
+  Framework: PyTorch
+- Name: pspnet_r18-d8_4xb2-80k_cityscapes-769x769
+  In Collection: PSPNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 75.9
+      mIoU(ms+flip): 77.86
+  Config: configs/pspnet/pspnet_r18-d8_4xb2-80k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-18-D8
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 1.9
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_769x769_80k_cityscapes/pspnet_r18-d8_769x769_80k_cityscapes_20201225_021458-3deefc62.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_769x769_80k_cityscapes/pspnet_r18-d8_769x769_80k_cityscapes-20201225_021458.log.json
+  Paper:
+    Title: Pyramid Scene Parsing Network
+    URL: https://arxiv.org/abs/1612.01105
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psp_head.py#L63
+  Framework: PyTorch
+- Name: pspnet_r50-d8_4xb2-80k_cityscapes-769x769
+  In Collection: PSPNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.59
+      mIoU(ms+flip): 80.69
+  Config: configs/pspnet/pspnet_r50-d8_4xb2-80k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D8
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_769x769_80k_cityscapes/pspnet_r50-d8_769x769_80k_cityscapes_20200606_210121-5ccf03dd.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_769x769_80k_cityscapes/pspnet_r50-d8_769x769_80k_cityscapes_20200606_210121.log.json
+  Paper:
+    Title: Pyramid Scene Parsing Network
+    URL: https://arxiv.org/abs/1612.01105
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psp_head.py#L63
+  Framework: PyTorch
+- Name: pspnet_r101-d8_4xb2-80k_cityscapes-769x769
+  In Collection: PSPNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.77
+      mIoU(ms+flip): 81.06
+  Config: configs/pspnet/pspnet_r101-d8_4xb2-80k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101-D8
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.oz1z1penmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_769x769_80k_cityscapes/pspnet_r101-d8_769x769_80k_cityscapes_20200606_225055-dba412fa.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_769x769_80k_cityscapes/pspnet_r101-d8_769x769_80k_cityscapes_20200606_225055.log.json
+  Paper:
+    Title: Pyramid Scene Parsing Network
+    URL: https://arxiv.org/abs/1612.01105
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psp_head.py#L63
+  Framework: PyTorch
+- Name: pspnet_r18b-d8_4xb2-80k_cityscapes-512x1024
+  In Collection: PSPNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 74.23
+      mIoU(ms+flip): 75.79
+  Config: configs/pspnet/pspnet_r18b-d8_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-18b-D8
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 1.5
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18b-d8_512x1024_80k_cityscapes/pspnet_r18b-d8_512x1024_80k_cityscapes_20201226_063116-26928a60.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18b-d8_512x1024_80k_cityscapes/pspnet_r18b-d8_512x1024_80k_cityscapes-20201226_063116.log.json
+  Paper:
+    Title: Pyramid Scene Parsing Network
+    URL: https://arxiv.org/abs/1612.01105
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psp_head.py#L63
+  Framework: PyTorch
+- Name: pspnet_r50b-d8_4xb2-80k_cityscapes-512x1024
+  In Collection: PSPNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.22
+      mIoU(ms+flip): 79.46
+  Config: configs/pspnet/pspnet_r50b-d8_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50b-D8
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 6.0
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50b-d8_512x1024_80k_cityscapes/pspnet_r50b-d8_512x1024_80k_cityscapes_20201225_094315-6344287a.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50b-d8_512x1024_80k_cityscapes/pspnet_r50b-d8_512x1024_80k_cityscapes-20201225_094315.log.json
+  Paper:
+    Title: Pyramid Scene Parsing Network
+    URL: https://arxiv.org/abs/1612.01105
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psp_head.py#L63
+  Framework: PyTorch
+- Name: pspnet_r101b-d8_4xb2-80k_cityscapes-512x1024
+  In Collection: PSPNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.69
+      mIoU(ms+flip): 80.79
+  Config: configs/pspnet/pspnet_r101b-d8_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101b-D8
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 9.5
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101b-d8_512x1024_80k_cityscapes/pspnet_r101b-d8_512x1024_80k_cityscapes_20201226_170012-3a4d38ab.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101b-d8_512x1024_80k_cityscapes/pspnet_r101b-d8_512x1024_80k_cityscapes-20201226_170012.log.json
+  Paper:
+    Title: Pyramid Scene Parsing Network
+    URL: https://arxiv.org/abs/1612.01105
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psp_head.py#L63
+  Framework: PyTorch
+- Name: pspnet_r18b-d8_4xb2-80k_cityscapes-769x769
+  In Collection: PSPNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 74.92
+      mIoU(ms+flip): 76.9
+  Config: configs/pspnet/pspnet_r18b-d8_4xb2-80k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-18b-D8
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 1.7
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18b-d8_769x769_80k_cityscapes/pspnet_r18b-d8_769x769_80k_cityscapes_20201226_080942-bf98d186.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18b-d8_769x769_80k_cityscapes/pspnet_r18b-d8_769x769_80k_cityscapes-20201226_080942.log.json
+  Paper:
+    Title: Pyramid Scene Parsing Network
+    URL: https://arxiv.org/abs/1612.01105
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psp_head.py#L63
+  Framework: PyTorch
+- Name: pspnet_r50b-d8_4xb2-80k_cityscapes-769x769
+  In Collection: PSPNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.5
+      mIoU(ms+flip): 79.96
+  Config: configs/pspnet/pspnet_r50b-d8_4xb2-80k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50b-D8
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 6.8
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50b-d8_769x769_80k_cityscapes/pspnet_r50b-d8_769x769_80k_cityscapes_20201225_094316-4c643cf6.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50b-d8_769x769_80k_cityscapes/pspnet_r50b-d8_769x769_80k_cityscapes-20201225_094316.log.json
+  Paper:
+    Title: Pyramid Scene Parsing Network
+    URL: https://arxiv.org/abs/1612.01105
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psp_head.py#L63
+  Framework: PyTorch
+- Name: pspnet_r101b-d8_4xb2-80k_cityscapes-769x769
+  In Collection: PSPNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.87
+      mIoU(ms+flip): 80.04
+  Config: configs/pspnet/pspnet_r101b-d8_4xb2-80k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101b-D8
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 10.8
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101b-d8_769x769_80k_cityscapes/pspnet_r101b-d8_769x769_80k_cityscapes_20201226_171823-f0e7c293.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101b-d8_769x769_80k_cityscapes/pspnet_r101b-d8_769x769_80k_cityscapes-20201226_171823.log.json
+  Paper:
+    Title: Pyramid Scene Parsing Network
+    URL: https://arxiv.org/abs/1612.01105
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psp_head.py#L63
+  Framework: PyTorch
+- Name: pspnet_r50b-d32_4xb2-80k_cityscapes-512x1024
+  In Collection: PSPNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 73.88
+      mIoU(ms+flip): 76.85
+  Config: configs/pspnet/pspnet_r50b-d32_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50-D32
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 3.0
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d32_512x1024_80k_cityscapes/pspnet_r50-d32_512x1024_80k_cityscapes_20220316_224840-9092b254.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d32_512x1024_80k_cityscapes/pspnet_r50-d32_512x1024_80k_cityscapes_20220316_224840.log.json
+  Paper:
+    Title: Pyramid Scene Parsing Network
+    URL: https://arxiv.org/abs/1612.01105
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psp_head.py#L63
+  Framework: PyTorch
+- Name: pspnet_r50-d32_rsb_4xb2-adamw-80k_cityscapes-512x1024
+  In Collection: PSPNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 74.09
+      mIoU(ms+flip): 77.18
+  Config: configs/pspnet/pspnet_r50-d32_rsb_4xb2-adamw-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50b-D32
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 3.1
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d32_rsb-pretrain_512x1024_adamw_80k_cityscapes/pspnet_r50-d32_rsb-pretrain_512x1024_adamw_80k_cityscapes_20220316_141229-dd9c9610.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d32_rsb-pretrain_512x1024_adamw_80k_cityscapes/pspnet_r50-d32_rsb-pretrain_512x1024_adamw_80k_cityscapes_20220316_141229.log.json
+  Paper:
+    Title: Pyramid Scene Parsing Network
+    URL: https://arxiv.org/abs/1612.01105
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psp_head.py#L63
+  Framework: PyTorch
+- Name: pspnet_r50b-d32_4xb2-80k_cityscapes-512x1024
+  In Collection: PSPNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 72.61
+      mIoU(ms+flip): 75.51
+  Config: configs/pspnet/pspnet_r50b-d32_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50b-D32
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 2.9
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50b-d32_512x1024_80k_cityscapes/pspnet_r50b-d32_512x1024_80k_cityscapes_20220311_152152-23bcaf8c.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50b-d32_512x1024_80k_cityscapes/pspnet_r50b-d32_512x1024_80k_cityscapes_20220311_152152.log.json
+  Paper:
+    Title: Pyramid Scene Parsing Network
+    URL: https://arxiv.org/abs/1612.01105
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psp_head.py#L63
+  Framework: PyTorch
+- Name: pspnet_r50-d8_4xb4-80k_ade20k-512x512
+  In Collection: PSPNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 41.13
+      mIoU(ms+flip): 41.94
+  Config: configs/pspnet/pspnet_r50-d8_4xb4-80k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 8.5
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_80k_ade20k/pspnet_r50-d8_512x512_80k_ade20k_20200615_014128-15a8b914.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_80k_ade20k/pspnet_r50-d8_512x512_80k_ade20k_20200615_014128.log.json
+  Paper:
+    Title: Pyramid Scene Parsing Network
+    URL: https://arxiv.org/abs/1612.01105
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psp_head.py#L63
+  Framework: PyTorch
+- Name: pspnet_r101-d8_4xb4-80k_ade20k-512x512
+  In Collection: PSPNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 43.57
+      mIoU(ms+flip): 44.35
+  Config: configs/pspnet/pspnet_r101-d8_4xb4-80k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 12.0
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_80k_ade20k/pspnet_r101-d8_512x512_80k_ade20k_20200614_031423-b6e782f0.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_80k_ade20k/pspnet_r101-d8_512x512_80k_ade20k_20200614_031423.log.json
+  Paper:
+    Title: Pyramid Scene Parsing Network
+    URL: https://arxiv.org/abs/1612.01105
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psp_head.py#L63
+  Framework: PyTorch
+- Name: pspnet_r50-d8_4xb4-160k_ade20k-512x512
+  In Collection: PSPNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 42.48
+      mIoU(ms+flip): 43.44
+  Config: configs/pspnet/pspnet_r50-d8_4xb4-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_160k_ade20k/pspnet_r50-d8_512x512_160k_ade20k_20200615_184358-1890b0bd.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_160k_ade20k/pspnet_r50-d8_512x512_160k_ade20k_20200615_184358.log.json
+  Paper:
+    Title: Pyramid Scene Parsing Network
+    URL: https://arxiv.org/abs/1612.01105
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psp_head.py#L63
+  Framework: PyTorch
+- Name: pspnet_r101-d8_4xb4-160k_ade20k-512x512
+  In Collection: PSPNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 44.39
+      mIoU(ms+flip): 45.35
+  Config: configs/pspnet/pspnet_r101-d8_4xb4-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_160k_ade20k/pspnet_r101-d8_512x512_160k_ade20k_20200615_100650-967c316f.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_160k_ade20k/pspnet_r101-d8_512x512_160k_ade20k_20200615_100650.log.json
+  Paper:
+    Title: Pyramid Scene Parsing Network
+    URL: https://arxiv.org/abs/1612.01105
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psp_head.py#L63
+  Framework: PyTorch
+- Name: pspnet_r50-d8_4xb4-20k_voc12aug-512x512
+  In Collection: PSPNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 76.78
+      mIoU(ms+flip): 77.61
+  Config: configs/pspnet/pspnet_r50-d8_4xb4-20k_voc12aug-512x512.py
+  Metadata:
+    Training Data: Pascal VOC 2012 + Aug
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 6.1
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_20k_voc12aug/pspnet_r50-d8_512x512_20k_voc12aug_20200617_101958-ed5dfbd9.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_20k_voc12aug/pspnet_r50-d8_512x512_20k_voc12aug_20200617_101958.log.json
+  Paper:
+    Title: Pyramid Scene Parsing Network
+    URL: https://arxiv.org/abs/1612.01105
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psp_head.py#L63
+  Framework: PyTorch
+- Name: pspnet_r101-d8_4xb4-20k_voc12aug-512x512
+  In Collection: PSPNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 78.47
+      mIoU(ms+flip): 79.25
+  Config: configs/pspnet/pspnet_r101-d8_4xb4-20k_voc12aug-512x512.py
+  Metadata:
+    Training Data: Pascal VOC 2012 + Aug
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 9.6
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_20k_voc12aug/pspnet_r101-d8_512x512_20k_voc12aug_20200617_102003-4aef3c9a.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_20k_voc12aug/pspnet_r101-d8_512x512_20k_voc12aug_20200617_102003.log.json
+  Paper:
+    Title: Pyramid Scene Parsing Network
+    URL: https://arxiv.org/abs/1612.01105
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psp_head.py#L63
+  Framework: PyTorch
+- Name: pspnet_r50-d8_4xb4-40k_voc12aug-512x512
+  In Collection: PSPNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 77.29
+      mIoU(ms+flip): 78.48
+  Config: configs/pspnet/pspnet_r50-d8_4xb4-40k_voc12aug-512x512.py
+  Metadata:
+    Training Data: Pascal VOC 2012 + Aug
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_40k_voc12aug/pspnet_r50-d8_512x512_40k_voc12aug_20200613_161222-ae9c1b8c.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_40k_voc12aug/pspnet_r50-d8_512x512_40k_voc12aug_20200613_161222.log.json
+  Paper:
+    Title: Pyramid Scene Parsing Network
+    URL: https://arxiv.org/abs/1612.01105
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psp_head.py#L63
+  Framework: PyTorch
+- Name: pspnet_r101-d8_4xb4-40k_voc12aug-512x512
+  In Collection: PSPNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 78.52
+      mIoU(ms+flip): 79.57
+  Config: configs/pspnet/pspnet_r101-d8_4xb4-40k_voc12aug-512x512.py
+  Metadata:
+    Training Data: Pascal VOC 2012 + Aug
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_40k_voc12aug/pspnet_r101-d8_512x512_40k_voc12aug_20200613_161222-bc933b18.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_40k_voc12aug/pspnet_r101-d8_512x512_40k_voc12aug_20200613_161222.log.json
+  Paper:
+    Title: Pyramid Scene Parsing Network
+    URL: https://arxiv.org/abs/1612.01105
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psp_head.py#L63
+  Framework: PyTorch
+- Name: pspnet_r101-d8_4xb4-40k_pascal-context-480x480
+  In Collection: PSPNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal Context
+    Metrics:
+      mIoU: 46.6
+      mIoU(ms+flip): 47.78
+  Config: configs/pspnet/pspnet_r101-d8_4xb4-40k_pascal-context-480x480.py
+  Metadata:
+    Training Data: Pascal Context
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 8.8
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_40k_pascal_context/pspnet_r101-d8_480x480_40k_pascal_context_20200911_211210-bf0f5d7c.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_40k_pascal_context/pspnet_r101-d8_480x480_40k_pascal_context-20200911_211210.log.json
+  Paper:
+    Title: Pyramid Scene Parsing Network
+    URL: https://arxiv.org/abs/1612.01105
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psp_head.py#L63
+  Framework: PyTorch
+- Name: pspnet_r101-d8_4xb4-80k_pascal-context-480x480
+  In Collection: PSPNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal Context
+    Metrics:
+      mIoU: 46.03
+      mIoU(ms+flip): 47.15
+  Config: configs/pspnet/pspnet_r101-d8_4xb4-80k_pascal-context-480x480.py
+  Metadata:
+    Training Data: Pascal Context
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_80k_pascal_context/pspnet_r101-d8_480x480_80k_pascal_context_20200911_190530-c86d6233.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_80k_pascal_context/pspnet_r101-d8_480x480_80k_pascal_context-20200911_190530.log.json
+  Paper:
+    Title: Pyramid Scene Parsing Network
+    URL: https://arxiv.org/abs/1612.01105
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psp_head.py#L63
+  Framework: PyTorch
+- Name: pspnet_r101-d8_4xb4-40k_pascal-context-59-480x480
+  In Collection: PSPNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal Context 59
+    Metrics:
+      mIoU: 52.02
+      mIoU(ms+flip): 53.54
+  Config: configs/pspnet/pspnet_r101-d8_4xb4-40k_pascal-context-59-480x480.py
+  Metadata:
+    Training Data: Pascal Context 59
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_40k_pascal_context_59/pspnet_r101-d8_480x480_40k_pascal_context_59_20210416_114524-86d44cd4.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_40k_pascal_context_59/pspnet_r101-d8_480x480_40k_pascal_context_59-20210416_114524.log.json
+  Paper:
+    Title: Pyramid Scene Parsing Network
+    URL: https://arxiv.org/abs/1612.01105
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psp_head.py#L63
+  Framework: PyTorch
+- Name: pspnet_r101-d8_4xb4-80k_pascal-context-59-480x480
+  In Collection: PSPNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal Context 59
+    Metrics:
+      mIoU: 52.47
+      mIoU(ms+flip): 53.99
+  Config: configs/pspnet/pspnet_r101-d8_4xb4-80k_pascal-context-59-480x480.py
+  Metadata:
+    Training Data: Pascal Context 59
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_80k_pascal_context_59/pspnet_r101-d8_480x480_80k_pascal_context_59_20210416_114418-fa6caaa2.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_80k_pascal_context_59/pspnet_r101-d8_480x480_80k_pascal_context_59-20210416_114418.log.json
+  Paper:
+    Title: Pyramid Scene Parsing Network
+    URL: https://arxiv.org/abs/1612.01105
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psp_head.py#L63
+  Framework: PyTorch
+- Name: pspnet_r50-d8_4xb4-20k_coco-stuff10k-512x512
+  In Collection: PSPNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: COCO-Stuff 10k
+    Metrics:
+      mIoU: 35.69
+      mIoU(ms+flip): 36.62
+  Config: configs/pspnet/pspnet_r50-d8_4xb4-20k_coco-stuff10k-512x512.py
+  Metadata:
+    Training Data: COCO-Stuff 10k
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 9.6
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_20k_coco-stuff10k/pspnet_r50-d8_512x512_4x4_20k_coco-stuff10k_20210820_203258-b88df27f.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_20k_coco-stuff10k/pspnet_r50-d8_512x512_4x4_20k_coco-stuff10k_20210820_203258.log.json
+  Paper:
+    Title: Pyramid Scene Parsing Network
+    URL: https://arxiv.org/abs/1612.01105
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psp_head.py#L63
+  Framework: PyTorch
+- Name: pspnet_r101-d8_4xb4-20k_coco-stuff10k-512x512
+  In Collection: PSPNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: COCO-Stuff 10k
+    Metrics:
+      mIoU: 37.26
+      mIoU(ms+flip): 38.52
+  Config: configs/pspnet/pspnet_r101-d8_4xb4-20k_coco-stuff10k-512x512.py
+  Metadata:
+    Training Data: COCO-Stuff 10k
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 13.2
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_20k_coco-stuff10k/pspnet_r101-d8_512x512_4x4_20k_coco-stuff10k_20210820_232135-76aae482.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_20k_coco-stuff10k/pspnet_r101-d8_512x512_4x4_20k_coco-stuff10k_20210820_232135.log.json
+  Paper:
+    Title: Pyramid Scene Parsing Network
+    URL: https://arxiv.org/abs/1612.01105
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psp_head.py#L63
+  Framework: PyTorch
+- Name: pspnet_r50-d8_4xb4-40k_coco-stuff10k-512x512
+  In Collection: PSPNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: COCO-Stuff 10k
+    Metrics:
+      mIoU: 36.33
+      mIoU(ms+flip): 37.24
+  Config: configs/pspnet/pspnet_r50-d8_4xb4-40k_coco-stuff10k-512x512.py
+  Metadata:
+    Training Data: COCO-Stuff 10k
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_40k_coco-stuff10k/pspnet_r50-d8_512x512_4x4_40k_coco-stuff10k_20210821_030857-92e2902b.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_40k_coco-stuff10k/pspnet_r50-d8_512x512_4x4_40k_coco-stuff10k_20210821_030857.log.json
+  Paper:
+    Title: Pyramid Scene Parsing Network
+    URL: https://arxiv.org/abs/1612.01105
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psp_head.py#L63
+  Framework: PyTorch
+- Name: pspnet_r101-d8_4xb4-40k_coco-stuff10k-512x512
+  In Collection: PSPNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: COCO-Stuff 10k
+    Metrics:
+      mIoU: 37.76
+      mIoU(ms+flip): 38.86
+  Config: configs/pspnet/pspnet_r101-d8_4xb4-40k_coco-stuff10k-512x512.py
+  Metadata:
+    Training Data: COCO-Stuff 10k
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_40k_coco-stuff10k/pspnet_r101-d8_512x512_4x4_40k_coco-stuff10k_20210821_014022-831aec95.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_40k_coco-stuff10k/pspnet_r101-d8_512x512_4x4_40k_coco-stuff10k_20210821_014022.log.json
+  Paper:
+    Title: Pyramid Scene Parsing Network
+    URL: https://arxiv.org/abs/1612.01105
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psp_head.py#L63
+  Framework: PyTorch
+- Name: pspnet_r50-d8_4xb4-80k_coco-stuff164k-512x512
+  In Collection: PSPNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: COCO-Stuff 164k
+    Metrics:
+      mIoU: 38.8
+      mIoU(ms+flip): 39.19
+  Config: configs/pspnet/pspnet_r50-d8_4xb4-80k_coco-stuff164k-512x512.py
+  Metadata:
+    Training Data: COCO-Stuff 164k
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 9.6
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_80k_coco-stuff164k/pspnet_r50-d8_512x512_4x4_80k_coco-stuff164k_20210707_152034-0e41b2db.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_80k_coco-stuff164k/pspnet_r50-d8_512x512_4x4_80k_coco-stuff164k_20210707_152034.log.json
+  Paper:
+    Title: Pyramid Scene Parsing Network
+    URL: https://arxiv.org/abs/1612.01105
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psp_head.py#L63
+  Framework: PyTorch
+- Name: pspnet_r101-d8_4xb4-80k_coco-stuff164k-512x512
+  In Collection: PSPNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: COCO-Stuff 164k
+    Metrics:
+      mIoU: 40.34
+      mIoU(ms+flip): 40.79
+  Config: configs/pspnet/pspnet_r101-d8_4xb4-80k_coco-stuff164k-512x512.py
+  Metadata:
+    Training Data: COCO-Stuff 164k
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 13.2
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_80k_coco-stuff164k/pspnet_r101-d8_512x512_4x4_80k_coco-stuff164k_20210707_152034-7eb41789.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_80k_coco-stuff164k/pspnet_r101-d8_512x512_4x4_80k_coco-stuff164k_20210707_152034.log.json
+  Paper:
+    Title: Pyramid Scene Parsing Network
+    URL: https://arxiv.org/abs/1612.01105
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psp_head.py#L63
+  Framework: PyTorch
+- Name: pspnet_r50-d8_4xb4-160k_coco-stuff164k-512x512
+  In Collection: PSPNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: COCO-Stuff 164k
+    Metrics:
+      mIoU: 39.64
+      mIoU(ms+flip): 39.97
+  Config: configs/pspnet/pspnet_r50-d8_4xb4-160k_coco-stuff164k-512x512.py
+  Metadata:
+    Training Data: COCO-Stuff 164k
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_160k_coco-stuff164k/pspnet_r50-d8_512x512_4x4_160k_coco-stuff164k_20210707_152004-51276a57.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_160k_coco-stuff164k/pspnet_r50-d8_512x512_4x4_160k_coco-stuff164k_20210707_152004.log.json
+  Paper:
+    Title: Pyramid Scene Parsing Network
+    URL: https://arxiv.org/abs/1612.01105
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psp_head.py#L63
+  Framework: PyTorch
+- Name: pspnet_r101-d8_4xb4-160k_coco-stuff164k-512x512
+  In Collection: PSPNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: COCO-Stuff 164k
+    Metrics:
+      mIoU: 41.28
+      mIoU(ms+flip): 41.66
+  Config: configs/pspnet/pspnet_r101-d8_4xb4-160k_coco-stuff164k-512x512.py
+  Metadata:
+    Training Data: COCO-Stuff 164k
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_160k_coco-stuff164k/pspnet_r101-d8_512x512_4x4_160k_coco-stuff164k_20210707_152004-4af9621b.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_160k_coco-stuff164k/pspnet_r101-d8_512x512_4x4_160k_coco-stuff164k_20210707_152004.log.json
+  Paper:
+    Title: Pyramid Scene Parsing Network
+    URL: https://arxiv.org/abs/1612.01105
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psp_head.py#L63
+  Framework: PyTorch
+- Name: pspnet_r50-d8_4xb4-320k_coco-stuff164k-512x512
+  In Collection: PSPNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: COCO-Stuff 164k
+    Metrics:
+      mIoU: 40.53
+      mIoU(ms+flip): 40.75
+  Config: configs/pspnet/pspnet_r50-d8_4xb4-320k_coco-stuff164k-512x512.py
+  Metadata:
+    Training Data: COCO-Stuff 164k
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_320k_coco-stuff164k/pspnet_r50-d8_512x512_4x4_320k_coco-stuff164k_20210707_152004-be9610cc.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_320k_coco-stuff164k/pspnet_r50-d8_512x512_4x4_320k_coco-stuff164k_20210707_152004.log.json
+  Paper:
+    Title: Pyramid Scene Parsing Network
+    URL: https://arxiv.org/abs/1612.01105
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psp_head.py#L63
+  Framework: PyTorch
+- Name: pspnet_r101-d8_4xb4-320k_coco-stuff164k-512x512
+  In Collection: PSPNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: COCO-Stuff 164k
+    Metrics:
+      mIoU: 41.95
+      mIoU(ms+flip): 42.42
+  Config: configs/pspnet/pspnet_r101-d8_4xb4-320k_coco-stuff164k-512x512.py
+  Metadata:
+    Training Data: COCO-Stuff 164k
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_320k_coco-stuff164k/pspnet_r101-d8_512x512_4x4_320k_coco-stuff164k_20210707_152004-72220c60.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_320k_coco-stuff164k/pspnet_r101-d8_512x512_4x4_320k_coco-stuff164k_20210707_152004.log.json
+  Paper:
+    Title: Pyramid Scene Parsing Network
+    URL: https://arxiv.org/abs/1612.01105
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psp_head.py#L63
+  Framework: PyTorch
+- Name: pspnet_r18-d8_4xb4-80k_loveda-512x512
+  In Collection: PSPNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: LoveDA
+    Metrics:
+      mIoU: 48.62
+      mIoU(ms+flip): 47.57
+  Config: configs/pspnet/pspnet_r18-d8_4xb4-80k_loveda-512x512.py
+  Metadata:
+    Training Data: LoveDA
+    Batch Size: 16
+    Architecture:
+    - R-18-D8
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 1.45
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_512x512_80k_loveda/pspnet_r18-d8_512x512_80k_loveda_20211105_052100-b97697f1.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_512x512_80k_loveda/pspnet_r18-d8_512x512_80k_loveda_20211105_052100.log.json
+  Paper:
+    Title: Pyramid Scene Parsing Network
+    URL: https://arxiv.org/abs/1612.01105
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psp_head.py#L63
+  Framework: PyTorch
+- Name: pspnet_r50-d8_4xb4-80k_loveda-512x512
+  In Collection: PSPNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: LoveDA
+    Metrics:
+      mIoU: 50.46
+      mIoU(ms+flip): 50.19
+  Config: configs/pspnet/pspnet_r50-d8_4xb4-80k_loveda-512x512.py
+  Metadata:
+    Training Data: LoveDA
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 6.14
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_80k_loveda/pspnet_r50-d8_512x512_80k_loveda_20211104_155728-88610f9f.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_80k_loveda/pspnet_r50-d8_512x512_80k_loveda_20211104_155728.log.json
+  Paper:
+    Title: Pyramid Scene Parsing Network
+    URL: https://arxiv.org/abs/1612.01105
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psp_head.py#L63
+  Framework: PyTorch
+- Name: pspnet_r101-d8_4xb4-80k_loveda-512x512
+  In Collection: PSPNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: LoveDA
+    Metrics:
+      mIoU: 51.86
+      mIoU(ms+flip): 51.34
+  Config: configs/pspnet/pspnet_r101-d8_4xb4-80k_loveda-512x512.py
+  Metadata:
+    Training Data: LoveDA
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 9.61
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_80k_loveda/pspnet_r101-d8_512x512_80k_loveda_20211104_153212-1c06c6a8.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_80k_loveda/pspnet_r101-d8_512x512_80k_loveda_20211104_153212.log.json
+  Paper:
+    Title: Pyramid Scene Parsing Network
+    URL: https://arxiv.org/abs/1612.01105
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psp_head.py#L63
+  Framework: PyTorch
+- Name: pspnet_r18-d8_4xb4-80k_potsdam-512x512
+  In Collection: PSPNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Potsdam
+    Metrics:
+      mIoU: 77.09
+      mIoU(ms+flip): 78.3
+  Config: configs/pspnet/pspnet_r18-d8_4xb4-80k_potsdam-512x512.py
+  Metadata:
+    Training Data: Potsdam
+    Batch Size: 16
+    Architecture:
+    - R-18-D8
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 1.5
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_4x4_512x512_80k_potsdam/pspnet_r18-d8_4x4_512x512_80k_potsdam_20211220_125612-7cd046e1.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_4x4_512x512_80k_potsdam/pspnet_r18-d8_4x4_512x512_80k_potsdam_20211220_125612.log.json
+  Paper:
+    Title: Pyramid Scene Parsing Network
+    URL: https://arxiv.org/abs/1612.01105
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psp_head.py#L63
+  Framework: PyTorch
+- Name: pspnet_r50-d8_4xb4-80k_potsdam-512x512
+  In Collection: PSPNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Potsdam
+    Metrics:
+      mIoU: 78.12
+      mIoU(ms+flip): 78.98
+  Config: configs/pspnet/pspnet_r50-d8_4xb4-80k_potsdam-512x512.py
+  Metadata:
+    Training Data: Potsdam
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 6.14
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_4x4_512x512_80k_potsdam/pspnet_r50-d8_4x4_512x512_80k_potsdam_20211219_043541-2dd5fe67.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_4x4_512x512_80k_potsdam/pspnet_r50-d8_4x4_512x512_80k_potsdam_20211219_043541.log.json
+  Paper:
+    Title: Pyramid Scene Parsing Network
+    URL: https://arxiv.org/abs/1612.01105
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psp_head.py#L63
+  Framework: PyTorch
+- Name: pspnet_r101-d8_4xb4-80k_potsdam-512x512
+  In Collection: PSPNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Potsdam
+    Metrics:
+      mIoU: 78.62
+      mIoU(ms+flip): 79.47
+  Config: configs/pspnet/pspnet_r101-d8_4xb4-80k_potsdam-512x512.py
+  Metadata:
+    Training Data: Potsdam
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 9.61
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_4x4_512x512_80k_potsdam/pspnet_r101-d8_4x4_512x512_80k_potsdam_20211220_125612-aed036c4.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_4x4_512x512_80k_potsdam/pspnet_r101-d8_4x4_512x512_80k_potsdam_20211220_125612.log.json
+  Paper:
+    Title: Pyramid Scene Parsing Network
+    URL: https://arxiv.org/abs/1612.01105
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psp_head.py#L63
+  Framework: PyTorch
+- Name: pspnet_r18-d8_4xb4-80k_vaihingen-512x512
+  In Collection: PSPNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Vaihingen
+    Metrics:
+      mIoU: 71.46
+      mIoU(ms+flip): 73.36
+  Config: configs/pspnet/pspnet_r18-d8_4xb4-80k_vaihingen-512x512.py
+  Metadata:
+    Training Data: Vaihingen
+    Batch Size: 16
+    Architecture:
+    - R-18-D8
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 1.45
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_4x4_512x512_80k_vaihingen/pspnet_r18-d8_4x4_512x512_80k_vaihingen_20211228_160355-52a8a6f6.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_4x4_512x512_80k_vaihingen/pspnet_r18-d8_4x4_512x512_80k_vaihingen_20211228_160355.log.json
+  Paper:
+    Title: Pyramid Scene Parsing Network
+    URL: https://arxiv.org/abs/1612.01105
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psp_head.py#L63
+  Framework: PyTorch
+- Name: pspnet_r50-d8_4xb4-80k_vaihingen-512x512
+  In Collection: PSPNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Vaihingen
+    Metrics:
+      mIoU: 72.36
+      mIoU(ms+flip): 73.75
+  Config: configs/pspnet/pspnet_r50-d8_4xb4-80k_vaihingen-512x512.py
+  Metadata:
+    Training Data: Vaihingen
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 6.14
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_4x4_512x512_80k_vaihingen/pspnet_r50-d8_4x4_512x512_80k_vaihingen_20211228_160355-382f8f5b.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_4x4_512x512_80k_vaihingen/pspnet_r50-d8_4x4_512x512_80k_vaihingen_20211228_160355.log.json
+  Paper:
+    Title: Pyramid Scene Parsing Network
+    URL: https://arxiv.org/abs/1612.01105
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psp_head.py#L63
+  Framework: PyTorch
+- Name: pspnet_r101-d8_4xb4-80k_vaihingen-512x512
+  In Collection: PSPNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Vaihingen
+    Metrics:
+      mIoU: 72.61
+      mIoU(ms+flip): 74.18
+  Config: configs/pspnet/pspnet_r101-d8_4xb4-80k_vaihingen-512x512.py
+  Metadata:
+    Training Data: Vaihingen
+    Batch Size: 16
+    Architecture:
+    - R-101-D8
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 9.61
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_4x4_512x512_80k_vaihingen/pspnet_r101-d8_4x4_512x512_80k_vaihingen_20211231_230806-8eba0a09.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_4x4_512x512_80k_vaihingen/pspnet_r101-d8_4x4_512x512_80k_vaihingen_20211231_230806.log.json
+  Paper:
+    Title: Pyramid Scene Parsing Network
+    URL: https://arxiv.org/abs/1612.01105
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psp_head.py#L63
+  Framework: PyTorch
+- Name: pspnet_r18-d8_4xb4-80k_isaid-896x896
+  In Collection: PSPNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: iSAID
+    Metrics:
+      mIoU: 60.22
+      mIoU(ms+flip): 61.25
+  Config: configs/pspnet/pspnet_r18-d8_4xb4-80k_isaid-896x896.py
+  Metadata:
+    Training Data: iSAID
+    Batch Size: 16
+    Architecture:
+    - R-18-D8
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 4.52
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_4x4_896x896_80k_isaid/pspnet_r18-d8_4x4_896x896_80k_isaid_20220110_180526-e84c0b6a.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_4x4_896x896_80k_isaid/pspnet_r18-d8_4x4_896x896_80k_isaid_20220110_180526.log.json
+  Paper:
+    Title: Pyramid Scene Parsing Network
+    URL: https://arxiv.org/abs/1612.01105
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psp_head.py#L63
+  Framework: PyTorch
+- Name: pspnet_r50-d8_4xb4-80k_isaid-896x896
+  In Collection: PSPNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: iSAID
+    Metrics:
+      mIoU: 65.36
+      mIoU(ms+flip): 66.48
+  Config: configs/pspnet/pspnet_r50-d8_4xb4-80k_isaid-896x896.py
+  Metadata:
+    Training Data: iSAID
+    Batch Size: 16
+    Architecture:
+    - R-50-D8
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 16.58
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_4x4_896x896_80k_isaid/pspnet_r50-d8_4x4_896x896_80k_isaid_20220110_180629-1f21dc32.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_4x4_896x896_80k_isaid/pspnet_r50-d8_4x4_896x896_80k_isaid_20220110_180629.log.json
+  Paper:
+    Title: Pyramid Scene Parsing Network
+    URL: https://arxiv.org/abs/1612.01105
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psp_head.py#L63
+  Framework: PyTorch
diff --git a/configs/pspnet/pspnet.yml b/configs/pspnet/pspnet.yml
deleted file mode 100644
index 7f811efd0f..0000000000
--- a/configs/pspnet/pspnet.yml
+++ /dev/null
@@ -1,1077 +0,0 @@
-Collections:
-- Name: PSPNet
-  Metadata:
-    Training Data:
-    - Cityscapes
-    - ADE20K
-    - Pascal VOC 2012 + Aug
-    - Pascal Context
-    - Pascal Context 59
-    - Dark Zurich and Nighttime Driving
-    - COCO-Stuff 10k
-    - COCO-Stuff 164k
-    - LoveDA
-    - Potsdam
-    - Vaihingen
-    - iSAID
-  Paper:
-    URL: https://arxiv.org/abs/1612.01105
-    Title: Pyramid Scene Parsing Network
-  README: configs/pspnet/README.md
-  Code:
-    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/psp_head.py#L63
-    Version: v0.17.0
-  Converted From:
-    Code: https://github.com/hszhao/PSPNet
-Models:
-- Name: pspnet_r50-d8_4xb2-40k_cityscapes-512x1024
-  In Collection: PSPNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,1024)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 245.7
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 6.1
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 77.85
-      mIoU(ms+flip): 79.18
-  Config: configs/pspnet/pspnet_r50-d8_4xb2-40k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth
-- Name: pspnet_r101-d8_4xb2-40k_cityscapes-512x1024
-  In Collection: PSPNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,1024)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 373.13
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 9.6
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 78.34
-      mIoU(ms+flip): 79.74
-  Config: configs/pspnet/pspnet_r101-d8_4xb2-40k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_40k_cityscapes/pspnet_r101-d8_512x1024_40k_cityscapes_20200604_232751-467e7cf4.pth
-- Name: pspnet_r50-d8_4xb2-40k_cityscapes-769x769
-  In Collection: PSPNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (769,769)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 568.18
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (769,769)
-    Training Memory (GB): 6.9
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 78.26
-      mIoU(ms+flip): 79.88
-  Config: configs/pspnet/pspnet_r50-d8_4xb2-40k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_769x769_40k_cityscapes/pspnet_r50-d8_769x769_40k_cityscapes_20200606_112725-86638686.pth
-- Name: pspnet_r101-d8_4xb2-40k_cityscapes-769x769
-  In Collection: PSPNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (769,769)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 869.57
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (769,769)
-    Training Memory (GB): 10.9
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 79.08
-      mIoU(ms+flip): 80.28
-  Config: configs/pspnet/pspnet_r101-d8_4xb2-40k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_769x769_40k_cityscapes/pspnet_r101-d8_769x769_40k_cityscapes_20200606_112753-61c6f5be.pth
-- Name: pspnet_r18-d8_4xb2-80k_cityscapes-512x1024
-  In Collection: PSPNet
-  Metadata:
-    backbone: R-18-D8
-    crop size: (512,1024)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 63.65
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 1.7
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 74.87
-      mIoU(ms+flip): 76.04
-  Config: configs/pspnet/pspnet_r18-d8_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_512x1024_80k_cityscapes/pspnet_r18-d8_512x1024_80k_cityscapes_20201225_021458-09ffa746.pth
-- Name: pspnet_r50-d8_4xb2-80k_cityscapes-512x1024
-  In Collection: PSPNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,1024)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 78.55
-      mIoU(ms+flip): 79.79
-  Config: configs/pspnet/pspnet_r50-d8_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes/pspnet_r50-d8_512x1024_80k_cityscapes_20200606_112131-2376f12b.pth
-- Name: pspnet_r50-d8-rsb_4xb2-adamw-80k_cityscapes-512x1024
-  In Collection: PSPNet
-  Metadata:
-    backbone: R-50b-D8 rsb
-    crop size: (512,1024)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 261.78
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 6.2
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 78.47
-      mIoU(ms+flip): 79.45
-  Config: configs/pspnet/pspnet_r50-d8-rsb_4xb2-adamw-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes/pspnet_r50-d8_rsb-pretrain_512x1024_adamw_80k_cityscapes_20220315_123238-588c30be.pth
-- Name: pspnet_r101-d8_4xb2-80k_cityscapes-512x1024
-  In Collection: PSPNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,1024)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 79.76
-      mIoU(ms+flip): 81.01
-  Config: configs/pspnet/pspnet_r101-d8_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_80k_cityscapes/pspnet_r101-d8_512x1024_80k_cityscapes_20200606_112211-e1e1100f.pth
-- Name: pspnet_r101-d8_4xb2-amp-80k_cityscapes-512x1024
-  In Collection: PSPNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,1024)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 114.03
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: AMP
-      resolution: (512,1024)
-    Training Memory (GB): 5.34
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 79.46
-  Config: configs/pspnet/pspnet_r101-d8_4xb2-amp-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_fp16_512x1024_80k_cityscapes/pspnet_r101-d8_fp16_512x1024_80k_cityscapes_20200717_230919-a0875e5c.pth
-- Name: pspnet_r18-d8_4xb2-80k_cityscapes-769x769
-  In Collection: PSPNet
-  Metadata:
-    backbone: R-18-D8
-    crop size: (769,769)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 161.29
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (769,769)
-    Training Memory (GB): 1.9
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 75.9
-      mIoU(ms+flip): 77.86
-  Config: configs/pspnet/pspnet_r18-d8_4xb2-80k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_769x769_80k_cityscapes/pspnet_r18-d8_769x769_80k_cityscapes_20201225_021458-3deefc62.pth
-- Name: pspnet_r50-d8_4xb2-80k_cityscapes-769x769
-  In Collection: PSPNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (769,769)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 79.59
-      mIoU(ms+flip): 80.69
-  Config: configs/pspnet/pspnet_r50-d8_4xb2-80k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_769x769_80k_cityscapes/pspnet_r50-d8_769x769_80k_cityscapes_20200606_210121-5ccf03dd.pth
-- Name: pspnet_r101-d8_4xb2-80k_cityscapes-769x769
-  In Collection: PSPNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (769,769)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 79.77
-      mIoU(ms+flip): 81.06
-  Config: configs/pspnet/pspnet_r101-d8_4xb2-80k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_769x769_80k_cityscapes/pspnet_r101-d8_769x769_80k_cityscapes_20200606_225055-dba412fa.pth
-- Name: pspnet_r18b-d8_4xb2-80k_cityscapes-512x1024
-  In Collection: PSPNet
-  Metadata:
-    backbone: R-18b-D8
-    crop size: (512,1024)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 61.43
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 1.5
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 74.23
-      mIoU(ms+flip): 75.79
-  Config: configs/pspnet/pspnet_r18b-d8_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18b-d8_512x1024_80k_cityscapes/pspnet_r18b-d8_512x1024_80k_cityscapes_20201226_063116-26928a60.pth
-- Name: pspnet_r50b-d8_4xb2-80k_cityscapes-512x1024
-  In Collection: PSPNet
-  Metadata:
-    backbone: R-50b-D8
-    crop size: (512,1024)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 232.56
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 6.0
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 78.22
-      mIoU(ms+flip): 79.46
-  Config: configs/pspnet/pspnet_r50b-d8_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50b-d8_512x1024_80k_cityscapes/pspnet_r50b-d8_512x1024_80k_cityscapes_20201225_094315-6344287a.pth
-- Name: pspnet_r101b-d8_4xb2-80k_cityscapes-512x1024
-  In Collection: PSPNet
-  Metadata:
-    backbone: R-101b-D8
-    crop size: (512,1024)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 362.32
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 9.5
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 79.69
-      mIoU(ms+flip): 80.79
-  Config: configs/pspnet/pspnet_r101b-d8_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101b-d8_512x1024_80k_cityscapes/pspnet_r101b-d8_512x1024_80k_cityscapes_20201226_170012-3a4d38ab.pth
-- Name: pspnet_r18b-d8_4xb2-80k_cityscapes-769x769
-  In Collection: PSPNet
-  Metadata:
-    backbone: R-18b-D8
-    crop size: (769,769)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 156.01
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (769,769)
-    Training Memory (GB): 1.7
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 74.92
-      mIoU(ms+flip): 76.9
-  Config: configs/pspnet/pspnet_r18b-d8_4xb2-80k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18b-d8_769x769_80k_cityscapes/pspnet_r18b-d8_769x769_80k_cityscapes_20201226_080942-bf98d186.pth
-- Name: pspnet_r50b-d8_4xb2-80k_cityscapes-769x769
-  In Collection: PSPNet
-  Metadata:
-    backbone: R-50b-D8
-    crop size: (769,769)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 531.91
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (769,769)
-    Training Memory (GB): 6.8
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 78.5
-      mIoU(ms+flip): 79.96
-  Config: configs/pspnet/pspnet_r50b-d8_4xb2-80k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50b-d8_769x769_80k_cityscapes/pspnet_r50b-d8_769x769_80k_cityscapes_20201225_094316-4c643cf6.pth
-- Name: pspnet_r101b-d8_4xb2-80k_cityscapes-769x769
-  In Collection: PSPNet
-  Metadata:
-    backbone: R-101b-D8
-    crop size: (769,769)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 854.7
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (769,769)
-    Training Memory (GB): 10.8
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 78.87
-      mIoU(ms+flip): 80.04
-  Config: configs/pspnet/pspnet_r101b-d8_4xb2-80k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101b-d8_769x769_80k_cityscapes/pspnet_r101b-d8_769x769_80k_cityscapes_20201226_171823-f0e7c293.pth
-- Name: pspnet_r50b-d32_4xb2-80k_cityscapes-512x1024
-  In Collection: PSPNet
-  Metadata:
-    backbone: R-50-D32
-    crop size: (512,1024)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 65.75
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 3.0
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 73.88
-      mIoU(ms+flip): 76.85
-  Config: configs/pspnet/pspnet_r50b-d32_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d32_512x1024_80k_cityscapes/pspnet_r50-d32_512x1024_80k_cityscapes_20220316_224840-9092b254.pth
-- Name: pspnet_r50-d32_rsb_4xb2-adamw-80k_cityscapes-512x1024
-  In Collection: PSPNet
-  Metadata:
-    backbone: R-50b-D32 rsb
-    crop size: (512,1024)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 62.19
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 3.1
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 74.09
-      mIoU(ms+flip): 77.18
-  Config: configs/pspnet/pspnet_r50-d32_rsb_4xb2-adamw-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d32_rsb-pretrain_512x1024_adamw_80k_cityscapes/pspnet_r50-d32_rsb-pretrain_512x1024_adamw_80k_cityscapes_20220316_141229-dd9c9610.pth
-- Name: pspnet_r50b-d32_4xb2-80k_cityscapes-512x1024
-  In Collection: PSPNet
-  Metadata:
-    backbone: R-50b-D32
-    crop size: (512,1024)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 64.89
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 2.9
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 72.61
-      mIoU(ms+flip): 75.51
-  Config: configs/pspnet/pspnet_r50b-d32_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50b-d32_512x1024_80k_cityscapes/pspnet_r50b-d32_512x1024_80k_cityscapes_20220311_152152-23bcaf8c.pth
-- Name: pspnet_r50-d8_4xb4-80k_ade20k-512x512
-  In Collection: PSPNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 42.5
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 8.5
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 41.13
-      mIoU(ms+flip): 41.94
-  Config: configs/pspnet/pspnet_r50-d8_4xb4-80k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_80k_ade20k/pspnet_r50-d8_512x512_80k_ade20k_20200615_014128-15a8b914.pth
-- Name: pspnet_r101-d8_4xb4-80k_ade20k-512x512
-  In Collection: PSPNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 65.36
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 12.0
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 43.57
-      mIoU(ms+flip): 44.35
-  Config: configs/pspnet/pspnet_r101-d8_4xb4-80k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_80k_ade20k/pspnet_r101-d8_512x512_80k_ade20k_20200614_031423-b6e782f0.pth
-- Name: pspnet_r50-d8_4xb4-160k_ade20k-512x512
-  In Collection: PSPNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 160000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 42.48
-      mIoU(ms+flip): 43.44
-  Config: configs/pspnet/pspnet_r50-d8_4xb4-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_160k_ade20k/pspnet_r50-d8_512x512_160k_ade20k_20200615_184358-1890b0bd.pth
-- Name: pspnet_r101-d8_4xb4-160k_ade20k-512x512
-  In Collection: PSPNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 160000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 44.39
-      mIoU(ms+flip): 45.35
-  Config: configs/pspnet/pspnet_r101-d8_4xb4-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_160k_ade20k/pspnet_r101-d8_512x512_160k_ade20k_20200615_100650-967c316f.pth
-- Name: pspnet_r50-d8_4xb4-20k_voc12aug-512x512
-  In Collection: PSPNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 20000
-    inference time (ms/im):
-    - value: 42.39
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 6.1
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal VOC 2012 + Aug
-    Metrics:
-      mIoU: 76.78
-      mIoU(ms+flip): 77.61
-  Config: configs/pspnet/pspnet_r50-d8_4xb4-20k_voc12aug-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_20k_voc12aug/pspnet_r50-d8_512x512_20k_voc12aug_20200617_101958-ed5dfbd9.pth
-- Name: pspnet_r101-d8_4xb4-20k_voc12aug-512x512
-  In Collection: PSPNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 20000
-    inference time (ms/im):
-    - value: 66.58
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 9.6
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal VOC 2012 + Aug
-    Metrics:
-      mIoU: 78.47
-      mIoU(ms+flip): 79.25
-  Config: configs/pspnet/pspnet_r101-d8_4xb4-20k_voc12aug-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_20k_voc12aug/pspnet_r101-d8_512x512_20k_voc12aug_20200617_102003-4aef3c9a.pth
-- Name: pspnet_r50-d8_4xb4-40k_voc12aug-512x512
-  In Collection: PSPNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 40000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal VOC 2012 + Aug
-    Metrics:
-      mIoU: 77.29
-      mIoU(ms+flip): 78.48
-  Config: configs/pspnet/pspnet_r50-d8_4xb4-40k_voc12aug-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_40k_voc12aug/pspnet_r50-d8_512x512_40k_voc12aug_20200613_161222-ae9c1b8c.pth
-- Name: pspnet_r101-d8_4xb4-40k_voc12aug-512x512
-  In Collection: PSPNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 40000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal VOC 2012 + Aug
-    Metrics:
-      mIoU: 78.52
-      mIoU(ms+flip): 79.57
-  Config: configs/pspnet/pspnet_r101-d8_4xb4-40k_voc12aug-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_40k_voc12aug/pspnet_r101-d8_512x512_40k_voc12aug_20200613_161222-bc933b18.pth
-- Name: pspnet_r101-d8_4xb4-40k_pascal-context-480x480
-  In Collection: PSPNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (480,480)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 103.31
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (480,480)
-    Training Memory (GB): 8.8
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal Context
-    Metrics:
-      mIoU: 46.6
-      mIoU(ms+flip): 47.78
-  Config: configs/pspnet/pspnet_r101-d8_4xb4-40k_pascal-context-480x480.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_40k_pascal_context/pspnet_r101-d8_480x480_40k_pascal_context_20200911_211210-bf0f5d7c.pth
-- Name: pspnet_r101-d8_4xb4-80k_pascal-context-480x480
-  In Collection: PSPNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (480,480)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal Context
-    Metrics:
-      mIoU: 46.03
-      mIoU(ms+flip): 47.15
-  Config: configs/pspnet/pspnet_r101-d8_4xb4-80k_pascal-context-480x480.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_80k_pascal_context/pspnet_r101-d8_480x480_80k_pascal_context_20200911_190530-c86d6233.pth
-- Name: pspnet_r101-d8_4xb4-40k_pascal-context-59-480x480
-  In Collection: PSPNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (480,480)
-    lr schd: 40000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal Context 59
-    Metrics:
-      mIoU: 52.02
-      mIoU(ms+flip): 53.54
-  Config: configs/pspnet/pspnet_r101-d8_4xb4-40k_pascal-context-59-480x480.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_40k_pascal_context_59/pspnet_r101-d8_480x480_40k_pascal_context_59_20210416_114524-86d44cd4.pth
-- Name: pspnet_r101-d8_4xb4-80k_pascal-context-59-480x480
-  In Collection: PSPNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (480,480)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal Context 59
-    Metrics:
-      mIoU: 52.47
-      mIoU(ms+flip): 53.99
-  Config: configs/pspnet/pspnet_r101-d8_4xb4-80k_pascal-context-59-480x480.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_80k_pascal_context_59/pspnet_r101-d8_480x480_80k_pascal_context_59_20210416_114418-fa6caaa2.pth
-- Name: pspnet_r50-d8_4xb4-20k_coco-stuff10k-512x512
-  In Collection: PSPNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 20000
-    inference time (ms/im):
-    - value: 48.78
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 9.6
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: COCO-Stuff 10k
-    Metrics:
-      mIoU: 35.69
-      mIoU(ms+flip): 36.62
-  Config: configs/pspnet/pspnet_r50-d8_4xb4-20k_coco-stuff10k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_20k_coco-stuff10k/pspnet_r50-d8_512x512_4x4_20k_coco-stuff10k_20210820_203258-b88df27f.pth
-- Name: pspnet_r101-d8_4xb4-20k_coco-stuff10k-512x512
-  In Collection: PSPNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 20000
-    inference time (ms/im):
-    - value: 90.09
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 13.2
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: COCO-Stuff 10k
-    Metrics:
-      mIoU: 37.26
-      mIoU(ms+flip): 38.52
-  Config: configs/pspnet/pspnet_r101-d8_4xb4-20k_coco-stuff10k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_20k_coco-stuff10k/pspnet_r101-d8_512x512_4x4_20k_coco-stuff10k_20210820_232135-76aae482.pth
-- Name: pspnet_r50-d8_4xb4-40k_coco-stuff10k-512x512
-  In Collection: PSPNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 40000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: COCO-Stuff 10k
-    Metrics:
-      mIoU: 36.33
-      mIoU(ms+flip): 37.24
-  Config: configs/pspnet/pspnet_r50-d8_4xb4-40k_coco-stuff10k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_40k_coco-stuff10k/pspnet_r50-d8_512x512_4x4_40k_coco-stuff10k_20210821_030857-92e2902b.pth
-- Name: pspnet_r101-d8_4xb4-40k_coco-stuff10k-512x512
-  In Collection: PSPNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 40000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: COCO-Stuff 10k
-    Metrics:
-      mIoU: 37.76
-      mIoU(ms+flip): 38.86
-  Config: configs/pspnet/pspnet_r101-d8_4xb4-40k_coco-stuff10k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_40k_coco-stuff10k/pspnet_r101-d8_512x512_4x4_40k_coco-stuff10k_20210821_014022-831aec95.pth
-- Name: pspnet_r50-d8_4xb4-80k_coco-stuff164k-512x512
-  In Collection: PSPNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 48.78
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 9.6
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: COCO-Stuff 164k
-    Metrics:
-      mIoU: 38.8
-      mIoU(ms+flip): 39.19
-  Config: configs/pspnet/pspnet_r50-d8_4xb4-80k_coco-stuff164k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_80k_coco-stuff164k/pspnet_r50-d8_512x512_4x4_80k_coco-stuff164k_20210707_152034-0e41b2db.pth
-- Name: pspnet_r101-d8_4xb4-80k_coco-stuff164k-512x512
-  In Collection: PSPNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 90.09
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 13.2
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: COCO-Stuff 164k
-    Metrics:
-      mIoU: 40.34
-      mIoU(ms+flip): 40.79
-  Config: configs/pspnet/pspnet_r101-d8_4xb4-80k_coco-stuff164k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_80k_coco-stuff164k/pspnet_r101-d8_512x512_4x4_80k_coco-stuff164k_20210707_152034-7eb41789.pth
-- Name: pspnet_r50-d8_4xb4-160k_coco-stuff164k-512x512
-  In Collection: PSPNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 160000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: COCO-Stuff 164k
-    Metrics:
-      mIoU: 39.64
-      mIoU(ms+flip): 39.97
-  Config: configs/pspnet/pspnet_r50-d8_4xb4-160k_coco-stuff164k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_160k_coco-stuff164k/pspnet_r50-d8_512x512_4x4_160k_coco-stuff164k_20210707_152004-51276a57.pth
-- Name: pspnet_r101-d8_4xb4-160k_coco-stuff164k-512x512
-  In Collection: PSPNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 160000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: COCO-Stuff 164k
-    Metrics:
-      mIoU: 41.28
-      mIoU(ms+flip): 41.66
-  Config: configs/pspnet/pspnet_r101-d8_4xb4-160k_coco-stuff164k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_160k_coco-stuff164k/pspnet_r101-d8_512x512_4x4_160k_coco-stuff164k_20210707_152004-4af9621b.pth
-- Name: pspnet_r50-d8_4xb4-320k_coco-stuff164k-512x512
-  In Collection: PSPNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 320000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: COCO-Stuff 164k
-    Metrics:
-      mIoU: 40.53
-      mIoU(ms+flip): 40.75
-  Config: configs/pspnet/pspnet_r50-d8_4xb4-320k_coco-stuff164k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_4x4_320k_coco-stuff164k/pspnet_r50-d8_512x512_4x4_320k_coco-stuff164k_20210707_152004-be9610cc.pth
-- Name: pspnet_r101-d8_4xb4-320k_coco-stuff164k-512x512
-  In Collection: PSPNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 320000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: COCO-Stuff 164k
-    Metrics:
-      mIoU: 41.95
-      mIoU(ms+flip): 42.42
-  Config: configs/pspnet/pspnet_r101-d8_4xb4-320k_coco-stuff164k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_4x4_320k_coco-stuff164k/pspnet_r101-d8_512x512_4x4_320k_coco-stuff164k_20210707_152004-72220c60.pth
-- Name: pspnet_r18-d8_4xb4-80k_loveda-512x512
-  In Collection: PSPNet
-  Metadata:
-    backbone: R-18-D8
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 37.22
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 1.45
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: LoveDA
-    Metrics:
-      mIoU: 48.62
-      mIoU(ms+flip): 47.57
-  Config: configs/pspnet/pspnet_r18-d8_4xb4-80k_loveda-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_512x512_80k_loveda/pspnet_r18-d8_512x512_80k_loveda_20211105_052100-b97697f1.pth
-- Name: pspnet_r50-d8_4xb4-80k_loveda-512x512
-  In Collection: PSPNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 151.52
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 6.14
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: LoveDA
-    Metrics:
-      mIoU: 50.46
-      mIoU(ms+flip): 50.19
-  Config: configs/pspnet/pspnet_r50-d8_4xb4-80k_loveda-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_80k_loveda/pspnet_r50-d8_512x512_80k_loveda_20211104_155728-88610f9f.pth
-- Name: pspnet_r101-d8_4xb4-80k_loveda-512x512
-  In Collection: PSPNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 218.34
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 9.61
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: LoveDA
-    Metrics:
-      mIoU: 51.86
-      mIoU(ms+flip): 51.34
-  Config: configs/pspnet/pspnet_r101-d8_4xb4-80k_loveda-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_80k_loveda/pspnet_r101-d8_512x512_80k_loveda_20211104_153212-1c06c6a8.pth
-- Name: pspnet_r18-d8_4xb4-80k_potsdam-512x512
-  In Collection: PSPNet
-  Metadata:
-    backbone: R-18-D8
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 11.75
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 1.5
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Potsdam
-    Metrics:
-      mIoU: 77.09
-      mIoU(ms+flip): 78.3
-  Config: configs/pspnet/pspnet_r18-d8_4xb4-80k_potsdam-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_4x4_512x512_80k_potsdam/pspnet_r18-d8_4x4_512x512_80k_potsdam_20211220_125612-7cd046e1.pth
-- Name: pspnet_r50-d8_4xb4-80k_potsdam-512x512
-  In Collection: PSPNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 33.1
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 6.14
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Potsdam
-    Metrics:
-      mIoU: 78.12
-      mIoU(ms+flip): 78.98
-  Config: configs/pspnet/pspnet_r50-d8_4xb4-80k_potsdam-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_4x4_512x512_80k_potsdam/pspnet_r50-d8_4x4_512x512_80k_potsdam_20211219_043541-2dd5fe67.pth
-- Name: pspnet_r101-d8_4xb4-80k_potsdam-512x512
-  In Collection: PSPNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 51.55
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 9.61
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Potsdam
-    Metrics:
-      mIoU: 78.62
-      mIoU(ms+flip): 79.47
-  Config: configs/pspnet/pspnet_r101-d8_4xb4-80k_potsdam-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_4x4_512x512_80k_potsdam/pspnet_r101-d8_4x4_512x512_80k_potsdam_20211220_125612-aed036c4.pth
-- Name: pspnet_r18-d8_4xb4-80k_vaihingen-512x512
-  In Collection: PSPNet
-  Metadata:
-    backbone: R-18-D8
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 11.76
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 1.45
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Vaihingen
-    Metrics:
-      mIoU: 71.46
-      mIoU(ms+flip): 73.36
-  Config: configs/pspnet/pspnet_r18-d8_4xb4-80k_vaihingen-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_4x4_512x512_80k_vaihingen/pspnet_r18-d8_4x4_512x512_80k_vaihingen_20211228_160355-52a8a6f6.pth
-- Name: pspnet_r50-d8_4xb4-80k_vaihingen-512x512
-  In Collection: PSPNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 33.01
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 6.14
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Vaihingen
-    Metrics:
-      mIoU: 72.36
-      mIoU(ms+flip): 73.75
-  Config: configs/pspnet/pspnet_r50-d8_4xb4-80k_vaihingen-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_4x4_512x512_80k_vaihingen/pspnet_r50-d8_4x4_512x512_80k_vaihingen_20211228_160355-382f8f5b.pth
-- Name: pspnet_r101-d8_4xb4-80k_vaihingen-512x512
-  In Collection: PSPNet
-  Metadata:
-    backbone: R-101-D8
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 50.08
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 9.61
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Vaihingen
-    Metrics:
-      mIoU: 72.61
-      mIoU(ms+flip): 74.18
-  Config: configs/pspnet/pspnet_r101-d8_4xb4-80k_vaihingen-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_4x4_512x512_80k_vaihingen/pspnet_r101-d8_4x4_512x512_80k_vaihingen_20211231_230806-8eba0a09.pth
-- Name: pspnet_r18-d8_4xb4-80k_isaid-896x896
-  In Collection: PSPNet
-  Metadata:
-    backbone: R-18-D8
-    crop size: (896,896)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 37.16
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (896,896)
-    Training Memory (GB): 4.52
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: iSAID
-    Metrics:
-      mIoU: 60.22
-      mIoU(ms+flip): 61.25
-  Config: configs/pspnet/pspnet_r18-d8_4xb4-80k_isaid-896x896.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_4x4_896x896_80k_isaid/pspnet_r18-d8_4x4_896x896_80k_isaid_20220110_180526-e84c0b6a.pth
-- Name: pspnet_r50-d8_4xb4-80k_isaid-896x896
-  In Collection: PSPNet
-  Metadata:
-    backbone: R-50-D8
-    crop size: (896,896)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 112.61
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (896,896)
-    Training Memory (GB): 16.58
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: iSAID
-    Metrics:
-      mIoU: 65.36
-      mIoU(ms+flip): 66.48
-  Config: configs/pspnet/pspnet_r50-d8_4xb4-80k_isaid-896x896.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_4x4_896x896_80k_isaid/pspnet_r50-d8_4x4_896x896_80k_isaid_20220110_180629-1f21dc32.pth
diff --git a/configs/resnest/README.md b/configs/resnest/README.md
index 7f07d147b7..d27bbe2807 100644
--- a/configs/resnest/README.md
+++ b/configs/resnest/README.md
@@ -1,6 +1,6 @@
 # ResNeSt
 
-[ResNeSt: Split-Attention Networks](https://arxiv.org/abs/2004.08955)
+> [ResNeSt: Split-Attention Networks](https://arxiv.org/abs/2004.08955)
 
 ## Introduction
 
@@ -22,6 +22,26 @@ It is well known that featuremap attention and multi-path representation are imp
 <img src="https://user-images.githubusercontent.com/24582831/142902526-3cf33345-7e40-47a6-985e-4381857e21df.png" width="60%"/>
 </div>
 
+## Results and models
+
+### Cityscapes
+
+| Method     | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                                            | download                                                                                                                                                                                                                                                                                                                                                                               |
+| ---------- | -------- | --------- | ------: | -------: | -------------- | ------ | ----: | ------------- | ------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| FCN        | S-101-D8 | 512x1024  |   80000 |     11.4 | 2.39           | V100   | 77.56 | 78.98         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/resnest/resnest_s101-d8_fcn_4xb2-80k_cityscapes-512x1024.py)           | [model](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/fcn_s101-d8_512x1024_80k_cityscapes/fcn_s101-d8_512x1024_80k_cityscapes_20200807_140631-f8d155b3.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/fcn_s101-d8_512x1024_80k_cityscapes/fcn_s101-d8_512x1024_80k_cityscapes-20200807_140631.log.json)                                         |
+| PSPNet     | S-101-D8 | 512x1024  |   80000 |     11.8 | 2.52           | V100   | 78.57 | 79.19         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/resnest/resnest_s101-d8_pspnet_4xb2-80k_cityscapes512x1024.py)         | [model](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/pspnet_s101-d8_512x1024_80k_cityscapes/pspnet_s101-d8_512x1024_80k_cityscapes_20200807_140631-c75f3b99.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/pspnet_s101-d8_512x1024_80k_cityscapes/pspnet_s101-d8_512x1024_80k_cityscapes-20200807_140631.log.json)                             |
+| DeepLabV3  | S-101-D8 | 512x1024  |   80000 |     11.9 | 1.88           | V100   | 79.67 | 80.51         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/resnest/resnest_s101-d8_deeplabv3_4xb2-80k_cityscapes-512x1024.py)     | [model](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3_s101-d8_512x1024_80k_cityscapes/deeplabv3_s101-d8_512x1024_80k_cityscapes_20200807_144429-b73c4270.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3_s101-d8_512x1024_80k_cityscapes/deeplabv3_s101-d8_512x1024_80k_cityscapes-20200807_144429.log.json)                 |
+| DeepLabV3+ | S-101-D8 | 512x1024  |   80000 |     13.2 | 2.36           | V100   | 79.62 | 80.27         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/resnest/resnest_s101-d8_deeplabv3plus_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3plus_s101-d8_512x1024_80k_cityscapes/deeplabv3plus_s101-d8_512x1024_80k_cityscapes_20200807_144429-1239eb43.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3plus_s101-d8_512x1024_80k_cityscapes/deeplabv3plus_s101-d8_512x1024_80k_cityscapes-20200807_144429.log.json) |
+
+### ADE20K
+
+| Method     | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                                        | download                                                                                                                                                                                                                                                                                                                                                               |
+| ---------- | -------- | --------- | ------: | -------: | -------------- | ------ | ----: | ------------- | --------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| FCN        | S-101-D8 | 512x512   |  160000 |     14.2 | 12.86          | V100   | 45.62 | 46.16         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/resnest/resnest_s101-d8_fcn_4xb4-160k_ade20k-512x512.py)           | [model](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/fcn_s101-d8_512x512_160k_ade20k/fcn_s101-d8_512x512_160k_ade20k_20200807_145416-d3160329.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/fcn_s101-d8_512x512_160k_ade20k/fcn_s101-d8_512x512_160k_ade20k-20200807_145416.log.json)                                         |
+| PSPNet     | S-101-D8 | 512x512   |  160000 |     14.2 | 13.02          | V100   | 45.44 | 46.28         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/resnest/resnest_s101-d8_pspnet_4xb4-160k_ade20k-512x512.py)        | [model](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/pspnet_s101-d8_512x512_160k_ade20k/pspnet_s101-d8_512x512_160k_ade20k_20200807_145416-a6daa92a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/pspnet_s101-d8_512x512_160k_ade20k/pspnet_s101-d8_512x512_160k_ade20k-20200807_145416.log.json)                             |
+| DeepLabV3  | S-101-D8 | 512x512   |  160000 |     14.6 | 9.28           | V100   | 45.71 | 46.59         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/resnest/resnest_s101-d8_deeplabv3_4xb4-160k_ade20k-512x512.py)     | [model](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3_s101-d8_512x512_160k_ade20k/deeplabv3_s101-d8_512x512_160k_ade20k_20200807_144503-17ecabe5.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3_s101-d8_512x512_160k_ade20k/deeplabv3_s101-d8_512x512_160k_ade20k-20200807_144503.log.json)                 |
+| DeepLabV3+ | S-101-D8 | 512x512   |  160000 |     16.2 | 11.96          | V100   | 46.47 | 47.27         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/resnest/resnest_s101-d8_deeplabv3plus_4xb4-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3plus_s101-d8_512x512_160k_ade20k/deeplabv3plus_s101-d8_512x512_160k_ade20k_20200807_144503-27b26226.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3plus_s101-d8_512x512_160k_ade20k/deeplabv3plus_s101-d8_512x512_160k_ade20k-20200807_144503.log.json) |
+
 ## Citation
 
 ```bibtex
@@ -32,23 +52,3 @@ journal={arXiv preprint arXiv:2004.08955},
 year={2020}
 }
 ```
-
-## Results and models
-
-### Cityscapes
-
-| Method     | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                                            | download                                                                                                                                                                                                                                                                                                                                                                               |
-| ---------- | -------- | --------- | ------: | -------: | -------------- | ----: | ------------- | ------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| FCN        | S-101-D8 | 512x1024  |   80000 |     11.4 | 2.39           | 77.56 | 78.98         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/resnest/resnest_s101-d8_fcn_4xb2-80k_cityscapes-512x1024.py)           | [model](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/fcn_s101-d8_512x1024_80k_cityscapes/fcn_s101-d8_512x1024_80k_cityscapes_20200807_140631-f8d155b3.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/fcn_s101-d8_512x1024_80k_cityscapes/fcn_s101-d8_512x1024_80k_cityscapes-20200807_140631.log.json)                                         |
-| PSPNet     | S-101-D8 | 512x1024  |   80000 |     11.8 | 2.52           | 78.57 | 79.19         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/resnest/resnest_s101-d8_pspnet_4xb2-80k_cityscapes512x1024.py)         | [model](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/pspnet_s101-d8_512x1024_80k_cityscapes/pspnet_s101-d8_512x1024_80k_cityscapes_20200807_140631-c75f3b99.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/pspnet_s101-d8_512x1024_80k_cityscapes/pspnet_s101-d8_512x1024_80k_cityscapes-20200807_140631.log.json)                             |
-| DeepLabV3  | S-101-D8 | 512x1024  |   80000 |     11.9 | 1.88           | 79.67 | 80.51         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/resnest/resnest_s101-d8_deeplabv3_4xb2-80k_cityscapes-512x1024.py)     | [model](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3_s101-d8_512x1024_80k_cityscapes/deeplabv3_s101-d8_512x1024_80k_cityscapes_20200807_144429-b73c4270.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3_s101-d8_512x1024_80k_cityscapes/deeplabv3_s101-d8_512x1024_80k_cityscapes-20200807_144429.log.json)                 |
-| DeepLabV3+ | S-101-D8 | 512x1024  |   80000 |     13.2 | 2.36           | 79.62 | 80.27         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/resnest/resnest_s101-d8_deeplabv3plus_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3plus_s101-d8_512x1024_80k_cityscapes/deeplabv3plus_s101-d8_512x1024_80k_cityscapes_20200807_144429-1239eb43.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3plus_s101-d8_512x1024_80k_cityscapes/deeplabv3plus_s101-d8_512x1024_80k_cityscapes-20200807_144429.log.json) |
-
-### ADE20K
-
-| Method     | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                                        | download                                                                                                                                                                                                                                                                                                                                                               |
-| ---------- | -------- | --------- | ------: | -------: | -------------- | ----: | ------------- | --------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| FCN        | S-101-D8 | 512x512   |  160000 |     14.2 | 12.86          | 45.62 | 46.16         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/resnest/resnest_s101-d8_fcn_4xb4-160k_ade20k-512x512.py)           | [model](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/fcn_s101-d8_512x512_160k_ade20k/fcn_s101-d8_512x512_160k_ade20k_20200807_145416-d3160329.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/fcn_s101-d8_512x512_160k_ade20k/fcn_s101-d8_512x512_160k_ade20k-20200807_145416.log.json)                                         |
-| PSPNet     | S-101-D8 | 512x512   |  160000 |     14.2 | 13.02          | 45.44 | 46.28         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/resnest/resnest_s101-d8_pspnet_4xb4-160k_ade20k-512x512.py)        | [model](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/pspnet_s101-d8_512x512_160k_ade20k/pspnet_s101-d8_512x512_160k_ade20k_20200807_145416-a6daa92a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/pspnet_s101-d8_512x512_160k_ade20k/pspnet_s101-d8_512x512_160k_ade20k-20200807_145416.log.json)                             |
-| DeepLabV3  | S-101-D8 | 512x512   |  160000 |     14.6 | 9.28           | 45.71 | 46.59         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/resnest/resnest_s101-d8_deeplabv3_4xb4-160k_ade20k-512x512.py)     | [model](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3_s101-d8_512x512_160k_ade20k/deeplabv3_s101-d8_512x512_160k_ade20k_20200807_144503-17ecabe5.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3_s101-d8_512x512_160k_ade20k/deeplabv3_s101-d8_512x512_160k_ade20k-20200807_144503.log.json)                 |
-| DeepLabV3+ | S-101-D8 | 512x512   |  160000 |     16.2 | 11.96          | 46.47 | 47.27         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/resnest/resnest_s101-d8_deeplabv3plus_4xb4-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3plus_s101-d8_512x512_160k_ade20k/deeplabv3plus_s101-d8_512x512_160k_ade20k_20200807_144503-27b26226.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3plus_s101-d8_512x512_160k_ade20k/deeplabv3plus_s101-d8_512x512_160k_ade20k-20200807_144503.log.json) |
diff --git a/configs/resnest/metafile.yaml b/configs/resnest/metafile.yaml
new file mode 100644
index 0000000000..0b8d41ebfd
--- /dev/null
+++ b/configs/resnest/metafile.yaml
@@ -0,0 +1,193 @@
+Models:
+- Name: resnest_s101-d8_fcn_4xb2-80k_cityscapes-512x1024
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.56
+      mIoU(ms+flip): 78.98
+  Config: configs/resnest/resnest_s101-d8_fcn_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - S-101-D8
+    - FCN
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 11.4
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/fcn_s101-d8_512x1024_80k_cityscapes/fcn_s101-d8_512x1024_80k_cityscapes_20200807_140631-f8d155b3.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/fcn_s101-d8_512x1024_80k_cityscapes/fcn_s101-d8_512x1024_80k_cityscapes-20200807_140631.log.json
+  Paper:
+    Title: 'ResNeSt: Split-Attention Networks'
+    URL: https://arxiv.org/abs/2004.08955
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/resnest.py#L271
+  Framework: PyTorch
+- Name: resnest_s101-d8_pspnet_4xb2-80k_cityscapes512x1024
+  In Collection: PSPNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.57
+      mIoU(ms+flip): 79.19
+  Config: configs/resnest/resnest_s101-d8_pspnet_4xb2-80k_cityscapes512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - S-101-D8
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 11.8
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/pspnet_s101-d8_512x1024_80k_cityscapes/pspnet_s101-d8_512x1024_80k_cityscapes_20200807_140631-c75f3b99.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/pspnet_s101-d8_512x1024_80k_cityscapes/pspnet_s101-d8_512x1024_80k_cityscapes-20200807_140631.log.json
+  Paper:
+    Title: 'ResNeSt: Split-Attention Networks'
+    URL: https://arxiv.org/abs/2004.08955
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/resnest.py#L271
+  Framework: PyTorch
+- Name: resnest_s101-d8_deeplabv3_4xb2-80k_cityscapes-512x1024
+  In Collection: DeepLabV3
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.67
+      mIoU(ms+flip): 80.51
+  Config: configs/resnest/resnest_s101-d8_deeplabv3_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - S-101-D8
+    - DeepLabV3
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 11.9
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3_s101-d8_512x1024_80k_cityscapes/deeplabv3_s101-d8_512x1024_80k_cityscapes_20200807_144429-b73c4270.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3_s101-d8_512x1024_80k_cityscapes/deeplabv3_s101-d8_512x1024_80k_cityscapes-20200807_144429.log.json
+  Paper:
+    Title: 'ResNeSt: Split-Attention Networks'
+    URL: https://arxiv.org/abs/2004.08955
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/resnest.py#L271
+  Framework: PyTorch
+- Name: resnest_s101-d8_deeplabv3plus_4xb2-80k_cityscapes-512x1024
+  In Collection: DeepLabV3+
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.62
+      mIoU(ms+flip): 80.27
+  Config: configs/resnest/resnest_s101-d8_deeplabv3plus_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - S-101-D8
+    - DeepLabV3+
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 13.2
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3plus_s101-d8_512x1024_80k_cityscapes/deeplabv3plus_s101-d8_512x1024_80k_cityscapes_20200807_144429-1239eb43.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3plus_s101-d8_512x1024_80k_cityscapes/deeplabv3plus_s101-d8_512x1024_80k_cityscapes-20200807_144429.log.json
+  Paper:
+    Title: 'ResNeSt: Split-Attention Networks'
+    URL: https://arxiv.org/abs/2004.08955
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/resnest.py#L271
+  Framework: PyTorch
+- Name: resnest_s101-d8_fcn_4xb4-160k_ade20k-512x512
+  In Collection: FCN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 45.62
+      mIoU(ms+flip): 46.16
+  Config: configs/resnest/resnest_s101-d8_fcn_4xb4-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - S-101-D8
+    - FCN
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 14.2
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/fcn_s101-d8_512x512_160k_ade20k/fcn_s101-d8_512x512_160k_ade20k_20200807_145416-d3160329.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/fcn_s101-d8_512x512_160k_ade20k/fcn_s101-d8_512x512_160k_ade20k-20200807_145416.log.json
+  Paper:
+    Title: 'ResNeSt: Split-Attention Networks'
+    URL: https://arxiv.org/abs/2004.08955
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/resnest.py#L271
+  Framework: PyTorch
+- Name: resnest_s101-d8_pspnet_4xb4-160k_ade20k-512x512
+  In Collection: PSPNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 45.44
+      mIoU(ms+flip): 46.28
+  Config: configs/resnest/resnest_s101-d8_pspnet_4xb4-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - S-101-D8
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 14.2
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/pspnet_s101-d8_512x512_160k_ade20k/pspnet_s101-d8_512x512_160k_ade20k_20200807_145416-a6daa92a.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/pspnet_s101-d8_512x512_160k_ade20k/pspnet_s101-d8_512x512_160k_ade20k-20200807_145416.log.json
+  Paper:
+    Title: 'ResNeSt: Split-Attention Networks'
+    URL: https://arxiv.org/abs/2004.08955
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/resnest.py#L271
+  Framework: PyTorch
+- Name: resnest_s101-d8_deeplabv3_4xb4-160k_ade20k-512x512
+  In Collection: DeepLabV3
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 45.71
+      mIoU(ms+flip): 46.59
+  Config: configs/resnest/resnest_s101-d8_deeplabv3_4xb4-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - S-101-D8
+    - DeepLabV3
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 14.6
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3_s101-d8_512x512_160k_ade20k/deeplabv3_s101-d8_512x512_160k_ade20k_20200807_144503-17ecabe5.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3_s101-d8_512x512_160k_ade20k/deeplabv3_s101-d8_512x512_160k_ade20k-20200807_144503.log.json
+  Paper:
+    Title: 'ResNeSt: Split-Attention Networks'
+    URL: https://arxiv.org/abs/2004.08955
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/resnest.py#L271
+  Framework: PyTorch
+- Name: resnest_s101-d8_deeplabv3plus_4xb4-160k_ade20k-512x512
+  In Collection: DeepLabV3+
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 46.47
+      mIoU(ms+flip): 47.27
+  Config: configs/resnest/resnest_s101-d8_deeplabv3plus_4xb4-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - S-101-D8
+    - DeepLabV3+
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 16.2
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3plus_s101-d8_512x512_160k_ade20k/deeplabv3plus_s101-d8_512x512_160k_ade20k_20200807_144503-27b26226.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3plus_s101-d8_512x512_160k_ade20k/deeplabv3plus_s101-d8_512x512_160k_ade20k-20200807_144503.log.json
+  Paper:
+    Title: 'ResNeSt: Split-Attention Networks'
+    URL: https://arxiv.org/abs/2004.08955
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/resnest.py#L271
+  Framework: PyTorch
diff --git a/configs/resnest/resnest.yml b/configs/resnest/resnest.yml
deleted file mode 100644
index ab897e3bd5..0000000000
--- a/configs/resnest/resnest.yml
+++ /dev/null
@@ -1,177 +0,0 @@
-Models:
-- Name: resnest_s101-d8_fcn_4xb2-80k_cityscapes-512x1024
-  In Collection: FCN
-  Metadata:
-    backbone: S-101-D8
-    crop size: (512,1024)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 418.41
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 11.4
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 77.56
-      mIoU(ms+flip): 78.98
-  Config: configs/resnest/resnest_s101-d8_fcn_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/fcn_s101-d8_512x1024_80k_cityscapes/fcn_s101-d8_512x1024_80k_cityscapes_20200807_140631-f8d155b3.pth
-- Name: resnest_s101-d8_pspnet_4xb2-80k_cityscapes512x1024
-  In Collection: PSPNet
-  Metadata:
-    backbone: S-101-D8
-    crop size: (512,1024)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 396.83
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 11.8
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 78.57
-      mIoU(ms+flip): 79.19
-  Config: configs/resnest/resnest_s101-d8_pspnet_4xb2-80k_cityscapes512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/pspnet_s101-d8_512x1024_80k_cityscapes/pspnet_s101-d8_512x1024_80k_cityscapes_20200807_140631-c75f3b99.pth
-- Name: resnest_s101-d8_deeplabv3_4xb2-80k_cityscapes-512x1024
-  In Collection: DeepLabV3
-  Metadata:
-    backbone: S-101-D8
-    crop size: (512,1024)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 531.91
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 11.9
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 79.67
-      mIoU(ms+flip): 80.51
-  Config: configs/resnest/resnest_s101-d8_deeplabv3_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3_s101-d8_512x1024_80k_cityscapes/deeplabv3_s101-d8_512x1024_80k_cityscapes_20200807_144429-b73c4270.pth
-- Name: resnest_s101-d8_deeplabv3plus_4xb2-80k_cityscapes-512x1024
-  In Collection: DeepLabV3+
-  Metadata:
-    backbone: S-101-D8
-    crop size: (512,1024)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 423.73
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 13.2
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 79.62
-      mIoU(ms+flip): 80.27
-  Config: configs/resnest/resnest_s101-d8_deeplabv3plus_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3plus_s101-d8_512x1024_80k_cityscapes/deeplabv3plus_s101-d8_512x1024_80k_cityscapes_20200807_144429-1239eb43.pth
-- Name: resnest_s101-d8_fcn_4xb4-160k_ade20k-512x512
-  In Collection: FCN
-  Metadata:
-    backbone: S-101-D8
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 77.76
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 14.2
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 45.62
-      mIoU(ms+flip): 46.16
-  Config: configs/resnest/resnest_s101-d8_fcn_4xb4-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/fcn_s101-d8_512x512_160k_ade20k/fcn_s101-d8_512x512_160k_ade20k_20200807_145416-d3160329.pth
-- Name: resnest_s101-d8_pspnet_4xb4-160k_ade20k-512x512
-  In Collection: PSPNet
-  Metadata:
-    backbone: S-101-D8
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 76.8
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 14.2
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 45.44
-      mIoU(ms+flip): 46.28
-  Config: configs/resnest/resnest_s101-d8_pspnet_4xb4-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/pspnet_s101-d8_512x512_160k_ade20k/pspnet_s101-d8_512x512_160k_ade20k_20200807_145416-a6daa92a.pth
-- Name: resnest_s101-d8_deeplabv3_4xb4-160k_ade20k-512x512
-  In Collection: DeepLabV3
-  Metadata:
-    backbone: S-101-D8
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 107.76
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 14.6
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 45.71
-      mIoU(ms+flip): 46.59
-  Config: configs/resnest/resnest_s101-d8_deeplabv3_4xb4-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3_s101-d8_512x512_160k_ade20k/deeplabv3_s101-d8_512x512_160k_ade20k_20200807_144503-17ecabe5.pth
-- Name: resnest_s101-d8_deeplabv3plus_4xb4-160k_ade20k-512x512
-  In Collection: DeepLabV3+
-  Metadata:
-    backbone: S-101-D8
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 83.61
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 16.2
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 46.47
-      mIoU(ms+flip): 47.27
-  Config: configs/resnest/resnest_s101-d8_deeplabv3plus_4xb4-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3plus_s101-d8_512x512_160k_ade20k/deeplabv3plus_s101-d8_512x512_160k_ade20k_20200807_144503-27b26226.pth
diff --git a/configs/segformer/README.md b/configs/segformer/README.md
index be64099da3..5dbf1215bc 100644
--- a/configs/segformer/README.md
+++ b/configs/segformer/README.md
@@ -1,6 +1,6 @@
 # SegFormer
 
-[SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203)
+> [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203)
 
 ## Introduction
 
@@ -22,17 +22,6 @@ We present SegFormer, a simple, efficient yet powerful semantic segmentation fra
 <img src="https://user-images.githubusercontent.com/24582831/142902600-e188073e-5744-4ba9-8dbf-9316e55c74aa.png" width="70%"/>
 </div>
 
-## Citation
-
-```bibtex
-@article{xie2021segformer,
-  title={SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers},
-  author={Xie, Enze and Wang, Wenhai and Yu, Zhiding and Anandkumar, Anima and Alvarez, Jose M and Luo, Ping},
-  journal={arXiv preprint arXiv:2105.15203},
-  year={2021}
-}
-```
-
 ## Usage
 
 To use other repositories' pre-trained models, it is necessary to convert keys.
@@ -49,15 +38,15 @@ This script convert model from `PRETRAIN_PATH` and store the converted model in
 
 ### ADE20K
 
-| Method    | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                             | download                                                                                                                                                                                                                                                                                                                                               |
-| --------- | -------- | --------- | ------: | -------: | -------------- | ----: | ------------- | ---------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| Segformer | MIT-B0   | 512x512   |  160000 |      2.1 | 51.32          | 37.41 | 38.34         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/segformer/segformer_mit-b0_8xb2-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b0_512x512_160k_ade20k/segformer_mit-b0_512x512_160k_ade20k_20210726_101530-8ffa8fda.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b0_512x512_160k_ade20k/segformer_mit-b0_512x512_160k_ade20k_20210726_101530.log.json) |
-| Segformer | MIT-B1   | 512x512   |  160000 |      2.6 | 47.66          | 40.97 | 42.54         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/segformer/segformer_mit-b1_8xb2-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b1_512x512_160k_ade20k/segformer_mit-b1_512x512_160k_ade20k_20210726_112106-d70e859d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b1_512x512_160k_ade20k/segformer_mit-b1_512x512_160k_ade20k_20210726_112106.log.json) |
-| Segformer | MIT-B2   | 512x512   |  160000 |      3.6 | 30.88          | 45.58 | 47.03         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/segformer/segformer_mit-b2_8xb2-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b2_512x512_160k_ade20k/segformer_mit-b2_512x512_160k_ade20k_20210726_112103-cbd414ac.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b2_512x512_160k_ade20k/segformer_mit-b2_512x512_160k_ade20k_20210726_112103.log.json) |
-| Segformer | MIT-B3   | 512x512   |  160000 |      4.8 | 22.11          | 47.82 | 48.81         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/segformer/segformer_mit-b3_8xb2-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b3_512x512_160k_ade20k/segformer_mit-b3_512x512_160k_ade20k_20210726_081410-962b98d2.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b3_512x512_160k_ade20k/segformer_mit-b3_512x512_160k_ade20k_20210726_081410.log.json) |
-| Segformer | MIT-B4   | 512x512   |  160000 |      6.1 | 15.45          | 48.46 | 49.76         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/segformer/segformer_mit-b4_8xb2-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b4_512x512_160k_ade20k/segformer_mit-b4_512x512_160k_ade20k_20210728_183055-7f509d7d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b4_512x512_160k_ade20k/segformer_mit-b4_512x512_160k_ade20k_20210728_183055.log.json) |
-| Segformer | MIT-B5   | 512x512   |  160000 |      7.2 | 11.89          | 49.13 | 50.22         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/segformer/segformer_mit-b5_8xb2-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b5_512x512_160k_ade20k/segformer_mit-b5_512x512_160k_ade20k_20210726_145235-94cedf59.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b5_512x512_160k_ade20k/segformer_mit-b5_512x512_160k_ade20k_20210726_145235.log.json) |
-| Segformer | MIT-B5   | 640x640   |  160000 |     11.5 | 11.30          | 49.62 | 50.36         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/segformer/segformer_mit-b5_8xb2-160k_ade20k-640x640.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b5_640x640_160k_ade20k/segformer_mit-b5_640x640_160k_ade20k_20210801_121243-41d2845b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b5_640x640_160k_ade20k/segformer_mit-b5_640x640_160k_ade20k_20210801_121243.log.json) |
+| Method    | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device   |  mIoU | mIoU(ms+flip) | config                                                                                                                             | download                                                                                                                                                                                                                                                                                                                                               |
+| --------- | -------- | --------- | ------: | -------: | -------------- | -------- | ----: | ------------- | ---------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| Segformer | MIT-B0   | 512x512   |  160000 |      2.1 | 51.32          | 1080 Ti  | 37.41 | 38.34         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/segformer/segformer_mit-b0_8xb2-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b0_512x512_160k_ade20k/segformer_mit-b0_512x512_160k_ade20k_20210726_101530-8ffa8fda.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b0_512x512_160k_ade20k/segformer_mit-b0_512x512_160k_ade20k_20210726_101530.log.json) |
+| Segformer | MIT-B1   | 512x512   |  160000 |      2.6 | 47.66          | TITAN Xp | 40.97 | 42.54         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/segformer/segformer_mit-b1_8xb2-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b1_512x512_160k_ade20k/segformer_mit-b1_512x512_160k_ade20k_20210726_112106-d70e859d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b1_512x512_160k_ade20k/segformer_mit-b1_512x512_160k_ade20k_20210726_112106.log.json) |
+| Segformer | MIT-B2   | 512x512   |  160000 |      3.6 | 30.88          | TITAN Xp | 45.58 | 47.03         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/segformer/segformer_mit-b2_8xb2-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b2_512x512_160k_ade20k/segformer_mit-b2_512x512_160k_ade20k_20210726_112103-cbd414ac.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b2_512x512_160k_ade20k/segformer_mit-b2_512x512_160k_ade20k_20210726_112103.log.json) |
+| Segformer | MIT-B3   | 512x512   |  160000 |      4.8 | 22.11          | V100     | 47.82 | 48.81         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/segformer/segformer_mit-b3_8xb2-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b3_512x512_160k_ade20k/segformer_mit-b3_512x512_160k_ade20k_20210726_081410-962b98d2.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b3_512x512_160k_ade20k/segformer_mit-b3_512x512_160k_ade20k_20210726_081410.log.json) |
+| Segformer | MIT-B4   | 512x512   |  160000 |      6.1 | 15.45          | V100     | 48.46 | 49.76         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/segformer/segformer_mit-b4_8xb2-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b4_512x512_160k_ade20k/segformer_mit-b4_512x512_160k_ade20k_20210728_183055-7f509d7d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b4_512x512_160k_ade20k/segformer_mit-b4_512x512_160k_ade20k_20210728_183055.log.json) |
+| Segformer | MIT-B5   | 512x512   |  160000 |      7.2 | 11.89          | V100     | 49.13 | 50.22         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/segformer/segformer_mit-b5_8xb2-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b5_512x512_160k_ade20k/segformer_mit-b5_512x512_160k_ade20k_20210726_145235-94cedf59.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b5_512x512_160k_ade20k/segformer_mit-b5_512x512_160k_ade20k_20210726_145235.log.json) |
+| Segformer | MIT-B5   | 640x640   |  160000 |     11.5 | 11.30          | V100     | 49.62 | 50.36         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/segformer/segformer_mit-b5_8xb2-160k_ade20k-640x640.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b5_640x640_160k_ade20k/segformer_mit-b5_640x640_160k_ade20k_20210801_121243-41d2845b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b5_640x640_160k_ade20k/segformer_mit-b5_640x640_160k_ade20k_20210801_121243.log.json) |
 
 Evaluation with AlignedResize:
 
@@ -91,11 +80,22 @@ test_pipeline = [
 
 The lower fps result is caused by the sliding window inference scheme (window size:1024x1024).
 
-| Method    | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                                   | download                                                                                                                                                                                                                                                                                                                                                                                       |
-| --------- | -------- | --------- | ------: | -------: | -------------- | ----: | ------------- | ---------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| Segformer | MIT-B0   | 1024x1024 |  160000 |     3.64 | 4.74           | 76.54 | 78.22         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/segformer/segformer_mit-b0_8xb1-160k_cityscapes-1024x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b0_8x1_1024x1024_160k_cityscapes/segformer_mit-b0_8x1_1024x1024_160k_cityscapes_20211208_101857-e7f88502.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b0_8x1_1024x1024_160k_cityscapes/segformer_mit-b0_8x1_1024x1024_160k_cityscapes_20211208_101857.log.json) |
-| Segformer | MIT-B1   | 1024x1024 |  160000 |     4.49 | 4.3            | 78.56 | 79.73         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/segformer/segformer_mit-b1_8xb1-160k_cityscapes-1024x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b1_8x1_1024x1024_160k_cityscapes/segformer_mit-b1_8x1_1024x1024_160k_cityscapes_20211208_064213-655c7b3f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b1_8x1_1024x1024_160k_cityscapes/segformer_mit-b1_8x1_1024x1024_160k_cityscapes_20211208_064213.log.json) |
-| Segformer | MIT-B2   | 1024x1024 |  160000 |     7.42 | 3.36           | 81.08 | 82.18         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/segformer/segformer_mit-b2_8xb1-160k_cityscapes-1024x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b2_8x1_1024x1024_160k_cityscapes/segformer_mit-b2_8x1_1024x1024_160k_cityscapes_20211207_134205-6096669a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b2_8x1_1024x1024_160k_cityscapes/segformer_mit-b2_8x1_1024x1024_160k_cityscapes_20211207_134205.log.json) |
-| Segformer | MIT-B3   | 1024x1024 |  160000 |    10.86 | 2.53           | 81.94 | 83.14         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/segformer/segformer_mit-b3_8xb1-160k_cityscapes-1024x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b3_8x1_1024x1024_160k_cityscapes/segformer_mit-b3_8x1_1024x1024_160k_cityscapes_20211206_224823-a8f8a177.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b3_8x1_1024x1024_160k_cityscapes/segformer_mit-b3_8x1_1024x1024_160k_cityscapes_20211206_224823.log.json) |
-| Segformer | MIT-B4   | 1024x1024 |  160000 |    15.07 | 1.88           | 81.89 | 83.38         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/segformer/segformer_mit-b4_8xb1-160k_cityscapes-1024x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b4_8x1_1024x1024_160k_cityscapes/segformer_mit-b4_8x1_1024x1024_160k_cityscapes_20211207_080709-07f6c333.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b4_8x1_1024x1024_160k_cityscapes/segformer_mit-b4_8x1_1024x1024_160k_cityscapes_20211207_080709.log.json) |
-| Segformer | MIT-B5   | 1024x1024 |  160000 |    18.00 | 1.39           | 82.25 | 83.48         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/segformer/segformer_mit-b5_8xb1-160k_cityscapes-1024x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b5_8x1_1024x1024_160k_cityscapes/segformer_mit-b5_8x1_1024x1024_160k_cityscapes_20211206_072934-87a052ec.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b5_8x1_1024x1024_160k_cityscapes/segformer_mit-b5_8x1_1024x1024_160k_cityscapes_20211206_072934.log.json) |
+| Method    | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                                   | download                                                                                                                                                                                                                                                                                                                                                                                       |
+| --------- | -------- | --------- | ------: | -------: | -------------- | ------ | ----: | ------------- | ---------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| Segformer | MIT-B0   | 1024x1024 |  160000 |     3.64 | 4.74           | V100   | 76.54 | 78.22         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/segformer/segformer_mit-b0_8xb1-160k_cityscapes-1024x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b0_8x1_1024x1024_160k_cityscapes/segformer_mit-b0_8x1_1024x1024_160k_cityscapes_20211208_101857-e7f88502.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b0_8x1_1024x1024_160k_cityscapes/segformer_mit-b0_8x1_1024x1024_160k_cityscapes_20211208_101857.log.json) |
+| Segformer | MIT-B1   | 1024x1024 |  160000 |     4.49 | 4.3            | V100   | 78.56 | 79.73         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/segformer/segformer_mit-b1_8xb1-160k_cityscapes-1024x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b1_8x1_1024x1024_160k_cityscapes/segformer_mit-b1_8x1_1024x1024_160k_cityscapes_20211208_064213-655c7b3f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b1_8x1_1024x1024_160k_cityscapes/segformer_mit-b1_8x1_1024x1024_160k_cityscapes_20211208_064213.log.json) |
+| Segformer | MIT-B2   | 1024x1024 |  160000 |     7.42 | 3.36           | V100   | 81.08 | 82.18         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/segformer/segformer_mit-b2_8xb1-160k_cityscapes-1024x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b2_8x1_1024x1024_160k_cityscapes/segformer_mit-b2_8x1_1024x1024_160k_cityscapes_20211207_134205-6096669a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b2_8x1_1024x1024_160k_cityscapes/segformer_mit-b2_8x1_1024x1024_160k_cityscapes_20211207_134205.log.json) |
+| Segformer | MIT-B3   | 1024x1024 |  160000 |    10.86 | 2.53           | V100   | 81.94 | 83.14         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/segformer/segformer_mit-b3_8xb1-160k_cityscapes-1024x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b3_8x1_1024x1024_160k_cityscapes/segformer_mit-b3_8x1_1024x1024_160k_cityscapes_20211206_224823-a8f8a177.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b3_8x1_1024x1024_160k_cityscapes/segformer_mit-b3_8x1_1024x1024_160k_cityscapes_20211206_224823.log.json) |
+| Segformer | MIT-B4   | 1024x1024 |  160000 |    15.07 | 1.88           | V100   | 81.89 | 83.38         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/segformer/segformer_mit-b4_8xb1-160k_cityscapes-1024x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b4_8x1_1024x1024_160k_cityscapes/segformer_mit-b4_8x1_1024x1024_160k_cityscapes_20211207_080709-07f6c333.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b4_8x1_1024x1024_160k_cityscapes/segformer_mit-b4_8x1_1024x1024_160k_cityscapes_20211207_080709.log.json) |
+| Segformer | MIT-B5   | 1024x1024 |  160000 |    18.00 | 1.39           | V100   | 82.25 | 83.48         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/segformer/segformer_mit-b5_8xb1-160k_cityscapes-1024x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b5_8x1_1024x1024_160k_cityscapes/segformer_mit-b5_8x1_1024x1024_160k_cityscapes_20211206_072934-87a052ec.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b5_8x1_1024x1024_160k_cityscapes/segformer_mit-b5_8x1_1024x1024_160k_cityscapes_20211206_072934.log.json) |
+
+## Citation
+
+```bibtex
+@article{xie2021segformer,
+  title={SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers},
+  author={Xie, Enze and Wang, Wenhai and Yu, Zhiding and Anandkumar, Anima and Alvarez, Jose M and Luo, Ping},
+  journal={arXiv preprint arXiv:2105.15203},
+  year={2021}
+}
+```
diff --git a/configs/segformer/metafile.yaml b/configs/segformer/metafile.yaml
new file mode 100644
index 0000000000..7fb38d745b
--- /dev/null
+++ b/configs/segformer/metafile.yaml
@@ -0,0 +1,340 @@
+Collections:
+- Name: Segformer
+  License: Apache License 2.0
+  Metadata:
+    Training Data:
+    - ADE20K
+    - Cityscapes
+  Paper:
+    Title: 'SegFormer: Simple and Efficient Design for Semantic Segmentation with
+      Transformers'
+    URL: https://arxiv.org/abs/2105.15203
+  README: configs/segformer/README.md
+  Frameworks:
+  - PyTorch
+Models:
+- Name: segformer_mit-b0_8xb2-160k_ade20k-512x512
+  In Collection: Segformer
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 37.41
+      mIoU(ms+flip): 38.34
+  Config: configs/segformer/segformer_mit-b0_8xb2-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - MIT-B0
+    - Segformer
+    Training Resources: 8x 1080 Ti GPUS
+    Memory (GB): 2.1
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b0_512x512_160k_ade20k/segformer_mit-b0_512x512_160k_ade20k_20210726_101530-8ffa8fda.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b0_512x512_160k_ade20k/segformer_mit-b0_512x512_160k_ade20k_20210726_101530.log.json
+  Paper:
+    Title: 'SegFormer: Simple and Efficient Design for Semantic Segmentation with
+      Transformers'
+    URL: https://arxiv.org/abs/2105.15203
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mit.py#L246
+  Framework: PyTorch
+- Name: segformer_mit-b1_8xb2-160k_ade20k-512x512
+  In Collection: Segformer
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 40.97
+      mIoU(ms+flip): 42.54
+  Config: configs/segformer/segformer_mit-b1_8xb2-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - MIT-B1
+    - Segformer
+    Training Resources: 8x TITAN Xp GPUS
+    Memory (GB): 2.6
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b1_512x512_160k_ade20k/segformer_mit-b1_512x512_160k_ade20k_20210726_112106-d70e859d.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b1_512x512_160k_ade20k/segformer_mit-b1_512x512_160k_ade20k_20210726_112106.log.json
+  Paper:
+    Title: 'SegFormer: Simple and Efficient Design for Semantic Segmentation with
+      Transformers'
+    URL: https://arxiv.org/abs/2105.15203
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mit.py#L246
+  Framework: PyTorch
+- Name: segformer_mit-b2_8xb2-160k_ade20k-512x512
+  In Collection: Segformer
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 45.58
+      mIoU(ms+flip): 47.03
+  Config: configs/segformer/segformer_mit-b2_8xb2-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - MIT-B2
+    - Segformer
+    Training Resources: 8x TITAN Xp GPUS
+    Memory (GB): 3.6
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b2_512x512_160k_ade20k/segformer_mit-b2_512x512_160k_ade20k_20210726_112103-cbd414ac.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b2_512x512_160k_ade20k/segformer_mit-b2_512x512_160k_ade20k_20210726_112103.log.json
+  Paper:
+    Title: 'SegFormer: Simple and Efficient Design for Semantic Segmentation with
+      Transformers'
+    URL: https://arxiv.org/abs/2105.15203
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mit.py#L246
+  Framework: PyTorch
+- Name: segformer_mit-b3_8xb2-160k_ade20k-512x512
+  In Collection: Segformer
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 47.82
+      mIoU(ms+flip): 48.81
+  Config: configs/segformer/segformer_mit-b3_8xb2-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - MIT-B3
+    - Segformer
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 4.8
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b3_512x512_160k_ade20k/segformer_mit-b3_512x512_160k_ade20k_20210726_081410-962b98d2.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b3_512x512_160k_ade20k/segformer_mit-b3_512x512_160k_ade20k_20210726_081410.log.json
+  Paper:
+    Title: 'SegFormer: Simple and Efficient Design for Semantic Segmentation with
+      Transformers'
+    URL: https://arxiv.org/abs/2105.15203
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mit.py#L246
+  Framework: PyTorch
+- Name: segformer_mit-b4_8xb2-160k_ade20k-512x512
+  In Collection: Segformer
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 48.46
+      mIoU(ms+flip): 49.76
+  Config: configs/segformer/segformer_mit-b4_8xb2-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - MIT-B4
+    - Segformer
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 6.1
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b4_512x512_160k_ade20k/segformer_mit-b4_512x512_160k_ade20k_20210728_183055-7f509d7d.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b4_512x512_160k_ade20k/segformer_mit-b4_512x512_160k_ade20k_20210728_183055.log.json
+  Paper:
+    Title: 'SegFormer: Simple and Efficient Design for Semantic Segmentation with
+      Transformers'
+    URL: https://arxiv.org/abs/2105.15203
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mit.py#L246
+  Framework: PyTorch
+- Name: segformer_mit-b5_8xb2-160k_ade20k-512x512
+  In Collection: Segformer
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 49.13
+      mIoU(ms+flip): 50.22
+  Config: configs/segformer/segformer_mit-b5_8xb2-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - MIT-B5
+    - Segformer
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 7.2
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b5_512x512_160k_ade20k/segformer_mit-b5_512x512_160k_ade20k_20210726_145235-94cedf59.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b5_512x512_160k_ade20k/segformer_mit-b5_512x512_160k_ade20k_20210726_145235.log.json
+  Paper:
+    Title: 'SegFormer: Simple and Efficient Design for Semantic Segmentation with
+      Transformers'
+    URL: https://arxiv.org/abs/2105.15203
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mit.py#L246
+  Framework: PyTorch
+- Name: segformer_mit-b5_8xb2-160k_ade20k-640x640
+  In Collection: Segformer
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 49.62
+      mIoU(ms+flip): 50.36
+  Config: configs/segformer/segformer_mit-b5_8xb2-160k_ade20k-640x640.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - MIT-B5
+    - Segformer
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 11.5
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b5_640x640_160k_ade20k/segformer_mit-b5_640x640_160k_ade20k_20210801_121243-41d2845b.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b5_640x640_160k_ade20k/segformer_mit-b5_640x640_160k_ade20k_20210801_121243.log.json
+  Paper:
+    Title: 'SegFormer: Simple and Efficient Design for Semantic Segmentation with
+      Transformers'
+    URL: https://arxiv.org/abs/2105.15203
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mit.py#L246
+  Framework: PyTorch
+- Name: segformer_mit-b0_8xb1-160k_cityscapes-1024x1024
+  In Collection: Segformer
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 76.54
+      mIoU(ms+flip): 78.22
+  Config: configs/segformer/segformer_mit-b0_8xb1-160k_cityscapes-1024x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - MIT-B0
+    - Segformer
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 3.64
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b0_8x1_1024x1024_160k_cityscapes/segformer_mit-b0_8x1_1024x1024_160k_cityscapes_20211208_101857-e7f88502.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b0_8x1_1024x1024_160k_cityscapes/segformer_mit-b0_8x1_1024x1024_160k_cityscapes_20211208_101857.log.json
+  Paper:
+    Title: 'SegFormer: Simple and Efficient Design for Semantic Segmentation with
+      Transformers'
+    URL: https://arxiv.org/abs/2105.15203
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mit.py#L246
+  Framework: PyTorch
+- Name: segformer_mit-b1_8xb1-160k_cityscapes-1024x1024
+  In Collection: Segformer
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.56
+      mIoU(ms+flip): 79.73
+  Config: configs/segformer/segformer_mit-b1_8xb1-160k_cityscapes-1024x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - MIT-B1
+    - Segformer
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 4.49
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b1_8x1_1024x1024_160k_cityscapes/segformer_mit-b1_8x1_1024x1024_160k_cityscapes_20211208_064213-655c7b3f.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b1_8x1_1024x1024_160k_cityscapes/segformer_mit-b1_8x1_1024x1024_160k_cityscapes_20211208_064213.log.json
+  Paper:
+    Title: 'SegFormer: Simple and Efficient Design for Semantic Segmentation with
+      Transformers'
+    URL: https://arxiv.org/abs/2105.15203
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mit.py#L246
+  Framework: PyTorch
+- Name: segformer_mit-b2_8xb1-160k_cityscapes-1024x1024
+  In Collection: Segformer
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 81.08
+      mIoU(ms+flip): 82.18
+  Config: configs/segformer/segformer_mit-b2_8xb1-160k_cityscapes-1024x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - MIT-B2
+    - Segformer
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 7.42
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b2_8x1_1024x1024_160k_cityscapes/segformer_mit-b2_8x1_1024x1024_160k_cityscapes_20211207_134205-6096669a.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b2_8x1_1024x1024_160k_cityscapes/segformer_mit-b2_8x1_1024x1024_160k_cityscapes_20211207_134205.log.json
+  Paper:
+    Title: 'SegFormer: Simple and Efficient Design for Semantic Segmentation with
+      Transformers'
+    URL: https://arxiv.org/abs/2105.15203
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mit.py#L246
+  Framework: PyTorch
+- Name: segformer_mit-b3_8xb1-160k_cityscapes-1024x1024
+  In Collection: Segformer
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 81.94
+      mIoU(ms+flip): 83.14
+  Config: configs/segformer/segformer_mit-b3_8xb1-160k_cityscapes-1024x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - MIT-B3
+    - Segformer
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 10.86
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b3_8x1_1024x1024_160k_cityscapes/segformer_mit-b3_8x1_1024x1024_160k_cityscapes_20211206_224823-a8f8a177.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b3_8x1_1024x1024_160k_cityscapes/segformer_mit-b3_8x1_1024x1024_160k_cityscapes_20211206_224823.log.json
+  Paper:
+    Title: 'SegFormer: Simple and Efficient Design for Semantic Segmentation with
+      Transformers'
+    URL: https://arxiv.org/abs/2105.15203
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mit.py#L246
+  Framework: PyTorch
+- Name: segformer_mit-b4_8xb1-160k_cityscapes-1024x1024
+  In Collection: Segformer
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 81.89
+      mIoU(ms+flip): 83.38
+  Config: configs/segformer/segformer_mit-b4_8xb1-160k_cityscapes-1024x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - MIT-B4
+    - Segformer
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 15.07
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b4_8x1_1024x1024_160k_cityscapes/segformer_mit-b4_8x1_1024x1024_160k_cityscapes_20211207_080709-07f6c333.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b4_8x1_1024x1024_160k_cityscapes/segformer_mit-b4_8x1_1024x1024_160k_cityscapes_20211207_080709.log.json
+  Paper:
+    Title: 'SegFormer: Simple and Efficient Design for Semantic Segmentation with
+      Transformers'
+    URL: https://arxiv.org/abs/2105.15203
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mit.py#L246
+  Framework: PyTorch
+- Name: segformer_mit-b5_8xb1-160k_cityscapes-1024x1024
+  In Collection: Segformer
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 82.25
+      mIoU(ms+flip): 83.48
+  Config: configs/segformer/segformer_mit-b5_8xb1-160k_cityscapes-1024x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - MIT-B5
+    - Segformer
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 18.0
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b5_8x1_1024x1024_160k_cityscapes/segformer_mit-b5_8x1_1024x1024_160k_cityscapes_20211206_072934-87a052ec.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b5_8x1_1024x1024_160k_cityscapes/segformer_mit-b5_8x1_1024x1024_160k_cityscapes_20211206_072934.log.json
+  Paper:
+    Title: 'SegFormer: Simple and Efficient Design for Semantic Segmentation with
+      Transformers'
+    URL: https://arxiv.org/abs/2105.15203
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mit.py#L246
+  Framework: PyTorch
diff --git a/configs/segformer/segformer.yml b/configs/segformer/segformer.yml
deleted file mode 100644
index 4a3818e16e..0000000000
--- a/configs/segformer/segformer.yml
+++ /dev/null
@@ -1,303 +0,0 @@
-Collections:
-- Name: Segformer
-  Metadata:
-    Training Data:
-    - ADE20K
-    - Cityscapes
-  Paper:
-    URL: https://arxiv.org/abs/2105.15203
-    Title: 'SegFormer: Simple and Efficient Design for Semantic Segmentation with
-      Transformers'
-  README: configs/segformer/README.md
-  Code:
-    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mit.py#L246
-    Version: v0.17.0
-  Converted From:
-    Code: https://github.com/NVlabs/SegFormer
-Models:
-- Name: segformer_mit-b0_8xb2-160k_ade20k-512x512
-  In Collection: Segformer
-  Metadata:
-    backbone: MIT-B0
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 19.49
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 2.1
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 37.41
-      mIoU(ms+flip): 38.34
-  Config: configs/segformer/segformer_mit-b0_8xb2-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b0_512x512_160k_ade20k/segformer_mit-b0_512x512_160k_ade20k_20210726_101530-8ffa8fda.pth
-- Name: segformer_mit-b1_8xb2-160k_ade20k-512x512
-  In Collection: Segformer
-  Metadata:
-    backbone: MIT-B1
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 20.98
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 2.6
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 40.97
-      mIoU(ms+flip): 42.54
-  Config: configs/segformer/segformer_mit-b1_8xb2-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b1_512x512_160k_ade20k/segformer_mit-b1_512x512_160k_ade20k_20210726_112106-d70e859d.pth
-- Name: segformer_mit-b2_8xb2-160k_ade20k-512x512
-  In Collection: Segformer
-  Metadata:
-    backbone: MIT-B2
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 32.38
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 3.6
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 45.58
-      mIoU(ms+flip): 47.03
-  Config: configs/segformer/segformer_mit-b2_8xb2-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b2_512x512_160k_ade20k/segformer_mit-b2_512x512_160k_ade20k_20210726_112103-cbd414ac.pth
-- Name: segformer_mit-b3_8xb2-160k_ade20k-512x512
-  In Collection: Segformer
-  Metadata:
-    backbone: MIT-B3
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 45.23
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 4.8
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 47.82
-      mIoU(ms+flip): 48.81
-  Config: configs/segformer/segformer_mit-b3_8xb2-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b3_512x512_160k_ade20k/segformer_mit-b3_512x512_160k_ade20k_20210726_081410-962b98d2.pth
-- Name: segformer_mit-b4_8xb2-160k_ade20k-512x512
-  In Collection: Segformer
-  Metadata:
-    backbone: MIT-B4
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 64.72
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 6.1
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 48.46
-      mIoU(ms+flip): 49.76
-  Config: configs/segformer/segformer_mit-b4_8xb2-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b4_512x512_160k_ade20k/segformer_mit-b4_512x512_160k_ade20k_20210728_183055-7f509d7d.pth
-- Name: segformer_mit-b5_8xb2-160k_ade20k-512x512
-  In Collection: Segformer
-  Metadata:
-    backbone: MIT-B5
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 84.1
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 7.2
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 49.13
-      mIoU(ms+flip): 50.22
-  Config: configs/segformer/segformer_mit-b5_8xb2-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b5_512x512_160k_ade20k/segformer_mit-b5_512x512_160k_ade20k_20210726_145235-94cedf59.pth
-- Name: segformer_mit-b5_8xb2-160k_ade20k-640x640
-  In Collection: Segformer
-  Metadata:
-    backbone: MIT-B5
-    crop size: (640,640)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 88.5
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (640,640)
-    Training Memory (GB): 11.5
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 49.62
-      mIoU(ms+flip): 50.36
-  Config: configs/segformer/segformer_mit-b5_8xb2-160k_ade20k-640x640.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b5_640x640_160k_ade20k/segformer_mit-b5_640x640_160k_ade20k_20210801_121243-41d2845b.pth
-- Name: segformer_mit-b0_8xb1-160k_cityscapes-1024x1024
-  In Collection: Segformer
-  Metadata:
-    backbone: MIT-B0
-    crop size: (1024,1024)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 210.97
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (1024,1024)
-    Training Memory (GB): 3.64
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 76.54
-      mIoU(ms+flip): 78.22
-  Config: configs/segformer/segformer_mit-b0_8xb1-160k_cityscapes-1024x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b0_8x1_1024x1024_160k_cityscapes/segformer_mit-b0_8x1_1024x1024_160k_cityscapes_20211208_101857-e7f88502.pth
-- Name: segformer_mit-b1_8xb1-160k_cityscapes-1024x1024
-  In Collection: Segformer
-  Metadata:
-    backbone: MIT-B1
-    crop size: (1024,1024)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 232.56
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (1024,1024)
-    Training Memory (GB): 4.49
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 78.56
-      mIoU(ms+flip): 79.73
-  Config: configs/segformer/segformer_mit-b1_8xb1-160k_cityscapes-1024x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b1_8x1_1024x1024_160k_cityscapes/segformer_mit-b1_8x1_1024x1024_160k_cityscapes_20211208_064213-655c7b3f.pth
-- Name: segformer_mit-b2_8xb1-160k_cityscapes-1024x1024
-  In Collection: Segformer
-  Metadata:
-    backbone: MIT-B2
-    crop size: (1024,1024)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 297.62
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (1024,1024)
-    Training Memory (GB): 7.42
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 81.08
-      mIoU(ms+flip): 82.18
-  Config: configs/segformer/segformer_mit-b2_8xb1-160k_cityscapes-1024x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b2_8x1_1024x1024_160k_cityscapes/segformer_mit-b2_8x1_1024x1024_160k_cityscapes_20211207_134205-6096669a.pth
-- Name: segformer_mit-b3_8xb1-160k_cityscapes-1024x1024
-  In Collection: Segformer
-  Metadata:
-    backbone: MIT-B3
-    crop size: (1024,1024)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 395.26
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (1024,1024)
-    Training Memory (GB): 10.86
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 81.94
-      mIoU(ms+flip): 83.14
-  Config: configs/segformer/segformer_mit-b3_8xb1-160k_cityscapes-1024x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b3_8x1_1024x1024_160k_cityscapes/segformer_mit-b3_8x1_1024x1024_160k_cityscapes_20211206_224823-a8f8a177.pth
-- Name: segformer_mit-b4_8xb1-160k_cityscapes-1024x1024
-  In Collection: Segformer
-  Metadata:
-    backbone: MIT-B4
-    crop size: (1024,1024)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 531.91
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (1024,1024)
-    Training Memory (GB): 15.07
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 81.89
-      mIoU(ms+flip): 83.38
-  Config: configs/segformer/segformer_mit-b4_8xb1-160k_cityscapes-1024x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b4_8x1_1024x1024_160k_cityscapes/segformer_mit-b4_8x1_1024x1024_160k_cityscapes_20211207_080709-07f6c333.pth
-- Name: segformer_mit-b5_8xb1-160k_cityscapes-1024x1024
-  In Collection: Segformer
-  Metadata:
-    backbone: MIT-B5
-    crop size: (1024,1024)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 719.42
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (1024,1024)
-    Training Memory (GB): 18.0
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 82.25
-      mIoU(ms+flip): 83.48
-  Config: configs/segformer/segformer_mit-b5_8xb1-160k_cityscapes-1024x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b5_8x1_1024x1024_160k_cityscapes/segformer_mit-b5_8x1_1024x1024_160k_cityscapes_20211206_072934-87a052ec.pth
diff --git a/configs/segmenter/README.md b/configs/segmenter/README.md
index 984ef9f510..a9a64ae421 100644
--- a/configs/segmenter/README.md
+++ b/configs/segmenter/README.md
@@ -1,6 +1,6 @@
 # Segmenter
 
-[Segmenter: Transformer for Semantic Segmentation](https://arxiv.org/abs/2105.05633)
+> [Segmenter: Transformer for Semantic Segmentation](https://arxiv.org/abs/2105.05633)
 
 ## Introduction
 
@@ -22,16 +22,6 @@ Image segmentation is often ambiguous at the level of individual image patches a
 <img src="https://user-images.githubusercontent.com/24582831/148507554-87eb80bd-02c7-4c31-b102-c6141e231ec8.png" width="70%"/>
 </div>
 
-```bibtex
-@inproceedings{strudel2021segmenter,
-  title={Segmenter: Transformer for semantic segmentation},
-  author={Strudel, Robin and Garcia, Ricardo and Laptev, Ivan and Schmid, Cordelia},
-  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
-  pages={7262--7272},
-  year={2021}
-}
-```
-
 ## Usage
 
 We have provided pretrained models converted from [ViT-AugReg](https://github.com/rwightman/pytorch-image-models/blob/f55c22bebf9d8afc449d317a723231ef72e0d662/timm/models/vision_transformer.py#L54-L106).
@@ -54,21 +44,33 @@ This script convert model from `PRETRAIN_PATH` and store the converted model in
 
 In our default setting, pretrained models and their corresponding [ViT-AugReg](https://github.com/rwightman/pytorch-image-models/blob/f55c22bebf9d8afc449d317a723231ef72e0d662/timm/models/vision_transformer.py#L54-L106) models could be defined below:
 
-| pretrained models     | original models                                                                                                                                                                   |
-| --------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| vit_tiny_p16_384.pth  | ['vit_tiny_patch16_384'](https://storage.googleapis.com/vit_models/augreg/Ti_16-i21k-300ep-lr_0.001-aug_none-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_384.npz)   |
-| vit_small_p16_384.pth | ['vit_small_patch16_384'](https://storage.googleapis.com/vit_models/augreg/S_16-i21k-300ep-lr_0.001-aug_light1-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_384.npz) |
-| vit_base_p16_384.pth  | ['vit_base_patch16_384'](https://storage.googleapis.com/vit_models/augreg/B_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.01-res_384.npz)  |
-| vit_large_p16_384.pth | ['vit_large_patch16_384'](https://storage.googleapis.com/vit_models/augreg/L_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.1-sd_0.1--imagenet2012-steps_20k-lr_0.01-res_384.npz) |
+| pretrained models     | original models                                                                                                                                                                 |
+| --------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| vit_tiny_p16_384.pth  | [vit_tiny_patch16_384](https://storage.googleapis.com/vit_models/augreg/Ti_16-i21k-300ep-lr_0.001-aug_none-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_384.npz)   |
+| vit_small_p16_384.pth | [vit_small_patch16_384](https://storage.googleapis.com/vit_models/augreg/S_16-i21k-300ep-lr_0.001-aug_light1-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_384.npz) |
+| vit_base_p16_384.pth  | [vit_base_patch16_384](https://storage.googleapis.com/vit_models/augreg/B_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.01-res_384.npz)  |
+| vit_large_p16_384.pth | [vit_large_patch16_384](https://storage.googleapis.com/vit_models/augreg/L_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.1-sd_0.1--imagenet2012-steps_20k-lr_0.01-res_384.npz) |
 
 ## Results and models
 
 ### ADE20K
 
-| Method           | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU  | mIoU(ms+flip) | config                                                                                                                                 | download                                                                                                                                                                                                                                                                                                                                                                                       |
-| ---------------- | -------- | --------- | ------- | -------- | -------------- | ----- | ------------- | -------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| Segmenter Mask   | ViT-T_16 | 512x512   | 160000  | 1.21     | 27.98          | 39.99 | 40.83         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/segmenter/segmenter_vit-t_mask_8xb1-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-t_mask_8x1_512x512_160k_ade20k/segmenter_vit-t_mask_8x1_512x512_160k_ade20k_20220105_151706-ffcf7509.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-t_mask_8x1_512x512_160k_ade20k/segmenter_vit-t_mask_8x1_512x512_160k_ade20k_20220105_151706.log.json)         |
-| Segmenter Linear | ViT-S_16 | 512x512   | 160000  | 1.78     | 28.07          | 45.75 | 46.82         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/segmenter/segmenter_vit-s_fcn_8xb1-160k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-s_linear_8x1_512x512_160k_ade20k/segmenter_vit-s_linear_8x1_512x512_160k_ade20k_20220105_151713-39658c46.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-s_linear_8x1_512x512_160k_ade20k/segmenter_vit-s_linear_8x1_512x512_160k_ade20k_20220105_151713.log.json) |
-| Segmenter Mask   | ViT-S_16 | 512x512   | 160000  | 2.03     | 24.80          | 46.19 | 47.85         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/segmenter/segmenter_vit-s_mask_8xb1-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-s_mask_8x1_512x512_160k_ade20k/segmenter_vit-s_mask_8x1_512x512_160k_ade20k_20220105_151706-511bb103.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-s_mask_8x1_512x512_160k_ade20k/segmenter_vit-s_mask_8x1_512x512_160k_ade20k_20220105_151706.log.json)         |
-| Segmenter Mask   | ViT-B_16 | 512x512   | 160000  | 4.20     | 13.20          | 49.60 | 51.07         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/segmenter/segmenter_vit-b_mask_8xb1-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-b_mask_8x1_512x512_160k_ade20k/segmenter_vit-b_mask_8x1_512x512_160k_ade20k_20220105_151706-bc533b08.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-b_mask_8x1_512x512_160k_ade20k/segmenter_vit-b_mask_8x1_512x512_160k_ade20k_20220105_151706.log.json)         |
-| Segmenter Mask   | ViT-L_16 | 640x640   | 160000  | 16.56    | 2.62           | 52.16 | 53.65         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/segmenter/segmenter_vit-l_mask_8xb1-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-l_mask_8x1_512x512_160k_ade20k/segmenter_vit-l_mask_8x1_512x512_160k_ade20k_20220105_162750-7ef345be.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-l_mask_8x1_512x512_160k_ade20k/segmenter_vit-l_mask_8x1_512x512_160k_ade20k_20220105_162750.log.json)         |
+| Method           | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device | mIoU  | mIoU(ms+flip) | config                                                                                                                                 | download                                                                                                                                                                                                                                                                                                                                                                                       |
+| ---------------- | -------- | --------- | ------- | -------- | -------------- | ------ | ----- | ------------- | -------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| Segmenter Mask   | ViT-T_16 | 512x512   | 160000  | 1.21     | 27.98          | V100   | 39.99 | 40.83         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/segmenter/segmenter_vit-t_mask_8xb1-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-t_mask_8x1_512x512_160k_ade20k/segmenter_vit-t_mask_8x1_512x512_160k_ade20k_20220105_151706-ffcf7509.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-t_mask_8x1_512x512_160k_ade20k/segmenter_vit-t_mask_8x1_512x512_160k_ade20k_20220105_151706.log.json)         |
+| Segmenter Linear | ViT-S_16 | 512x512   | 160000  | 1.78     | 28.07          | V100   | 45.75 | 46.82         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/segmenter/segmenter_vit-s_fcn_8xb1-160k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-s_linear_8x1_512x512_160k_ade20k/segmenter_vit-s_linear_8x1_512x512_160k_ade20k_20220105_151713-39658c46.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-s_linear_8x1_512x512_160k_ade20k/segmenter_vit-s_linear_8x1_512x512_160k_ade20k_20220105_151713.log.json) |
+| Segmenter Mask   | ViT-S_16 | 512x512   | 160000  | 2.03     | 24.80          | V100   | 46.19 | 47.85         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/segmenter/segmenter_vit-s_mask_8xb1-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-s_mask_8x1_512x512_160k_ade20k/segmenter_vit-s_mask_8x1_512x512_160k_ade20k_20220105_151706-511bb103.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-s_mask_8x1_512x512_160k_ade20k/segmenter_vit-s_mask_8x1_512x512_160k_ade20k_20220105_151706.log.json)         |
+| Segmenter Mask   | ViT-B_16 | 512x512   | 160000  | 4.20     | 13.20          | V100   | 49.60 | 51.07         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/segmenter/segmenter_vit-b_mask_8xb1-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-b_mask_8x1_512x512_160k_ade20k/segmenter_vit-b_mask_8x1_512x512_160k_ade20k_20220105_151706-bc533b08.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-b_mask_8x1_512x512_160k_ade20k/segmenter_vit-b_mask_8x1_512x512_160k_ade20k_20220105_151706.log.json)         |
+| Segmenter Mask   | ViT-L_16 | 640x640   | 160000  | 16.56    | 2.62           | V100   | 52.16 | 53.65         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/segmenter/segmenter_vit-l_mask_8xb1-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-l_mask_8x1_512x512_160k_ade20k/segmenter_vit-l_mask_8x1_512x512_160k_ade20k_20220105_162750-7ef345be.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-l_mask_8x1_512x512_160k_ade20k/segmenter_vit-l_mask_8x1_512x512_160k_ade20k_20220105_162750.log.json)         |
+
+## Citation
+
+```bibtex
+@inproceedings{strudel2021segmenter,
+  title={Segmenter: Transformer for semantic segmentation},
+  author={Strudel, Robin and Garcia, Ricardo and Laptev, Ivan and Schmid, Cordelia},
+  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
+  pages={7262--7272},
+  year={2021}
+}
+```
diff --git a/configs/segmenter/metafile.yaml b/configs/segmenter/metafile.yaml
new file mode 100644
index 0000000000..ff2aa448bb
--- /dev/null
+++ b/configs/segmenter/metafile.yaml
@@ -0,0 +1,138 @@
+Collections:
+- Name: Segmenter
+  License: Apache License 2.0
+  Metadata:
+    Training Data:
+    - ADE20K
+  Paper:
+    Title: 'Segmenter: Transformer for Semantic Segmentation'
+    URL: https://arxiv.org/abs/2105.05633
+  README: configs/segmenter/README.md
+  Frameworks:
+  - PyTorch
+Models:
+- Name: segmenter_vit-t_mask_8xb1-160k_ade20k-512x512
+  In Collection: Segmenter
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 39.99
+      mIoU(ms+flip): 40.83
+  Config: configs/segmenter/segmenter_vit-t_mask_8xb1-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 8
+    Architecture:
+    - ViT-T_16
+    - Segmenter
+    - Mask
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 1.21
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-t_mask_8x1_512x512_160k_ade20k/segmenter_vit-t_mask_8x1_512x512_160k_ade20k_20220105_151706-ffcf7509.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-t_mask_8x1_512x512_160k_ade20k/segmenter_vit-t_mask_8x1_512x512_160k_ade20k_20220105_151706.log.json
+  Paper:
+    Title: 'Segmenter: Transformer for Semantic Segmentation'
+    URL: https://arxiv.org/abs/2105.05633
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.21.0/mmseg/models/decode_heads/segmenter_mask_head.py#L15
+  Framework: PyTorch
+- Name: segmenter_vit-s_fcn_8xb1-160k_ade20k-512x512
+  In Collection: Segmenter
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 45.75
+      mIoU(ms+flip): 46.82
+  Config: configs/segmenter/segmenter_vit-s_fcn_8xb1-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 8
+    Architecture:
+    - ViT-S_16
+    - Segmenter
+    - Linear
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 1.78
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-s_linear_8x1_512x512_160k_ade20k/segmenter_vit-s_linear_8x1_512x512_160k_ade20k_20220105_151713-39658c46.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-s_linear_8x1_512x512_160k_ade20k/segmenter_vit-s_linear_8x1_512x512_160k_ade20k_20220105_151713.log.json
+  Paper:
+    Title: 'Segmenter: Transformer for Semantic Segmentation'
+    URL: https://arxiv.org/abs/2105.05633
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.21.0/mmseg/models/decode_heads/segmenter_mask_head.py#L15
+  Framework: PyTorch
+- Name: segmenter_vit-s_mask_8xb1-160k_ade20k-512x512
+  In Collection: Segmenter
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 46.19
+      mIoU(ms+flip): 47.85
+  Config: configs/segmenter/segmenter_vit-s_mask_8xb1-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 8
+    Architecture:
+    - ViT-S_16
+    - Segmenter
+    - Mask
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 2.03
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-s_mask_8x1_512x512_160k_ade20k/segmenter_vit-s_mask_8x1_512x512_160k_ade20k_20220105_151706-511bb103.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-s_mask_8x1_512x512_160k_ade20k/segmenter_vit-s_mask_8x1_512x512_160k_ade20k_20220105_151706.log.json
+  Paper:
+    Title: 'Segmenter: Transformer for Semantic Segmentation'
+    URL: https://arxiv.org/abs/2105.05633
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.21.0/mmseg/models/decode_heads/segmenter_mask_head.py#L15
+  Framework: PyTorch
+- Name: segmenter_vit-b_mask_8xb1-160k_ade20k-512x512
+  In Collection: Segmenter
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 49.6
+      mIoU(ms+flip): 51.07
+  Config: configs/segmenter/segmenter_vit-b_mask_8xb1-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 8
+    Architecture:
+    - ViT-B_16
+    - Segmenter
+    - Mask
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 4.2
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-b_mask_8x1_512x512_160k_ade20k/segmenter_vit-b_mask_8x1_512x512_160k_ade20k_20220105_151706-bc533b08.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-b_mask_8x1_512x512_160k_ade20k/segmenter_vit-b_mask_8x1_512x512_160k_ade20k_20220105_151706.log.json
+  Paper:
+    Title: 'Segmenter: Transformer for Semantic Segmentation'
+    URL: https://arxiv.org/abs/2105.05633
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.21.0/mmseg/models/decode_heads/segmenter_mask_head.py#L15
+  Framework: PyTorch
+- Name: segmenter_vit-l_mask_8xb1-160k_ade20k-512x512
+  In Collection: Segmenter
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 52.16
+      mIoU(ms+flip): 53.65
+  Config: configs/segmenter/segmenter_vit-l_mask_8xb1-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 8
+    Architecture:
+    - ViT-L_16
+    - Segmenter
+    - Mask
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 16.56
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-l_mask_8x1_512x512_160k_ade20k/segmenter_vit-l_mask_8x1_512x512_160k_ade20k_20220105_162750-7ef345be.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-l_mask_8x1_512x512_160k_ade20k/segmenter_vit-l_mask_8x1_512x512_160k_ade20k_20220105_162750.log.json
+  Paper:
+    Title: 'Segmenter: Transformer for Semantic Segmentation'
+    URL: https://arxiv.org/abs/2105.05633
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.21.0/mmseg/models/decode_heads/segmenter_mask_head.py#L15
+  Framework: PyTorch
diff --git a/configs/segmenter/segmenter.yml b/configs/segmenter/segmenter.yml
deleted file mode 100644
index 1069f003b7..0000000000
--- a/configs/segmenter/segmenter.yml
+++ /dev/null
@@ -1,125 +0,0 @@
-Collections:
-- Name: Segmenter
-  Metadata:
-    Training Data:
-    - ADE20K
-  Paper:
-    URL: https://arxiv.org/abs/2105.05633
-    Title: 'Segmenter: Transformer for Semantic Segmentation'
-  README: configs/segmenter/README.md
-  Code:
-    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.21.0/mmseg/models/decode_heads/segmenter_mask_head.py#L15
-    Version: v0.21.0
-  Converted From:
-    Code: https://github.com/rstrudel/segmenter
-Models:
-- Name: segmenter_vit-t_mask_8xb1-160k_ade20k-512x512
-  In Collection: Segmenter
-  Metadata:
-    backbone: ViT-T_16
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 35.74
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 1.21
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 39.99
-      mIoU(ms+flip): 40.83
-  Config: configs/segmenter/segmenter_vit-t_mask_8xb1-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-t_mask_8x1_512x512_160k_ade20k/segmenter_vit-t_mask_8x1_512x512_160k_ade20k_20220105_151706-ffcf7509.pth
-- Name: segmenter_vit-s_fcn_8xb1-160k_ade20k-512x512
-  In Collection: Segmenter
-  Metadata:
-    backbone: ViT-S_16
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 35.63
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 1.78
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 45.75
-      mIoU(ms+flip): 46.82
-  Config: configs/segmenter/segmenter_vit-s_fcn_8xb1-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-s_linear_8x1_512x512_160k_ade20k/segmenter_vit-s_linear_8x1_512x512_160k_ade20k_20220105_151713-39658c46.pth
-- Name: segmenter_vit-s_mask_8xb1-160k_ade20k-512x512
-  In Collection: Segmenter
-  Metadata:
-    backbone: ViT-S_16
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 40.32
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 2.03
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 46.19
-      mIoU(ms+flip): 47.85
-  Config: configs/segmenter/segmenter_vit-s_mask_8xb1-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-s_mask_8x1_512x512_160k_ade20k/segmenter_vit-s_mask_8x1_512x512_160k_ade20k_20220105_151706-511bb103.pth
-- Name: segmenter_vit-b_mask_8xb1-160k_ade20k-512x512
-  In Collection: Segmenter
-  Metadata:
-    backbone: ViT-B_16
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 75.76
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 4.2
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 49.6
-      mIoU(ms+flip): 51.07
-  Config: configs/segmenter/segmenter_vit-b_mask_8xb1-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-b_mask_8x1_512x512_160k_ade20k/segmenter_vit-b_mask_8x1_512x512_160k_ade20k_20220105_151706-bc533b08.pth
-- Name: segmenter_vit-l_mask_8xb1-160k_ade20k-512x512
-  In Collection: Segmenter
-  Metadata:
-    backbone: ViT-L_16
-    crop size: (640,640)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 381.68
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (640,640)
-    Training Memory (GB): 16.56
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 52.16
-      mIoU(ms+flip): 53.65
-  Config: configs/segmenter/segmenter_vit-l_mask_8xb1-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segmenter/segmenter_vit-l_mask_8x1_512x512_160k_ade20k/segmenter_vit-l_mask_8x1_512x512_160k_ade20k_20220105_162750-7ef345be.pth
diff --git a/configs/segnext/README.md b/configs/segnext/README.md
index 315f4e23e8..06b63be546 100644
--- a/configs/segnext/README.md
+++ b/configs/segnext/README.md
@@ -26,12 +26,12 @@ We present SegNeXt, a simple convolutional network architecture for semantic seg
 
 ### ADE20K
 
-| Method  | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU  | mIoU(ms+flip) | config                                                                                                                                 | download                                                                                                                                                                                                                                                                                                                                                                                   |
-| ------- | -------- | --------- | ------- | -------- | -------------- | ----- | ------------- | -------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| SegNeXt | MSCAN-T  | 512x512   | 160000  | 17.88    | 52.38          | 41.50 | 42.59         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/segnext/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244-05bd8466.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244.log.json) |
-| SegNeXt | MSCAN-S  | 512x512   | 160000  | 21.47    | 42.27          | 44.16 | 45.81         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/segnext/segnext_mscan-s_1xb16-adamw-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-s_1x16_512x512_adamw_160k_ade20k/segnext_mscan-s_1x16_512x512_adamw_160k_ade20k_20230214_113014-43013668.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-s_1x16_512x512_adamw_160k_ade20k/segnext_mscan-s_1x16_512x512_adamw_160k_ade20k_20230214_113014.log.json) |
-| SegNeXt | MSCAN-B  | 512x512   | 160000  | 31.03    | 35.15          | 48.03 | 49.68         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/segnext/segnext_mscan-b_1xb16-adamw-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-b_1x16_512x512_adamw_160k_ade20k/segnext_mscan-b_1x16_512x512_adamw_160k_ade20k_20230209_172053-b6f6c70c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-b_1x16_512x512_adamw_160k_ade20k/segnext_mscan-b_1x16_512x512_adamw_160k_ade20k_20230209_172053.log.json) |
-| SegNeXt | MSCAN-L  | 512x512   | 160000  | 43.32    | 22.91          | 50.99 | 52.10         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/segnext/segnext_mscan-l_1xb16-adamw-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-l_1x16_512x512_adamw_160k_ade20k/segnext_mscan-l_1x16_512x512_adamw_160k_ade20k_20230209_172055-19b14b63.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-l_1x16_512x512_adamw_160k_ade20k/segnext_mscan-l_1x16_512x512_adamw_160k_ade20k_20230209_172055.log.json) |
+| Method  | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device | mIoU  | mIoU(ms+flip) | config                                                                                                                                 | download                                                                                                                                                                                                                                                                                                                                                                                   |
+| ------- | -------- | --------- | ------- | -------- | -------------- | ------ | ----- | ------------- | -------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| SegNeXt | MSCAN-T  | 512x512   | 160000  | 17.88    | 52.38          | A100   | 41.50 | 42.59         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/segnext/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244-05bd8466.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244.log.json) |
+| SegNeXt | MSCAN-S  | 512x512   | 160000  | 21.47    | 42.27          | A100   | 44.16 | 45.81         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/segnext/segnext_mscan-s_1xb16-adamw-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-s_1x16_512x512_adamw_160k_ade20k/segnext_mscan-s_1x16_512x512_adamw_160k_ade20k_20230214_113014-43013668.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-s_1x16_512x512_adamw_160k_ade20k/segnext_mscan-s_1x16_512x512_adamw_160k_ade20k_20230214_113014.log.json) |
+| SegNeXt | MSCAN-B  | 512x512   | 160000  | 31.03    | 35.15          | A100   | 48.03 | 49.68         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/segnext/segnext_mscan-b_1xb16-adamw-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-b_1x16_512x512_adamw_160k_ade20k/segnext_mscan-b_1x16_512x512_adamw_160k_ade20k_20230209_172053-b6f6c70c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-b_1x16_512x512_adamw_160k_ade20k/segnext_mscan-b_1x16_512x512_adamw_160k_ade20k_20230209_172053.log.json) |
+| SegNeXt | MSCAN-L  | 512x512   | 160000  | 43.32    | 22.91          | A100   | 50.99 | 52.10         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/segnext/segnext_mscan-l_1xb16-adamw-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-l_1x16_512x512_adamw_160k_ade20k/segnext_mscan-l_1x16_512x512_adamw_160k_ade20k_20230209_172055-19b14b63.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-l_1x16_512x512_adamw_160k_ade20k/segnext_mscan-l_1x16_512x512_adamw_160k_ade20k_20230209_172055.log.json) |
 
 Note:
 
diff --git a/configs/segnext/metafile.yaml b/configs/segnext/metafile.yaml
new file mode 100644
index 0000000000..faa11c9784
--- /dev/null
+++ b/configs/segnext/metafile.yaml
@@ -0,0 +1,109 @@
+Collections:
+- Name: SegNeXt
+  License: Apache License 2.0
+  Metadata:
+    Training Data:
+    - ADE20K
+  Paper:
+    Title: 'SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation'
+    URL: https://arxiv.org/abs/2209.08575
+  README: configs/segnext/README.md
+  Frameworks:
+  - PyTorch
+Models:
+- Name: segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512
+  In Collection: SegNeXt
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 41.5
+      mIoU(ms+flip): 42.59
+  Config: configs/segnext/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - MSCAN-T
+    - SegNeXt
+    Training Resources: 1x A100 GPUS
+    Memory (GB): 17.88
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244-05bd8466.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244.log.json
+  Paper:
+    Title: 'SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation'
+    URL: https://arxiv.org/abs/2209.08575
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/mmseg/models/backbones/mscan.py#L328
+  Framework: PyTorch
+- Name: segnext_mscan-s_1xb16-adamw-160k_ade20k-512x512
+  In Collection: SegNeXt
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 44.16
+      mIoU(ms+flip): 45.81
+  Config: configs/segnext/segnext_mscan-s_1xb16-adamw-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - MSCAN-S
+    - SegNeXt
+    Training Resources: 1x A100 GPUS
+    Memory (GB): 21.47
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-s_1x16_512x512_adamw_160k_ade20k/segnext_mscan-s_1x16_512x512_adamw_160k_ade20k_20230214_113014-43013668.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-s_1x16_512x512_adamw_160k_ade20k/segnext_mscan-s_1x16_512x512_adamw_160k_ade20k_20230214_113014.log.json
+  Paper:
+    Title: 'SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation'
+    URL: https://arxiv.org/abs/2209.08575
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/mmseg/models/backbones/mscan.py#L328
+  Framework: PyTorch
+- Name: segnext_mscan-b_1xb16-adamw-160k_ade20k-512x512
+  In Collection: SegNeXt
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 48.03
+      mIoU(ms+flip): 49.68
+  Config: configs/segnext/segnext_mscan-b_1xb16-adamw-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - MSCAN-B
+    - SegNeXt
+    Training Resources: 1x A100 GPUS
+    Memory (GB): 31.03
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-b_1x16_512x512_adamw_160k_ade20k/segnext_mscan-b_1x16_512x512_adamw_160k_ade20k_20230209_172053-b6f6c70c.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-b_1x16_512x512_adamw_160k_ade20k/segnext_mscan-b_1x16_512x512_adamw_160k_ade20k_20230209_172053.log.json
+  Paper:
+    Title: 'SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation'
+    URL: https://arxiv.org/abs/2209.08575
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/mmseg/models/backbones/mscan.py#L328
+  Framework: PyTorch
+- Name: segnext_mscan-l_1xb16-adamw-160k_ade20k-512x512
+  In Collection: SegNeXt
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 50.99
+      mIoU(ms+flip): 52.1
+  Config: configs/segnext/segnext_mscan-l_1xb16-adamw-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - MSCAN-L
+    - SegNeXt
+    Training Resources: 1x A100 GPUS
+    Memory (GB): 43.32
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-l_1x16_512x512_adamw_160k_ade20k/segnext_mscan-l_1x16_512x512_adamw_160k_ade20k_20230209_172055-19b14b63.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-l_1x16_512x512_adamw_160k_ade20k/segnext_mscan-l_1x16_512x512_adamw_160k_ade20k_20230209_172055.log.json
+  Paper:
+    Title: 'SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation'
+    URL: https://arxiv.org/abs/2209.08575
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/mmseg/models/backbones/mscan.py#L328
+  Framework: PyTorch
diff --git a/configs/segnext/segnext.yml b/configs/segnext/segnext.yml
deleted file mode 100644
index 3bcdea8403..0000000000
--- a/configs/segnext/segnext.yml
+++ /dev/null
@@ -1,103 +0,0 @@
-Collections:
-- Name: SegNeXt
-  Metadata:
-    Training Data:
-    - ADE20K
-  Paper:
-    URL: https://arxiv.org/abs/2209.08575
-    Title: 'SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation'
-  README: configs/segnext/README.md
-  Code:
-    URL: https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/mmseg/models/backbones/mscan.py#L328
-    Version: dev-1.x
-  Converted From:
-    Code: https://github.com/visual-attention-network/segnext
-Models:
-- Name: segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512
-  In Collection: SegNeXt
-  Metadata:
-    backbone: MSCAN-T
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 19.09
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 17.88
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 41.5
-      mIoU(ms+flip): 42.59
-  Config: configs/segnext/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244-05bd8466.pth
-- Name: segnext_mscan-s_1xb16-adamw-160k_ade20k-512x512
-  In Collection: SegNeXt
-  Metadata:
-    backbone: MSCAN-S
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 23.66
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 21.47
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 44.16
-      mIoU(ms+flip): 45.81
-  Config: configs/segnext/segnext_mscan-s_1xb16-adamw-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-s_1x16_512x512_adamw_160k_ade20k/segnext_mscan-s_1x16_512x512_adamw_160k_ade20k_20230214_113014-43013668.pth
-- Name: segnext_mscan-b_1xb16-adamw-160k_ade20k-512x512
-  In Collection: SegNeXt
-  Metadata:
-    backbone: MSCAN-B
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 28.45
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 31.03
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 48.03
-      mIoU(ms+flip): 49.68
-  Config: configs/segnext/segnext_mscan-b_1xb16-adamw-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-b_1x16_512x512_adamw_160k_ade20k/segnext_mscan-b_1x16_512x512_adamw_160k_ade20k_20230209_172053-b6f6c70c.pth
-- Name: segnext_mscan-l_1xb16-adamw-160k_ade20k-512x512
-  In Collection: SegNeXt
-  Metadata:
-    backbone: MSCAN-L
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 43.65
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 43.32
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 50.99
-      mIoU(ms+flip): 52.1
-  Config: configs/segnext/segnext_mscan-l_1xb16-adamw-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-l_1x16_512x512_adamw_160k_ade20k/segnext_mscan-l_1x16_512x512_adamw_160k_ade20k_20230209_172055-19b14b63.pth
diff --git a/configs/sem_fpn/README.md b/configs/sem_fpn/README.md
index fcef72d2a5..8bbbd0eee0 100644
--- a/configs/sem_fpn/README.md
+++ b/configs/sem_fpn/README.md
@@ -1,6 +1,6 @@
 # Semantic FPN
 
-[Panoptic Feature Pyramid Networks](https://arxiv.org/abs/1901.02446)
+> [Panoptic Feature Pyramid Networks](https://arxiv.org/abs/1901.02446)
 
 ## Introduction
 
@@ -22,6 +22,22 @@ The recently introduced panoptic segmentation task has renewed our community's i
 <img src="https://user-images.githubusercontent.com/24582831/142902694-03ed2131-9104-467b-ace1-c74c62fb7177.png" width="60%"/>
 </div>
 
+## Results and models
+
+### Cityscapes
+
+| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                       | download                                                                                                                                                                                                                                                                                                                           |
+| ------ | -------- | --------- | ------: | -------: | -------------- | ------ | ----: | ------------- | ---------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| FPN    | R-50     | 512x1024  |   80000 |      2.8 | 13.54          | V100   | 74.52 | 76.08         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/sem_fpn/fpn_r50_4xb2-80k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r50_512x1024_80k_cityscapes/fpn_r50_512x1024_80k_cityscapes_20200717_021437-94018a0d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r50_512x1024_80k_cityscapes/fpn_r50_512x1024_80k_cityscapes-20200717_021437.log.json)     |
+| FPN    | R-101    | 512x1024  |   80000 |      3.9 | 10.29          | V100   | 75.80 | 77.40         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/sem_fpn/fpn_r101_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r101_512x1024_80k_cityscapes/fpn_r101_512x1024_80k_cityscapes_20200717_012416-c5800d4c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r101_512x1024_80k_cityscapes/fpn_r101_512x1024_80k_cityscapes-20200717_012416.log.json) |
+
+### ADE20K
+
+| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                   | download                                                                                                                                                                                                                                                                                                           |
+| ------ | -------- | --------- | ------: | -------: | -------------- | ------ | ----: | ------------- | ------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| FPN    | R-50     | 512x512   |  160000 |      4.9 | 55.77          | V100   | 37.49 | 39.09         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/sem_fpn/fpn_r50_4xb4-160k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r50_512x512_160k_ade20k/fpn_r50_512x512_160k_ade20k_20200718_131734-5b5a6ab9.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r50_512x512_160k_ade20k/fpn_r50_512x512_160k_ade20k-20200718_131734.log.json)     |
+| FPN    | R-101    | 512x512   |  160000 |      5.9 | 40.58          | V100   | 39.35 | 40.72         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/sem_fpn/fpn_r101_4xb4-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r101_512x512_160k_ade20k/fpn_r101_512x512_160k_ade20k_20200718_131734-306b5004.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r101_512x512_160k_ade20k/fpn_r101_512x512_160k_ade20k-20200718_131734.log.json) |
+
 ## Citation
 
 ```bibtex
@@ -33,19 +49,3 @@ The recently introduced panoptic segmentation task has renewed our community's i
   year={2019}
 }
 ```
-
-## Results and models
-
-### Cityscapes
-
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                       | download                                                                                                                                                                                                                                                                                                                           |
-| ------ | -------- | --------- | ------: | -------: | -------------- | ----: | ------------- | ---------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| FPN    | R-50     | 512x1024  |   80000 |      2.8 | 13.54          | 74.52 | 76.08         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/sem_fpn/fpn_r50_4xb2-80k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r50_512x1024_80k_cityscapes/fpn_r50_512x1024_80k_cityscapes_20200717_021437-94018a0d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r50_512x1024_80k_cityscapes/fpn_r50_512x1024_80k_cityscapes-20200717_021437.log.json)     |
-| FPN    | R-101    | 512x1024  |   80000 |      3.9 | 10.29          | 75.80 | 77.40         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/sem_fpn/fpn_r101_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r101_512x1024_80k_cityscapes/fpn_r101_512x1024_80k_cityscapes_20200717_012416-c5800d4c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r101_512x1024_80k_cityscapes/fpn_r101_512x1024_80k_cityscapes-20200717_012416.log.json) |
-
-### ADE20K
-
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                   | download                                                                                                                                                                                                                                                                                                           |
-| ------ | -------- | --------- | ------: | -------: | -------------- | ----: | ------------- | ------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| FPN    | R-50     | 512x512   |  160000 |      4.9 | 55.77          | 37.49 | 39.09         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/sem_fpn/fpn_r50_4xb4-160k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r50_512x512_160k_ade20k/fpn_r50_512x512_160k_ade20k_20200718_131734-5b5a6ab9.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r50_512x512_160k_ade20k/fpn_r50_512x512_160k_ade20k-20200718_131734.log.json)     |
-| FPN    | R-101    | 512x512   |  160000 |      5.9 | 40.58          | 39.35 | 40.72         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/sem_fpn/fpn_r101_4xb4-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r101_512x512_160k_ade20k/fpn_r101_512x512_160k_ade20k_20200718_131734-306b5004.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r101_512x512_160k_ade20k/fpn_r101_512x512_160k_ade20k-20200718_131734.log.json) |
diff --git a/configs/sem_fpn/metafile.yaml b/configs/sem_fpn/metafile.yaml
new file mode 100644
index 0000000000..e734897245
--- /dev/null
+++ b/configs/sem_fpn/metafile.yaml
@@ -0,0 +1,110 @@
+Collections:
+- Name: FPN
+  License: Apache License 2.0
+  Metadata:
+    Training Data:
+    - Cityscapes
+    - ADE20K
+  Paper:
+    Title: Panoptic Feature Pyramid Networks
+    URL: https://arxiv.org/abs/1901.02446
+  README: configs/sem_fpn/README.md
+  Frameworks:
+  - PyTorch
+Models:
+- Name: fpn_r50_4xb2-80k_cityscapes-512x1024
+  In Collection: FPN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 74.52
+      mIoU(ms+flip): 76.08
+  Config: configs/sem_fpn/fpn_r50_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50
+    - FPN
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 2.8
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r50_512x1024_80k_cityscapes/fpn_r50_512x1024_80k_cityscapes_20200717_021437-94018a0d.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r50_512x1024_80k_cityscapes/fpn_r50_512x1024_80k_cityscapes-20200717_021437.log.json
+  Paper:
+    Title: Panoptic Feature Pyramid Networks
+    URL: https://arxiv.org/abs/1901.02446
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fpn_head.py#L12
+  Framework: PyTorch
+- Name: fpn_r101_4xb2-80k_cityscapes-512x1024
+  In Collection: FPN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 75.8
+      mIoU(ms+flip): 77.4
+  Config: configs/sem_fpn/fpn_r101_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101
+    - FPN
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 3.9
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r101_512x1024_80k_cityscapes/fpn_r101_512x1024_80k_cityscapes_20200717_012416-c5800d4c.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r101_512x1024_80k_cityscapes/fpn_r101_512x1024_80k_cityscapes-20200717_012416.log.json
+  Paper:
+    Title: Panoptic Feature Pyramid Networks
+    URL: https://arxiv.org/abs/1901.02446
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fpn_head.py#L12
+  Framework: PyTorch
+- Name: fpn_r50_4xb4-160k_ade20k-512x512
+  In Collection: FPN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 37.49
+      mIoU(ms+flip): 39.09
+  Config: configs/sem_fpn/fpn_r50_4xb4-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-50
+    - FPN
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 4.9
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r50_512x512_160k_ade20k/fpn_r50_512x512_160k_ade20k_20200718_131734-5b5a6ab9.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r50_512x512_160k_ade20k/fpn_r50_512x512_160k_ade20k-20200718_131734.log.json
+  Paper:
+    Title: Panoptic Feature Pyramid Networks
+    URL: https://arxiv.org/abs/1901.02446
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fpn_head.py#L12
+  Framework: PyTorch
+- Name: fpn_r101_4xb4-160k_ade20k-512x512
+  In Collection: FPN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 39.35
+      mIoU(ms+flip): 40.72
+  Config: configs/sem_fpn/fpn_r101_4xb4-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-101
+    - FPN
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 5.9
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r101_512x512_160k_ade20k/fpn_r101_512x512_160k_ade20k_20200718_131734-306b5004.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r101_512x512_160k_ade20k/fpn_r101_512x512_160k_ade20k-20200718_131734.log.json
+  Paper:
+    Title: Panoptic Feature Pyramid Networks
+    URL: https://arxiv.org/abs/1901.02446
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fpn_head.py#L12
+  Framework: PyTorch
diff --git a/configs/sem_fpn/sem_fpn.yml b/configs/sem_fpn/sem_fpn.yml
deleted file mode 100644
index 79ed0b81c4..0000000000
--- a/configs/sem_fpn/sem_fpn.yml
+++ /dev/null
@@ -1,104 +0,0 @@
-Collections:
-- Name: FPN
-  Metadata:
-    Training Data:
-    - Cityscapes
-    - ADE20K
-  Paper:
-    URL: https://arxiv.org/abs/1901.02446
-    Title: Panoptic Feature Pyramid Networks
-  README: configs/sem_fpn/README.md
-  Code:
-    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fpn_head.py#L12
-    Version: v0.17.0
-  Converted From:
-    Code: https://github.com/facebookresearch/detectron2
-Models:
-- Name: fpn_r50_4xb2-80k_cityscapes-512x1024
-  In Collection: FPN
-  Metadata:
-    backbone: R-50
-    crop size: (512,1024)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 73.86
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 2.8
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 74.52
-      mIoU(ms+flip): 76.08
-  Config: configs/sem_fpn/fpn_r50_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r50_512x1024_80k_cityscapes/fpn_r50_512x1024_80k_cityscapes_20200717_021437-94018a0d.pth
-- Name: fpn_r101_4xb2-80k_cityscapes-512x1024
-  In Collection: FPN
-  Metadata:
-    backbone: R-101
-    crop size: (512,1024)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 97.18
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 3.9
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 75.8
-      mIoU(ms+flip): 77.4
-  Config: configs/sem_fpn/fpn_r101_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r101_512x1024_80k_cityscapes/fpn_r101_512x1024_80k_cityscapes_20200717_012416-c5800d4c.pth
-- Name: fpn_r50_4xb4-160k_ade20k-512x512
-  In Collection: FPN
-  Metadata:
-    backbone: R-50
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 17.93
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 4.9
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 37.49
-      mIoU(ms+flip): 39.09
-  Config: configs/sem_fpn/fpn_r50_4xb4-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r50_512x512_160k_ade20k/fpn_r50_512x512_160k_ade20k_20200718_131734-5b5a6ab9.pth
-- Name: fpn_r101_4xb4-160k_ade20k-512x512
-  In Collection: FPN
-  Metadata:
-    backbone: R-101
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 24.64
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 5.9
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 39.35
-      mIoU(ms+flip): 40.72
-  Config: configs/sem_fpn/fpn_r101_4xb4-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r101_512x512_160k_ade20k/fpn_r101_512x512_160k_ade20k_20200718_131734-306b5004.pth
diff --git a/configs/setr/README.md b/configs/setr/README.md
index 1aa3f245a7..556c625f41 100644
--- a/configs/setr/README.md
+++ b/configs/setr/README.md
@@ -1,6 +1,6 @@
 # SETR
 
-[Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers](https://arxiv.org/abs/2012.15840)
+> [Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers](https://arxiv.org/abs/2012.15840)
 
 ## Introduction
 
@@ -26,17 +26,6 @@ Most recent semantic segmentation methods adopt a fully-convolutional network (F
 This head has two version head.
 ```
 
-## Citation
-
-```bibtex
-@article{zheng2020rethinking,
-  title={Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers},
-  author={Zheng, Sixiao and Lu, Jiachen and Zhao, Hengshuang and Zhu, Xiatian and Luo, Zekun and Wang, Yabiao and Fu, Yanwei and Feng, Jianfeng and Xiang, Tao and Torr, Philip HS and others},
-  journal={arXiv preprint arXiv:2012.15840},
-  year={2020}
-}
-```
-
 ## Usage
 
 You can download the pretrain from [here](https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_p16_384-b3be5167.pth). Then you can convert its keys with the script `vit2mmseg.py` in the tools directory.
@@ -58,17 +47,28 @@ This script convert the model from `PRETRAIN_PATH` and store the converted model
 
 ### ADE20K
 
-| Method     | Backbone | Crop Size | Batch Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU  | mIoU(ms+flip) | config                                                                                                                        | download                                                                                                                                                                                                                                                                                                                             |
-| ---------- | -------- | --------- | ---------- | ------- | -------- | -------------- | ----- | ------------: | ----------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| SETR Naive | ViT-L    | 512x512   | 16         | 160000  | 18.40    | 4.72           | 48.28 |         49.56 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/setr/setr_vit-l_naive_8xb2-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_naive_512x512_160k_b16_ade20k/setr_naive_512x512_160k_b16_ade20k_20210619_191258-061f24f5.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_naive_512x512_160k_b16_ade20k/setr_naive_512x512_160k_b16_ade20k_20210619_191258.log.json) |
-| SETR PUP   | ViT-L    | 512x512   | 16         | 160000  | 19.54    | 4.50           | 48.24 |         49.99 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/setr/setr_vit-l_pup_8xb2-160k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_pup_512x512_160k_b16_ade20k/setr_pup_512x512_160k_b16_ade20k_20210619_191343-7e0ce826.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_pup_512x512_160k_b16_ade20k/setr_pup_512x512_160k_b16_ade20k_20210619_191343.log.json)         |
-| SETR MLA   | ViT-L    | 512x512   | 8          | 160000  | 10.96    | -              | 47.34 |         49.05 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/setr/setr_vit-l-mla_8xb1-160k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_mla_512x512_160k_b8_ade20k/setr_mla_512x512_160k_b8_ade20k_20210619_191118-c6d21df0.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_mla_512x512_160k_b8_ade20k/setr_mla_512x512_160k_b8_ade20k_20210619_191118.log.json)             |
-| SETR MLA   | ViT-L    | 512x512   | 16         | 160000  | 17.30    | 5.25           | 47.39 |         49.37 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/setr/setr_vit-l_mla_8xb2-160k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_mla_512x512_160k_b16_ade20k/setr_mla_512x512_160k_b16_ade20k_20210619_191057-f9741de7.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_mla_512x512_160k_b16_ade20k/setr_mla_512x512_160k_b16_ade20k_20210619_191057.log.json)         |
+| Method     | Backbone | Crop Size | Batch Size | Lr schd | Mem (GB) | Inf time (fps) | Device | mIoU  | mIoU(ms+flip) | config                                                                                                                        | download                                                                                                                                                                                                                                                                                                                             |
+| ---------- | -------- | --------- | ---------- | ------- | -------- | -------------- | ------ | ----- | ------------: | ----------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| SETR Naive | ViT-L    | 512x512   | 16         | 160000  | 18.40    | 4.72           | V100   | 48.28 |         49.56 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/setr/setr_vit-l_naive_8xb2-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_naive_512x512_160k_b16_ade20k/setr_naive_512x512_160k_b16_ade20k_20210619_191258-061f24f5.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_naive_512x512_160k_b16_ade20k/setr_naive_512x512_160k_b16_ade20k_20210619_191258.log.json) |
+| SETR PUP   | ViT-L    | 512x512   | 16         | 160000  | 19.54    | 4.50           | V100   | 48.24 |         49.99 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/setr/setr_vit-l_pup_8xb2-160k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_pup_512x512_160k_b16_ade20k/setr_pup_512x512_160k_b16_ade20k_20210619_191343-7e0ce826.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_pup_512x512_160k_b16_ade20k/setr_pup_512x512_160k_b16_ade20k_20210619_191343.log.json)         |
+| SETR MLA   | ViT-L    | 512x512   | 8          | 160000  | 10.96    | -              | V100   | 47.34 |         49.05 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/setr/setr_vit-l-mla_8xb1-160k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_mla_512x512_160k_b8_ade20k/setr_mla_512x512_160k_b8_ade20k_20210619_191118-c6d21df0.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_mla_512x512_160k_b8_ade20k/setr_mla_512x512_160k_b8_ade20k_20210619_191118.log.json)             |
+| SETR MLA   | ViT-L    | 512x512   | 16         | 160000  | 17.30    | 5.25           | V100   | 47.39 |         49.37 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/setr/setr_vit-l_mla_8xb2-160k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_mla_512x512_160k_b16_ade20k/setr_mla_512x512_160k_b16_ade20k_20210619_191057-f9741de7.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_mla_512x512_160k_b16_ade20k/setr_mla_512x512_160k_b16_ade20k_20210619_191057.log.json)         |
 
 ### Cityscapes
 
-| Method     | Backbone | Crop Size | Batch Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU  | mIoU(ms+flip) | config                                                                                                                           | download                                                                                                                                                                                                                                                                                                                                                                                 |
-| ---------- | -------- | --------- | ---------- | ------- | -------- | -------------- | ----- | ------------: | -------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| SETR Naive | ViT-L    | 768x768   | 8          | 80000   | 24.06    | 0.39           | 78.10 |         80.22 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/setr/setr_vit-l_naive_8xb1-80k_cityscapes-768x768.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_naive_vit-large_8x1_768x768_80k_cityscapes/setr_naive_vit-large_8x1_768x768_80k_cityscapes_20211123_000505-20728e80.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_naive_vit-large_8x1_768x768_80k_cityscapes/setr_naive_vit-large_8x1_768x768_80k_cityscapes_20211123_000505.log.json) |
-| SETR PUP   | ViT-L    | 768x768   | 8          | 80000   | 27.96    | 0.37           | 79.21 |         81.02 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/setr/setr_vit-l_pup_8xb1-80k_cityscapes-768x768.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_pup_vit-large_8x1_768x768_80k_cityscapes/setr_pup_vit-large_8x1_768x768_80k_cityscapes_20211122_155115-f6f37b8f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_pup_vit-large_8x1_768x768_80k_cityscapes/setr_pup_vit-large_8x1_768x768_80k_cityscapes_20211122_155115.log.json)         |
-| SETR MLA   | ViT-L    | 768x768   | 8          | 80000   | 24.10    | 0.41           | 77.00 |         79.59 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/setr/setr_vit-l_mla_8xb1-80k_cityscapes-768x768.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_mla_vit-large_8x1_768x768_80k_cityscapes/setr_mla_vit-large_8x1_768x768_80k_cityscapes_20211119_101003-7f8dccbe.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_mla_vit-large_8x1_768x768_80k_cityscapes/setr_mla_vit-large_8x1_768x768_80k_cityscapes_20211119_101003.log.json)         |
+| Method     | Backbone | Crop Size | Batch Size | Lr schd | Mem (GB) | Inf time (fps) | Device | mIoU  | mIoU(ms+flip) | config                                                                                                                           | download                                                                                                                                                                                                                                                                                                                                                                                 |
+| ---------- | -------- | --------- | ---------- | ------- | -------- | -------------- | ------ | ----- | ------------: | -------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| SETR Naive | ViT-L    | 768x768   | 8          | 80000   | 24.06    | 0.39           | V100   | 78.10 |         80.22 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/setr/setr_vit-l_naive_8xb1-80k_cityscapes-768x768.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_naive_vit-large_8x1_768x768_80k_cityscapes/setr_naive_vit-large_8x1_768x768_80k_cityscapes_20211123_000505-20728e80.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_naive_vit-large_8x1_768x768_80k_cityscapes/setr_naive_vit-large_8x1_768x768_80k_cityscapes_20211123_000505.log.json) |
+| SETR PUP   | ViT-L    | 768x768   | 8          | 80000   | 27.96    | 0.37           | V100   | 79.21 |         81.02 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/setr/setr_vit-l_pup_8xb1-80k_cityscapes-768x768.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_pup_vit-large_8x1_768x768_80k_cityscapes/setr_pup_vit-large_8x1_768x768_80k_cityscapes_20211122_155115-f6f37b8f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_pup_vit-large_8x1_768x768_80k_cityscapes/setr_pup_vit-large_8x1_768x768_80k_cityscapes_20211122_155115.log.json)         |
+| SETR MLA   | ViT-L    | 768x768   | 8          | 80000   | 24.10    | 0.41           | V100   | 77.00 |         79.59 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/setr/setr_vit-l_mla_8xb1-80k_cityscapes-768x768.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_mla_vit-large_8x1_768x768_80k_cityscapes/setr_mla_vit-large_8x1_768x768_80k_cityscapes_20211119_101003-7f8dccbe.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_mla_vit-large_8x1_768x768_80k_cityscapes/setr_mla_vit-large_8x1_768x768_80k_cityscapes_20211119_101003.log.json)         |
+
+## Citation
+
+```bibtex
+@article{zheng2020rethinking,
+  title={Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers},
+  author={Zheng, Sixiao and Lu, Jiachen and Zhao, Hengshuang and Zhu, Xiatian and Luo, Zekun and Wang, Yabiao and Fu, Yanwei and Feng, Jianfeng and Xiang, Tao and Torr, Philip HS and others},
+  journal={arXiv preprint arXiv:2012.15840},
+  year={2020}
+}
+```
diff --git a/configs/setr/metafile.yaml b/configs/setr/metafile.yaml
new file mode 100644
index 0000000000..8e6bc087dd
--- /dev/null
+++ b/configs/setr/metafile.yaml
@@ -0,0 +1,197 @@
+Collections:
+- Name: SETR
+  License: Apache License 2.0
+  Metadata:
+    Training Data:
+    - ADE20K
+    - Cityscapes
+  Paper:
+    Title: Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective
+      with Transformers
+    URL: https://arxiv.org/abs/2012.15840
+  README: configs/setr/README.md
+  Frameworks:
+  - PyTorch
+Models:
+- Name: setr_vit-l_naive_8xb2-160k_ade20k-512x512
+  In Collection: SETR
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 48.28
+      mIoU(ms+flip): 49.56
+  Config: configs/setr/setr_vit-l_naive_8xb2-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - ViT-L
+    - SETR
+    - Naive
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 18.4
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_naive_512x512_160k_b16_ade20k/setr_naive_512x512_160k_b16_ade20k_20210619_191258-061f24f5.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_naive_512x512_160k_b16_ade20k/setr_naive_512x512_160k_b16_ade20k_20210619_191258.log.json
+  Paper:
+    Title: Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective
+      with Transformers
+    URL: https://arxiv.org/abs/2012.15840
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/setr_up_head.py#L11
+  Framework: PyTorch
+- Name: setr_vit-l_pup_8xb2-160k_ade20k-512x512
+  In Collection: SETR
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 48.24
+      mIoU(ms+flip): 49.99
+  Config: configs/setr/setr_vit-l_pup_8xb2-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - ViT-L
+    - SETR
+    - PUP
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 19.54
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_pup_512x512_160k_b16_ade20k/setr_pup_512x512_160k_b16_ade20k_20210619_191343-7e0ce826.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_pup_512x512_160k_b16_ade20k/setr_pup_512x512_160k_b16_ade20k_20210619_191343.log.json
+  Paper:
+    Title: Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective
+      with Transformers
+    URL: https://arxiv.org/abs/2012.15840
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/setr_up_head.py#L11
+  Framework: PyTorch
+- Name: setr_vit-l-mla_8xb1-160k_ade20k-512x512
+  In Collection: SETR
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 47.34
+      mIoU(ms+flip): 49.05
+  Config: configs/setr/setr_vit-l-mla_8xb1-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 8
+    Architecture:
+    - ViT-L
+    - SETR
+    - MLA
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 10.96
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_mla_512x512_160k_b8_ade20k/setr_mla_512x512_160k_b8_ade20k_20210619_191118-c6d21df0.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_mla_512x512_160k_b8_ade20k/setr_mla_512x512_160k_b8_ade20k_20210619_191118.log.json
+  Paper:
+    Title: Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective
+      with Transformers
+    URL: https://arxiv.org/abs/2012.15840
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/setr_up_head.py#L11
+  Framework: PyTorch
+- Name: setr_vit-l_mla_8xb2-160k_ade20k-512x512
+  In Collection: SETR
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 47.39
+      mIoU(ms+flip): 49.37
+  Config: configs/setr/setr_vit-l_mla_8xb2-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - ViT-L
+    - SETR
+    - MLA
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 17.3
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_mla_512x512_160k_b16_ade20k/setr_mla_512x512_160k_b16_ade20k_20210619_191057-f9741de7.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_mla_512x512_160k_b16_ade20k/setr_mla_512x512_160k_b16_ade20k_20210619_191057.log.json
+  Paper:
+    Title: Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective
+      with Transformers
+    URL: https://arxiv.org/abs/2012.15840
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/setr_up_head.py#L11
+  Framework: PyTorch
+- Name: setr_vit-l_naive_8xb1-80k_cityscapes-768x768
+  In Collection: SETR
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.1
+      mIoU(ms+flip): 80.22
+  Config: configs/setr/setr_vit-l_naive_8xb1-80k_cityscapes-768x768.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - ViT-L
+    - SETR
+    - Naive
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 24.06
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_naive_vit-large_8x1_768x768_80k_cityscapes/setr_naive_vit-large_8x1_768x768_80k_cityscapes_20211123_000505-20728e80.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_naive_vit-large_8x1_768x768_80k_cityscapes/setr_naive_vit-large_8x1_768x768_80k_cityscapes_20211123_000505.log.json
+  Paper:
+    Title: Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective
+      with Transformers
+    URL: https://arxiv.org/abs/2012.15840
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/setr_up_head.py#L11
+  Framework: PyTorch
+- Name: setr_vit-l_pup_8xb1-80k_cityscapes-768x768
+  In Collection: SETR
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.21
+      mIoU(ms+flip): 81.02
+  Config: configs/setr/setr_vit-l_pup_8xb1-80k_cityscapes-768x768.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - ViT-L
+    - SETR
+    - PUP
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 27.96
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_pup_vit-large_8x1_768x768_80k_cityscapes/setr_pup_vit-large_8x1_768x768_80k_cityscapes_20211122_155115-f6f37b8f.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_pup_vit-large_8x1_768x768_80k_cityscapes/setr_pup_vit-large_8x1_768x768_80k_cityscapes_20211122_155115.log.json
+  Paper:
+    Title: Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective
+      with Transformers
+    URL: https://arxiv.org/abs/2012.15840
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/setr_up_head.py#L11
+  Framework: PyTorch
+- Name: setr_vit-l_mla_8xb1-80k_cityscapes-768x768
+  In Collection: SETR
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.0
+      mIoU(ms+flip): 79.59
+  Config: configs/setr/setr_vit-l_mla_8xb1-80k_cityscapes-768x768.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - ViT-L
+    - SETR
+    - MLA
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 24.1
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_mla_vit-large_8x1_768x768_80k_cityscapes/setr_mla_vit-large_8x1_768x768_80k_cityscapes_20211119_101003-7f8dccbe.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_mla_vit-large_8x1_768x768_80k_cityscapes/setr_mla_vit-large_8x1_768x768_80k_cityscapes_20211119_101003.log.json
+  Paper:
+    Title: Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective
+      with Transformers
+    URL: https://arxiv.org/abs/2012.15840
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/setr_up_head.py#L11
+  Framework: PyTorch
diff --git a/configs/setr/setr.yml b/configs/setr/setr.yml
deleted file mode 100644
index 6a9987089d..0000000000
--- a/configs/setr/setr.yml
+++ /dev/null
@@ -1,164 +0,0 @@
-Collections:
-- Name: SETR
-  Metadata:
-    Training Data:
-    - ADE20K
-    - Cityscapes
-  Paper:
-    URL: https://arxiv.org/abs/2012.15840
-    Title: Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective
-      with Transformers
-  README: configs/setr/README.md
-  Code:
-    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/setr_up_head.py#L11
-    Version: v0.17.0
-  Converted From:
-    Code: https://github.com/fudan-zvg/SETR
-Models:
-- Name: setr_vit-l_naive_8xb2-160k_ade20k-512x512
-  In Collection: SETR
-  Metadata:
-    backbone: ViT-L
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 211.86
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 18.4
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 48.28
-      mIoU(ms+flip): 49.56
-  Config: configs/setr/setr_vit-l_naive_8xb2-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_naive_512x512_160k_b16_ade20k/setr_naive_512x512_160k_b16_ade20k_20210619_191258-061f24f5.pth
-- Name: setr_vit-l_pup_8xb2-160k_ade20k-512x512
-  In Collection: SETR
-  Metadata:
-    backbone: ViT-L
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 222.22
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 19.54
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 48.24
-      mIoU(ms+flip): 49.99
-  Config: configs/setr/setr_vit-l_pup_8xb2-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_pup_512x512_160k_b16_ade20k/setr_pup_512x512_160k_b16_ade20k_20210619_191343-7e0ce826.pth
-- Name: setr_vit-l-mla_8xb1-160k_ade20k-512x512
-  In Collection: SETR
-  Metadata:
-    backbone: ViT-L
-    crop size: (512,512)
-    lr schd: 160000
-    Training Memory (GB): 10.96
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 47.34
-      mIoU(ms+flip): 49.05
-  Config: configs/setr/setr_vit-l-mla_8xb1-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_mla_512x512_160k_b8_ade20k/setr_mla_512x512_160k_b8_ade20k_20210619_191118-c6d21df0.pth
-- Name: setr_vit-l_mla_8xb2-160k_ade20k-512x512
-  In Collection: SETR
-  Metadata:
-    backbone: ViT-L
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 190.48
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 17.3
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 47.39
-      mIoU(ms+flip): 49.37
-  Config: configs/setr/setr_vit-l_mla_8xb2-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_mla_512x512_160k_b16_ade20k/setr_mla_512x512_160k_b16_ade20k_20210619_191057-f9741de7.pth
-- Name: setr_vit-l_naive_8xb1-80k_cityscapes-768x768
-  In Collection: SETR
-  Metadata:
-    backbone: ViT-L
-    crop size: (768,768)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 2564.1
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (768,768)
-    Training Memory (GB): 24.06
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 78.1
-      mIoU(ms+flip): 80.22
-  Config: configs/setr/setr_vit-l_naive_8xb1-80k_cityscapes-768x768.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_naive_vit-large_8x1_768x768_80k_cityscapes/setr_naive_vit-large_8x1_768x768_80k_cityscapes_20211123_000505-20728e80.pth
-- Name: setr_vit-l_pup_8xb1-80k_cityscapes-768x768
-  In Collection: SETR
-  Metadata:
-    backbone: ViT-L
-    crop size: (768,768)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 2702.7
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (768,768)
-    Training Memory (GB): 27.96
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 79.21
-      mIoU(ms+flip): 81.02
-  Config: configs/setr/setr_vit-l_pup_8xb1-80k_cityscapes-768x768.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_pup_vit-large_8x1_768x768_80k_cityscapes/setr_pup_vit-large_8x1_768x768_80k_cityscapes_20211122_155115-f6f37b8f.pth
-- Name: setr_vit-l_mla_8xb1-80k_cityscapes-768x768
-  In Collection: SETR
-  Metadata:
-    backbone: ViT-L
-    crop size: (768,768)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 2439.02
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (768,768)
-    Training Memory (GB): 24.1
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 77.0
-      mIoU(ms+flip): 79.59
-  Config: configs/setr/setr_vit-l_mla_8xb1-80k_cityscapes-768x768.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/setr/setr_mla_vit-large_8x1_768x768_80k_cityscapes/setr_mla_vit-large_8x1_768x768_80k_cityscapes_20211119_101003-7f8dccbe.pth
diff --git a/configs/stdc/README.md b/configs/stdc/README.md
index 639e6b6986..1efd65482f 100644
--- a/configs/stdc/README.md
+++ b/configs/stdc/README.md
@@ -1,6 +1,6 @@
 # STDC
 
-[Rethinking BiSeNet For Real-time Semantic Segmentation](https://arxiv.org/abs/2104.13188)
+> [Rethinking BiSeNet For Real-time Semantic Segmentation](https://arxiv.org/abs/2104.13188)
 
 ## Introduction
 
@@ -22,18 +22,6 @@ BiSeNet has been proved to be a popular two-stream network for real-time segment
 <img src="https://user-images.githubusercontent.com/24582831/143640374-d0709587-edb2-4821-bb60-340035f6ad8f.png" width="60%"/>
 </div>
 
-## Citation
-
-```bibtex
-@inproceedings{fan2021rethinking,
-  title={Rethinking BiSeNet For Real-time Semantic Segmentation},
-  author={Fan, Mingyuan and Lai, Shenqi and Huang, Junshi and Wei, Xiaoming and Chai, Zhenhua and Luo, Junfeng and Wei, Xiaolin},
-  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
-  pages={9716--9725},
-  year={2021}
-}
-```
-
 ## Usage
 
 We have provided [ImageNet Pretrained STDCNet Weights](https://drive.google.com/drive/folders/1wROFwRt8qWHD4jSo8Zu1gp1d6oYJ3ns1) models converted from [official repo](https://github.com/MichaelFan01/STDC-Seg).
@@ -58,12 +46,12 @@ This script convert model from `PRETRAIN_PATH` and store the converted model in
 
 ### Cityscapes
 
-| Method               | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                           | download                                                                                                                                                                                                                                                                                                                                             |
-| -------------------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------- | -------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| STDC 1 (No Pretrain) | STDC1    | 512x1024  |   80000 | 7.15     | 23.06          | 71.82 | 73.89         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/stdc/stdc1_4xb12-80k_cityscapes-512x1024.py)          | [model](https://download.openmmlab.com/mmsegmentation/v0.5/stdc/stdc1_512x1024_80k_cityscapes/stdc1_512x1024_80k_cityscapes_20220224_073048-74e6920a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/stdc/stdc1_512x1024_80k_cityscapes/stdc1_512x1024_80k_cityscapes_20220224_073048.log.json)                                     |
-| STDC 1               | STDC1    | 512x1024  |   80000 | -        | -              | 74.94 | 76.97         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/stdc/stdc1_in1k-pre_4xb12-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/stdc/stdc1_in1k-pre_512x1024_80k_cityscapes/stdc1_in1k-pre_512x1024_80k_cityscapes_20220224_141648-3d4c2981.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/stdc/stdc1_in1k-pre_512x1024_80k_cityscapes/stdc1_in1k-pre_512x1024_80k_cityscapes_20220224_141648.log.json) |
-| STDC 2 (No Pretrain) | STDC2    | 512x1024  |   80000 | 8.27     | 23.71          | 73.15 | 76.13         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/stdc/stdc2_4xb12-80k_cityscapes-512x1024.py)          | [model](https://download.openmmlab.com/mmsegmentation/v0.5/stdc/stdc2_512x1024_80k_cityscapes/stdc2_512x1024_80k_cityscapes_20220222_132015-fb1e3a1a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/stdc/stdc2_512x1024_80k_cityscapes/stdc2_512x1024_80k_cityscapes_20220222_132015.log.json)                                     |
-| STDC 2               | STDC2    | 512x1024  |   80000 | -        | -              | 76.67 | 78.67         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/stdc/stdc2_in1k-pre_4xb12-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/stdc/stdc2_in1k-pre_512x1024_80k_cityscapes/stdc2_in1k-pre_512x1024_80k_cityscapes_20220224_073048-1f8f0f6c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/stdc/stdc2_in1k-pre_512x1024_80k_cityscapes/stdc2_in1k-pre_512x1024_80k_cityscapes_20220224_073048.log.json) |
+| Method | Backbone             | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                           | download                                                                                                                                                                                                                                                                                                                                             |
+| ------ | -------------------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------- | -------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| STDC   | STDC1 (No Pretrain)  | 512x1024  |   80000 | 7.15     | 23.06          | V100   | 71.82 | 73.89         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/stdc/stdc1_4xb12-80k_cityscapes-512x1024.py)          | [model](https://download.openmmlab.com/mmsegmentation/v0.5/stdc/stdc1_512x1024_80k_cityscapes/stdc1_512x1024_80k_cityscapes_20220224_073048-74e6920a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/stdc/stdc1_512x1024_80k_cityscapes/stdc1_512x1024_80k_cityscapes_20220224_073048.log.json)                                     |
+| STDC   | STDC1                | 512x1024  |   80000 | -        | -              | V100   | 74.94 | 76.97         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/stdc/stdc1_in1k-pre_4xb12-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/stdc/stdc1_in1k-pre_512x1024_80k_cityscapes/stdc1_in1k-pre_512x1024_80k_cityscapes_20220224_141648-3d4c2981.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/stdc/stdc1_in1k-pre_512x1024_80k_cityscapes/stdc1_in1k-pre_512x1024_80k_cityscapes_20220224_141648.log.json) |
+| STDC   | STDC2  (No Pretrain) | 512x1024  |   80000 | 8.27     | 23.71          | V100   | 73.15 | 76.13         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/stdc/stdc2_4xb12-80k_cityscapes-512x1024.py)          | [model](https://download.openmmlab.com/mmsegmentation/v0.5/stdc/stdc2_512x1024_80k_cityscapes/stdc2_512x1024_80k_cityscapes_20220222_132015-fb1e3a1a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/stdc/stdc2_512x1024_80k_cityscapes/stdc2_512x1024_80k_cityscapes_20220222_132015.log.json)                                     |
+| STDC   | STDC2                | 512x1024  |   80000 | -        | -              | V100   | 76.67 | 78.67         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/stdc/stdc2_in1k-pre_4xb12-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/stdc/stdc2_in1k-pre_512x1024_80k_cityscapes/stdc2_in1k-pre_512x1024_80k_cityscapes_20220224_073048-1f8f0f6c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/stdc/stdc2_in1k-pre_512x1024_80k_cityscapes/stdc2_in1k-pre_512x1024_80k_cityscapes_20220224_073048.log.json) |
 
 Note:
 
@@ -71,3 +59,15 @@ Note:
 - `No Pretrain` means the model is trained from scratch.
 - The FPS is for reference only. The environment is also different from paper setting, whose input size is `512x1024` and `768x1536`, i.e., 50% and 75% of our input size, respectively and using TensorRT.
 - The parameter `fusion_kernel` in `STDCHead` is not learnable. In official repo, `find_unused_parameters=True` is set [here](https://github.com/MichaelFan01/STDC-Seg/blob/59ff37fbd693b99972c76fcefe97caa14aeb619f/train.py#L220). You may check it by printing model parameters of original repo on your own.
+
+## Citation
+
+```bibtex
+@inproceedings{fan2021rethinking,
+  title={Rethinking BiSeNet For Real-time Semantic Segmentation},
+  author={Fan, Mingyuan and Lai, Shenqi and Huang, Junshi and Wei, Xiaoming and Chai, Zhenhua and Luo, Junfeng and Wei, Xiaolin},
+  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
+  pages={9716--9725},
+  year={2021}
+}
+```
diff --git a/configs/stdc/metafile.yaml b/configs/stdc/metafile.yaml
new file mode 100644
index 0000000000..93cb14f50b
--- /dev/null
+++ b/configs/stdc/metafile.yaml
@@ -0,0 +1,107 @@
+Collections:
+- Name: STDC
+  License: Apache License 2.0
+  Metadata:
+    Training Data:
+    - Cityscapes
+  Paper:
+    Title: Rethinking BiSeNet For Real-time Semantic Segmentation
+    URL: https://arxiv.org/abs/2104.13188
+  README: configs/stdc/README.md
+  Frameworks:
+  - PyTorch
+Models:
+- Name: stdc1_4xb12-80k_cityscapes-512x1024
+  In Collection: STDC
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 71.82
+      mIoU(ms+flip): 73.89
+  Config: configs/stdc/stdc1_4xb12-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 48
+    Architecture:
+    - STDC1
+    - STDC
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 7.15
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/stdc/stdc1_512x1024_80k_cityscapes/stdc1_512x1024_80k_cityscapes_20220224_073048-74e6920a.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/stdc/stdc1_512x1024_80k_cityscapes/stdc1_512x1024_80k_cityscapes_20220224_073048.log.json
+  Paper:
+    Title: Rethinking BiSeNet For Real-time Semantic Segmentation
+    URL: https://arxiv.org/abs/2104.13188
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.20.0/mmseg/models/backbones/stdc.py#L394
+  Framework: PyTorch
+- Name: stdc1_in1k-pre_4xb12-80k_cityscapes-512x1024
+  In Collection: STDC
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 74.94
+      mIoU(ms+flip): 76.97
+  Config: configs/stdc/stdc1_in1k-pre_4xb12-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 48
+    Architecture:
+    - STDC1
+    - STDC
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/stdc/stdc1_in1k-pre_512x1024_80k_cityscapes/stdc1_in1k-pre_512x1024_80k_cityscapes_20220224_141648-3d4c2981.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/stdc/stdc1_in1k-pre_512x1024_80k_cityscapes/stdc1_in1k-pre_512x1024_80k_cityscapes_20220224_141648.log.json
+  Paper:
+    Title: Rethinking BiSeNet For Real-time Semantic Segmentation
+    URL: https://arxiv.org/abs/2104.13188
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.20.0/mmseg/models/backbones/stdc.py#L394
+  Framework: PyTorch
+- Name: stdc2_4xb12-80k_cityscapes-512x1024
+  In Collection: STDC
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 73.15
+      mIoU(ms+flip): 76.13
+  Config: configs/stdc/stdc2_4xb12-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 48
+    Architecture:
+    - STDC2
+    - STDC
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 8.27
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/stdc/stdc2_512x1024_80k_cityscapes/stdc2_512x1024_80k_cityscapes_20220222_132015-fb1e3a1a.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/stdc/stdc2_512x1024_80k_cityscapes/stdc2_512x1024_80k_cityscapes_20220222_132015.log.json
+  Paper:
+    Title: Rethinking BiSeNet For Real-time Semantic Segmentation
+    URL: https://arxiv.org/abs/2104.13188
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.20.0/mmseg/models/backbones/stdc.py#L394
+  Framework: PyTorch
+- Name: stdc2_in1k-pre_4xb12-80k_cityscapes-512x1024
+  In Collection: STDC
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 76.67
+      mIoU(ms+flip): 78.67
+  Config: configs/stdc/stdc2_in1k-pre_4xb12-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 48
+    Architecture:
+    - STDC2
+    - STDC
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/stdc/stdc2_in1k-pre_512x1024_80k_cityscapes/stdc2_in1k-pre_512x1024_80k_cityscapes_20220224_073048-1f8f0f6c.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/stdc/stdc2_in1k-pre_512x1024_80k_cityscapes/stdc2_in1k-pre_512x1024_80k_cityscapes_20220224_073048.log.json
+  Paper:
+    Title: Rethinking BiSeNet For Real-time Semantic Segmentation
+    URL: https://arxiv.org/abs/2104.13188
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.20.0/mmseg/models/backbones/stdc.py#L394
+  Framework: PyTorch
diff --git a/configs/stdc/stdc.yml b/configs/stdc/stdc.yml
deleted file mode 100644
index 22fb37eeba..0000000000
--- a/configs/stdc/stdc.yml
+++ /dev/null
@@ -1,87 +0,0 @@
-Collections:
-- Name: STDC
-  Metadata:
-    Training Data:
-    - Cityscapes
-  Paper:
-    URL: https://arxiv.org/abs/2104.13188
-    Title: Rethinking BiSeNet For Real-time Semantic Segmentation
-  README: configs/stdc/README.md
-  Code:
-    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.20.0/mmseg/models/backbones/stdc.py#L394
-    Version: v0.20.0
-  Converted From:
-    Code: https://github.com/MichaelFan01/STDC-Seg
-Models:
-- Name: stdc1_4xb12-80k_cityscapes-512x1024
-  In Collection: STDC
-  Metadata:
-    backbone: STDC1
-    crop size: (512,1024)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 43.37
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 7.15
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 71.82
-      mIoU(ms+flip): 73.89
-  Config: configs/stdc/stdc1_4xb12-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/stdc/stdc1_512x1024_80k_cityscapes/stdc1_512x1024_80k_cityscapes_20220224_073048-74e6920a.pth
-- Name: stdc1_in1k-pre_4xb12-80k_cityscapes-512x1024
-  In Collection: STDC
-  Metadata:
-    backbone: STDC1
-    crop size: (512,1024)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 74.94
-      mIoU(ms+flip): 76.97
-  Config: configs/stdc/stdc1_in1k-pre_4xb12-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/stdc/stdc1_in1k-pre_512x1024_80k_cityscapes/stdc1_in1k-pre_512x1024_80k_cityscapes_20220224_141648-3d4c2981.pth
-- Name: stdc2_4xb12-80k_cityscapes-512x1024
-  In Collection: STDC
-  Metadata:
-    backbone: STDC2
-    crop size: (512,1024)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 42.18
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 8.27
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 73.15
-      mIoU(ms+flip): 76.13
-  Config: configs/stdc/stdc2_4xb12-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/stdc/stdc2_512x1024_80k_cityscapes/stdc2_512x1024_80k_cityscapes_20220222_132015-fb1e3a1a.pth
-- Name: stdc2_in1k-pre_4xb12-80k_cityscapes-512x1024
-  In Collection: STDC
-  Metadata:
-    backbone: STDC2
-    crop size: (512,1024)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 76.67
-      mIoU(ms+flip): 78.67
-  Config: configs/stdc/stdc2_in1k-pre_4xb12-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/stdc/stdc2_in1k-pre_512x1024_80k_cityscapes/stdc2_in1k-pre_512x1024_80k_cityscapes_20220224_073048-1f8f0f6c.pth
diff --git a/configs/swin/README.md b/configs/swin/README.md
index 4ab20e80b8..18fcbae8bc 100644
--- a/configs/swin/README.md
+++ b/configs/swin/README.md
@@ -1,6 +1,6 @@
 # Swin Transformer
 
-[Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030)
+> [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030)
 
 ## Introduction
 
@@ -22,17 +22,6 @@ This paper presents a new vision Transformer, called Swin Transformer, that capa
 <img src="https://user-images.githubusercontent.com/24582831/142902882-3fb9014c-11b6-47e9-aa14-500dfe7cbb1c.png" width="80%"/>
 </div>
 
-## Citation
-
-```bibtex
-@article{liu2021Swin,
-  title={Swin Transformer: Hierarchical Vision Transformer using Shifted Windows},
-  author={Liu, Ze and Lin, Yutong and Cao, Yue and Hu, Han and Wei, Yixuan and Zhang, Zheng and Lin, Stephen and Guo, Baining},
-  journal={arXiv preprint arXiv:2103.14030},
-  year={2021}
-}
-```
-
 ## Usage
 
 We have provided pretrained models converted from [official repo](https://github.com/microsoft/Swin-Transformer)．
@@ -66,11 +55,22 @@ In our default setting, pretrained models and their corresponding [original mode
 
 ### ADE20K
 
-| Method  | Backbone | Crop Size | pretrain     | pretrain img size | Batch Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU  | mIoU(ms+flip) | config                                                                                                                                                          | download                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             |
-| ------- | -------- | --------- | ------------ | ----------------- | ---------- | ------- | -------- | -------------- | ----- | ------------: | --------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| UPerNet | Swin-T   | 512x512   | ImageNet-1K  | 224x224           | 16         | 160000  | 5.02     | 21.06          | 44.41 |         45.79 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/swin/swin-tiny-patch4-window7-in1k-pre_upernet_8xb2-160k_ade20k-512x512.py)           | [model](https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542.log.json)         |
-| UPerNet | Swin-S   | 512x512   | ImageNet-1K  | 224x224           | 16         | 160000  | 6.17     | 14.72          | 47.72 |         49.24 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/swin/swin-small-patch4-window7-in1k-pre_upernet_8xb2-160k_ade20k-512x512.py)          | [model](https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015.log.json)     |
-| UPerNet | Swin-B   | 512x512   | ImageNet-1K  | 224x224           | 16         | 160000  | 7.61     | 12.65          | 47.99 |         49.57 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/swin/swin-base-patch4-window7-in1k-pre_upernet_8xb2-160k_ade20k-512x512.py)           | [model](https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192340-593b0e13.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192340.log.json)         |
-| UPerNet | Swin-B   | 512x512   | ImageNet-22K | 224x224           | 16         | 160000  | -        | -              | 50.13 |          51.9 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/swin/swin-base-patch4-window7-in22k-pre_upernet_8xb2-160k_ade20k-512x512.py)          | [model](https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_22K/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_22K_20210526_211650-762e2178.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_22K/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_22K_20210526_211650.log.json)     |
-| UPerNet | Swin-B   | 512x512   | ImageNet-1K  | 384x384           | 16         | 160000  | 8.52     | 12.10          | 48.35 |         49.65 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/swin/swin-base-patch4-window12-in1k-384x384-pre_upernet_8xb2-160k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_1K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_1K_20210531_132020-05b22ea4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_1K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_1K_20210531_132020.log.json)     |
-| UPerNet | Swin-B   | 512x512   | ImageNet-22K | 384x384           | 16         | 160000  | -        | -              | 50.76 |          52.4 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/swin/swin-base-patch4-window12-in22k-384x384-pre_upernet_8xb2-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459.log.json) |
+| Method  | Backbone | Crop Size | pretrain     | pretrain img size | Batch Size | Lr schd | Mem (GB) | Inf time (fps) | Device | mIoU  | mIoU(ms+flip) | config                                                                                                                                                          | download                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             |
+| ------- | -------- | --------- | ------------ | ----------------- | ---------- | ------- | -------- | -------------- | ------ | ----- | ------------: | --------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| UPerNet | Swin-T   | 512x512   | ImageNet-1K  | 224x224           | 16         | 160000  | 5.02     | 21.06          | V100   | 44.41 |         45.79 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/swin/swin-tiny-patch4-window7-in1k-pre_upernet_8xb2-160k_ade20k-512x512.py)           | [model](https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542.log.json)         |
+| UPerNet | Swin-S   | 512x512   | ImageNet-1K  | 224x224           | 16         | 160000  | 6.17     | 14.72          | V100   | 47.72 |         49.24 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/swin/swin-small-patch4-window7-in1k-pre_upernet_8xb2-160k_ade20k-512x512.py)          | [model](https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015.log.json)     |
+| UPerNet | Swin-B   | 512x512   | ImageNet-1K  | 224x224           | 16         | 160000  | 7.61     | 12.65          | V100   | 47.99 |         49.57 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/swin/swin-base-patch4-window7-in1k-pre_upernet_8xb2-160k_ade20k-512x512.py)           | [model](https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192340-593b0e13.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192340.log.json)         |
+| UPerNet | Swin-B   | 512x512   | ImageNet-22K | 224x224           | 16         | 160000  | -        | -              | V100   | 50.13 |          51.9 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/swin/swin-base-patch4-window7-in22k-pre_upernet_8xb2-160k_ade20k-512x512.py)          | [model](https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_22K/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_22K_20210526_211650-762e2178.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_22K/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_22K_20210526_211650.log.json)     |
+| UPerNet | Swin-B   | 512x512   | ImageNet-1K  | 384x384           | 16         | 160000  | 8.52     | 12.10          | V100   | 48.35 |         49.65 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/swin/swin-base-patch4-window12-in1k-384x384-pre_upernet_8xb2-160k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_1K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_1K_20210531_132020-05b22ea4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_1K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_1K_20210531_132020.log.json)     |
+| UPerNet | Swin-B   | 512x512   | ImageNet-22K | 384x384           | 16         | 160000  | -        | -              | V100   | 50.76 |          52.4 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/swin/swin-base-patch4-window12-in22k-384x384-pre_upernet_8xb2-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459.log.json) |
+
+## Citation
+
+```bibtex
+@article{liu2021Swin,
+  title={Swin Transformer: Hierarchical Vision Transformer using Shifted Windows},
+  author={Liu, Ze and Lin, Yutong and Cao, Yue and Hu, Han and Wei, Yixuan and Zhang, Zheng and Lin, Stephen and Guo, Baining},
+  journal={arXiv preprint arXiv:2103.14030},
+  year={2021}
+}
+```
diff --git a/configs/swin/metafile.yaml b/configs/swin/metafile.yaml
new file mode 100644
index 0000000000..67a4e07551
--- /dev/null
+++ b/configs/swin/metafile.yaml
@@ -0,0 +1,143 @@
+Models:
+- Name: swin-tiny-patch4-window7-in1k-pre_upernet_8xb2-160k_ade20k-512x512
+  In Collection: UPerNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 44.41
+      mIoU(ms+flip): 45.79
+  Config: configs/swin/swin-tiny-patch4-window7-in1k-pre_upernet_8xb2-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - Swin-T
+    - UPerNet
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 5.02
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542.log.json
+  Paper:
+    Title: 'Swin Transformer: Hierarchical Vision Transformer using Shifted Windows'
+    URL: https://arxiv.org/abs/2103.14030
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/swin.py#L524
+  Framework: PyTorch
+- Name: swin-small-patch4-window7-in1k-pre_upernet_8xb2-160k_ade20k-512x512
+  In Collection: UPerNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 47.72
+      mIoU(ms+flip): 49.24
+  Config: configs/swin/swin-small-patch4-window7-in1k-pre_upernet_8xb2-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - Swin-S
+    - UPerNet
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 6.17
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015.log.json
+  Paper:
+    Title: 'Swin Transformer: Hierarchical Vision Transformer using Shifted Windows'
+    URL: https://arxiv.org/abs/2103.14030
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/swin.py#L524
+  Framework: PyTorch
+- Name: swin-base-patch4-window7-in1k-pre_upernet_8xb2-160k_ade20k-512x512
+  In Collection: UPerNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 47.99
+      mIoU(ms+flip): 49.57
+  Config: configs/swin/swin-base-patch4-window7-in1k-pre_upernet_8xb2-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - Swin-B
+    - UPerNet
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 7.61
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192340-593b0e13.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192340.log.json
+  Paper:
+    Title: 'Swin Transformer: Hierarchical Vision Transformer using Shifted Windows'
+    URL: https://arxiv.org/abs/2103.14030
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/swin.py#L524
+  Framework: PyTorch
+- Name: swin-base-patch4-window7-in22k-pre_upernet_8xb2-160k_ade20k-512x512
+  In Collection: UPerNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 50.13
+      mIoU(ms+flip): 51.9
+  Config: configs/swin/swin-base-patch4-window7-in22k-pre_upernet_8xb2-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - Swin-B
+    - UPerNet
+    Training Resources: 8x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_22K/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_22K_20210526_211650-762e2178.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_22K/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_22K_20210526_211650.log.json
+  Paper:
+    Title: 'Swin Transformer: Hierarchical Vision Transformer using Shifted Windows'
+    URL: https://arxiv.org/abs/2103.14030
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/swin.py#L524
+  Framework: PyTorch
+- Name: swin-base-patch4-window12-in1k-384x384-pre_upernet_8xb2-160k_ade20k-512x512
+  In Collection: UPerNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 48.35
+      mIoU(ms+flip): 49.65
+  Config: configs/swin/swin-base-patch4-window12-in1k-384x384-pre_upernet_8xb2-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - Swin-B
+    - UPerNet
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 8.52
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_1K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_1K_20210531_132020-05b22ea4.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_1K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_1K_20210531_132020.log.json
+  Paper:
+    Title: 'Swin Transformer: Hierarchical Vision Transformer using Shifted Windows'
+    URL: https://arxiv.org/abs/2103.14030
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/swin.py#L524
+  Framework: PyTorch
+- Name: swin-base-patch4-window12-in22k-384x384-pre_upernet_8xb2-160k_ade20k-512x512
+  In Collection: UPerNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 50.76
+      mIoU(ms+flip): 52.4
+  Config: configs/swin/swin-base-patch4-window12-in22k-384x384-pre_upernet_8xb2-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - Swin-B
+    - UPerNet
+    Training Resources: 8x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459.log.json
+  Paper:
+    Title: 'Swin Transformer: Hierarchical Vision Transformer using Shifted Windows'
+    URL: https://arxiv.org/abs/2103.14030
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/swin.py#L524
+  Framework: PyTorch
diff --git a/configs/swin/swin.yml b/configs/swin/swin.yml
deleted file mode 100644
index 783d839c8d..0000000000
--- a/configs/swin/swin.yml
+++ /dev/null
@@ -1,117 +0,0 @@
-Models:
-- Name: swin-tiny-patch4-window7-in1k-pre_upernet_8xb2-160k_ade20k-512x512
-  In Collection: UPerNet
-  Metadata:
-    backbone: Swin-T
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 47.48
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 5.02
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 44.41
-      mIoU(ms+flip): 45.79
-  Config: configs/swin/swin-tiny-patch4-window7-in1k-pre_upernet_8xb2-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth
-- Name: swin-small-patch4-window7-in1k-pre_upernet_8xb2-160k_ade20k-512x512
-  In Collection: UPerNet
-  Metadata:
-    backbone: Swin-S
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 67.93
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 6.17
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 47.72
-      mIoU(ms+flip): 49.24
-  Config: configs/swin/swin-small-patch4-window7-in1k-pre_upernet_8xb2-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth
-- Name: swin-base-patch4-window7-in1k-pre_upernet_8xb2-160k_ade20k-512x512
-  In Collection: UPerNet
-  Metadata:
-    backbone: Swin-B
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 79.05
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 7.61
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 47.99
-      mIoU(ms+flip): 49.57
-  Config: configs/swin/swin-base-patch4-window7-in1k-pre_upernet_8xb2-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192340-593b0e13.pth
-- Name: swin-base-patch4-window7-in22k-pre_upernet_8xb2-160k_ade20k-512x512
-  In Collection: UPerNet
-  Metadata:
-    backbone: Swin-B
-    crop size: (512,512)
-    lr schd: 160000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 50.13
-      mIoU(ms+flip): 51.9
-  Config: configs/swin/swin-base-patch4-window7-in22k-pre_upernet_8xb2-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_22K/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_22K_20210526_211650-762e2178.pth
-- Name: swin-base-patch4-window12-in1k-384x384-pre_upernet_8xb2-160k_ade20k-512x512
-  In Collection: UPerNet
-  Metadata:
-    backbone: Swin-B
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 82.64
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 8.52
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 48.35
-      mIoU(ms+flip): 49.65
-  Config: configs/swin/swin-base-patch4-window12-in1k-384x384-pre_upernet_8xb2-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_1K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_1K_20210531_132020-05b22ea4.pth
-- Name: swin-base-patch4-window12-in22k-384x384-pre_upernet_8xb2-160k_ade20k-512x512
-  In Collection: UPerNet
-  Metadata:
-    backbone: Swin-B
-    crop size: (512,512)
-    lr schd: 160000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 50.76
-      mIoU(ms+flip): 52.4
-  Config: configs/swin/swin-base-patch4-window12-in22k-384x384-pre_upernet_8xb2-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth
diff --git a/configs/twins/README.md b/configs/twins/README.md
index 3e741802e6..306b65fac8 100644
--- a/configs/twins/README.md
+++ b/configs/twins/README.md
@@ -1,6 +1,6 @@
 # Twins
 
-[Twins: Revisiting the Design of Spatial Attention in Vision Transformers](https://arxiv.org/pdf/2104.13840.pdf)
+> [Twins: Revisiting the Design of Spatial Attention in Vision Transformers](https://arxiv.org/pdf/2104.13840.pdf)
 
 ## Introduction
 
@@ -22,17 +22,6 @@ Very recently, a variety of vision transformer architectures for dense predictio
 <img src="https://user-images.githubusercontent.com/24582831/145021310-57826cf5-5e03-4c7c-9081-ffa744bdae27.png" width="80%"/>
 </div>
 
-## Citation
-
-```bibtex
-@article{chu2021twins,
-  title={Twins: Revisiting spatial attention design in vision transformers},
-  author={Chu, Xiangxiang and Tian, Zhi and Wang, Yuqing and Zhang, Bo and Ren, Haibing and Wei, Xiaolin and Xia, Huaxia and Shen, Chunhua},
-  journal={arXiv preprint arXiv:2104.13840},
-  year={2021}altgvt
-}
-```
-
 ## Usage
 
 We have provided pretrained models converted from [official repo](https://github.com/Meituan-AutoML/Twins).
@@ -55,22 +44,33 @@ python tools/model_converters/twins2mmseg.py ./alt_gvt_base.pth ./pretrained/alt
 
 ### ADE20K
 
-| Method              | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU  | mIoU(ms+flip) | config                                                                                                                                 | download                                                                                                                                                                                                                                                                                                                                                                                       |
-| ------------------- | -------- | --------- | ------- | -------- | -------------- | ----- | ------------- | -------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| Twins-FPN           | PCPVT-S  | 512x512   | 80000   | 6.60     | 27.15          | 43.26 | 44.11         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/twins/twins_pcpvt-s_fpn_fpnhead_8xb4-80k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_pcpvt-s_fpn_fpnhead_8x4_512x512_80k_ade20k/twins_pcpvt-s_fpn_fpnhead_8x4_512x512_80k_ade20k_20211201_204132-41acd132.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_pcpvt-s_fpn_fpnhead_8x4_512x512_80k_ade20k/twins_pcpvt-s_fpn_fpnhead_8x4_512x512_80k_ade20k_20211201_204132.log.json) |
-| Twins-UPerNet       | PCPVT-S  | 512x512   | 160000  | 9.67     | 14.24          | 46.04 | 46.92         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/twins/twins_pcpvt-s_uperhead_8xb4-160k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_pcpvt-s_uperhead_8x4_512x512_160k_ade20k/twins_pcpvt-s_uperhead_8x4_512x512_160k_ade20k_20211201_233537-8e99c07a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_pcpvt-s_uperhead_8x4_512x512_160k_ade20k/twins_pcpvt-s_uperhead_8x4_512x512_160k_ade20k_20211201_233537.log.json)         |
-| Twins-FPN           | PCPVT-B  | 512x512   | 80000   | 8.41     | 19.67          | 45.66 | 46.48         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/twins/twins_pcpvt-b_fpn_fpnhead_8xb4-80k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_pcpvt-b_fpn_fpnhead_8x4_512x512_80k_ade20k/twins_pcpvt-b_fpn_fpnhead_8x4_512x512_80k_ade20k_20211130_141019-d396db72.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_pcpvt-b_fpn_fpnhead_8x4_512x512_80k_ade20k/twins_pcpvt-b_fpn_fpnhead_8x4_512x512_80k_ade20k_20211130_141019.log.json) |
-| Twins-UPerNet (8x2) | PCPVT-B  | 512x512   | 160000  | 6.46     | 12.04          | 47.91 | 48.64         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/twins/twins_pcpvt-b_uperhead_8xb2-160k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_pcpvt-b_uperhead_8x2_512x512_160k_ade20k/twins_pcpvt-b_uperhead_8x2_512x512_160k_ade20k_20211130_141020-02094ea5.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_pcpvt-b_uperhead_8x2_512x512_160k_ade20k/twins_pcpvt-b_uperhead_8x2_512x512_160k_ade20k_20211130_141020.log.json)         |
-| Twins-FPN           | PCPVT-L  | 512x512   | 80000   | 10.78    | 14.32          | 45.94 | 46.70         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/twins/twins_pcpvt-l_fpn_fpnhead_8xb4-80k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_pcpvt-l_fpn_fpnhead_8x4_512x512_80k_ade20k/twins_pcpvt-l_fpn_fpnhead_8x4_512x512_80k_ade20k_20211201_105226-bc6d61dc.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_pcpvt-l_fpn_fpnhead_8x4_512x512_80k_ade20k/twins_pcpvt-l_fpn_fpnhead_8x4_512x512_80k_ade20k_20211201_105226.log.json) |
-| Twins-UPerNet (8x2) | PCPVT-L  | 512x512   | 160000  | 7.82     | 10.70          | 49.35 | 50.08         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/twins/twins_pcpvt-l_uperhead_8xb2-160k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_pcpvt-l_uperhead_8x2_512x512_160k_ade20k/twins_pcpvt-l_uperhead_8x2_512x512_160k_ade20k_20211201_075053-c6095c07.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_pcpvt-l_uperhead_8x2_512x512_160k_ade20k/twins_pcpvt-l_uperhead_8x2_512x512_160k_ade20k_20211201_075053.log.json)         |
-| Twins-FPN           | SVT-S    | 512x512   | 80000   | 5.80     | 29.79          | 44.47 | 45.42         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/twins/twins_svt-s_fpn_fpnhead_8xb4-80k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_svt-s_fpn_fpnhead_8x4_512x512_80k_ade20k/twins_svt-s_fpn_fpnhead_8x4_512x512_80k_ade20k_20211130_141006-0a0d3317.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_svt-s_fpn_fpnhead_8x4_512x512_80k_ade20k/twins_svt-s_fpn_fpnhead_8x4_512x512_80k_ade20k_20211130_141006.log.json)         |
-| Twins-UPerNet (8x2) | SVT-S    | 512x512   | 160000  | 4.93     | 15.09          | 46.08 | 46.96         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/twins/twins_svt-s_uperhead_8xb2-160k_ade20k-512x512.py)     | [model](https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_svt-s_uperhead_8x2_512x512_160k_ade20k/twins_svt-s_uperhead_8x2_512x512_160k_ade20k_20211130_141005-e48a2d94.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_svt-s_uperhead_8x2_512x512_160k_ade20k/twins_svt-s_uperhead_8x2_512x512_160k_ade20k_20211130_141005.log.json)                 |
-| Twins-FPN           | SVT-B    | 512x512   | 80000   | 8.75     | 21.10          | 46.77 | 47.47         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/twins/twins_svt-b_fpn_fpnhead_8xb4-80k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_svt-b_fpn_fpnhead_8x4_512x512_80k_ade20k/twins_svt-b_fpn_fpnhead_8x4_512x512_80k_ade20k_20211201_113849-88b2907c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_svt-b_fpn_fpnhead_8x4_512x512_80k_ade20k/twins_svt-b_fpn_fpnhead_8x4_512x512_80k_ade20k_20211201_113849.log.json)         |
-| Twins-UPerNet (8x2) | SVT-B    | 512x512   | 160000  | 6.77     | 12.66          | 48.04 | 48.87         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/twins/twins_svt-b_uperhead_8xb2-160k_ade20k-512x512.py)     | [model](https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_svt-b_uperhead_8x2_512x512_160k_ade20k/twins_svt-b_uperhead_8x2_512x512_160k_ade20k_20211202_040826-0943a1f1.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_svt-b_uperhead_8x2_512x512_160k_ade20k/twins_svt-b_uperhead_8x2_512x512_160k_ade20k_20211202_040826.log.json)                 |
-| Twins-FPN           | SVT-L    | 512x512   | 80000   | 11.20    | 17.80          | 46.55 | 47.74         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/twins/twins_svt-l_fpn_fpnhead_8xb4-80k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_svt-l_fpn_fpnhead_8x4_512x512_80k_ade20k/twins_svt-l_fpn_fpnhead_8x4_512x512_80k_ade20k_20211130_141005-1d59bee2.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_svt-l_fpn_fpnhead_8x4_512x512_80k_ade20k/twins_svt-l_fpn_fpnhead_8x4_512x512_80k_ade20k_20211130_141005.log.json)         |
-| Twins-UPerNet (8x2) | SVT-L    | 512x512   | 160000  | 8.41     | 10.73          | 49.65 | 50.63         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/twins/twins_pcpvt-l_uperhead_8xb2-160k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_svt-l_uperhead_8x2_512x512_160k_ade20k/twins_svt-l_uperhead_8x2_512x512_160k_ade20k_20211130_141005-3e2cae61.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_svt-l_uperhead_8x2_512x512_160k_ade20k/twins_svt-l_uperhead_8x2_512x512_160k_ade20k_20211130_141005.log.json)                 |
+| Method  | Backbone            | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device | mIoU  | mIoU(ms+flip) | config                                                                                                                                 | download                                                                                                                                                                                                                                                                                                                                                                                       |
+| ------- | ------------------- | --------- | ------- | -------- | -------------- | ------ | ----- | ------------- | -------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| FPN     | Twins-PCPVT-S       | 512x512   | 80000   | 6.60     | 27.15          | V100   | 43.26 | 44.11         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/twins/twins_pcpvt-s_fpn_fpnhead_8xb4-80k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_pcpvt-s_fpn_fpnhead_8x4_512x512_80k_ade20k/twins_pcpvt-s_fpn_fpnhead_8x4_512x512_80k_ade20k_20211201_204132-41acd132.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_pcpvt-s_fpn_fpnhead_8x4_512x512_80k_ade20k/twins_pcpvt-s_fpn_fpnhead_8x4_512x512_80k_ade20k_20211201_204132.log.json) |
+| UPerNet | Twins-PCPVT-S       | 512x512   | 160000  | 9.67     | 14.24          | V100   | 46.04 | 46.92         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/twins/twins_pcpvt-s_uperhead_8xb4-160k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_pcpvt-s_uperhead_8x4_512x512_160k_ade20k/twins_pcpvt-s_uperhead_8x4_512x512_160k_ade20k_20211201_233537-8e99c07a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_pcpvt-s_uperhead_8x4_512x512_160k_ade20k/twins_pcpvt-s_uperhead_8x4_512x512_160k_ade20k_20211201_233537.log.json)         |
+| FPN     | Twins-PCPVT-B       | 512x512   | 80000   | 8.41     | 19.67          | V100   | 45.66 | 46.48         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/twins/twins_pcpvt-b_fpn_fpnhead_8xb4-80k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_pcpvt-b_fpn_fpnhead_8x4_512x512_80k_ade20k/twins_pcpvt-b_fpn_fpnhead_8x4_512x512_80k_ade20k_20211130_141019-d396db72.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_pcpvt-b_fpn_fpnhead_8x4_512x512_80k_ade20k/twins_pcpvt-b_fpn_fpnhead_8x4_512x512_80k_ade20k_20211130_141019.log.json) |
+| UPerNet | Twins-PCPVT-B (8x2) | 512x512   | 160000  | 6.46     | 12.04          | V100   | 47.91 | 48.64         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/twins/twins_pcpvt-b_uperhead_8xb2-160k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_pcpvt-b_uperhead_8x2_512x512_160k_ade20k/twins_pcpvt-b_uperhead_8x2_512x512_160k_ade20k_20211130_141020-02094ea5.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_pcpvt-b_uperhead_8x2_512x512_160k_ade20k/twins_pcpvt-b_uperhead_8x2_512x512_160k_ade20k_20211130_141020.log.json)         |
+| FPN     | Twins-PCPVT-L       | 512x512   | 80000   | 10.78    | 14.32          | V100   | 45.94 | 46.70         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/twins/twins_pcpvt-l_fpn_fpnhead_8xb4-80k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_pcpvt-l_fpn_fpnhead_8x4_512x512_80k_ade20k/twins_pcpvt-l_fpn_fpnhead_8x4_512x512_80k_ade20k_20211201_105226-bc6d61dc.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_pcpvt-l_fpn_fpnhead_8x4_512x512_80k_ade20k/twins_pcpvt-l_fpn_fpnhead_8x4_512x512_80k_ade20k_20211201_105226.log.json) |
+| UPerNet | Twins-PCPVT-L (8x2) | 512x512   | 160000  | 7.82     | 10.70          | V100   | 49.35 | 50.08         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/twins/twins_pcpvt-l_uperhead_8xb2-160k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_pcpvt-l_uperhead_8x2_512x512_160k_ade20k/twins_pcpvt-l_uperhead_8x2_512x512_160k_ade20k_20211201_075053-c6095c07.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_pcpvt-l_uperhead_8x2_512x512_160k_ade20k/twins_pcpvt-l_uperhead_8x2_512x512_160k_ade20k_20211201_075053.log.json)         |
+| FPN     | Twins-SVT-S         | 512x512   | 80000   | 5.80     | 29.79          | V100   | 44.47 | 45.42         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/twins/twins_svt-s_fpn_fpnhead_8xb4-80k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_svt-s_fpn_fpnhead_8x4_512x512_80k_ade20k/twins_svt-s_fpn_fpnhead_8x4_512x512_80k_ade20k_20211130_141006-0a0d3317.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_svt-s_fpn_fpnhead_8x4_512x512_80k_ade20k/twins_svt-s_fpn_fpnhead_8x4_512x512_80k_ade20k_20211130_141006.log.json)         |
+| UPerNet | SVT-S  (8x2)        | 512x512   | 160000  | 4.93     | 15.09          | V100   | 46.08 | 46.96         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/twins/twins_svt-s_uperhead_8xb2-160k_ade20k-512x512.py)     | [model](https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_svt-s_uperhead_8x2_512x512_160k_ade20k/twins_svt-s_uperhead_8x2_512x512_160k_ade20k_20211130_141005-e48a2d94.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_svt-s_uperhead_8x2_512x512_160k_ade20k/twins_svt-s_uperhead_8x2_512x512_160k_ade20k_20211130_141005.log.json)                 |
+| FPN     | Twins-SVT-B         | 512x512   | 80000   | 8.75     | 21.10          | V100   | 46.77 | 47.47         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/twins/twins_svt-b_fpn_fpnhead_8xb4-80k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_svt-b_fpn_fpnhead_8x4_512x512_80k_ade20k/twins_svt-b_fpn_fpnhead_8x4_512x512_80k_ade20k_20211201_113849-88b2907c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_svt-b_fpn_fpnhead_8x4_512x512_80k_ade20k/twins_svt-b_fpn_fpnhead_8x4_512x512_80k_ade20k_20211201_113849.log.json)         |
+| UPerNet | Twins-SVT-B  (8x2)  | 512x512   | 160000  | 6.77     | 12.66          | V100   | 48.04 | 48.87         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/twins/twins_svt-b_uperhead_8xb2-160k_ade20k-512x512.py)     | [model](https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_svt-b_uperhead_8x2_512x512_160k_ade20k/twins_svt-b_uperhead_8x2_512x512_160k_ade20k_20211202_040826-0943a1f1.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_svt-b_uperhead_8x2_512x512_160k_ade20k/twins_svt-b_uperhead_8x2_512x512_160k_ade20k_20211202_040826.log.json)                 |
+| FPN     | Twins-SVT-L         | 512x512   | 80000   | 11.20    | 17.80          | V100   | 46.55 | 47.74         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/twins/twins_svt-l_fpn_fpnhead_8xb4-80k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_svt-l_fpn_fpnhead_8x4_512x512_80k_ade20k/twins_svt-l_fpn_fpnhead_8x4_512x512_80k_ade20k_20211130_141005-1d59bee2.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_svt-l_fpn_fpnhead_8x4_512x512_80k_ade20k/twins_svt-l_fpn_fpnhead_8x4_512x512_80k_ade20k_20211130_141005.log.json)         |
+| UPerNet | Twins-SVT-L  (8x2)  | 512x512   | 160000  | 8.41     | 10.73          | V100   | 49.65 | 50.63         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/twins/twins_pcpvt-l_uperhead_8xb2-160k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_svt-l_uperhead_8x2_512x512_160k_ade20k/twins_svt-l_uperhead_8x2_512x512_160k_ade20k_20211130_141005-3e2cae61.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_svt-l_uperhead_8x2_512x512_160k_ade20k/twins_svt-l_uperhead_8x2_512x512_160k_ade20k_20211130_141005.log.json)                 |
 
 Note:
 
 - `8x2` means 8 GPUs with 2 samples per GPU in training. Default setting of Twins on ADE20K is 8 GPUs with 4 samples per GPU in training.
 - `UPerNet` and `FPN` are decoder heads utilized in corresponding Twins model, which is `UPerHead` and `FPNHead`, respectively. Specifically, models in [official repo](https://github.com/Meituan-AutoML/Twins) all use `UPerHead`.
+
+## Citation
+
+```bibtex
+@article{chu2021twins,
+  title={Twins: Revisiting spatial attention design in vision transformers},
+  author={Chu, Xiangxiang and Tian, Zhi and Wang, Yuqing and Zhang, Bo and Ren, Haibing and Wei, Xiaolin and Xia, Huaxia and Shen, Chunhua},
+  journal={arXiv preprint arXiv:2104.13840},
+  year={2021}altgvt
+}
+```
diff --git a/configs/twins/metafile.yaml b/configs/twins/metafile.yaml
new file mode 100644
index 0000000000..0de78d9d2e
--- /dev/null
+++ b/configs/twins/metafile.yaml
@@ -0,0 +1,289 @@
+Models:
+- Name: twins_pcpvt-s_fpn_fpnhead_8xb4-80k_ade20k-512x512
+  In Collection: FPN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 43.26
+      mIoU(ms+flip): 44.11
+  Config: configs/twins/twins_pcpvt-s_fpn_fpnhead_8xb4-80k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 32
+    Architecture:
+    - Twins-PCPVT-S
+    - FPN
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 6.6
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_pcpvt-s_fpn_fpnhead_8x4_512x512_80k_ade20k/twins_pcpvt-s_fpn_fpnhead_8x4_512x512_80k_ade20k_20211201_204132-41acd132.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_pcpvt-s_fpn_fpnhead_8x4_512x512_80k_ade20k/twins_pcpvt-s_fpn_fpnhead_8x4_512x512_80k_ade20k_20211201_204132.log.json
+  Paper:
+    Title: 'Twins: Revisiting the Design of Spatial Attention in Vision Transformers'
+    URL: https://arxiv.org/pdf/2104.13840.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.20.0/mmseg/models/backbones/twins.py#L352
+  Framework: PyTorch
+- Name: twins_pcpvt-s_uperhead_8xb4-160k_ade20k-512x512
+  In Collection: UPerNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 46.04
+      mIoU(ms+flip): 46.92
+  Config: configs/twins/twins_pcpvt-s_uperhead_8xb4-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 32
+    Architecture:
+    - Twins-PCPVT-S
+    - UPerNet
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 9.67
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_pcpvt-s_uperhead_8x4_512x512_160k_ade20k/twins_pcpvt-s_uperhead_8x4_512x512_160k_ade20k_20211201_233537-8e99c07a.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_pcpvt-s_uperhead_8x4_512x512_160k_ade20k/twins_pcpvt-s_uperhead_8x4_512x512_160k_ade20k_20211201_233537.log.json
+  Paper:
+    Title: 'Twins: Revisiting the Design of Spatial Attention in Vision Transformers'
+    URL: https://arxiv.org/pdf/2104.13840.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.20.0/mmseg/models/backbones/twins.py#L352
+  Framework: PyTorch
+- Name: twins_pcpvt-b_fpn_fpnhead_8xb4-80k_ade20k-512x512
+  In Collection: FPN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 45.66
+      mIoU(ms+flip): 46.48
+  Config: configs/twins/twins_pcpvt-b_fpn_fpnhead_8xb4-80k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 32
+    Architecture:
+    - Twins-PCPVT-B
+    - FPN
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 8.41
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_pcpvt-b_fpn_fpnhead_8x4_512x512_80k_ade20k/twins_pcpvt-b_fpn_fpnhead_8x4_512x512_80k_ade20k_20211130_141019-d396db72.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_pcpvt-b_fpn_fpnhead_8x4_512x512_80k_ade20k/twins_pcpvt-b_fpn_fpnhead_8x4_512x512_80k_ade20k_20211130_141019.log.json
+  Paper:
+    Title: 'Twins: Revisiting the Design of Spatial Attention in Vision Transformers'
+    URL: https://arxiv.org/pdf/2104.13840.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.20.0/mmseg/models/backbones/twins.py#L352
+  Framework: PyTorch
+- Name: twins_pcpvt-b_uperhead_8xb2-160k_ade20k-512x512
+  In Collection: UPerNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 47.91
+      mIoU(ms+flip): 48.64
+  Config: configs/twins/twins_pcpvt-b_uperhead_8xb2-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - Twins-PCPVT-B
+    - UPerNet
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 6.46
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_pcpvt-b_uperhead_8x2_512x512_160k_ade20k/twins_pcpvt-b_uperhead_8x2_512x512_160k_ade20k_20211130_141020-02094ea5.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_pcpvt-b_uperhead_8x2_512x512_160k_ade20k/twins_pcpvt-b_uperhead_8x2_512x512_160k_ade20k_20211130_141020.log.json
+  Paper:
+    Title: 'Twins: Revisiting the Design of Spatial Attention in Vision Transformers'
+    URL: https://arxiv.org/pdf/2104.13840.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.20.0/mmseg/models/backbones/twins.py#L352
+  Framework: PyTorch
+- Name: twins_pcpvt-l_fpn_fpnhead_8xb4-80k_ade20k-512x512
+  In Collection: FPN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 45.94
+      mIoU(ms+flip): 46.7
+  Config: configs/twins/twins_pcpvt-l_fpn_fpnhead_8xb4-80k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 32
+    Architecture:
+    - Twins-PCPVT-L
+    - FPN
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 10.78
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_pcpvt-l_fpn_fpnhead_8x4_512x512_80k_ade20k/twins_pcpvt-l_fpn_fpnhead_8x4_512x512_80k_ade20k_20211201_105226-bc6d61dc.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_pcpvt-l_fpn_fpnhead_8x4_512x512_80k_ade20k/twins_pcpvt-l_fpn_fpnhead_8x4_512x512_80k_ade20k_20211201_105226.log.json
+  Paper:
+    Title: 'Twins: Revisiting the Design of Spatial Attention in Vision Transformers'
+    URL: https://arxiv.org/pdf/2104.13840.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.20.0/mmseg/models/backbones/twins.py#L352
+  Framework: PyTorch
+- Name: twins_pcpvt-l_uperhead_8xb2-160k_ade20k-512x512
+  In Collection: UPerNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 49.35
+      mIoU(ms+flip): 50.08
+  Config: configs/twins/twins_pcpvt-l_uperhead_8xb2-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - Twins-PCPVT-L
+    - UPerNet
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 7.82
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_pcpvt-l_uperhead_8x2_512x512_160k_ade20k/twins_pcpvt-l_uperhead_8x2_512x512_160k_ade20k_20211201_075053-c6095c07.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_pcpvt-l_uperhead_8x2_512x512_160k_ade20k/twins_pcpvt-l_uperhead_8x2_512x512_160k_ade20k_20211201_075053.log.json
+  Paper:
+    Title: 'Twins: Revisiting the Design of Spatial Attention in Vision Transformers'
+    URL: https://arxiv.org/pdf/2104.13840.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.20.0/mmseg/models/backbones/twins.py#L352
+  Framework: PyTorch
+- Name: twins_svt-s_fpn_fpnhead_8xb4-80k_ade20k-512x512
+  In Collection: FPN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 44.47
+      mIoU(ms+flip): 45.42
+  Config: configs/twins/twins_svt-s_fpn_fpnhead_8xb4-80k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 32
+    Architecture:
+    - Twins-SVT-S
+    - FPN
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 5.8
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_svt-s_fpn_fpnhead_8x4_512x512_80k_ade20k/twins_svt-s_fpn_fpnhead_8x4_512x512_80k_ade20k_20211130_141006-0a0d3317.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_svt-s_fpn_fpnhead_8x4_512x512_80k_ade20k/twins_svt-s_fpn_fpnhead_8x4_512x512_80k_ade20k_20211130_141006.log.json
+  Paper:
+    Title: 'Twins: Revisiting the Design of Spatial Attention in Vision Transformers'
+    URL: https://arxiv.org/pdf/2104.13840.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.20.0/mmseg/models/backbones/twins.py#L352
+  Framework: PyTorch
+- Name: twins_svt-s_uperhead_8xb2-160k_ade20k-512x512
+  In Collection: UPerNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 46.08
+      mIoU(ms+flip): 46.96
+  Config: configs/twins/twins_svt-s_uperhead_8xb2-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - SVT-S
+    - UPerNet
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 4.93
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_svt-s_uperhead_8x2_512x512_160k_ade20k/twins_svt-s_uperhead_8x2_512x512_160k_ade20k_20211130_141005-e48a2d94.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_svt-s_uperhead_8x2_512x512_160k_ade20k/twins_svt-s_uperhead_8x2_512x512_160k_ade20k_20211130_141005.log.json
+  Paper:
+    Title: 'Twins: Revisiting the Design of Spatial Attention in Vision Transformers'
+    URL: https://arxiv.org/pdf/2104.13840.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.20.0/mmseg/models/backbones/twins.py#L352
+  Framework: PyTorch
+- Name: twins_svt-b_fpn_fpnhead_8xb4-80k_ade20k-512x512
+  In Collection: FPN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 46.77
+      mIoU(ms+flip): 47.47
+  Config: configs/twins/twins_svt-b_fpn_fpnhead_8xb4-80k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 32
+    Architecture:
+    - Twins-SVT-B
+    - FPN
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 8.75
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_svt-b_fpn_fpnhead_8x4_512x512_80k_ade20k/twins_svt-b_fpn_fpnhead_8x4_512x512_80k_ade20k_20211201_113849-88b2907c.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_svt-b_fpn_fpnhead_8x4_512x512_80k_ade20k/twins_svt-b_fpn_fpnhead_8x4_512x512_80k_ade20k_20211201_113849.log.json
+  Paper:
+    Title: 'Twins: Revisiting the Design of Spatial Attention in Vision Transformers'
+    URL: https://arxiv.org/pdf/2104.13840.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.20.0/mmseg/models/backbones/twins.py#L352
+  Framework: PyTorch
+- Name: twins_svt-b_uperhead_8xb2-160k_ade20k-512x512
+  In Collection: UPerNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 48.04
+      mIoU(ms+flip): 48.87
+  Config: configs/twins/twins_svt-b_uperhead_8xb2-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - Twins-SVT-B
+    - UPerNet
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 6.77
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_svt-b_uperhead_8x2_512x512_160k_ade20k/twins_svt-b_uperhead_8x2_512x512_160k_ade20k_20211202_040826-0943a1f1.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_svt-b_uperhead_8x2_512x512_160k_ade20k/twins_svt-b_uperhead_8x2_512x512_160k_ade20k_20211202_040826.log.json
+  Paper:
+    Title: 'Twins: Revisiting the Design of Spatial Attention in Vision Transformers'
+    URL: https://arxiv.org/pdf/2104.13840.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.20.0/mmseg/models/backbones/twins.py#L352
+  Framework: PyTorch
+- Name: twins_svt-l_fpn_fpnhead_8xb4-80k_ade20k-512x512
+  In Collection: FPN
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 46.55
+      mIoU(ms+flip): 47.74
+  Config: configs/twins/twins_svt-l_fpn_fpnhead_8xb4-80k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 32
+    Architecture:
+    - Twins-SVT-L
+    - FPN
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 11.2
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_svt-l_fpn_fpnhead_8x4_512x512_80k_ade20k/twins_svt-l_fpn_fpnhead_8x4_512x512_80k_ade20k_20211130_141005-1d59bee2.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_svt-l_fpn_fpnhead_8x4_512x512_80k_ade20k/twins_svt-l_fpn_fpnhead_8x4_512x512_80k_ade20k_20211130_141005.log.json
+  Paper:
+    Title: 'Twins: Revisiting the Design of Spatial Attention in Vision Transformers'
+    URL: https://arxiv.org/pdf/2104.13840.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.20.0/mmseg/models/backbones/twins.py#L352
+  Framework: PyTorch
+- Name: twins_pcpvt-l_uperhead_8xb2-160k_ade20k-512x512
+  In Collection: UPerNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 49.65
+      mIoU(ms+flip): 50.63
+  Config: configs/twins/twins_pcpvt-l_uperhead_8xb2-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - Twins-SVT-L
+    - UPerNet
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 8.41
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_svt-l_uperhead_8x2_512x512_160k_ade20k/twins_svt-l_uperhead_8x2_512x512_160k_ade20k_20211130_141005-3e2cae61.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_svt-l_uperhead_8x2_512x512_160k_ade20k/twins_svt-l_uperhead_8x2_512x512_160k_ade20k_20211130_141005.log.json
+  Paper:
+    Title: 'Twins: Revisiting the Design of Spatial Attention in Vision Transformers'
+    URL: https://arxiv.org/pdf/2104.13840.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.20.0/mmseg/models/backbones/twins.py#L352
+  Framework: PyTorch
diff --git a/configs/twins/twins.yml b/configs/twins/twins.yml
deleted file mode 100644
index 48d25c682f..0000000000
--- a/configs/twins/twins.yml
+++ /dev/null
@@ -1,265 +0,0 @@
-Models:
-- Name: twins_pcpvt-s_fpn_fpnhead_8xb4-80k_ade20k-512x512
-  In Collection: FPN
-  Metadata:
-    backbone: PCPVT-S
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 36.83
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 6.6
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 43.26
-      mIoU(ms+flip): 44.11
-  Config: configs/twins/twins_pcpvt-s_fpn_fpnhead_8xb4-80k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_pcpvt-s_fpn_fpnhead_8x4_512x512_80k_ade20k/twins_pcpvt-s_fpn_fpnhead_8x4_512x512_80k_ade20k_20211201_204132-41acd132.pth
-- Name: twins_pcpvt-s_uperhead_8xb4-160k_ade20k-512x512
-  In Collection: UPerNet
-  Metadata:
-    backbone: PCPVT-S
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 70.22
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 9.67
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 46.04
-      mIoU(ms+flip): 46.92
-  Config: configs/twins/twins_pcpvt-s_uperhead_8xb4-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_pcpvt-s_uperhead_8x4_512x512_160k_ade20k/twins_pcpvt-s_uperhead_8x4_512x512_160k_ade20k_20211201_233537-8e99c07a.pth
-- Name: twins_pcpvt-b_fpn_fpnhead_8xb4-80k_ade20k-512x512
-  In Collection: FPN
-  Metadata:
-    backbone: PCPVT-B
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 50.84
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 8.41
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 45.66
-      mIoU(ms+flip): 46.48
-  Config: configs/twins/twins_pcpvt-b_fpn_fpnhead_8xb4-80k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_pcpvt-b_fpn_fpnhead_8x4_512x512_80k_ade20k/twins_pcpvt-b_fpn_fpnhead_8x4_512x512_80k_ade20k_20211130_141019-d396db72.pth
-- Name: twins_pcpvt-b_uperhead_8xb2-160k_ade20k-512x512
-  In Collection: UPerNet
-  Metadata:
-    backbone: PCPVT-B
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 83.06
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 6.46
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 47.91
-      mIoU(ms+flip): 48.64
-  Config: configs/twins/twins_pcpvt-b_uperhead_8xb2-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_pcpvt-b_uperhead_8x2_512x512_160k_ade20k/twins_pcpvt-b_uperhead_8x2_512x512_160k_ade20k_20211130_141020-02094ea5.pth
-- Name: twins_pcpvt-l_fpn_fpnhead_8xb4-80k_ade20k-512x512
-  In Collection: FPN
-  Metadata:
-    backbone: PCPVT-L
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 69.83
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 10.78
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 45.94
-      mIoU(ms+flip): 46.7
-  Config: configs/twins/twins_pcpvt-l_fpn_fpnhead_8xb4-80k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_pcpvt-l_fpn_fpnhead_8x4_512x512_80k_ade20k/twins_pcpvt-l_fpn_fpnhead_8x4_512x512_80k_ade20k_20211201_105226-bc6d61dc.pth
-- Name: twins_pcpvt-l_uperhead_8xb2-160k_ade20k-512x512
-  In Collection: UPerNet
-  Metadata:
-    backbone: PCPVT-L
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 93.46
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 7.82
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 49.35
-      mIoU(ms+flip): 50.08
-  Config: configs/twins/twins_pcpvt-l_uperhead_8xb2-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_pcpvt-l_uperhead_8x2_512x512_160k_ade20k/twins_pcpvt-l_uperhead_8x2_512x512_160k_ade20k_20211201_075053-c6095c07.pth
-- Name: twins_svt-s_fpn_fpnhead_8xb4-80k_ade20k-512x512
-  In Collection: FPN
-  Metadata:
-    backbone: SVT-S
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 33.57
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 5.8
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 44.47
-      mIoU(ms+flip): 45.42
-  Config: configs/twins/twins_svt-s_fpn_fpnhead_8xb4-80k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_svt-s_fpn_fpnhead_8x4_512x512_80k_ade20k/twins_svt-s_fpn_fpnhead_8x4_512x512_80k_ade20k_20211130_141006-0a0d3317.pth
-- Name: twins_svt-s_uperhead_8xb2-160k_ade20k-512x512
-  In Collection: UPerNet
-  Metadata:
-    backbone: SVT-S
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 66.27
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 4.93
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 46.08
-      mIoU(ms+flip): 46.96
-  Config: configs/twins/twins_svt-s_uperhead_8xb2-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_svt-s_uperhead_8x2_512x512_160k_ade20k/twins_svt-s_uperhead_8x2_512x512_160k_ade20k_20211130_141005-e48a2d94.pth
-- Name: twins_svt-b_fpn_fpnhead_8xb4-80k_ade20k-512x512
-  In Collection: FPN
-  Metadata:
-    backbone: SVT-B
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 47.39
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 8.75
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 46.77
-      mIoU(ms+flip): 47.47
-  Config: configs/twins/twins_svt-b_fpn_fpnhead_8xb4-80k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_svt-b_fpn_fpnhead_8x4_512x512_80k_ade20k/twins_svt-b_fpn_fpnhead_8x4_512x512_80k_ade20k_20211201_113849-88b2907c.pth
-- Name: twins_svt-b_uperhead_8xb2-160k_ade20k-512x512
-  In Collection: UPerNet
-  Metadata:
-    backbone: SVT-B
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 78.99
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 6.77
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 48.04
-      mIoU(ms+flip): 48.87
-  Config: configs/twins/twins_svt-b_uperhead_8xb2-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_svt-b_uperhead_8x2_512x512_160k_ade20k/twins_svt-b_uperhead_8x2_512x512_160k_ade20k_20211202_040826-0943a1f1.pth
-- Name: twins_svt-l_fpn_fpnhead_8xb4-80k_ade20k-512x512
-  In Collection: FPN
-  Metadata:
-    backbone: SVT-L
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 56.18
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 11.2
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 46.55
-      mIoU(ms+flip): 47.74
-  Config: configs/twins/twins_svt-l_fpn_fpnhead_8xb4-80k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_svt-l_fpn_fpnhead_8x4_512x512_80k_ade20k/twins_svt-l_fpn_fpnhead_8x4_512x512_80k_ade20k_20211130_141005-1d59bee2.pth
-- Name: twins_pcpvt-l_uperhead_8xb2-160k_ade20k-512x512
-  In Collection: UPerNet
-  Metadata:
-    backbone: SVT-L
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 93.2
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 8.41
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 49.65
-      mIoU(ms+flip): 50.63
-  Config: configs/twins/twins_pcpvt-l_uperhead_8xb2-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/twins/twins_svt-l_uperhead_8x2_512x512_160k_ade20k/twins_svt-l_uperhead_8x2_512x512_160k_ade20k_20211130_141005-3e2cae61.pth
diff --git a/configs/unet/README.md b/configs/unet/README.md
index f3dc261c22..e42cd0f91e 100644
--- a/configs/unet/README.md
+++ b/configs/unet/README.md
@@ -1,6 +1,6 @@
 # UNet
 
-[U-Net: Convolutional Networks for Biomedical Image Segmentation](https://arxiv.org/abs/1505.04597)
+> [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://arxiv.org/abs/1505.04597)
 
 ## Introduction
 
@@ -22,71 +22,71 @@ There is large consent that successful training of deep networks requires many t
 <img src="https://user-images.githubusercontent.com/24582831/142902977-20fe689d-a147-4d92-9690-dbfde8b68dbe.png" width="70%"/>
 </div>
 
-## Citation
-
-```bibtex
-@inproceedings{ronneberger2015u,
-  title={U-net: Convolutional networks for biomedical image segmentation},
-  author={Ronneberger, Olaf and Fischer, Philipp and Brox, Thomas},
-  booktitle={International Conference on Medical image computing and computer-assisted intervention},
-  pages={234--241},
-  year={2015},
-  organization={Springer}
-}
-```
-
 ## Results and models
 
 ### Cityscapes
 
-| Method     | Backbone    | Loss          | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                            | download                                                                                                                                                                                                                                                                                                                                                                     |
-| ---------- | ----------- | ------------- | --------- | ------: | -------- | -------------- | ----: | ------------: | --------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| UNet + FCN | UNet-S5-D16 | Cross Entropy | 512x1024  |  160000 | 17.91    | 3.05           | 69.10 |         71.05 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/unet/unet-s5-d16_fcn_4xb4-160k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes_20211210_145204-6860854e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes_20211210_145204.log.json) |
+| Method     | Backbone    | Loss          | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                            | download                                                                                                                                                                                                                                                                                                                                                                     |
+| ---------- | ----------- | ------------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | --------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| UNet + FCN | UNet-S5-D16 | Cross Entropy | 512x1024  |  160000 | 17.91    | 3.05           | V100   | 69.10 |         71.05 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/unet/unet-s5-d16_fcn_4xb4-160k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes_20211210_145204-6860854e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes_20211210_145204.log.json) |
 
 ### DRIVE
 
-| Method           | Backbone    | Loss                 | Image Size | Crop Size | Stride | Lr schd | Mem (GB) | Inf time (fps) | mDice |  Dice | config                                                                                                                                         | download                                                                                                                                                                                                                                                                                                                                                                                                         |
-| ---------------- | ----------- | -------------------- | ---------- | --------- | -----: | ------- | -------- | -------------: | ----: | ----: | ---------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| UNet + FCN       | UNet-S5-D16 | Cross Entropy        | 584x565    | 64x64     |  42x42 | 40000   | 0.680    |              - | 88.38 | 78.67 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/unet/unet-s5-d16_fcn_4xb4-40k_drive-64x64.py)                       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_64x64_40k_drive/fcn_unet_s5-d16_64x64_40k_drive_20201223_191051-5daf6d3b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_64x64_40k_drive/unet_s5-d16_64x64_40k_drive-20201223_191051.log.json)                                                                                                 |
-| UNet + FCN       | UNet-S5-D16 | Cross Entropy + Dice | 584x565    | 64x64     |  42x42 | 40000   | 0.582    |              - | 88.71 | 79.32 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/unet/unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_drive-64x64.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201820-785de5c2.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201820.log.json)                         |
-| UNet + PSPNet    | UNet-S5-D16 | Cross Entropy        | 584x565    | 64x64     |  42x42 | 40000   | 0.599    |              - | 88.35 | 78.62 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/unet/unet-s5-d16_pspnet_4xb4-40k_drive-64x64.py)                    | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_64x64_40k_drive/pspnet_unet_s5-d16_64x64_40k_drive_20201227_181818-aac73387.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_64x64_40k_drive/pspnet_unet_s5-d16_64x64_40k_drive-20201227_181818.log.json)                                                                             |
-| UNet + PSPNet    | UNet-S5-D16 | Cross Entropy + Dice | 584x565    | 64x64     |  42x42 | 40000   | 0.585    |              - | 88.76 | 79.42 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/unet/unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_drive-64x64.py)    | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201821-22b3e3ba.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201821.log.json)             |
-| UNet + DeepLabV3 | UNet-S5-D16 | Cross Entropy        | 584x565    | 64x64     |  42x42 | 40000   | 0.596    |              - | 88.38 | 78.69 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/unet/unet-s5-d16_deeplabv3_4xb4-40k_drive-64x64.py)                 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_64x64_40k_drive/deeplabv3_unet_s5-d16_64x64_40k_drive_20201226_094047-0671ff20.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_64x64_40k_drive/deeplabv3_unet_s5-d16_64x64_40k_drive-20201226_094047.log.json)                                                                 |
-| UNet + DeepLabV3 | UNet-S5-D16 | Cross Entropy + Dice | 584x565    | 64x64     |  42x42 | 40000   | 0.582    |              - | 88.84 | 79.56 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/unet/unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_drive-64x64.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201825-6bf0efd7.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201825.log.json) |
+| Method           | Backbone    | Loss                 | Image Size | Crop Size | Stride | Lr schd | Mem (GB) | Inf time (fps) | Device | mDice |  Dice | config                                                                                                                                         | download                                                                                                                                                                                                                                                                                                                                                                                                         |
+| ---------------- | ----------- | -------------------- | ---------- | --------- | -----: | ------- | -------- | -------------: | ------ | ----: | ----: | ---------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| UNet + FCN       | UNet-S5-D16 | Cross Entropy        | 584x565    | 64x64     |  42x42 | 40000   | 0.680    |              - | V100   | 88.38 | 78.67 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/unet/unet-s5-d16_fcn_4xb4-40k_drive-64x64.py)                       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_64x64_40k_drive/fcn_unet_s5-d16_64x64_40k_drive_20201223_191051-5daf6d3b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_64x64_40k_drive/unet_s5-d16_64x64_40k_drive-20201223_191051.log.json)                                                                                                 |
+| UNet + FCN       | UNet-S5-D16 | Cross Entropy + Dice | 584x565    | 64x64     |  42x42 | 40000   | 0.582    |              - | V100   | 88.71 | 79.32 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/unet/unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_drive-64x64.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201820-785de5c2.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201820.log.json)                         |
+| UNet + PSPNet    | UNet-S5-D16 | Cross Entropy        | 584x565    | 64x64     |  42x42 | 40000   | 0.599    |              - | V100   | 88.35 | 78.62 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/unet/unet-s5-d16_pspnet_4xb4-40k_drive-64x64.py)                    | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_64x64_40k_drive/pspnet_unet_s5-d16_64x64_40k_drive_20201227_181818-aac73387.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_64x64_40k_drive/pspnet_unet_s5-d16_64x64_40k_drive-20201227_181818.log.json)                                                                             |
+| UNet + PSPNet    | UNet-S5-D16 | Cross Entropy + Dice | 584x565    | 64x64     |  42x42 | 40000   | 0.585    |              - | V100   | 88.76 | 79.42 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/unet/unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_drive-64x64.py)    | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201821-22b3e3ba.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201821.log.json)             |
+| UNet + DeepLabV3 | UNet-S5-D16 | Cross Entropy        | 584x565    | 64x64     |  42x42 | 40000   | 0.596    |              - | V100   | 88.38 | 78.69 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/unet/unet-s5-d16_deeplabv3_4xb4-40k_drive-64x64.py)                 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_64x64_40k_drive/deeplabv3_unet_s5-d16_64x64_40k_drive_20201226_094047-0671ff20.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_64x64_40k_drive/deeplabv3_unet_s5-d16_64x64_40k_drive-20201226_094047.log.json)                                                                 |
+| UNet + DeepLabV3 | UNet-S5-D16 | Cross Entropy + Dice | 584x565    | 64x64     |  42x42 | 40000   | 0.582    |              - | V100   | 88.84 | 79.56 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/unet/unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_drive-64x64.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201825-6bf0efd7.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201825.log.json) |
 
 ### STARE
 
-| Method           | Backbone    | Loss                 | Image Size | Crop Size | Stride | Lr schd | Mem (GB) | Inf time (fps) | mDice |  Dice | config                                                                                                                                           | download                                                                                                                                                                                                                                                                                                                                                                                                                 |
-| ---------------- | ----------- | -------------------- | ---------- | --------- | -----: | ------- | -------- | -------------: | ----: | ----: | ------------------------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| UNet + FCN       | UNet-S5-D16 | Cross Entropy        | 605x700    | 128x128   |  85x85 | 40000   | 0.968    |              - | 89.78 | 81.02 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/unet/unet-s5-d16_fcn_4xb4-40k_stare-128x128.py)                       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_128x128_40k_stare/fcn_unet_s5-d16_128x128_40k_stare_20201223_191051-7d77e78b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_128x128_40k_stare/unet_s5-d16_128x128_40k_stare-20201223_191051.log.json)                                                                                                 |
-| UNet + FCN       | UNet-S5-D16 | Cross Entropy + Dice | 605x700    | 128x128   |  85x85 | 40000   | 0.986    |              - | 90.65 | 82.70 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/unet/unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_stare-128x128.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201821-f75705a9.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201821.log.json)                         |
-| UNet + PSPNet    | UNet-S5-D16 | Cross Entropy        | 605x700    | 128x128   |  85x85 | 40000   | 0.982    |              - | 89.89 | 81.22 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/unet/unet-s5-d16_pspnet_4xb4-40k_stare-128x128.py)                    | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_stare/pspnet_unet_s5-d16_128x128_40k_stare_20201227_181818-3c2923c4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_stare/pspnet_unet_s5-d16_128x128_40k_stare-20201227_181818.log.json)                                                                             |
-| UNet + PSPNet    | UNet-S5-D16 | Cross Entropy + Dice | 605x700    | 128x128   |  85x85 | 40000   | 1.028    |              - | 90.72 | 82.84 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/unet/unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_stare-128x128.py)    | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201823-f1063ef7.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201823.log.json)             |
-| UNet + DeepLabV3 | UNet-S5-D16 | Cross Entropy        | 605x700    | 128x128   |  85x85 | 40000   | 0.999    |              - | 89.73 | 80.93 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/unet/unet-s5-d16_deeplabv3_4xb4-40k_stare-128x128.py)                 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_stare/deeplabv3_unet_s5-d16_128x128_40k_stare_20201226_094047-93dcb93c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_stare/deeplabv3_unet_s5-d16_128x128_40k_stare-20201226_094047.log.json)                                                                 |
-| UNet + DeepLabV3 | UNet-S5-D16 | Cross Entropy + Dice | 605x700    | 128x128   |  85x85 | 40000   | 1.010    |              - | 90.65 | 82.71 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/unet/unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_stare-128x128.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201825-21db614c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201825.log.json) |
+| Method           | Backbone    | Loss                 | Image Size | Crop Size | Stride | Lr schd | Mem (GB) | Inf time (fps) | Device | mDice |  Dice | config                                                                                                                                           | download                                                                                                                                                                                                                                                                                                                                                                                                                 |
+| ---------------- | ----------- | -------------------- | ---------- | --------- | -----: | ------- | -------- | -------------: | ------ | ----: | ----: | ------------------------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| UNet + FCN       | UNet-S5-D16 | Cross Entropy        | 605x700    | 128x128   |  85x85 | 40000   | 0.968    |              - | V100   | 89.78 | 81.02 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/unet/unet-s5-d16_fcn_4xb4-40k_stare-128x128.py)                       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_128x128_40k_stare/fcn_unet_s5-d16_128x128_40k_stare_20201223_191051-7d77e78b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_128x128_40k_stare/unet_s5-d16_128x128_40k_stare-20201223_191051.log.json)                                                                                                 |
+| UNet + FCN       | UNet-S5-D16 | Cross Entropy + Dice | 605x700    | 128x128   |  85x85 | 40000   | 0.986    |              - | V100   | 90.65 | 82.70 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/unet/unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_stare-128x128.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201821-f75705a9.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201821.log.json)                         |
+| UNet + PSPNet    | UNet-S5-D16 | Cross Entropy        | 605x700    | 128x128   |  85x85 | 40000   | 0.982    |              - | V100   | 89.89 | 81.22 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/unet/unet-s5-d16_pspnet_4xb4-40k_stare-128x128.py)                    | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_stare/pspnet_unet_s5-d16_128x128_40k_stare_20201227_181818-3c2923c4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_stare/pspnet_unet_s5-d16_128x128_40k_stare-20201227_181818.log.json)                                                                             |
+| UNet + PSPNet    | UNet-S5-D16 | Cross Entropy + Dice | 605x700    | 128x128   |  85x85 | 40000   | 1.028    |              - | V100   | 90.72 | 82.84 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/unet/unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_stare-128x128.py)    | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201823-f1063ef7.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201823.log.json)             |
+| UNet + DeepLabV3 | UNet-S5-D16 | Cross Entropy        | 605x700    | 128x128   |  85x85 | 40000   | 0.999    |              - | V100   | 89.73 | 80.93 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/unet/unet-s5-d16_deeplabv3_4xb4-40k_stare-128x128.py)                 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_stare/deeplabv3_unet_s5-d16_128x128_40k_stare_20201226_094047-93dcb93c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_stare/deeplabv3_unet_s5-d16_128x128_40k_stare-20201226_094047.log.json)                                                                 |
+| UNet + DeepLabV3 | UNet-S5-D16 | Cross Entropy + Dice | 605x700    | 128x128   |  85x85 | 40000   | 1.010    |              - | V100   | 90.65 | 82.71 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/unet/unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_stare-128x128.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201825-21db614c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201825.log.json) |
 
 ### CHASE_DB1
 
-| Method           | Backbone    | Loss                 | Image Size | Crop Size | Stride | Lr schd | Mem (GB) | Inf time (fps) | mDice |  Dice | config                                                                                                                                               | download                                                                                                                                                                                                                                                                                                                                                                                                                                 |
-| ---------------- | ----------- | -------------------- | ---------- | --------- | -----: | ------- | -------- | -------------: | ----: | ----: | ---------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| UNet + FCN       | UNet-S5-D16 | Cross Entropy        | 960x999    | 128x128   |  85x85 | 40000   | 0.968    |              - | 89.46 | 80.24 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/unet/unet-s5-d16_fcn_4xb4-40k_chase-db1-128x128.py)                       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_128x128_40k_chase_db1/fcn_unet_s5-d16_128x128_40k_chase_db1_20201223_191051-11543527.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_128x128_40k_chase_db1/unet_s5-d16_128x128_40k_chase_db1-20201223_191051.log.json)                                                                                                 |
-| UNet + FCN       | UNet-S5-D16 | Cross Entropy + Dice | 960x999    | 128x128   |  85x85 | 40000   | 0.986    |              - | 89.52 | 80.40 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/unet/unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_chase-db1-128x128.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201821-1c4eb7cf.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201821.log.json)                         |
-| UNet + PSPNet    | UNet-S5-D16 | Cross Entropy        | 960x999    | 128x128   |  85x85 | 40000   | 0.982    |              - | 89.52 | 80.36 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/unet/unet-s5-d16_pspnet_4xb4-40k_chase-db1-128x128.py)                    | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1/pspnet_unet_s5-d16_128x128_40k_chase_db1_20201227_181818-68d4e609.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1/pspnet_unet_s5-d16_128x128_40k_chase_db1-20201227_181818.log.json)                                                                             |
-| UNet + PSPNet    | UNet-S5-D16 | Cross Entropy + Dice | 960x999    | 128x128   |  85x85 | 40000   | 1.028    |              - | 89.45 | 80.28 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/unet/unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_chase-db1-128x128.py)    | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201823-c0802c4d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201823.log.json)             |
-| UNet + DeepLabV3 | UNet-S5-D16 | Cross Entropy        | 960x999    | 128x128   |  85x85 | 40000   | 0.999    |              - | 89.57 | 80.47 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/unet/unet_s5-d16_deeplabv3_4xb4-40k_chase-db1-128x128.py)                 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1/deeplabv3_unet_s5-d16_128x128_40k_chase_db1_20201226_094047-4c5aefa3.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1/deeplabv3_unet_s5-d16_128x128_40k_chase_db1-20201226_094047.log.json)                                                                 |
-| UNet + DeepLabV3 | UNet-S5-D16 | Cross Entropy + Dice | 960x999    | 128x128   |  85x85 | 40000   | 1.010    |              - | 89.49 | 80.37 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/unet/unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_chase-db1-128x128.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201825-4ef29df5.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201825.log.json) |
+| Method           | Backbone    | Loss                 | Image Size | Crop Size | Stride | Lr schd | Mem (GB) | Inf time (fps) | Device | mDice |  Dice | config                                                                                                                                               | download                                                                                                                                                                                                                                                                                                                                                                                                                                 |
+| ---------------- | ----------- | -------------------- | ---------- | --------- | -----: | ------- | -------- | -------------: | ------ | ----: | ----: | ---------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| UNet + FCN       | UNet-S5-D16 | Cross Entropy        | 960x999    | 128x128   |  85x85 | 40000   | 0.968    |              - | V100   | 89.46 | 80.24 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/unet/unet-s5-d16_fcn_4xb4-40k_chase-db1-128x128.py)                       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_128x128_40k_chase_db1/fcn_unet_s5-d16_128x128_40k_chase_db1_20201223_191051-11543527.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_128x128_40k_chase_db1/unet_s5-d16_128x128_40k_chase_db1-20201223_191051.log.json)                                                                                                 |
+| UNet + FCN       | UNet-S5-D16 | Cross Entropy + Dice | 960x999    | 128x128   |  85x85 | 40000   | 0.986    |              - | V100   | 89.52 | 80.40 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/unet/unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_chase-db1-128x128.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201821-1c4eb7cf.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201821.log.json)                         |
+| UNet + PSPNet    | UNet-S5-D16 | Cross Entropy        | 960x999    | 128x128   |  85x85 | 40000   | 0.982    |              - | V100   | 89.52 | 80.36 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/unet/unet-s5-d16_pspnet_4xb4-40k_chase-db1-128x128.py)                    | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1/pspnet_unet_s5-d16_128x128_40k_chase_db1_20201227_181818-68d4e609.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1/pspnet_unet_s5-d16_128x128_40k_chase_db1-20201227_181818.log.json)                                                                             |
+| UNet + PSPNet    | UNet-S5-D16 | Cross Entropy + Dice | 960x999    | 128x128   |  85x85 | 40000   | 1.028    |              - | V100   | 89.45 | 80.28 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/unet/unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_chase-db1-128x128.py)    | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201823-c0802c4d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201823.log.json)             |
+| UNet + DeepLabV3 | UNet-S5-D16 | Cross Entropy        | 960x999    | 128x128   |  85x85 | 40000   | 0.999    |              - | V100   | 89.57 | 80.47 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/unet/unet_s5-d16_deeplabv3_4xb4-40k_chase-db1-128x128.py)                 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1/deeplabv3_unet_s5-d16_128x128_40k_chase_db1_20201226_094047-4c5aefa3.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1/deeplabv3_unet_s5-d16_128x128_40k_chase_db1-20201226_094047.log.json)                                                                 |
+| UNet + DeepLabV3 | UNet-S5-D16 | Cross Entropy + Dice | 960x999    | 128x128   |  85x85 | 40000   | 1.010    |              - | V100   | 89.49 | 80.37 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/unet/unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_chase-db1-128x128.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201825-4ef29df5.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201825.log.json) |
 
 ### HRF
 
-| Method           | Backbone    | Loss                 | Image Size | Crop Size |  Stride | Lr schd | Mem (GB) | Inf time (fps) | mDice |  Dice | config                                                                                                                                         | download                                                                                                                                                                                                                                                                                                                                                                                                         |
-| ---------------- | ----------- | -------------------- | ---------- | --------- | ------: | ------- | -------- | -------------: | ----: | ----: | ---------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| UNet + FCN       | UNet-S5-D16 | Cross Entropy        | 2336x3504  | 256x256   | 170x170 | 40000   | 2.525    |              - | 88.92 | 79.45 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/unet/unet-s5-d16_fcn_4xb4-40k_hrf-256x256.py)                       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_256x256_40k_hrf/fcn_unet_s5-d16_256x256_40k_hrf_20201223_173724-d89cf1ed.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_256x256_40k_hrf/unet_s5-d16_256x256_40k_hrf-20201223_173724.log.json)                                                                                                 |
-| UNet + FCN       | UNet-S5-D16 | Cross Entropy + Dice | 2336x3504  | 256x256   | 170x170 | 40000   | 2.623    |              - | 89.64 | 80.87 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/unet/unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_hrf-256x256.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_201821-c314da8a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_201821.log.json)                         |
-| UNet + PSPNet    | UNet-S5-D16 | Cross Entropy        | 2336x3504  | 256x256   | 170x170 | 40000   | 2.588    |              - | 89.24 | 80.07 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/unet/unet-s5-d16_pspnet_4xb4-40k_hrf-256x256.py)                    | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_256x256_40k_hrf/pspnet_unet_s5-d16_256x256_40k_hrf_20201227_181818-fdb7e29b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_256x256_40k_hrf/pspnet_unet_s5-d16_256x256_40k_hrf-20201227_181818.log.json)                                                                             |
-| UNet + PSPNet    | UNet-S5-D16 | Cross Entropy + Dice | 2336x3504  | 256x256   | 170x170 | 40000   | 2.798    |              - | 89.69 | 80.96 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/unet/unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_hrf-256x256.py)    | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_201823-53d492fa.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_201823.log.json)             |
-| UNet + DeepLabV3 | UNet-S5-D16 | Cross Entropy        | 2336x3504  | 256x256   | 170x170 | 40000   | 2.604    |              - | 89.32 | 80.21 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/unet/unet-s5-d16_deeplabv3_4xb4-40k_hrf-256x256.py)                 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf/deeplabv3_unet_s5-d16_256x256_40k_hrf_20201226_094047-3a1fdf85.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf/deeplabv3_unet_s5-d16_256x256_40k_hrf-20201226_094047.log.json)                                                                 |
-| UNet + DeepLabV3 | UNet-S5-D16 | Cross Entropy + Dice | 2336x3504  | 256x256   | 170x170 | 40000   | 2.607    |              - | 89.56 | 80.71 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/unet/unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_hrf-256x256.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_202032-59daf7a4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_202032.log.json) |
+| Method           | Backbone    | Loss                 | Image Size | Crop Size |  Stride | Lr schd | Mem (GB) | Inf time (fps) | Device | mDice |  Dice | config                                                                                                                                         | download                                                                                                                                                                                                                                                                                                                                                                                                         |
+| ---------------- | ----------- | -------------------- | ---------- | --------- | ------: | ------- | -------- | -------------: | ------ | ----: | ----: | ---------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| UNet + FCN       | UNet-S5-D16 | Cross Entropy        | 2336x3504  | 256x256   | 170x170 | 40000   | 2.525    |              - | V100   | 88.92 | 79.45 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/unet/unet-s5-d16_fcn_4xb4-40k_hrf-256x256.py)                       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_256x256_40k_hrf/fcn_unet_s5-d16_256x256_40k_hrf_20201223_173724-d89cf1ed.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_256x256_40k_hrf/unet_s5-d16_256x256_40k_hrf-20201223_173724.log.json)                                                                                                 |
+| UNet + FCN       | UNet-S5-D16 | Cross Entropy + Dice | 2336x3504  | 256x256   | 170x170 | 40000   | 2.623    |              - | V100   | 89.64 | 80.87 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/unet/unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_hrf-256x256.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_201821-c314da8a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_201821.log.json)                         |
+| UNet + PSPNet    | UNet-S5-D16 | Cross Entropy        | 2336x3504  | 256x256   | 170x170 | 40000   | 2.588    |              - | V100   | 89.24 | 80.07 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/unet/unet-s5-d16_pspnet_4xb4-40k_hrf-256x256.py)                    | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_256x256_40k_hrf/pspnet_unet_s5-d16_256x256_40k_hrf_20201227_181818-fdb7e29b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_256x256_40k_hrf/pspnet_unet_s5-d16_256x256_40k_hrf-20201227_181818.log.json)                                                                             |
+| UNet + PSPNet    | UNet-S5-D16 | Cross Entropy + Dice | 2336x3504  | 256x256   | 170x170 | 40000   | 2.798    |              - | V100   | 89.69 | 80.96 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/unet/unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_hrf-256x256.py)    | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_201823-53d492fa.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_201823.log.json)             |
+| UNet + DeepLabV3 | UNet-S5-D16 | Cross Entropy        | 2336x3504  | 256x256   | 170x170 | 40000   | 2.604    |              - | V100   | 89.32 | 80.21 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/unet/unet-s5-d16_deeplabv3_4xb4-40k_hrf-256x256.py)                 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf/deeplabv3_unet_s5-d16_256x256_40k_hrf_20201226_094047-3a1fdf85.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf/deeplabv3_unet_s5-d16_256x256_40k_hrf-20201226_094047.log.json)                                                                 |
+| UNet + DeepLabV3 | UNet-S5-D16 | Cross Entropy + Dice | 2336x3504  | 256x256   | 170x170 | 40000   | 2.607    |              - | V100   | 89.56 | 80.71 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/unet/unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_hrf-256x256.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_202032-59daf7a4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_202032.log.json) |
 
 Note:
 
 - In  `DRIVE`, `STARE`, `CHASE_DB1`, and `HRF` dataset, `mDice` is mean dice of background and vessel, while `Dice` is dice metric of vessel(foreground) only.
+
+## Citation
+
+```bibtex
+@inproceedings{ronneberger2015u,
+  title={U-net: Convolutional networks for biomedical image segmentation},
+  author={Ronneberger, Olaf and Fischer, Philipp and Brox, Thomas},
+  booktitle={International Conference on Medical image computing and computer-assisted intervention},
+  pages={234--241},
+  year={2015},
+  organization={Springer}
+}
+```
diff --git a/configs/unet/metafile.yaml b/configs/unet/metafile.yaml
new file mode 100644
index 0000000000..1eafbc6d08
--- /dev/null
+++ b/configs/unet/metafile.yaml
@@ -0,0 +1,642 @@
+Collections:
+- Name: UNet
+  License: Apache License 2.0
+  Metadata:
+    Training Data:
+    - Cityscapes
+    - DRIVE
+    - STARE
+    - CHASE_DB1
+    - HRF
+  Paper:
+    Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
+    URL: https://arxiv.org/abs/1505.04597
+  README: configs/unet/README.md
+  Frameworks:
+  - PyTorch
+Models:
+- Name: unet-s5-d16_fcn_4xb4-160k_cityscapes-512x1024
+  In Collection: UNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 69.1
+      mIoU(ms+flip): 71.05
+  Config: configs/unet/unet-s5-d16_fcn_4xb4-160k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 16
+    Architecture:
+    - UNet-S5-D16
+    - UNet
+    - FCN
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 17.91
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes_20211210_145204-6860854e.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes_20211210_145204.log.json
+  Paper:
+    Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
+    URL: https://arxiv.org/abs/1505.04597
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
+  Framework: PyTorch
+- Name: unet-s5-d16_fcn_4xb4-40k_drive-64x64
+  In Collection: UNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: DRIVE
+    Metrics:
+      mDice: 88.38
+      Dice: 78.67
+  Config: configs/unet/unet-s5-d16_fcn_4xb4-40k_drive-64x64.py
+  Metadata:
+    Training Data: DRIVE
+    Batch Size: 16
+    Architecture:
+    - UNet-S5-D16
+    - UNet
+    - FCN
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 0.68
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_64x64_40k_drive/fcn_unet_s5-d16_64x64_40k_drive_20201223_191051-5daf6d3b.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_64x64_40k_drive/unet_s5-d16_64x64_40k_drive-20201223_191051.log.json
+  Paper:
+    Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
+    URL: https://arxiv.org/abs/1505.04597
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
+  Framework: PyTorch
+- Name: unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_drive-64x64
+  In Collection: UNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: DRIVE
+    Metrics:
+      mDice: 88.71
+      Dice: 79.32
+  Config: configs/unet/unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_drive-64x64.py
+  Metadata:
+    Training Data: DRIVE
+    Batch Size: 16
+    Architecture:
+    - UNet-S5-D16
+    - UNet
+    - FCN
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 0.582
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201820-785de5c2.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201820.log.json
+  Paper:
+    Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
+    URL: https://arxiv.org/abs/1505.04597
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
+  Framework: PyTorch
+- Name: unet-s5-d16_pspnet_4xb4-40k_drive-64x64
+  In Collection: UNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: DRIVE
+    Metrics:
+      mDice: 88.35
+      Dice: 78.62
+  Config: configs/unet/unet-s5-d16_pspnet_4xb4-40k_drive-64x64.py
+  Metadata:
+    Training Data: DRIVE
+    Batch Size: 16
+    Architecture:
+    - UNet-S5-D16
+    - UNet
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 0.599
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_64x64_40k_drive/pspnet_unet_s5-d16_64x64_40k_drive_20201227_181818-aac73387.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_64x64_40k_drive/pspnet_unet_s5-d16_64x64_40k_drive-20201227_181818.log.json
+  Paper:
+    Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
+    URL: https://arxiv.org/abs/1505.04597
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
+  Framework: PyTorch
+- Name: unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_drive-64x64
+  In Collection: UNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: DRIVE
+    Metrics:
+      mDice: 88.76
+      Dice: 79.42
+  Config: configs/unet/unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_drive-64x64.py
+  Metadata:
+    Training Data: DRIVE
+    Batch Size: 16
+    Architecture:
+    - UNet-S5-D16
+    - UNet
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 0.585
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201821-22b3e3ba.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201821.log.json
+  Paper:
+    Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
+    URL: https://arxiv.org/abs/1505.04597
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
+  Framework: PyTorch
+- Name: unet-s5-d16_deeplabv3_4xb4-40k_drive-64x64
+  In Collection: UNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: DRIVE
+    Metrics:
+      mDice: 88.38
+      Dice: 78.69
+  Config: configs/unet/unet-s5-d16_deeplabv3_4xb4-40k_drive-64x64.py
+  Metadata:
+    Training Data: DRIVE
+    Batch Size: 16
+    Architecture:
+    - UNet-S5-D16
+    - UNet
+    - DeepLabV3
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 0.596
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_64x64_40k_drive/deeplabv3_unet_s5-d16_64x64_40k_drive_20201226_094047-0671ff20.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_64x64_40k_drive/deeplabv3_unet_s5-d16_64x64_40k_drive-20201226_094047.log.json
+  Paper:
+    Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
+    URL: https://arxiv.org/abs/1505.04597
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
+  Framework: PyTorch
+- Name: unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_drive-64x64
+  In Collection: UNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: DRIVE
+    Metrics:
+      mDice: 88.84
+      Dice: 79.56
+  Config: configs/unet/unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_drive-64x64.py
+  Metadata:
+    Training Data: DRIVE
+    Batch Size: 16
+    Architecture:
+    - UNet-S5-D16
+    - UNet
+    - DeepLabV3
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 0.582
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201825-6bf0efd7.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201825.log.json
+  Paper:
+    Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
+    URL: https://arxiv.org/abs/1505.04597
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
+  Framework: PyTorch
+- Name: unet-s5-d16_fcn_4xb4-40k_stare-128x128
+  In Collection: UNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: STARE
+    Metrics:
+      mDice: 89.78
+      Dice: 81.02
+  Config: configs/unet/unet-s5-d16_fcn_4xb4-40k_stare-128x128.py
+  Metadata:
+    Training Data: STARE
+    Batch Size: 16
+    Architecture:
+    - UNet-S5-D16
+    - UNet
+    - FCN
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 0.968
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_128x128_40k_stare/fcn_unet_s5-d16_128x128_40k_stare_20201223_191051-7d77e78b.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_128x128_40k_stare/unet_s5-d16_128x128_40k_stare-20201223_191051.log.json
+  Paper:
+    Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
+    URL: https://arxiv.org/abs/1505.04597
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
+  Framework: PyTorch
+- Name: unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_stare-128x128
+  In Collection: UNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: STARE
+    Metrics:
+      mDice: 90.65
+      Dice: 82.7
+  Config: configs/unet/unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_stare-128x128.py
+  Metadata:
+    Training Data: STARE
+    Batch Size: 16
+    Architecture:
+    - UNet-S5-D16
+    - UNet
+    - FCN
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 0.986
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201821-f75705a9.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201821.log.json
+  Paper:
+    Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
+    URL: https://arxiv.org/abs/1505.04597
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
+  Framework: PyTorch
+- Name: unet-s5-d16_pspnet_4xb4-40k_stare-128x128
+  In Collection: UNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: STARE
+    Metrics:
+      mDice: 89.89
+      Dice: 81.22
+  Config: configs/unet/unet-s5-d16_pspnet_4xb4-40k_stare-128x128.py
+  Metadata:
+    Training Data: STARE
+    Batch Size: 16
+    Architecture:
+    - UNet-S5-D16
+    - UNet
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 0.982
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_stare/pspnet_unet_s5-d16_128x128_40k_stare_20201227_181818-3c2923c4.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_stare/pspnet_unet_s5-d16_128x128_40k_stare-20201227_181818.log.json
+  Paper:
+    Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
+    URL: https://arxiv.org/abs/1505.04597
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
+  Framework: PyTorch
+- Name: unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_stare-128x128
+  In Collection: UNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: STARE
+    Metrics:
+      mDice: 90.72
+      Dice: 82.84
+  Config: configs/unet/unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_stare-128x128.py
+  Metadata:
+    Training Data: STARE
+    Batch Size: 16
+    Architecture:
+    - UNet-S5-D16
+    - UNet
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 1.028
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201823-f1063ef7.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201823.log.json
+  Paper:
+    Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
+    URL: https://arxiv.org/abs/1505.04597
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
+  Framework: PyTorch
+- Name: unet-s5-d16_deeplabv3_4xb4-40k_stare-128x128
+  In Collection: UNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: STARE
+    Metrics:
+      mDice: 89.73
+      Dice: 80.93
+  Config: configs/unet/unet-s5-d16_deeplabv3_4xb4-40k_stare-128x128.py
+  Metadata:
+    Training Data: STARE
+    Batch Size: 16
+    Architecture:
+    - UNet-S5-D16
+    - UNet
+    - DeepLabV3
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 0.999
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_stare/deeplabv3_unet_s5-d16_128x128_40k_stare_20201226_094047-93dcb93c.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_stare/deeplabv3_unet_s5-d16_128x128_40k_stare-20201226_094047.log.json
+  Paper:
+    Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
+    URL: https://arxiv.org/abs/1505.04597
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
+  Framework: PyTorch
+- Name: unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_stare-128x128
+  In Collection: UNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: STARE
+    Metrics:
+      mDice: 90.65
+      Dice: 82.71
+  Config: configs/unet/unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_stare-128x128.py
+  Metadata:
+    Training Data: STARE
+    Batch Size: 16
+    Architecture:
+    - UNet-S5-D16
+    - UNet
+    - DeepLabV3
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 1.01
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201825-21db614c.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201825.log.json
+  Paper:
+    Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
+    URL: https://arxiv.org/abs/1505.04597
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
+  Framework: PyTorch
+- Name: unet-s5-d16_fcn_4xb4-40k_chase-db1-128x128
+  In Collection: UNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: CHASE_DB1
+    Metrics:
+      mDice: 89.46
+      Dice: 80.24
+  Config: configs/unet/unet-s5-d16_fcn_4xb4-40k_chase-db1-128x128.py
+  Metadata:
+    Training Data: CHASE_DB1
+    Batch Size: 16
+    Architecture:
+    - UNet-S5-D16
+    - UNet
+    - FCN
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 0.968
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_128x128_40k_chase_db1/fcn_unet_s5-d16_128x128_40k_chase_db1_20201223_191051-11543527.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_128x128_40k_chase_db1/unet_s5-d16_128x128_40k_chase_db1-20201223_191051.log.json
+  Paper:
+    Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
+    URL: https://arxiv.org/abs/1505.04597
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
+  Framework: PyTorch
+- Name: unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_chase-db1-128x128
+  In Collection: UNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: CHASE_DB1
+    Metrics:
+      mDice: 89.52
+      Dice: 80.4
+  Config: configs/unet/unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_chase-db1-128x128.py
+  Metadata:
+    Training Data: CHASE_DB1
+    Batch Size: 16
+    Architecture:
+    - UNet-S5-D16
+    - UNet
+    - FCN
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 0.986
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201821-1c4eb7cf.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201821.log.json
+  Paper:
+    Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
+    URL: https://arxiv.org/abs/1505.04597
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
+  Framework: PyTorch
+- Name: unet-s5-d16_pspnet_4xb4-40k_chase-db1-128x128
+  In Collection: UNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: CHASE_DB1
+    Metrics:
+      mDice: 89.52
+      Dice: 80.36
+  Config: configs/unet/unet-s5-d16_pspnet_4xb4-40k_chase-db1-128x128.py
+  Metadata:
+    Training Data: CHASE_DB1
+    Batch Size: 16
+    Architecture:
+    - UNet-S5-D16
+    - UNet
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 0.982
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1/pspnet_unet_s5-d16_128x128_40k_chase_db1_20201227_181818-68d4e609.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1/pspnet_unet_s5-d16_128x128_40k_chase_db1-20201227_181818.log.json
+  Paper:
+    Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
+    URL: https://arxiv.org/abs/1505.04597
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
+  Framework: PyTorch
+- Name: unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_chase-db1-128x128
+  In Collection: UNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: CHASE_DB1
+    Metrics:
+      mDice: 89.45
+      Dice: 80.28
+  Config: configs/unet/unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_chase-db1-128x128.py
+  Metadata:
+    Training Data: CHASE_DB1
+    Batch Size: 16
+    Architecture:
+    - UNet-S5-D16
+    - UNet
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 1.028
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201823-c0802c4d.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201823.log.json
+  Paper:
+    Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
+    URL: https://arxiv.org/abs/1505.04597
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
+  Framework: PyTorch
+- Name: unet_s5-d16_deeplabv3_4xb4-40k_chase-db1-128x128
+  In Collection: UNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: CHASE_DB1
+    Metrics:
+      mDice: 89.57
+      Dice: 80.47
+  Config: configs/unet/unet_s5-d16_deeplabv3_4xb4-40k_chase-db1-128x128.py
+  Metadata:
+    Training Data: CHASE_DB1
+    Batch Size: 16
+    Architecture:
+    - UNet-S5-D16
+    - UNet
+    - DeepLabV3
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 0.999
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1/deeplabv3_unet_s5-d16_128x128_40k_chase_db1_20201226_094047-4c5aefa3.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1/deeplabv3_unet_s5-d16_128x128_40k_chase_db1-20201226_094047.log.json
+  Paper:
+    Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
+    URL: https://arxiv.org/abs/1505.04597
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
+  Framework: PyTorch
+- Name: unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_chase-db1-128x128
+  In Collection: UNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: CHASE_DB1
+    Metrics:
+      mDice: 89.49
+      Dice: 80.37
+  Config: configs/unet/unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_chase-db1-128x128.py
+  Metadata:
+    Training Data: CHASE_DB1
+    Batch Size: 16
+    Architecture:
+    - UNet-S5-D16
+    - UNet
+    - DeepLabV3
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 1.01
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201825-4ef29df5.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201825.log.json
+  Paper:
+    Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
+    URL: https://arxiv.org/abs/1505.04597
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
+  Framework: PyTorch
+- Name: unet-s5-d16_fcn_4xb4-40k_hrf-256x256
+  In Collection: UNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: HRF
+    Metrics:
+      mDice: 88.92
+      Dice: 79.45
+  Config: configs/unet/unet-s5-d16_fcn_4xb4-40k_hrf-256x256.py
+  Metadata:
+    Training Data: HRF
+    Batch Size: 16
+    Architecture:
+    - UNet-S5-D16
+    - UNet
+    - FCN
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 2.525
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_256x256_40k_hrf/fcn_unet_s5-d16_256x256_40k_hrf_20201223_173724-d89cf1ed.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_256x256_40k_hrf/unet_s5-d16_256x256_40k_hrf-20201223_173724.log.json
+  Paper:
+    Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
+    URL: https://arxiv.org/abs/1505.04597
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
+  Framework: PyTorch
+- Name: unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_hrf-256x256
+  In Collection: UNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: HRF
+    Metrics:
+      mDice: 89.64
+      Dice: 80.87
+  Config: configs/unet/unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_hrf-256x256.py
+  Metadata:
+    Training Data: HRF
+    Batch Size: 16
+    Architecture:
+    - UNet-S5-D16
+    - UNet
+    - FCN
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 2.623
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_201821-c314da8a.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_201821.log.json
+  Paper:
+    Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
+    URL: https://arxiv.org/abs/1505.04597
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
+  Framework: PyTorch
+- Name: unet-s5-d16_pspnet_4xb4-40k_hrf-256x256
+  In Collection: UNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: HRF
+    Metrics:
+      mDice: 89.24
+      Dice: 80.07
+  Config: configs/unet/unet-s5-d16_pspnet_4xb4-40k_hrf-256x256.py
+  Metadata:
+    Training Data: HRF
+    Batch Size: 16
+    Architecture:
+    - UNet-S5-D16
+    - UNet
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 2.588
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_256x256_40k_hrf/pspnet_unet_s5-d16_256x256_40k_hrf_20201227_181818-fdb7e29b.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_256x256_40k_hrf/pspnet_unet_s5-d16_256x256_40k_hrf-20201227_181818.log.json
+  Paper:
+    Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
+    URL: https://arxiv.org/abs/1505.04597
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
+  Framework: PyTorch
+- Name: unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_hrf-256x256
+  In Collection: UNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: HRF
+    Metrics:
+      mDice: 89.69
+      Dice: 80.96
+  Config: configs/unet/unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_hrf-256x256.py
+  Metadata:
+    Training Data: HRF
+    Batch Size: 16
+    Architecture:
+    - UNet-S5-D16
+    - UNet
+    - PSPNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 2.798
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_201823-53d492fa.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_201823.log.json
+  Paper:
+    Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
+    URL: https://arxiv.org/abs/1505.04597
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
+  Framework: PyTorch
+- Name: unet-s5-d16_deeplabv3_4xb4-40k_hrf-256x256
+  In Collection: UNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: HRF
+    Metrics:
+      mDice: 89.32
+      Dice: 80.21
+  Config: configs/unet/unet-s5-d16_deeplabv3_4xb4-40k_hrf-256x256.py
+  Metadata:
+    Training Data: HRF
+    Batch Size: 16
+    Architecture:
+    - UNet-S5-D16
+    - UNet
+    - DeepLabV3
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 2.604
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf/deeplabv3_unet_s5-d16_256x256_40k_hrf_20201226_094047-3a1fdf85.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf/deeplabv3_unet_s5-d16_256x256_40k_hrf-20201226_094047.log.json
+  Paper:
+    Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
+    URL: https://arxiv.org/abs/1505.04597
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
+  Framework: PyTorch
+- Name: unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_hrf-256x256
+  In Collection: UNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: HRF
+    Metrics:
+      mDice: 89.56
+      Dice: 80.71
+  Config: configs/unet/unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_hrf-256x256.py
+  Metadata:
+    Training Data: HRF
+    Batch Size: 16
+    Architecture:
+    - UNet-S5-D16
+    - UNet
+    - DeepLabV3
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 2.607
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_202032-59daf7a4.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_202032.log.json
+  Paper:
+    Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
+    URL: https://arxiv.org/abs/1505.04597
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
+  Framework: PyTorch
diff --git a/configs/unet/unet.yml b/configs/unet/unet.yml
deleted file mode 100644
index 4a01ce33e2..0000000000
--- a/configs/unet/unet.yml
+++ /dev/null
@@ -1,377 +0,0 @@
-Collections:
-- Name: UNet
-  Metadata:
-    Training Data:
-    - Cityscapes
-    - DRIVE
-    - STARE
-    - CHASE_DB1
-    - HRF
-  Paper:
-    URL: https://arxiv.org/abs/1505.04597
-    Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
-  README: configs/unet/README.md
-  Code:
-    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
-    Version: v0.17.0
-  Converted From:
-    Code: http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net
-Models:
-- Name: unet-s5-d16_fcn_4xb4-160k_cityscapes-512x1024
-  In Collection: UNet
-  Metadata:
-    backbone: UNet-S5-D16
-    crop size: (512,1024)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 327.87
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 17.91
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 69.1
-      mIoU(ms+flip): 71.05
-  Config: configs/unet/unet-s5-d16_fcn_4xb4-160k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes_20211210_145204-6860854e.pth
-- Name: unet-s5-d16_fcn_4xb4-40k_drive-64x64
-  In Collection: UNet
-  Metadata:
-    backbone: UNet-S5-D16
-    crop size: (64,64)
-    lr schd: 40000
-    Training Memory (GB): 0.68
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: DRIVE
-    Metrics:
-      Dice: 78.67
-  Config: configs/unet/unet-s5-d16_fcn_4xb4-40k_drive-64x64.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_64x64_40k_drive/fcn_unet_s5-d16_64x64_40k_drive_20201223_191051-5daf6d3b.pth
-- Name: unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_drive-64x64
-  In Collection: UNet
-  Metadata:
-    backbone: UNet-S5-D16
-    crop size: (64,64)
-    lr schd: 40000
-    Training Memory (GB): 0.582
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: DRIVE
-    Metrics:
-      Dice: 79.32
-  Config: configs/unet/unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_drive-64x64.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201820-785de5c2.pth
-- Name: unet-s5-d16_pspnet_4xb4-40k_drive-64x64
-  In Collection: UNet
-  Metadata:
-    backbone: UNet-S5-D16
-    crop size: (64,64)
-    lr schd: 40000
-    Training Memory (GB): 0.599
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: DRIVE
-    Metrics:
-      Dice: 78.62
-  Config: configs/unet/unet-s5-d16_pspnet_4xb4-40k_drive-64x64.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_64x64_40k_drive/pspnet_unet_s5-d16_64x64_40k_drive_20201227_181818-aac73387.pth
-- Name: unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_drive-64x64
-  In Collection: UNet
-  Metadata:
-    backbone: UNet-S5-D16
-    crop size: (64,64)
-    lr schd: 40000
-    Training Memory (GB): 0.585
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: DRIVE
-    Metrics:
-      Dice: 79.42
-  Config: configs/unet/unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_drive-64x64.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201821-22b3e3ba.pth
-- Name: unet-s5-d16_deeplabv3_4xb4-40k_drive-64x64
-  In Collection: UNet
-  Metadata:
-    backbone: UNet-S5-D16
-    crop size: (64,64)
-    lr schd: 40000
-    Training Memory (GB): 0.596
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: DRIVE
-    Metrics:
-      Dice: 78.69
-  Config: configs/unet/unet-s5-d16_deeplabv3_4xb4-40k_drive-64x64.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_64x64_40k_drive/deeplabv3_unet_s5-d16_64x64_40k_drive_20201226_094047-0671ff20.pth
-- Name: unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_drive-64x64
-  In Collection: UNet
-  Metadata:
-    backbone: UNet-S5-D16
-    crop size: (64,64)
-    lr schd: 40000
-    Training Memory (GB): 0.582
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: DRIVE
-    Metrics:
-      Dice: 79.56
-  Config: configs/unet/unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_drive-64x64.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201825-6bf0efd7.pth
-- Name: unet-s5-d16_fcn_4xb4-40k_stare-128x128
-  In Collection: UNet
-  Metadata:
-    backbone: UNet-S5-D16
-    crop size: (128,128)
-    lr schd: 40000
-    Training Memory (GB): 0.968
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: STARE
-    Metrics:
-      Dice: 81.02
-  Config: configs/unet/unet-s5-d16_fcn_4xb4-40k_stare-128x128.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_128x128_40k_stare/fcn_unet_s5-d16_128x128_40k_stare_20201223_191051-7d77e78b.pth
-- Name: unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_stare-128x128
-  In Collection: UNet
-  Metadata:
-    backbone: UNet-S5-D16
-    crop size: (128,128)
-    lr schd: 40000
-    Training Memory (GB): 0.986
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: STARE
-    Metrics:
-      Dice: 82.7
-  Config: configs/unet/unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_stare-128x128.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201821-f75705a9.pth
-- Name: unet-s5-d16_pspnet_4xb4-40k_stare-128x128
-  In Collection: UNet
-  Metadata:
-    backbone: UNet-S5-D16
-    crop size: (128,128)
-    lr schd: 40000
-    Training Memory (GB): 0.982
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: STARE
-    Metrics:
-      Dice: 81.22
-  Config: configs/unet/unet-s5-d16_pspnet_4xb4-40k_stare-128x128.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_stare/pspnet_unet_s5-d16_128x128_40k_stare_20201227_181818-3c2923c4.pth
-- Name: unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_stare-128x128
-  In Collection: UNet
-  Metadata:
-    backbone: UNet-S5-D16
-    crop size: (128,128)
-    lr schd: 40000
-    Training Memory (GB): 1.028
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: STARE
-    Metrics:
-      Dice: 82.84
-  Config: configs/unet/unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_stare-128x128.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201823-f1063ef7.pth
-- Name: unet-s5-d16_deeplabv3_4xb4-40k_stare-128x128
-  In Collection: UNet
-  Metadata:
-    backbone: UNet-S5-D16
-    crop size: (128,128)
-    lr schd: 40000
-    Training Memory (GB): 0.999
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: STARE
-    Metrics:
-      Dice: 80.93
-  Config: configs/unet/unet-s5-d16_deeplabv3_4xb4-40k_stare-128x128.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_stare/deeplabv3_unet_s5-d16_128x128_40k_stare_20201226_094047-93dcb93c.pth
-- Name: unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_stare-128x128
-  In Collection: UNet
-  Metadata:
-    backbone: UNet-S5-D16
-    crop size: (128,128)
-    lr schd: 40000
-    Training Memory (GB): 1.01
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: STARE
-    Metrics:
-      Dice: 82.71
-  Config: configs/unet/unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_stare-128x128.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201825-21db614c.pth
-- Name: unet-s5-d16_fcn_4xb4-40k_chase-db1-128x128
-  In Collection: UNet
-  Metadata:
-    backbone: UNet-S5-D16
-    crop size: (128,128)
-    lr schd: 40000
-    Training Memory (GB): 0.968
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: CHASE_DB1
-    Metrics:
-      Dice: 80.24
-  Config: configs/unet/unet-s5-d16_fcn_4xb4-40k_chase-db1-128x128.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_128x128_40k_chase_db1/fcn_unet_s5-d16_128x128_40k_chase_db1_20201223_191051-11543527.pth
-- Name: unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_chase-db1-128x128
-  In Collection: UNet
-  Metadata:
-    backbone: UNet-S5-D16
-    crop size: (128,128)
-    lr schd: 40000
-    Training Memory (GB): 0.986
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: CHASE_DB1
-    Metrics:
-      Dice: 80.4
-  Config: configs/unet/unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_chase-db1-128x128.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201821-1c4eb7cf.pth
-- Name: unet-s5-d16_pspnet_4xb4-40k_chase-db1-128x128
-  In Collection: UNet
-  Metadata:
-    backbone: UNet-S5-D16
-    crop size: (128,128)
-    lr schd: 40000
-    Training Memory (GB): 0.982
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: CHASE_DB1
-    Metrics:
-      Dice: 80.36
-  Config: configs/unet/unet-s5-d16_pspnet_4xb4-40k_chase-db1-128x128.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1/pspnet_unet_s5-d16_128x128_40k_chase_db1_20201227_181818-68d4e609.pth
-- Name: unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_chase-db1-128x128
-  In Collection: UNet
-  Metadata:
-    backbone: UNet-S5-D16
-    crop size: (128,128)
-    lr schd: 40000
-    Training Memory (GB): 1.028
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: CHASE_DB1
-    Metrics:
-      Dice: 80.28
-  Config: configs/unet/unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_chase-db1-128x128.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201823-c0802c4d.pth
-- Name: unet_s5-d16_deeplabv3_4xb4-40k_chase-db1-128x128
-  In Collection: UNet
-  Metadata:
-    backbone: UNet-S5-D16
-    crop size: (128,128)
-    lr schd: 40000
-    Training Memory (GB): 0.999
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: CHASE_DB1
-    Metrics:
-      Dice: 80.47
-  Config: configs/unet/unet_s5-d16_deeplabv3_4xb4-40k_chase-db1-128x128.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1/deeplabv3_unet_s5-d16_128x128_40k_chase_db1_20201226_094047-4c5aefa3.pth
-- Name: unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_chase-db1-128x128
-  In Collection: UNet
-  Metadata:
-    backbone: UNet-S5-D16
-    crop size: (128,128)
-    lr schd: 40000
-    Training Memory (GB): 1.01
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: CHASE_DB1
-    Metrics:
-      Dice: 80.37
-  Config: configs/unet/unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_chase-db1-128x128.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201825-4ef29df5.pth
-- Name: unet-s5-d16_fcn_4xb4-40k_hrf-256x256
-  In Collection: UNet
-  Metadata:
-    backbone: UNet-S5-D16
-    crop size: (256,256)
-    lr schd: 40000
-    Training Memory (GB): 2.525
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: HRF
-    Metrics:
-      Dice: 79.45
-  Config: configs/unet/unet-s5-d16_fcn_4xb4-40k_hrf-256x256.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_256x256_40k_hrf/fcn_unet_s5-d16_256x256_40k_hrf_20201223_173724-d89cf1ed.pth
-- Name: unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_hrf-256x256
-  In Collection: UNet
-  Metadata:
-    backbone: UNet-S5-D16
-    crop size: (256,256)
-    lr schd: 40000
-    Training Memory (GB): 2.623
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: HRF
-    Metrics:
-      Dice: 80.87
-  Config: configs/unet/unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_hrf-256x256.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_201821-c314da8a.pth
-- Name: unet-s5-d16_pspnet_4xb4-40k_hrf-256x256
-  In Collection: UNet
-  Metadata:
-    backbone: UNet-S5-D16
-    crop size: (256,256)
-    lr schd: 40000
-    Training Memory (GB): 2.588
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: HRF
-    Metrics:
-      Dice: 80.07
-  Config: configs/unet/unet-s5-d16_pspnet_4xb4-40k_hrf-256x256.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_256x256_40k_hrf/pspnet_unet_s5-d16_256x256_40k_hrf_20201227_181818-fdb7e29b.pth
-- Name: unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_hrf-256x256
-  In Collection: UNet
-  Metadata:
-    backbone: UNet-S5-D16
-    crop size: (256,256)
-    lr schd: 40000
-    Training Memory (GB): 2.798
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: HRF
-    Metrics:
-      Dice: 80.96
-  Config: configs/unet/unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_hrf-256x256.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_201823-53d492fa.pth
-- Name: unet-s5-d16_deeplabv3_4xb4-40k_hrf-256x256
-  In Collection: UNet
-  Metadata:
-    backbone: UNet-S5-D16
-    crop size: (256,256)
-    lr schd: 40000
-    Training Memory (GB): 2.604
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: HRF
-    Metrics:
-      Dice: 80.21
-  Config: configs/unet/unet-s5-d16_deeplabv3_4xb4-40k_hrf-256x256.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf/deeplabv3_unet_s5-d16_256x256_40k_hrf_20201226_094047-3a1fdf85.pth
-- Name: unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_hrf-256x256
-  In Collection: UNet
-  Metadata:
-    backbone: UNet-S5-D16
-    crop size: (256,256)
-    lr schd: 40000
-    Training Memory (GB): 2.607
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: HRF
-    Metrics:
-      Dice: 80.71
-  Config: configs/unet/unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_hrf-256x256.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_202032-59daf7a4.pth
diff --git a/configs/upernet/README.md b/configs/upernet/README.md
index e4a5ee4381..7d128090bf 100644
--- a/configs/upernet/README.md
+++ b/configs/upernet/README.md
@@ -1,6 +1,6 @@
 # UPerNet
 
-[Unified Perceptual Parsing for Scene Understanding](https://arxiv.org/pdf/1807.10221.pdf)
+> [Unified Perceptual Parsing for Scene Understanding](https://arxiv.org/pdf/1807.10221.pdf)
 
 ## Introduction
 
@@ -22,6 +22,39 @@ Humans recognize the visual world at multiple levels: we effortlessly categorize
 <img src="https://user-images.githubusercontent.com/24582831/142903077-44e8e0da-7276-4bda-bd2b-0df1680ca845.png" width="70%"/>
 </div>
 
+## Results and models
+
+### Cityscapes
+
+| Method  | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                           | download                                                                                                                                                                                                                                                                                                                                           |
+| ------- | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| UPerNet | R-50     | 512x1024  |   40000 | 6.4      | 4.25           | V100   | 77.10 |         78.37 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/upernet/upernet_r50_4xb2-40k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_40k_cityscapes/upernet_r50_512x1024_40k_cityscapes_20200605_094827-aa54cb54.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_40k_cityscapes/upernet_r50_512x1024_40k_cityscapes_20200605_094827.log.json)     |
+| UPerNet | R-101    | 512x1024  |   40000 | 7.4      | 3.79           | V100   | 78.69 |         80.11 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/upernet/upernet_r101_4xb2-40k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_40k_cityscapes/upernet_r101_512x1024_40k_cityscapes_20200605_094933-ebce3b10.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_40k_cityscapes/upernet_r101_512x1024_40k_cityscapes_20200605_094933.log.json) |
+| UPerNet | R-50     | 769x769   |   40000 | 7.2      | 1.76           | V100   | 77.98 |         79.70 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/upernet/upernet_r50_4xb2-40k_cityscapes-769x769.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_40k_cityscapes/upernet_r50_769x769_40k_cityscapes_20200530_033048-92d21539.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_40k_cityscapes/upernet_r50_769x769_40k_cityscapes_20200530_033048.log.json)         |
+| UPerNet | R-101    | 769x769   |   40000 | 8.4      | 1.56           | V100   | 79.03 |         80.77 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/upernet/upernet_r101_4xb2-40k_cityscapes-769x769.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_40k_cityscapes/upernet_r101_769x769_40k_cityscapes_20200530_040819-83c95d01.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_40k_cityscapes/upernet_r101_769x769_40k_cityscapes_20200530_040819.log.json)     |
+| UPerNet | R-50     | 512x1024  |   80000 | -        | -              | V100   | 78.19 |         79.19 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/upernet/upernet_r50_4xb2-80k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_80k_cityscapes/upernet_r50_512x1024_80k_cityscapes_20200607_052207-848beca8.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_80k_cityscapes/upernet_r50_512x1024_80k_cityscapes_20200607_052207.log.json)     |
+| UPerNet | R-101    | 512x1024  |   80000 | -        | -              | V100   | 79.40 |         80.46 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/upernet/upernet_r101_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_80k_cityscapes/upernet_r101_512x1024_80k_cityscapes_20200607_002403-f05f2345.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_80k_cityscapes/upernet_r101_512x1024_80k_cityscapes_20200607_002403.log.json) |
+| UPerNet | R-50     | 769x769   |   80000 | -        | -              | V100   | 79.39 |         80.92 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/upernet/upernet_r50_4xb2-80k_cityscapes-769x769.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_80k_cityscapes/upernet_r50_769x769_80k_cityscapes_20200607_005107-82ae7d15.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_80k_cityscapes/upernet_r50_769x769_80k_cityscapes_20200607_005107.log.json)         |
+| UPerNet | R-101    | 769x769   |   80000 | -        | -              | V100   | 80.10 |         81.49 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/upernet/upernet_r101_4xb2-80k_cityscapes-769x769.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_80k_cityscapes/upernet_r101_769x769_80k_cityscapes_20200607_001014-082fc334.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_80k_cityscapes/upernet_r101_769x769_80k_cityscapes_20200607_001014.log.json)     |
+
+### ADE20K
+
+| Method  | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                       | download                                                                                                                                                                                                                                                                                                                           |
+| ------- | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ---------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| UPerNet | R-50     | 512x512   |   80000 | 8.1      | 23.40          | V100   | 40.70 |         41.81 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/upernet/upernet_r50_4xb4-80k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_80k_ade20k/upernet_r50_512x512_80k_ade20k_20200614_144127-ecc8377b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_80k_ade20k/upernet_r50_512x512_80k_ade20k_20200614_144127.log.json)         |
+| UPerNet | R-101    | 512x512   |   80000 | 9.1      | 20.34          | V100   | 42.91 |         43.96 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/upernet/upernet_r101_4xb4-80k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_80k_ade20k/upernet_r101_512x512_80k_ade20k_20200614_185117-32e4db94.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_80k_ade20k/upernet_r101_512x512_80k_ade20k_20200614_185117.log.json)     |
+| UPerNet | R-50     | 512x512   |  160000 | -        | -              | V100   | 42.05 |         42.78 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/upernet/upernet_r50_4xb4-160k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_160k_ade20k/upernet_r50_512x512_160k_ade20k_20200615_184328-8534de8d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_160k_ade20k/upernet_r50_512x512_160k_ade20k_20200615_184328.log.json)     |
+| UPerNet | R-101    | 512x512   |  160000 | -        | -              | V100   | 43.82 |         44.85 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/upernet/upernet_r101_4xb4-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_160k_ade20k/upernet_r101_512x512_160k_ade20k_20200615_161951-91b32684.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_160k_ade20k/upernet_r101_512x512_160k_ade20k_20200615_161951.log.json) |
+
+### Pascal VOC 2012 + Aug
+
+| Method  | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                        | download                                                                                                                                                                                                                                                                                                                               |
+| ------- | -------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| UPerNet | R-50     | 512x512   |   20000 | 6.4      | 23.17          | V100   | 74.82 |         76.35 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/upernet/upernet_r50_4xb4-20k_voc12aug-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_20k_voc12aug/upernet_r50_512x512_20k_voc12aug_20200617_165330-5b5890a7.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_20k_voc12aug/upernet_r50_512x512_20k_voc12aug_20200617_165330.log.json)     |
+| UPerNet | R-101    | 512x512   |   20000 | 7.5      | 19.98          | V100   | 77.10 |         78.29 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/upernet/upernet_r101_4xb4-20k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_20k_voc12aug/upernet_r101_512x512_20k_voc12aug_20200617_165629-f14e7f27.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_20k_voc12aug/upernet_r101_512x512_20k_voc12aug_20200617_165629.log.json) |
+| UPerNet | R-50     | 512x512   |   40000 | -        | -              | V100   | 75.92 |         77.44 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/upernet/upernet_r50_4xb4-40k_voc12aug-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_40k_voc12aug/upernet_r50_512x512_40k_voc12aug_20200613_162257-ca9bcc6b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_40k_voc12aug/upernet_r50_512x512_40k_voc12aug_20200613_162257.log.json)     |
+| UPerNet | R-101    | 512x512   |   40000 | -        | -              | V100   | 77.43 |         78.56 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/upernet/upernet_r101_4xb4-40k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_40k_voc12aug/upernet_r101_512x512_40k_voc12aug_20200613_163549-e26476ac.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_40k_voc12aug/upernet_r101_512x512_40k_voc12aug_20200613_163549.log.json) |
+
 ## Citation
 
 ```bibtex
@@ -33,36 +66,3 @@ Humans recognize the visual world at multiple levels: we effortlessly categorize
   year={2018}
 }
 ```
-
-## Results and models
-
-### Cityscapes
-
-| Method  | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                           | download                                                                                                                                                                                                                                                                                                                                           |
-| ------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| UPerNet | R-50     | 512x1024  |   40000 | 6.4      | 4.25           | 77.10 |         78.37 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/upernet/upernet_r50_4xb2-40k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_40k_cityscapes/upernet_r50_512x1024_40k_cityscapes_20200605_094827-aa54cb54.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_40k_cityscapes/upernet_r50_512x1024_40k_cityscapes_20200605_094827.log.json)     |
-| UPerNet | R-101    | 512x1024  |   40000 | 7.4      | 3.79           | 78.69 |         80.11 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/upernet/upernet_r101_4xb2-40k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_40k_cityscapes/upernet_r101_512x1024_40k_cityscapes_20200605_094933-ebce3b10.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_40k_cityscapes/upernet_r101_512x1024_40k_cityscapes_20200605_094933.log.json) |
-| UPerNet | R-50     | 769x769   |   40000 | 7.2      | 1.76           | 77.98 |         79.70 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/upernet/upernet_r50_4xb2-40k_cityscapes-769x769.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_40k_cityscapes/upernet_r50_769x769_40k_cityscapes_20200530_033048-92d21539.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_40k_cityscapes/upernet_r50_769x769_40k_cityscapes_20200530_033048.log.json)         |
-| UPerNet | R-101    | 769x769   |   40000 | 8.4      | 1.56           | 79.03 |         80.77 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/upernet/upernet_r101_4xb2-40k_cityscapes-769x769.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_40k_cityscapes/upernet_r101_769x769_40k_cityscapes_20200530_040819-83c95d01.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_40k_cityscapes/upernet_r101_769x769_40k_cityscapes_20200530_040819.log.json)     |
-| UPerNet | R-50     | 512x1024  |   80000 | -        | -              | 78.19 |         79.19 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/upernet/upernet_r50_4xb2-80k_cityscapes-512x1024.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_80k_cityscapes/upernet_r50_512x1024_80k_cityscapes_20200607_052207-848beca8.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_80k_cityscapes/upernet_r50_512x1024_80k_cityscapes_20200607_052207.log.json)     |
-| UPerNet | R-101    | 512x1024  |   80000 | -        | -              | 79.40 |         80.46 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/upernet/upernet_r101_4xb2-80k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_80k_cityscapes/upernet_r101_512x1024_80k_cityscapes_20200607_002403-f05f2345.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_80k_cityscapes/upernet_r101_512x1024_80k_cityscapes_20200607_002403.log.json) |
-| UPerNet | R-50     | 769x769   |   80000 | -        | -              | 79.39 |         80.92 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/upernet/upernet_r50_4xb2-80k_cityscapes-769x769.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_80k_cityscapes/upernet_r50_769x769_80k_cityscapes_20200607_005107-82ae7d15.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_80k_cityscapes/upernet_r50_769x769_80k_cityscapes_20200607_005107.log.json)         |
-| UPerNet | R-101    | 769x769   |   80000 | -        | -              | 80.10 |         81.49 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/upernet/upernet_r101_4xb2-80k_cityscapes-769x769.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_80k_cityscapes/upernet_r101_769x769_80k_cityscapes_20200607_001014-082fc334.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_80k_cityscapes/upernet_r101_769x769_80k_cityscapes_20200607_001014.log.json)     |
-
-### ADE20K
-
-| Method  | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                       | download                                                                                                                                                                                                                                                                                                                           |
-| ------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ---------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| UPerNet | R-50     | 512x512   |   80000 | 8.1      | 23.40          | 40.70 |         41.81 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/upernet/upernet_r50_4xb4-80k_ade20k-512x512.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_80k_ade20k/upernet_r50_512x512_80k_ade20k_20200614_144127-ecc8377b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_80k_ade20k/upernet_r50_512x512_80k_ade20k_20200614_144127.log.json)         |
-| UPerNet | R-101    | 512x512   |   80000 | 9.1      | 20.34          | 42.91 |         43.96 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/upernet/upernet_r101_4xb4-80k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_80k_ade20k/upernet_r101_512x512_80k_ade20k_20200614_185117-32e4db94.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_80k_ade20k/upernet_r101_512x512_80k_ade20k_20200614_185117.log.json)     |
-| UPerNet | R-50     | 512x512   |  160000 | -        | -              | 42.05 |         42.78 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/upernet/upernet_r50_4xb4-160k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_160k_ade20k/upernet_r50_512x512_160k_ade20k_20200615_184328-8534de8d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_160k_ade20k/upernet_r50_512x512_160k_ade20k_20200615_184328.log.json)     |
-| UPerNet | R-101    | 512x512   |  160000 | -        | -              | 43.82 |         44.85 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/upernet/upernet_r101_4xb4-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_160k_ade20k/upernet_r101_512x512_160k_ade20k_20200615_161951-91b32684.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_160k_ade20k/upernet_r101_512x512_160k_ade20k_20200615_161951.log.json) |
-
-### Pascal VOC 2012 + Aug
-
-| Method  | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                        | download                                                                                                                                                                                                                                                                                                                               |
-| ------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| UPerNet | R-50     | 512x512   |   20000 | 6.4      | 23.17          | 74.82 |         76.35 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/upernet/upernet_r50_4xb4-20k_voc12aug-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_20k_voc12aug/upernet_r50_512x512_20k_voc12aug_20200617_165330-5b5890a7.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_20k_voc12aug/upernet_r50_512x512_20k_voc12aug_20200617_165330.log.json)     |
-| UPerNet | R-101    | 512x512   |   20000 | 7.5      | 19.98          | 77.10 |         78.29 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/upernet/upernet_r101_4xb4-20k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_20k_voc12aug/upernet_r101_512x512_20k_voc12aug_20200617_165629-f14e7f27.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_20k_voc12aug/upernet_r101_512x512_20k_voc12aug_20200617_165629.log.json) |
-| UPerNet | R-50     | 512x512   |   40000 | -        | -              | 75.92 |         77.44 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/upernet/upernet_r50_4xb4-40k_voc12aug-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_40k_voc12aug/upernet_r50_512x512_40k_voc12aug_20200613_162257-ca9bcc6b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_40k_voc12aug/upernet_r50_512x512_40k_voc12aug_20200613_162257.log.json)     |
-| UPerNet | R-101    | 512x512   |   40000 | -        | -              | 77.43 |         78.56 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/upernet/upernet_r101_4xb4-40k_voc12aug-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_40k_voc12aug/upernet_r101_512x512_40k_voc12aug_20200613_163549-e26476ac.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_40k_voc12aug/upernet_r101_512x512_40k_voc12aug_20200613_163549.log.json) |
diff --git a/configs/upernet/metafile.yaml b/configs/upernet/metafile.yaml
new file mode 100644
index 0000000000..f6ad8187f2
--- /dev/null
+++ b/configs/upernet/metafile.yaml
@@ -0,0 +1,391 @@
+Collections:
+- Name: UPerNet
+  License: Apache License 2.0
+  Metadata:
+    Training Data:
+    - Cityscapes
+    - ADE20K
+    - Pascal VOC 2012 + Aug
+  Paper:
+    Title: Unified Perceptual Parsing for Scene Understanding
+    URL: https://arxiv.org/pdf/1807.10221.pdf
+  README: configs/upernet/README.md
+  Frameworks:
+  - PyTorch
+Models:
+- Name: upernet_r50_4xb2-40k_cityscapes-512x1024
+  In Collection: UPerNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.1
+      mIoU(ms+flip): 78.37
+  Config: configs/upernet/upernet_r50_4xb2-40k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50
+    - UPerNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 6.4
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_40k_cityscapes/upernet_r50_512x1024_40k_cityscapes_20200605_094827-aa54cb54.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_40k_cityscapes/upernet_r50_512x1024_40k_cityscapes_20200605_094827.log.json
+  Paper:
+    Title: Unified Perceptual Parsing for Scene Understanding
+    URL: https://arxiv.org/pdf/1807.10221.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
+  Framework: PyTorch
+- Name: upernet_r101_4xb2-40k_cityscapes-512x1024
+  In Collection: UPerNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.69
+      mIoU(ms+flip): 80.11
+  Config: configs/upernet/upernet_r101_4xb2-40k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101
+    - UPerNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 7.4
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_40k_cityscapes/upernet_r101_512x1024_40k_cityscapes_20200605_094933-ebce3b10.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_40k_cityscapes/upernet_r101_512x1024_40k_cityscapes_20200605_094933.log.json
+  Paper:
+    Title: Unified Perceptual Parsing for Scene Understanding
+    URL: https://arxiv.org/pdf/1807.10221.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
+  Framework: PyTorch
+- Name: upernet_r50_4xb2-40k_cityscapes-769x769
+  In Collection: UPerNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.98
+      mIoU(ms+flip): 79.7
+  Config: configs/upernet/upernet_r50_4xb2-40k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50
+    - UPerNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 7.2
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_40k_cityscapes/upernet_r50_769x769_40k_cityscapes_20200530_033048-92d21539.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_40k_cityscapes/upernet_r50_769x769_40k_cityscapes_20200530_033048.log.json
+  Paper:
+    Title: Unified Perceptual Parsing for Scene Understanding
+    URL: https://arxiv.org/pdf/1807.10221.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
+  Framework: PyTorch
+- Name: upernet_r101_4xb2-40k_cityscapes-769x769
+  In Collection: UPerNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.03
+      mIoU(ms+flip): 80.77
+  Config: configs/upernet/upernet_r101_4xb2-40k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101
+    - UPerNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 8.4
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_40k_cityscapes/upernet_r101_769x769_40k_cityscapes_20200530_040819-83c95d01.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_40k_cityscapes/upernet_r101_769x769_40k_cityscapes_20200530_040819.log.json
+  Paper:
+    Title: Unified Perceptual Parsing for Scene Understanding
+    URL: https://arxiv.org/pdf/1807.10221.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
+  Framework: PyTorch
+- Name: upernet_r50_4xb2-80k_cityscapes-512x1024
+  In Collection: UPerNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.19
+      mIoU(ms+flip): 79.19
+  Config: configs/upernet/upernet_r50_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50
+    - UPerNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_80k_cityscapes/upernet_r50_512x1024_80k_cityscapes_20200607_052207-848beca8.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_80k_cityscapes/upernet_r50_512x1024_80k_cityscapes_20200607_052207.log.json
+  Paper:
+    Title: Unified Perceptual Parsing for Scene Understanding
+    URL: https://arxiv.org/pdf/1807.10221.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
+  Framework: PyTorch
+- Name: upernet_r101_4xb2-80k_cityscapes-512x1024
+  In Collection: UPerNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.4
+      mIoU(ms+flip): 80.46
+  Config: configs/upernet/upernet_r101_4xb2-80k_cityscapes-512x1024.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101
+    - UPerNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_80k_cityscapes/upernet_r101_512x1024_80k_cityscapes_20200607_002403-f05f2345.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_80k_cityscapes/upernet_r101_512x1024_80k_cityscapes_20200607_002403.log.json
+  Paper:
+    Title: Unified Perceptual Parsing for Scene Understanding
+    URL: https://arxiv.org/pdf/1807.10221.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
+  Framework: PyTorch
+- Name: upernet_r50_4xb2-80k_cityscapes-769x769
+  In Collection: UPerNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.39
+      mIoU(ms+flip): 80.92
+  Config: configs/upernet/upernet_r50_4xb2-80k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-50
+    - UPerNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_80k_cityscapes/upernet_r50_769x769_80k_cityscapes_20200607_005107-82ae7d15.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_80k_cityscapes/upernet_r50_769x769_80k_cityscapes_20200607_005107.log.json
+  Paper:
+    Title: Unified Perceptual Parsing for Scene Understanding
+    URL: https://arxiv.org/pdf/1807.10221.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
+  Framework: PyTorch
+- Name: upernet_r101_4xb2-80k_cityscapes-769x769
+  In Collection: UPerNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 80.1
+      mIoU(ms+flip): 81.49
+  Config: configs/upernet/upernet_r101_4xb2-80k_cityscapes-769x769.py
+  Metadata:
+    Training Data: Cityscapes
+    Batch Size: 8
+    Architecture:
+    - R-101
+    - UPerNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_80k_cityscapes/upernet_r101_769x769_80k_cityscapes_20200607_001014-082fc334.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_80k_cityscapes/upernet_r101_769x769_80k_cityscapes_20200607_001014.log.json
+  Paper:
+    Title: Unified Perceptual Parsing for Scene Understanding
+    URL: https://arxiv.org/pdf/1807.10221.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
+  Framework: PyTorch
+- Name: upernet_r50_4xb4-80k_ade20k-512x512
+  In Collection: UPerNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 40.7
+      mIoU(ms+flip): 41.81
+  Config: configs/upernet/upernet_r50_4xb4-80k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-50
+    - UPerNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 8.1
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_80k_ade20k/upernet_r50_512x512_80k_ade20k_20200614_144127-ecc8377b.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_80k_ade20k/upernet_r50_512x512_80k_ade20k_20200614_144127.log.json
+  Paper:
+    Title: Unified Perceptual Parsing for Scene Understanding
+    URL: https://arxiv.org/pdf/1807.10221.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
+  Framework: PyTorch
+- Name: upernet_r101_4xb4-80k_ade20k-512x512
+  In Collection: UPerNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 42.91
+      mIoU(ms+flip): 43.96
+  Config: configs/upernet/upernet_r101_4xb4-80k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-101
+    - UPerNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 9.1
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_80k_ade20k/upernet_r101_512x512_80k_ade20k_20200614_185117-32e4db94.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_80k_ade20k/upernet_r101_512x512_80k_ade20k_20200614_185117.log.json
+  Paper:
+    Title: Unified Perceptual Parsing for Scene Understanding
+    URL: https://arxiv.org/pdf/1807.10221.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
+  Framework: PyTorch
+- Name: upernet_r50_4xb4-160k_ade20k-512x512
+  In Collection: UPerNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 42.05
+      mIoU(ms+flip): 42.78
+  Config: configs/upernet/upernet_r50_4xb4-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-50
+    - UPerNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_160k_ade20k/upernet_r50_512x512_160k_ade20k_20200615_184328-8534de8d.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_160k_ade20k/upernet_r50_512x512_160k_ade20k_20200615_184328.log.json
+  Paper:
+    Title: Unified Perceptual Parsing for Scene Understanding
+    URL: https://arxiv.org/pdf/1807.10221.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
+  Framework: PyTorch
+- Name: upernet_r101_4xb4-160k_ade20k-512x512
+  In Collection: UPerNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 43.82
+      mIoU(ms+flip): 44.85
+  Config: configs/upernet/upernet_r101_4xb4-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - R-101
+    - UPerNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_160k_ade20k/upernet_r101_512x512_160k_ade20k_20200615_161951-91b32684.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_160k_ade20k/upernet_r101_512x512_160k_ade20k_20200615_161951.log.json
+  Paper:
+    Title: Unified Perceptual Parsing for Scene Understanding
+    URL: https://arxiv.org/pdf/1807.10221.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
+  Framework: PyTorch
+- Name: upernet_r50_4xb4-20k_voc12aug-512x512
+  In Collection: UPerNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 74.82
+      mIoU(ms+flip): 76.35
+  Config: configs/upernet/upernet_r50_4xb4-20k_voc12aug-512x512.py
+  Metadata:
+    Training Data: Pascal VOC 2012 + Aug
+    Batch Size: 16
+    Architecture:
+    - R-50
+    - UPerNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 6.4
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_20k_voc12aug/upernet_r50_512x512_20k_voc12aug_20200617_165330-5b5890a7.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_20k_voc12aug/upernet_r50_512x512_20k_voc12aug_20200617_165330.log.json
+  Paper:
+    Title: Unified Perceptual Parsing for Scene Understanding
+    URL: https://arxiv.org/pdf/1807.10221.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
+  Framework: PyTorch
+- Name: upernet_r101_4xb4-20k_voc12aug-512x512
+  In Collection: UPerNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 77.1
+      mIoU(ms+flip): 78.29
+  Config: configs/upernet/upernet_r101_4xb4-20k_voc12aug-512x512.py
+  Metadata:
+    Training Data: Pascal VOC 2012 + Aug
+    Batch Size: 16
+    Architecture:
+    - R-101
+    - UPerNet
+    Training Resources: 4x V100 GPUS
+    Memory (GB): 7.5
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_20k_voc12aug/upernet_r101_512x512_20k_voc12aug_20200617_165629-f14e7f27.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_20k_voc12aug/upernet_r101_512x512_20k_voc12aug_20200617_165629.log.json
+  Paper:
+    Title: Unified Perceptual Parsing for Scene Understanding
+    URL: https://arxiv.org/pdf/1807.10221.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
+  Framework: PyTorch
+- Name: upernet_r50_4xb4-40k_voc12aug-512x512
+  In Collection: UPerNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 75.92
+      mIoU(ms+flip): 77.44
+  Config: configs/upernet/upernet_r50_4xb4-40k_voc12aug-512x512.py
+  Metadata:
+    Training Data: Pascal VOC 2012 + Aug
+    Batch Size: 16
+    Architecture:
+    - R-50
+    - UPerNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_40k_voc12aug/upernet_r50_512x512_40k_voc12aug_20200613_162257-ca9bcc6b.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_40k_voc12aug/upernet_r50_512x512_40k_voc12aug_20200613_162257.log.json
+  Paper:
+    Title: Unified Perceptual Parsing for Scene Understanding
+    URL: https://arxiv.org/pdf/1807.10221.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
+  Framework: PyTorch
+- Name: upernet_r101_4xb4-40k_voc12aug-512x512
+  In Collection: UPerNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 77.43
+      mIoU(ms+flip): 78.56
+  Config: configs/upernet/upernet_r101_4xb4-40k_voc12aug-512x512.py
+  Metadata:
+    Training Data: Pascal VOC 2012 + Aug
+    Batch Size: 16
+    Architecture:
+    - R-101
+    - UPerNet
+    Training Resources: 4x V100 GPUS
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_40k_voc12aug/upernet_r101_512x512_40k_voc12aug_20200613_163549-e26476ac.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_40k_voc12aug/upernet_r101_512x512_40k_voc12aug_20200613_163549.log.json
+  Paper:
+    Title: Unified Perceptual Parsing for Scene Understanding
+    URL: https://arxiv.org/pdf/1807.10221.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
+  Framework: PyTorch
diff --git a/configs/upernet/upernet.yml b/configs/upernet/upernet.yml
deleted file mode 100644
index 6892fcf06a..0000000000
--- a/configs/upernet/upernet.yml
+++ /dev/null
@@ -1,305 +0,0 @@
-Collections:
-- Name: UPerNet
-  Metadata:
-    Training Data:
-    - Cityscapes
-    - ADE20K
-    - Pascal VOC 2012 + Aug
-  Paper:
-    URL: https://arxiv.org/pdf/1807.10221.pdf
-    Title: Unified Perceptual Parsing for Scene Understanding
-  README: configs/upernet/README.md
-  Code:
-    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
-    Version: v0.17.0
-  Converted From:
-    Code: https://github.com/CSAILVision/unifiedparsing
-Models:
-- Name: upernet_r50_4xb2-40k_cityscapes-512x1024
-  In Collection: UPerNet
-  Metadata:
-    backbone: R-50
-    crop size: (512,1024)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 235.29
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 6.4
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 77.1
-      mIoU(ms+flip): 78.37
-  Config: configs/upernet/upernet_r50_4xb2-40k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_40k_cityscapes/upernet_r50_512x1024_40k_cityscapes_20200605_094827-aa54cb54.pth
-- Name: upernet_r101_4xb2-40k_cityscapes-512x1024
-  In Collection: UPerNet
-  Metadata:
-    backbone: R-101
-    crop size: (512,1024)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 263.85
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,1024)
-    Training Memory (GB): 7.4
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 78.69
-      mIoU(ms+flip): 80.11
-  Config: configs/upernet/upernet_r101_4xb2-40k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_40k_cityscapes/upernet_r101_512x1024_40k_cityscapes_20200605_094933-ebce3b10.pth
-- Name: upernet_r50_4xb2-40k_cityscapes-769x769
-  In Collection: UPerNet
-  Metadata:
-    backbone: R-50
-    crop size: (769,769)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 568.18
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (769,769)
-    Training Memory (GB): 7.2
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 77.98
-      mIoU(ms+flip): 79.7
-  Config: configs/upernet/upernet_r50_4xb2-40k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_40k_cityscapes/upernet_r50_769x769_40k_cityscapes_20200530_033048-92d21539.pth
-- Name: upernet_r101_4xb2-40k_cityscapes-769x769
-  In Collection: UPerNet
-  Metadata:
-    backbone: R-101
-    crop size: (769,769)
-    lr schd: 40000
-    inference time (ms/im):
-    - value: 641.03
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (769,769)
-    Training Memory (GB): 8.4
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 79.03
-      mIoU(ms+flip): 80.77
-  Config: configs/upernet/upernet_r101_4xb2-40k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_40k_cityscapes/upernet_r101_769x769_40k_cityscapes_20200530_040819-83c95d01.pth
-- Name: upernet_r50_4xb2-80k_cityscapes-512x1024
-  In Collection: UPerNet
-  Metadata:
-    backbone: R-50
-    crop size: (512,1024)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 78.19
-      mIoU(ms+flip): 79.19
-  Config: configs/upernet/upernet_r50_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_80k_cityscapes/upernet_r50_512x1024_80k_cityscapes_20200607_052207-848beca8.pth
-- Name: upernet_r101_4xb2-80k_cityscapes-512x1024
-  In Collection: UPerNet
-  Metadata:
-    backbone: R-101
-    crop size: (512,1024)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 79.4
-      mIoU(ms+flip): 80.46
-  Config: configs/upernet/upernet_r101_4xb2-80k_cityscapes-512x1024.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_80k_cityscapes/upernet_r101_512x1024_80k_cityscapes_20200607_002403-f05f2345.pth
-- Name: upernet_r50_4xb2-80k_cityscapes-769x769
-  In Collection: UPerNet
-  Metadata:
-    backbone: R-50
-    crop size: (769,769)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 79.39
-      mIoU(ms+flip): 80.92
-  Config: configs/upernet/upernet_r50_4xb2-80k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_80k_cityscapes/upernet_r50_769x769_80k_cityscapes_20200607_005107-82ae7d15.pth
-- Name: upernet_r101_4xb2-80k_cityscapes-769x769
-  In Collection: UPerNet
-  Metadata:
-    backbone: R-101
-    crop size: (769,769)
-    lr schd: 80000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Cityscapes
-    Metrics:
-      mIoU: 80.1
-      mIoU(ms+flip): 81.49
-  Config: configs/upernet/upernet_r101_4xb2-80k_cityscapes-769x769.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_80k_cityscapes/upernet_r101_769x769_80k_cityscapes_20200607_001014-082fc334.pth
-- Name: upernet_r50_4xb4-80k_ade20k-512x512
-  In Collection: UPerNet
-  Metadata:
-    backbone: R-50
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 42.74
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 8.1
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 40.7
-      mIoU(ms+flip): 41.81
-  Config: configs/upernet/upernet_r50_4xb4-80k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_80k_ade20k/upernet_r50_512x512_80k_ade20k_20200614_144127-ecc8377b.pth
-- Name: upernet_r101_4xb4-80k_ade20k-512x512
-  In Collection: UPerNet
-  Metadata:
-    backbone: R-101
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 49.16
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 9.1
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 42.91
-      mIoU(ms+flip): 43.96
-  Config: configs/upernet/upernet_r101_4xb4-80k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_80k_ade20k/upernet_r101_512x512_80k_ade20k_20200614_185117-32e4db94.pth
-- Name: upernet_r50_4xb4-160k_ade20k-512x512
-  In Collection: UPerNet
-  Metadata:
-    backbone: R-50
-    crop size: (512,512)
-    lr schd: 160000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 42.05
-      mIoU(ms+flip): 42.78
-  Config: configs/upernet/upernet_r50_4xb4-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_160k_ade20k/upernet_r50_512x512_160k_ade20k_20200615_184328-8534de8d.pth
-- Name: upernet_r101_4xb4-160k_ade20k-512x512
-  In Collection: UPerNet
-  Metadata:
-    backbone: R-101
-    crop size: (512,512)
-    lr schd: 160000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 43.82
-      mIoU(ms+flip): 44.85
-  Config: configs/upernet/upernet_r101_4xb4-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_160k_ade20k/upernet_r101_512x512_160k_ade20k_20200615_161951-91b32684.pth
-- Name: upernet_r50_4xb4-20k_voc12aug-512x512
-  In Collection: UPerNet
-  Metadata:
-    backbone: R-50
-    crop size: (512,512)
-    lr schd: 20000
-    inference time (ms/im):
-    - value: 43.16
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 6.4
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal VOC 2012 + Aug
-    Metrics:
-      mIoU: 74.82
-      mIoU(ms+flip): 76.35
-  Config: configs/upernet/upernet_r50_4xb4-20k_voc12aug-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_20k_voc12aug/upernet_r50_512x512_20k_voc12aug_20200617_165330-5b5890a7.pth
-- Name: upernet_r101_4xb4-20k_voc12aug-512x512
-  In Collection: UPerNet
-  Metadata:
-    backbone: R-101
-    crop size: (512,512)
-    lr schd: 20000
-    inference time (ms/im):
-    - value: 50.05
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 7.5
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal VOC 2012 + Aug
-    Metrics:
-      mIoU: 77.1
-      mIoU(ms+flip): 78.29
-  Config: configs/upernet/upernet_r101_4xb4-20k_voc12aug-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_20k_voc12aug/upernet_r101_512x512_20k_voc12aug_20200617_165629-f14e7f27.pth
-- Name: upernet_r50_4xb4-40k_voc12aug-512x512
-  In Collection: UPerNet
-  Metadata:
-    backbone: R-50
-    crop size: (512,512)
-    lr schd: 40000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal VOC 2012 + Aug
-    Metrics:
-      mIoU: 75.92
-      mIoU(ms+flip): 77.44
-  Config: configs/upernet/upernet_r50_4xb4-40k_voc12aug-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_40k_voc12aug/upernet_r50_512x512_40k_voc12aug_20200613_162257-ca9bcc6b.pth
-- Name: upernet_r101_4xb4-40k_voc12aug-512x512
-  In Collection: UPerNet
-  Metadata:
-    backbone: R-101
-    crop size: (512,512)
-    lr schd: 40000
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: Pascal VOC 2012 + Aug
-    Metrics:
-      mIoU: 77.43
-      mIoU(ms+flip): 78.56
-  Config: configs/upernet/upernet_r101_4xb4-40k_voc12aug-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_40k_voc12aug/upernet_r101_512x512_40k_voc12aug_20200613_163549-e26476ac.pth
diff --git a/configs/vit/README.md b/configs/vit/README.md
index b7f242549d..26601d498f 100644
--- a/configs/vit/README.md
+++ b/configs/vit/README.md
@@ -1,6 +1,6 @@
 # Vision Transformer
 
-[An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/pdf/2010.11929.pdf)
+> [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/pdf/2010.11929.pdf)
 
 ## Introduction
 
@@ -22,17 +22,6 @@ While the Transformer architecture has become the de-facto standard for natural
 <img src="https://user-images.githubusercontent.com/24582831/142903144-f80a12cc-8698-48ab-843c-49dedf558121.png" width="70%"/>
 </div>
 
-## Citation
-
-```bibtex
-@article{dosoViTskiy2020,
-  title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
-  author={DosoViTskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and  Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil},
-  journal={arXiv preprint arXiv:2010.11929},
-  year={2020}
-}
-```
-
 ## Usage
 
 To use other repositories' pre-trained models, it is necessary to convert keys.
@@ -55,16 +44,27 @@ This script convert model from `PRETRAIN_PATH` and store the converted model in
 
 ### ADE20K
 
-| Method  | Backbone          | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                                     | download                                                                                                                                                                                                                                                                                                                   |
-| ------- | ----------------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| UPerNet | ViT-B + MLN       | 512x512   |   80000 | 9.20     | 6.94           | 47.71 |         49.51 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/vit/vit_vit-b16_mln_upernet_8xb2-80k_ade20k-512x512.py)         | [model](https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_vit-b16_mln_512x512_80k_ade20k/upernet_vit-b16_mln_512x512_80k_ade20k_20210624_130547-0403cee1.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_vit-b16_mln_512x512_80k_ade20k/20210624_130547.log.json)                |
-| UPerNet | ViT-B + MLN       | 512x512   |  160000 | 9.20     | 7.58           | 46.75 |         48.46 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/vit/vit_vit-b16_mln_upernet_8xb2-160k_ade20k-512x512.py)        | [model](https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_vit-b16_mln_512x512_160k_ade20k/upernet_vit-b16_mln_512x512_160k_ade20k_20210624_130547-852fa768.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_vit-b16_mln_512x512_160k_ade20k/20210623_192432.log.json)             |
-| UPerNet | ViT-B + LN + MLN  | 512x512   |  160000 | 9.21     | 6.82           | 47.73 |         49.95 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/vit/vit_vit-b16-ln_mln_upernet_8xb2-160k_ade20k-512x512.py)     | [model](https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_vit-b16_ln_mln_512x512_160k_ade20k/upernet_vit-b16_ln_mln_512x512_160k_ade20k_20210621_172828-f444c077.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_vit-b16_ln_mln_512x512_160k_ade20k/20210621_172828.log.json)    |
-| UPerNet | DeiT-S            | 512x512   |   80000 | 4.68     | 29.85          | 42.96 |         43.79 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/vit/vit_deit-s16_upernet_8xb2-80k_ade20k-512x512.py)            | [model](https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-s16_512x512_80k_ade20k/upernet_deit-s16_512x512_80k_ade20k_20210624_095228-afc93ec2.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-s16_512x512_80k_ade20k/20210624_095228.log.json)                         |
-| UPerNet | DeiT-S            | 512x512   |  160000 | 4.68     | 29.19          | 42.87 |         43.79 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/vit/vit_deit-s16_upernet_8xb2-160k_ade20k-512x512.py)           | [model](https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-s16_512x512_160k_ade20k/upernet_deit-s16_512x512_160k_ade20k_20210621_160903-5110d916.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-s16_512x512_160k_ade20k/20210621_160903.log.json)                      |
-| UPerNet | DeiT-S + MLN      | 512x512   |  160000 | 5.69     | 11.18          | 43.82 |         45.07 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/vit/vit_deit-s16_mln_upernet_8xb2-160k_ade20k-512x512.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-s16_mln_512x512_160k_ade20k/upernet_deit-s16_mln_512x512_160k_ade20k_20210621_161021-fb9a5dfb.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-s16_mln_512x512_160k_ade20k/20210621_161021.log.json)          |
-| UPerNet | DeiT-S + LN + MLN | 512x512   |  160000 | 5.69     | 12.39          | 43.52 |         45.01 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/vit/vit_deit-s16-ln_mln_upernet_512x512_160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-s16_ln_mln_512x512_160k_ade20k/upernet_deit-s16_ln_mln_512x512_160k_ade20k_20210621_161021-c0cd652f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-s16_ln_mln_512x512_160k_ade20k/20210621_161021.log.json) |
-| UPerNet | DeiT-B            | 512x512   |   80000 | 7.75     | 9.69           | 45.24 |         46.73 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/vit/vit_deit-b16_upernet_8xb2-80k_ade20k-512x512.py)            | [model](https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-b16_512x512_80k_ade20k/upernet_deit-b16_512x512_80k_ade20k_20210624_130529-1e090789.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-b16_512x512_80k_ade20k/20210624_130529.log.json)                         |
-| UPerNet | DeiT-B            | 512x512   |  160000 | 7.75     | 10.39          | 45.36 |         47.16 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/vit/vit_deit-b16_upernet_8xb2-160k_ade20k-512x512.py)           | [model](https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-b16_512x512_160k_ade20k/upernet_deit-b16_512x512_160k_ade20k_20210621_180100-828705d7.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-b16_512x512_160k_ade20k/20210621_180100.log.json)                      |
-| UPerNet | DeiT-B + MLN      | 512x512   |  160000 | 9.21     | 7.78           | 45.46 |         47.16 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/vit/vit_deit-b16_mln_upernet_8xb2-160k_ade20k-512x512.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-b16_mln_512x512_160k_ade20k/upernet_deit-b16_mln_512x512_160k_ade20k_20210621_191949-4e1450f3.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-b16_mln_512x512_160k_ade20k/20210621_191949.log.json)          |
-| UPerNet | DeiT-B + LN + MLN | 512x512   |  160000 | 9.21     | 7.75           | 45.37 |         47.23 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/vit/vit_deit-b16-ln_mln_upernet_8xb2-160k_ade20k-512x512.py)    | [model](https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-b16_ln_mln_512x512_160k_ade20k/upernet_deit-b16_ln_mln_512x512_160k_ade20k_20210623_153535-8a959c14.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-b16_ln_mln_512x512_160k_ade20k/20210623_153535.log.json) |
+| Method  | Backbone          | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device |  mIoU | mIoU(ms+flip) | config                                                                                                                                  | download                                                                                                                                                                                                                                                                                                                   |
+| ------- | ----------------- | --------- | ------: | -------- | -------------- | ------ | ----: | ------------: | --------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| UPerNet | ViT-B + MLN       | 512x512   |   80000 | 9.20     | 6.94           | V100   | 47.71 |         49.51 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/vit/vit_vit-b16_mln_upernet_8xb2-80k_ade20k-512x512.py)      | [model](https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_vit-b16_mln_512x512_80k_ade20k/upernet_vit-b16_mln_512x512_80k_ade20k_20210624_130547-0403cee1.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_vit-b16_mln_512x512_80k_ade20k/20210624_130547.log.json)                |
+| UPerNet | ViT-B + MLN       | 512x512   |  160000 | 9.20     | 7.58           | V100   | 46.75 |         48.46 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/vit/vit_vit-b16_mln_upernet_8xb2-160k_ade20k-512x512.py)     | [model](https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_vit-b16_mln_512x512_160k_ade20k/upernet_vit-b16_mln_512x512_160k_ade20k_20210624_130547-852fa768.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_vit-b16_mln_512x512_160k_ade20k/20210623_192432.log.json)             |
+| UPerNet | ViT-B + LN + MLN  | 512x512   |  160000 | 9.21     | 6.82           | V100   | 47.73 |         49.95 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/vit/vit_vit-b16-ln_mln_upernet_8xb2-160k_ade20k-512x512.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_vit-b16_ln_mln_512x512_160k_ade20k/upernet_vit-b16_ln_mln_512x512_160k_ade20k_20210621_172828-f444c077.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_vit-b16_ln_mln_512x512_160k_ade20k/20210621_172828.log.json)    |
+| UPerNet | DeiT-S            | 512x512   |   80000 | 4.68     | 29.85          | V100   | 42.96 |         43.79 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/vit/vit_deit-s16_upernet_8xb2-80k_ade20k-512x512.py)         | [model](https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-s16_512x512_80k_ade20k/upernet_deit-s16_512x512_80k_ade20k_20210624_095228-afc93ec2.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-s16_512x512_80k_ade20k/20210624_095228.log.json)                         |
+| UPerNet | DeiT-S            | 512x512   |  160000 | 4.68     | 29.19          | V100   | 42.87 |         43.79 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/vit/vit_deit-s16_upernet_8xb2-160k_ade20k-512x512.py)        | [model](https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-s16_512x512_160k_ade20k/upernet_deit-s16_512x512_160k_ade20k_20210621_160903-5110d916.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-s16_512x512_160k_ade20k/20210621_160903.log.json)                      |
+| UPerNet | DeiT-S + MLN      | 512x512   |  160000 | 5.69     | 11.18          | V100   | 43.82 |         45.07 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/vit/vit_deit-s16_mln_upernet_8xb2-160k_ade20k-512x512.py)    | [model](https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-s16_mln_512x512_160k_ade20k/upernet_deit-s16_mln_512x512_160k_ade20k_20210621_161021-fb9a5dfb.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-s16_mln_512x512_160k_ade20k/20210621_161021.log.json)          |
+| UPerNet | DeiT-S + LN + MLN | 512x512   |  160000 | 5.69     | 12.39          | V100   | 43.52 |         45.01 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/vit/vit_deit-s16-ln_mln_upernet_8xb2-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-s16_ln_mln_512x512_160k_ade20k/upernet_deit-s16_ln_mln_512x512_160k_ade20k_20210621_161021-c0cd652f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-s16_ln_mln_512x512_160k_ade20k/20210621_161021.log.json) |
+| UPerNet | DeiT-B            | 512x512   |   80000 | 7.75     | 9.69           | V100   | 45.24 |         46.73 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/vit/vit_deit-b16_upernet_8xb2-80k_ade20k-512x512.py)         | [model](https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-b16_512x512_80k_ade20k/upernet_deit-b16_512x512_80k_ade20k_20210624_130529-1e090789.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-b16_512x512_80k_ade20k/20210624_130529.log.json)                         |
+| UPerNet | DeiT-B            | 512x512   |  160000 | 7.75     | 10.39          | V100   | 45.36 |         47.16 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/vit/vit_deit-b16_upernet_8xb2-160k_ade20k-512x512.py)        | [model](https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-b16_512x512_160k_ade20k/upernet_deit-b16_512x512_160k_ade20k_20210621_180100-828705d7.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-b16_512x512_160k_ade20k/20210621_180100.log.json)                      |
+| UPerNet | DeiT-B + MLN      | 512x512   |  160000 | 9.21     | 7.78           | V100   | 45.46 |         47.16 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/vit/vit_deit-b16_mln_upernet_8xb2-160k_ade20k-512x512.py)    | [model](https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-b16_mln_512x512_160k_ade20k/upernet_deit-b16_mln_512x512_160k_ade20k_20210621_191949-4e1450f3.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-b16_mln_512x512_160k_ade20k/20210621_191949.log.json)          |
+| UPerNet | DeiT-B + LN + MLN | 512x512   |  160000 | 9.21     | 7.75           | V100   | 45.37 |         47.23 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/vit/vit_deit-b16-ln_mln_upernet_8xb2-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-b16_ln_mln_512x512_160k_ade20k/upernet_deit-b16_ln_mln_512x512_160k_ade20k_20210623_153535-8a959c14.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-b16_ln_mln_512x512_160k_ade20k/20210623_153535.log.json) |
+
+## Citation
+
+```bibtex
+@article{dosoViTskiy2020,
+  title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
+  author={DosoViTskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and  Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil},
+  journal={arXiv preprint arXiv:2010.11929},
+  year={2020}
+}
+```
diff --git a/configs/vit/metafile.yaml b/configs/vit/metafile.yaml
new file mode 100644
index 0000000000..68e254a5f9
--- /dev/null
+++ b/configs/vit/metafile.yaml
@@ -0,0 +1,265 @@
+Models:
+- Name: vit_vit-b16_mln_upernet_8xb2-80k_ade20k-512x512
+  In Collection: UPerNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 47.71
+      mIoU(ms+flip): 49.51
+  Config: configs/vit/vit_vit-b16_mln_upernet_8xb2-80k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - ViT-B
+    - UPerNet
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 9.2
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_vit-b16_mln_512x512_80k_ade20k/upernet_vit-b16_mln_512x512_80k_ade20k_20210624_130547-0403cee1.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_vit-b16_mln_512x512_80k_ade20k/20210624_130547.log.json
+  Paper:
+    Title: 'An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale'
+    URL: https://arxiv.org/pdf/2010.11929.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/vit.py#L98
+  Framework: PyTorch
+- Name: vit_vit-b16_mln_upernet_8xb2-160k_ade20k-512x512
+  In Collection: UPerNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 46.75
+      mIoU(ms+flip): 48.46
+  Config: configs/vit/vit_vit-b16_mln_upernet_8xb2-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - ViT-B
+    - UPerNet
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 9.2
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_vit-b16_mln_512x512_160k_ade20k/upernet_vit-b16_mln_512x512_160k_ade20k_20210624_130547-852fa768.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_vit-b16_mln_512x512_160k_ade20k/20210623_192432.log.json
+  Paper:
+    Title: 'An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale'
+    URL: https://arxiv.org/pdf/2010.11929.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/vit.py#L98
+  Framework: PyTorch
+- Name: vit_vit-b16-ln_mln_upernet_8xb2-160k_ade20k-512x512
+  In Collection: UPerNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 47.73
+      mIoU(ms+flip): 49.95
+  Config: configs/vit/vit_vit-b16-ln_mln_upernet_8xb2-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - ViT-B
+    - UPerNet
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 9.21
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_vit-b16_ln_mln_512x512_160k_ade20k/upernet_vit-b16_ln_mln_512x512_160k_ade20k_20210621_172828-f444c077.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_vit-b16_ln_mln_512x512_160k_ade20k/20210621_172828.log.json
+  Paper:
+    Title: 'An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale'
+    URL: https://arxiv.org/pdf/2010.11929.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/vit.py#L98
+  Framework: PyTorch
+- Name: vit_deit-s16_upernet_8xb2-80k_ade20k-512x512
+  In Collection: UPerNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 42.96
+      mIoU(ms+flip): 43.79
+  Config: configs/vit/vit_deit-s16_upernet_8xb2-80k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - DeiT-S
+    - UPerNet
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 4.68
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-s16_512x512_80k_ade20k/upernet_deit-s16_512x512_80k_ade20k_20210624_095228-afc93ec2.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-s16_512x512_80k_ade20k/20210624_095228.log.json
+  Paper:
+    Title: 'An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale'
+    URL: https://arxiv.org/pdf/2010.11929.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/vit.py#L98
+  Framework: PyTorch
+- Name: vit_deit-s16_upernet_8xb2-160k_ade20k-512x512
+  In Collection: UPerNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 42.87
+      mIoU(ms+flip): 43.79
+  Config: configs/vit/vit_deit-s16_upernet_8xb2-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - DeiT-S
+    - UPerNet
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 4.68
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-s16_512x512_160k_ade20k/upernet_deit-s16_512x512_160k_ade20k_20210621_160903-5110d916.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-s16_512x512_160k_ade20k/20210621_160903.log.json
+  Paper:
+    Title: 'An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale'
+    URL: https://arxiv.org/pdf/2010.11929.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/vit.py#L98
+  Framework: PyTorch
+- Name: vit_deit-s16_mln_upernet_8xb2-160k_ade20k-512x512
+  In Collection: UPerNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 43.82
+      mIoU(ms+flip): 45.07
+  Config: configs/vit/vit_deit-s16_mln_upernet_8xb2-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - DeiT-S
+    - UPerNet
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 5.69
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-s16_mln_512x512_160k_ade20k/upernet_deit-s16_mln_512x512_160k_ade20k_20210621_161021-fb9a5dfb.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-s16_mln_512x512_160k_ade20k/20210621_161021.log.json
+  Paper:
+    Title: 'An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale'
+    URL: https://arxiv.org/pdf/2010.11929.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/vit.py#L98
+  Framework: PyTorch
+- Name: vit_deit-s16-ln_mln_upernet_8xb2-160k_ade20k-512x512
+  In Collection: UPerNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 43.52
+      mIoU(ms+flip): 45.01
+  Config: configs/vit/vit_deit-s16-ln_mln_upernet_8xb2-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - DeiT-S
+    - UPerNet
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 5.69
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-s16_ln_mln_512x512_160k_ade20k/upernet_deit-s16_ln_mln_512x512_160k_ade20k_20210621_161021-c0cd652f.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-s16_ln_mln_512x512_160k_ade20k/20210621_161021.log.json
+  Paper:
+    Title: 'An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale'
+    URL: https://arxiv.org/pdf/2010.11929.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/vit.py#L98
+  Framework: PyTorch
+- Name: vit_deit-b16_upernet_8xb2-80k_ade20k-512x512
+  In Collection: UPerNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 45.24
+      mIoU(ms+flip): 46.73
+  Config: configs/vit/vit_deit-b16_upernet_8xb2-80k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - DeiT-B
+    - UPerNet
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 7.75
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-b16_512x512_80k_ade20k/upernet_deit-b16_512x512_80k_ade20k_20210624_130529-1e090789.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-b16_512x512_80k_ade20k/20210624_130529.log.json
+  Paper:
+    Title: 'An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale'
+    URL: https://arxiv.org/pdf/2010.11929.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/vit.py#L98
+  Framework: PyTorch
+- Name: vit_deit-b16_upernet_8xb2-160k_ade20k-512x512
+  In Collection: UPerNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 45.36
+      mIoU(ms+flip): 47.16
+  Config: configs/vit/vit_deit-b16_upernet_8xb2-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - DeiT-B
+    - UPerNet
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 7.75
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-b16_512x512_160k_ade20k/upernet_deit-b16_512x512_160k_ade20k_20210621_180100-828705d7.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-b16_512x512_160k_ade20k/20210621_180100.log.json
+  Paper:
+    Title: 'An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale'
+    URL: https://arxiv.org/pdf/2010.11929.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/vit.py#L98
+  Framework: PyTorch
+- Name: vit_deit-b16_mln_upernet_8xb2-160k_ade20k-512x512
+  In Collection: UPerNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 45.46
+      mIoU(ms+flip): 47.16
+  Config: configs/vit/vit_deit-b16_mln_upernet_8xb2-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - DeiT-B
+    - UPerNet
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 9.21
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-b16_mln_512x512_160k_ade20k/upernet_deit-b16_mln_512x512_160k_ade20k_20210621_191949-4e1450f3.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-b16_mln_512x512_160k_ade20k/20210621_191949.log.json
+  Paper:
+    Title: 'An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale'
+    URL: https://arxiv.org/pdf/2010.11929.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/vit.py#L98
+  Framework: PyTorch
+- Name: vit_deit-b16-ln_mln_upernet_8xb2-160k_ade20k-512x512
+  In Collection: UPerNet
+  Results:
+    Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 45.37
+      mIoU(ms+flip): 47.23
+  Config: configs/vit/vit_deit-b16-ln_mln_upernet_8xb2-160k_ade20k-512x512.py
+  Metadata:
+    Training Data: ADE20K
+    Batch Size: 16
+    Architecture:
+    - DeiT-B
+    - UPerNet
+    Training Resources: 8x V100 GPUS
+    Memory (GB): 9.21
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-b16_ln_mln_512x512_160k_ade20k/upernet_deit-b16_ln_mln_512x512_160k_ade20k_20210623_153535-8a959c14.pth
+  Training log: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-b16_ln_mln_512x512_160k_ade20k/20210623_153535.log.json
+  Paper:
+    Title: 'An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale'
+    URL: https://arxiv.org/pdf/2010.11929.pdf
+  Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/vit.py#L98
+  Framework: PyTorch
diff --git a/configs/vit/vit.yml b/configs/vit/vit.yml
deleted file mode 100644
index 613d866ac4..0000000000
--- a/configs/vit/vit.yml
+++ /dev/null
@@ -1,243 +0,0 @@
-Models:
-- Name: vit_vit-b16_mln_upernet_8xb2-80k_ade20k-512x512
-  In Collection: UPerNet
-  Metadata:
-    backbone: ViT-B + MLN
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 144.09
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 9.2
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 47.71
-      mIoU(ms+flip): 49.51
-  Config: configs/vit/vit_vit-b16_mln_upernet_8xb2-80k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_vit-b16_mln_512x512_80k_ade20k/upernet_vit-b16_mln_512x512_80k_ade20k_20210624_130547-0403cee1.pth
-- Name: vit_vit-b16_mln_upernet_8xb2-160k_ade20k-512x512
-  In Collection: UPerNet
-  Metadata:
-    backbone: ViT-B + MLN
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 131.93
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 9.2
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 46.75
-      mIoU(ms+flip): 48.46
-  Config: configs/vit/vit_vit-b16_mln_upernet_8xb2-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_vit-b16_mln_512x512_160k_ade20k/upernet_vit-b16_mln_512x512_160k_ade20k_20210624_130547-852fa768.pth
-- Name: vit_vit-b16-ln_mln_upernet_8xb2-160k_ade20k-512x512
-  In Collection: UPerNet
-  Metadata:
-    backbone: ViT-B + LN + MLN
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 146.63
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 9.21
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 47.73
-      mIoU(ms+flip): 49.95
-  Config: configs/vit/vit_vit-b16-ln_mln_upernet_8xb2-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_vit-b16_ln_mln_512x512_160k_ade20k/upernet_vit-b16_ln_mln_512x512_160k_ade20k_20210621_172828-f444c077.pth
-- Name: vit_deit-s16_upernet_8xb2-80k_ade20k-512x512
-  In Collection: UPerNet
-  Metadata:
-    backbone: DeiT-S
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 33.5
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 4.68
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 42.96
-      mIoU(ms+flip): 43.79
-  Config: configs/vit/vit_deit-s16_upernet_8xb2-80k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-s16_512x512_80k_ade20k/upernet_deit-s16_512x512_80k_ade20k_20210624_095228-afc93ec2.pth
-- Name: vit_deit-s16_upernet_8xb2-160k_ade20k-512x512
-  In Collection: UPerNet
-  Metadata:
-    backbone: DeiT-S
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 34.26
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 4.68
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 42.87
-      mIoU(ms+flip): 43.79
-  Config: configs/vit/vit_deit-s16_upernet_8xb2-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-s16_512x512_160k_ade20k/upernet_deit-s16_512x512_160k_ade20k_20210621_160903-5110d916.pth
-- Name: vit_deit-s16_mln_upernet_8xb2-160k_ade20k-512x512
-  In Collection: UPerNet
-  Metadata:
-    backbone: DeiT-S + MLN
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 89.45
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 5.69
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 43.82
-      mIoU(ms+flip): 45.07
-  Config: configs/vit/vit_deit-s16_mln_upernet_8xb2-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-s16_mln_512x512_160k_ade20k/upernet_deit-s16_mln_512x512_160k_ade20k_20210621_161021-fb9a5dfb.pth
-- Name: vit_deit-s16-ln_mln_upernet_512x512_160k_ade20k-512x512
-  In Collection: UPerNet
-  Metadata:
-    backbone: DeiT-S + LN + MLN
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 80.71
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 5.69
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 43.52
-      mIoU(ms+flip): 45.01
-  Config: configs/vit/vit_deit-s16-ln_mln_upernet_512x512_160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-s16_ln_mln_512x512_160k_ade20k/upernet_deit-s16_ln_mln_512x512_160k_ade20k_20210621_161021-c0cd652f.pth
-- Name: vit_deit-b16_upernet_8xb2-80k_ade20k-512x512
-  In Collection: UPerNet
-  Metadata:
-    backbone: DeiT-B
-    crop size: (512,512)
-    lr schd: 80000
-    inference time (ms/im):
-    - value: 103.2
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 7.75
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 45.24
-      mIoU(ms+flip): 46.73
-  Config: configs/vit/vit_deit-b16_upernet_8xb2-80k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-b16_512x512_80k_ade20k/upernet_deit-b16_512x512_80k_ade20k_20210624_130529-1e090789.pth
-- Name: vit_deit-b16_upernet_8xb2-160k_ade20k-512x512
-  In Collection: UPerNet
-  Metadata:
-    backbone: DeiT-B
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 96.25
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 7.75
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 45.36
-      mIoU(ms+flip): 47.16
-  Config: configs/vit/vit_deit-b16_upernet_8xb2-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-b16_512x512_160k_ade20k/upernet_deit-b16_512x512_160k_ade20k_20210621_180100-828705d7.pth
-- Name: vit_deit-b16_mln_upernet_8xb2-160k_ade20k-512x512
-  In Collection: UPerNet
-  Metadata:
-    backbone: DeiT-B + MLN
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 128.53
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 9.21
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 45.46
-      mIoU(ms+flip): 47.16
-  Config: configs/vit/vit_deit-b16_mln_upernet_8xb2-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-b16_mln_512x512_160k_ade20k/upernet_deit-b16_mln_512x512_160k_ade20k_20210621_191949-4e1450f3.pth
-- Name: vit_deit-b16-ln_mln_upernet_8xb2-160k_ade20k-512x512
-  In Collection: UPerNet
-  Metadata:
-    backbone: DeiT-B + LN + MLN
-    crop size: (512,512)
-    lr schd: 160000
-    inference time (ms/im):
-    - value: 129.03
-      hardware: V100
-      backend: PyTorch
-      batch size: 1
-      mode: FP32
-      resolution: (512,512)
-    Training Memory (GB): 9.21
-  Results:
-  - Task: Semantic Segmentation
-    Dataset: ADE20K
-    Metrics:
-      mIoU: 45.37
-      mIoU(ms+flip): 47.23
-  Config: configs/vit/vit_deit-b16-ln_mln_upernet_8xb2-160k_ade20k-512x512.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-b16_ln_mln_512x512_160k_ade20k/upernet_deit-b16_ln_mln_512x512_160k_ade20k_20210623_153535-8a959c14.pth
diff --git a/configs/vit/vit_deit-s16-ln_mln_upernet_512x512_160k_ade20k-512x512.py b/configs/vit/vit_deit-s16-ln_mln_upernet_8xb2-160k_ade20k-512x512.py
similarity index 100%
rename from configs/vit/vit_deit-s16-ln_mln_upernet_512x512_160k_ade20k-512x512.py
rename to configs/vit/vit_deit-s16-ln_mln_upernet_8xb2-160k_ade20k-512x512.py
diff --git a/model-index.yml b/model-index.yml
index 130031a303..5e87c386dd 100644
--- a/model-index.yml
+++ b/model-index.yml
@@ -1,50 +1,50 @@
 Import:
-- configs/ann/ann.yml
-- configs/apcnet/apcnet.yml
-- configs/beit/beit.yml
-- configs/bisenetv1/bisenetv1.yml
-- configs/bisenetv2/bisenetv2.yml
-- configs/ccnet/ccnet.yml
-- configs/cgnet/cgnet.yml
-- configs/convnext/convnext.yml
-- configs/danet/danet.yml
-- configs/deeplabv3/deeplabv3.yml
-- configs/deeplabv3plus/deeplabv3plus.yml
-- configs/dmnet/dmnet.yml
-- configs/dnlnet/dnlnet.yml
-- configs/dpt/dpt.yml
-- configs/emanet/emanet.yml
-- configs/encnet/encnet.yml
-- configs/erfnet/erfnet.yml
-- configs/fastfcn/fastfcn.yml
-- configs/fastscnn/fastscnn.yml
-- configs/fcn/fcn.yml
-- configs/gcnet/gcnet.yml
-- configs/hrnet/hrnet.yml
-- configs/icnet/icnet.yml
-- configs/isanet/isanet.yml
-- configs/knet/knet.yml
-- configs/mae/mae.yml
-- configs/mask2former/mask2former.yml
-- configs/maskformer/maskformer.yml
-- configs/mobilenet_v2/mobilenet_v2.yml
-- configs/mobilenet_v3/mobilenet_v3.yml
-- configs/nonlocal_net/nonlocal_net.yml
-- configs/ocrnet/ocrnet.yml
-- configs/pidnet/pidnet.yml
-- configs/point_rend/point_rend.yml
-- configs/poolformer/poolformer.yml
-- configs/psanet/psanet.yml
-- configs/pspnet/pspnet.yml
-- configs/resnest/resnest.yml
-- configs/segformer/segformer.yml
-- configs/segmenter/segmenter.yml
-- configs/segnext/segnext.yml
-- configs/sem_fpn/sem_fpn.yml
-- configs/setr/setr.yml
-- configs/stdc/stdc.yml
-- configs/swin/swin.yml
-- configs/twins/twins.yml
-- configs/unet/unet.yml
-- configs/upernet/upernet.yml
-- configs/vit/vit.yml
+- configs/ann/metafile.yaml
+- configs/apcnet/metafile.yaml
+- configs/beit/metafile.yaml
+- configs/bisenetv1/metafile.yaml
+- configs/bisenetv2/metafile.yaml
+- configs/ccnet/metafile.yaml
+- configs/cgnet/metafile.yaml
+- configs/convnext/metafile.yaml
+- configs/danet/metafile.yaml
+- configs/deeplabv3/metafile.yaml
+- configs/deeplabv3plus/metafile.yaml
+- configs/dmnet/metafile.yaml
+- configs/dnlnet/metafile.yaml
+- configs/dpt/metafile.yaml
+- configs/emanet/metafile.yaml
+- configs/encnet/metafile.yaml
+- configs/erfnet/metafile.yaml
+- configs/fastfcn/metafile.yaml
+- configs/fastscnn/metafile.yaml
+- configs/fcn/metafile.yaml
+- configs/gcnet/metafile.yaml
+- configs/hrnet/metafile.yaml
+- configs/icnet/metafile.yaml
+- configs/isanet/metafile.yaml
+- configs/knet/metafile.yaml
+- configs/mae/metafile.yaml
+- configs/mask2former/metafile.yaml
+- configs/maskformer/metafile.yaml
+- configs/mobilenet_v2/metafile.yaml
+- configs/mobilenet_v3/metafile.yaml
+- configs/nonlocal_net/metafile.yaml
+- configs/ocrnet/metafile.yaml
+- configs/pidnet/metafile.yaml
+- configs/point_rend/metafile.yaml
+- configs/poolformer/metafile.yaml
+- configs/psanet/metafile.yaml
+- configs/pspnet/metafile.yaml
+- configs/resnest/metafile.yaml
+- configs/segformer/metafile.yaml
+- configs/segmenter/metafile.yaml
+- configs/segnext/metafile.yaml
+- configs/sem_fpn/metafile.yaml
+- configs/setr/metafile.yaml
+- configs/stdc/metafile.yaml
+- configs/swin/metafile.yaml
+- configs/twins/metafile.yaml
+- configs/unet/metafile.yaml
+- configs/upernet/metafile.yaml
+- configs/vit/metafile.yaml