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diff --git a/‎.gitignore
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+146
diff --git a/‎README.md
+78-2 b/‎README.md
+78-2
diff --git a/‎docs/AWESOME_REID.md
+47 b/‎docs/AWESOME_REID.md
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diff --git a/‎docs/MODEL_ZOO.md
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diff --git a/‎docs/Makefile
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+19
@@ -0,0 +1,146 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+torchreid.egg-info/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+
+.idea/
+hhh/
+imgs/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+
+# ReID
+reid-data/
+log/
+saved-models/
+model-zoo/
+debug.py
+
+# Cython eval code
+*.c
+*.html
+
+# OS X
+.DS_Store
+.Spotlight-V100
+.Trashes
+._*
@@ -1,2 +1,78 @@
-PyTorch code for CVPR'2020 paper “Pose-guided Visible Part Matching for Occluded Person ReID”
-Code will be release soon.
+# CVPR2020 Pose-guided Visible Part Matching for Occluded Person ReID
+This is the pytorch implementation of the CVPR2020 paper *"Pose-guided Visible Part Matching for Occluded Person ReID"*
+
+## Dependencies
+-Python2.7\
+-Pytorch 0.4\
+-Numpy
+
+## Related Project
+Our code is based on [deep-person-reid](https://github.com/KaiyangZhou/deep-person-reid). We adopt [openpose](https://github.com/CMU-Perceptual-Computing-Lab/openpose) to extract pose landmarks and part affinity fields.
+
+## Dataset Preparation
+Download the raw datasets [Occluded-REID, P-DukeMTMC-reID](https://github.com/tinajia2012/ICME2018_Occluded-Person-Reidentification_datasets), and [Partial-Reid](https://pan.baidu.com/s/1VhPUVJOLvkhgbJiUoEnJWg) (code:zdl8) which is released by [Partial Person Re-identification](https://www.cv-foundation.org/openaccess/content_iccv_2015/html/Zheng_Partial_Person_Re-Identification_ICCV_2015_paper.html). Instructions regarding how to prepare [Market1501](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Zheng_Scalable_Person_Re-Identification_ICCV_2015_paper.pdf) datasets can be found [here](https://github.com/KaiyangZhou/deep-person-reid/blob/master/DATASETS.md). And then place them under the directory like:
+
+```
+PVPM_experiments/data/
+├── ICME2018_Occluded-Person-Reidentification_datasets
+│   ├── Occluded_Duke
+│   └── Occluded_REID
+├── Market-1501-v15.09.15
+└── Partial-REID_Dataset
+```
+
+## Pose extraction
+Install openopse as described [here](https://github.com/CMU-Perceptual-Computing-Lab/openpose).\
+Change path to your own dataset root and run sh files in /scripts:
+```
+sh openpose_occluded_reid.sh
+sh openpose_market.sh
+``` 
+
+## To Train PCB baseline
+
+``` 
+python scripts/main.py --root PATH_TO_DATAROOT \
+ -s market1501 -t market1501\
+ --save-dir PATH_TO_EXPERIMENT_FOLDER/market_PCB\
+ -a pcb_p6 --gpu-devices 0 --fixbase-epoch 0\
+ --open-layers classifier fc\
+ --new-layers classifier em\
+ --transforms random_flip\
+ --optim sgd --lr 0.02\
+ --stepsize 25 50\
+ --staged-lr --height 384 --width 192\
+ --batch-size 32 --base-lr-mult 0.5
+```
+## To train PVPM
+```
+python scripts/main.py --load-pose --root PATH_TO_DATAROOT
+ -s market1501\
+ -t occlusion_reid p_duke partial_reid\
+ --save-dir PATH_TO_EXPERIMENT_FOLDER/PVPM\
+ -a pose_p6s --gpu-devices 0\
+ --fixbase-epoch 30\
+ --open-layers pose_subnet\
+ --new-layers pose_subnet\
+ --transforms random_flip\
+ --optim sgd --lr 0.02\
+ --stepsize 15 25 --staged-lr\
+ --height 384 --width 128\
+ --batch-size 32\
+ --start-eval 20\
+ --eval-freq 10\
+ --load-weights PATH_TO_EXPERIMENT_FOLDER/market_PCB/model.pth.tar-60\
+ --train-sampler RandomIdentitySampler\
+ --reg-matching-score-epoch 0\
+ --graph-matching
+ --max-epoch 30
+ --part-score
+```
+
+ 
+
+
+
+
+
+
@@ -0,0 +1,47 @@
+# Awesome-ReID
+Here is a collection of ReID-related research with links to papers and code. You are welcome to submit [PR](https://help.github.com/articles/creating-a-pull-request/)s if you find something missing.
+
+## Conferences
+- **[CVPR 2019](#cvpr-2019)**
+- **[AAAI 2019](#aaai-2019)**
+- **[NeurIPS 2018](#neurips-2018)**
+- **[ECCV 2018](#eccv-2018)**
+- **[CVPR 2018](#cvpr-2018)**
+- **[ArXiv](#arxiv)**
+
+### CVPR 2019
+- Joint Discriminative and Generative Learning for Person Re-identification. [[paper](https://arxiv.org/abs/1904.07223)][[code](https://github.com/NVlabs/DG-Net)]
+- Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-identification. [[paper](https://arxiv.org/abs/1904.01990)][[code](https://github.com/zhunzhong07/ECN)]
+- Dissecting Person Re-identification from the Viewpoint of Viewpoint. [[paper](https://arxiv.org/abs/1812.02162)][[code](https://github.com/sxzrt/Dissecting-Person-Re-ID-from-the-Viewpoint-of-Viewpoint)]
+- Unsupervised Person Re-identification by Soft Multilabel Learning. [[paper](https://arxiv.org/abs/1903.06325)][[code](https://github.com/KovenYu/MAR)]
+- Patch-based Discriminative Feature Learning for Unsupervised Person Re-identification. [[paper](https://kovenyu.com/publication/2019-cvpr-pedal/)][[code](https://github.com/QizeYang/PAUL)]
+
+
+### AAAI 2019
+- Spatial and Temporal Mutual Promotion for Video-based Person Re-identification. [[paper](https://arxiv.org/abs/1812.10305)][[code](https://github.com/yolomax/person-reid-lib)]
+- Spatial-Temporal Person Re-identification. [[paper](https://arxiv.org/abs/1812.03282)][[code](https://github.com/Wanggcong/Spatial-Temporal-Re-identification)]
+- Horizontal Pyramid Matching for Person Re-identification. [[paper](https://arxiv.org/abs/1804.05275)][[code](https://github.com/OasisYang/HPM)]
+- Backbone Can Not be Trained at Once: Rolling Back to Pre-trained Network for Person Re-identification. [[paper](https://arxiv.org/abs/1901.06140)][[code](https://github.com/youngminPIL/rollback)]
+- A Bottom-Up Clustering Approach to Unsupervised Person Re-identification. [[paper](https://vana77.github.io/vana77.github.io/images/AAAI19.pdf)][[code](https://github.com/vana77/Bottom-up-Clustering-Person-Re-identification)]
+
+### NeurIPS 2018
+- FD-GAN: Pose-guided Feature Distilling GAN for Robust Person Re-identification. [[paper](https://arxiv.org/abs/1810.02936)][[code](https://github.com/yxgeee/FD-GAN)]
+
+### ECCV 2018
+- Generalizing A Person Retrieval Model Hetero- and Homogeneously. [[paper](http://openaccess.thecvf.com/content_ECCV_2018/papers/Zhun_Zhong_Generalizing_A_Person_ECCV_2018_paper.pdf)][[code](https://github.com/zhunzhong07/HHL)]
+- Pose-Normalized Image Generation for Person Re-identification. [[paper](https://arxiv.org/abs/1712.02225)][[code](https://github.com/naiq/PN_GAN)]
+
+### CVPR 2018
+- Camera Style Adaptation for Person Re-Identification. [[paper](https://arxiv.org/abs/1711.10295)][[code](https://github.com/zhunzhong07/CamStyle)]
+- Deep Group-Shuffling Random Walk for Person Re-Identification. [[paper](https://arxiv.org/abs/1807.11178)][[code](https://github.com/YantaoShen/kpm_rw_person_reid)]
+- End-to-End Deep Kronecker-Product Matching for Person Re-identification. [[paper](https://arxiv.org/abs/1807.11182)][[code](https://github.com/YantaoShen/kpm_rw_person_reid)]
+- Features for Multi-Target Multi-Camera Tracking and Re-Identification. [[paper](https://arxiv.org/abs/1803.10859)][[code](https://github.com/ergysr/DeepCC)]
+- Group Consistent Similarity Learning via Deep CRF for Person Re-Identification. [[paper](http://openaccess.thecvf.com/content_cvpr_2018/papers/Chen_Group_Consistent_Similarity_CVPR_2018_paper.pdf)][[code](https://github.com/dapengchen123/crf_affinity)]
+- Harmonious Attention Network for Person Re-Identification. [[paper](https://arxiv.org/abs/1802.08122)][[code](https://github.com/KaiyangZhou/deep-person-reid)]
+- Human Semantic Parsing for Person Re-Identification. [[paper](https://arxiv.org/abs/1804.00216)][[code](https://github.com/emrahbasaran/SPReID)]
+- Multi-Level Factorisation Net for Person Re-Identification. [[paper](https://arxiv.org/abs/1803.09132)][[code](https://github.com/KaiyangZhou/deep-person-reid)]
+-  Resource Aware Person Re-identification across Multiple Resolutions. [[paper](https://arxiv.org/abs/1805.08805)][[code](https://github.com/mileyan/DARENet)]
+- Exploit the Unknown Gradually: One-Shot Video-Based Person Re-Identification by Stepwise Learning. [[paper](https://yu-wu.net/pdf/CVPR2018_Exploit-Unknown-Gradually.pdf)][[code](https://github.com/Yu-Wu/Exploit-Unknown-Gradually)]
+
+### ArXiv
+- Revisiting Temporal Modeling for Video-based Person ReID. [[paper](https://arxiv.org/abs/1805.02104)][[code](https://github.com/jiyanggao/Video-Person-ReID)]
@@ -0,0 +1,81 @@
+# Model Zoo
+
+In general,
+- results are presented in the format of *<Rank-1 (mAP)>*, unless specified otherwise.
+- when computing model size and FLOPs, only layers that are used at test time are considered (see `torchreid.utils.compute_model_complexity`).
+- asterisk (\*) means the model is trained from scratch.
+- `combineall=True` means all images in the dataset are used for model training.
+- for the cuhk03 dataset, we use the 767/700 split by [Zhong et al. CVPR'17](https://arxiv.org/abs/1701.08398).
+- [label smoothing regularizer](https://arxiv.org/abs/1512.00567) is used in the softmax loss.
+
+
+## ImageNet pretrained models
+
+
+| Model | Download |
+| :--- | :---: |
+| shufflenet | [model](https://mega.nz/#!RDpUlQCY!tr_5xBEkelzDjveIYBBcGcovNCOrgfiJO9kiidz9fZM) |
+| mobilenetv2_x1_0 | [model](https://mega.nz/#!NKp2wAIA!1NH1pbNzY_M2hVk_hdsxNM1NUOWvvGPHhaNr-fASF6c) |
+| mobilenetv2_x1_4 | [model](https://mega.nz/#!RGhgEIwS!xN2s2ZdyqI6vQ3EwgmRXLEW3khr9tpXg96G9SUJugGk) |
+| mlfn | [model](https://mega.nz/#!YHxAhaxC!yu9E6zWl0x5zscSouTdbZu8gdFFytDdl-RAdD2DEfpk) |
+| osnet_x1_0 | [model](https://mega.nz/#!YK5GRARL!F90NsNB2XHjXGZFC3Lrw1GMic0oMw4fnfuDUnSrPAYM) |
+| osnet_x0_75 | [model](https://mega.nz/#!NPxilYBA!Se414Wtts__7eY6J5FIrowynvjUUG7a8Z5zUPfJN33s) |
+| osnet_x0_5 | [model](https://mega.nz/#!NO4ihQSJ!oMIRSZ0HlJF_8FKUbXT8Ei0vzH0xUYs5tWaf_KLrODg) |
+| osnet_x0_25 | [model](https://mega.nz/#!IDwQwaxT!TbQ_33gPK-ZchPFTf43UMc45rlNKWiWMqH4rTXB1T7k) |
+| osnet_ibn_x1_0 | [model](https://mega.nz/#!8Wo2kSDR!bNvgu4V0VkCQp_L2ZUDaudYKYRCkkSNdzcA1CcZGZTE) |
+
+
+## Same-domain ReID
+
+
+| Model | # Param (10^6) | GFLOPs | Loss | Input | Transforms | Distance | market1501  | dukemtmcreid | msmt17 |
+| :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
+| resnet50 | 23.5 | 2.7 | softmax | (256, 128) | `random_flip`, `random_crop` | `euclidean` | [87.9 (70.4)](https://mega.nz/#!FKZjVKaZ!4v_FR8pTvuHoMQIKdstJ_YCsRrtZW2hwWxc-T0JIlHE) | [78.3 (58.9)](https://mega.nz/#!JPZjCYhK!YVJbE_4vTc8DX19Rt_FB77YY4BaEA1P6Xb5sNJGep2M) | [63.2 (33.9)](https://mega.nz/#!APAxDY4Z!Iou9x8s3ATdYS2SlK2oiJbHrhvlzH7F1gE2qjM-GJGw) |
+| resnet50_fc512 | 24.6 | 4.1 | softmax | (256, 128) | `random_flip`, `random_crop` | `euclidean` | [90.8 (75.3)](https://mega.nz/#!EaZjhKyS!lBvD3vAJ4DOmElZkNa7gyPM1RE661GUd2v9kK84gSZE) | [81.0 (64.0)](https://mega.nz/#!lXYDSKZa!lumiXkY2H5Sm8gEgTWPBdWKv3ujy4zjrffjERaXkc9I) | [69.6 (38.4)](https://mega.nz/#!9PQTXIpL!iI5wgieTCn0Jm-pyg9RCu0RkH43pV3ntHhr1PeqSyT4) |
+| mlfn | 32.5 | 2.8 | softmax | (256, 128) | `random_flip`, `random_crop` | `euclidean` | [90.1 (74.3)](https://mega.nz/#!kHQ3ESLT!NoGc8eHEBZOJZM19THh3DFfRBXIPXzM-sdLmF1mvTXA) | [81.1 (63.2)](https://mega.nz/#!8PQXUCaI!mJO1vD9tI739hkNBj2QWUt0VPcZ-s89fSMMGPPP1msc) | [66.4 (37.2)](https://mega.nz/#!paIXFQCS!W3ZGkxyF1idwvQzTRDE2p0DhNDki2SBJRfp7S_Cwphk) |
+| hacnn<sup>*</sup> | 4.5 | 0.5 | softmax | (160, 64) | `random_flip`, `random_crop` | `euclidean` | [90.9 (75.6)](https://mega.nz/#!ULQXUQBK!S-8v_pR2xBD3ZpuY0I7Bqift-eX_V84gajHMDG6zUac) | [80.1 (63.2)](https://mega.nz/#!wPJTkAQR!XkKd39lsmBZMrCh3JjF6vnNafBZkouVIVdeBqQKdSzA) | [64.7 (37.2)](https://mega.nz/#!AXAziKjL!JtMwHz2UYy58gDMQLGakSmF3JOr72o8zmkqlQA-LIpQ) |
+| mobilenetv2_x1_0 | 2.2 | 0.2 | softmax | (256, 128) | `random_flip`, `random_crop` | `euclidean` | [85.6 (67.3)](https://mega.nz/#!8KYTFAIB!3dL35WQLxSoTSClDTv0kxa81k3fh5hXmAWA4_a3qiOI) | [74.2 (54.7)](https://mega.nz/#!hbRXDSCL!YYgqJ6PVUf4clgtUuK2s5FRhYJdU3yTibLscwOTNnDk) | [57.4 (29.3)](https://mega.nz/#!5SJTmCYb!ZQ8O2MN9JF4-WDAeX04Xex1KyuBYQ_o2aoMIsTgQ748) |
+| mobilenetv2_x1_4 | 4.3 | 0.4 | softmax | (256, 128) | `random_flip`, `random_crop` | `euclidean` | [87.0 (68.5)](https://mega.nz/#!4XZhEKCS!6lTuTRbHIWU5nzJzTPDGykA7sPME8_1ISGsUYFJXZWA) | [76.2 (55.8)](https://mega.nz/#!JbQVDIYQ!-7pnjIfpIDt1EoQOvpvuIEcTj3Qg8SE6o_3ZPGWrIcw) | [60.1 (31.5)](https://mega.nz/#!gOYDAQrK!sMJO7c_X4iIxoVfV_tXYdzeDJByPo5XkUjEN7Z2JTmM) |
+| osnet_x1_0 | 2.2 | 0.98 | softmax | (256, 128) | `random_flip` | `euclidean` | [94.2 (82.6)](https://mega.nz/#!hLoyTSba!fqt7GcKrHJhwe9BtuK0ozgVAQcrlMG8Pm6JsSfr5HEI) | [87.0 (70.2)](https://mega.nz/#!ETwGhQYB!h2gHN-H3J4X4WqcJXy2b0pPKl28paydkiS-PDHsEgPM) | [74.9 (43.8)](https://mega.nz/#!hWxE2aJA!NGcxu5uYH1qI6DfBTu0KFoi_NfoA0TJcBFW-g43pC0I) |
+| osnet_x0_75 | 1.3 | 0.57 | softmax | (256, 128) | `random_flip` | `euclidean` | [93.7 (81.2)](https://mega.nz/#!JO4WAaJa!nQuoqZnYfy0xu7vs2mp28AFceya-ZhrXTry837jvoDQ) | [85.8 (69.8)](https://mega.nz/#!lOgkEIoI!fQ5vuYIABIOcRxF-OK-6YxtEufWhyVkYkGB4qPoRYJ4) | [72.8 (41.4)](https://mega.nz/#!0exGXI5a!rxtzBayyRK0on0HFq9XO0UtWEBhbV86dFitljhjeWcs) |
+| osnet_x0_5 | 0.6 | 0.27 | softmax | (256, 128) | `random_flip` | `euclidean` | [92.5 (79.8)](https://mega.nz/#!QCx0RArD!hqz3Mh0Iif5d8PpQW0frxa-Tepn2a2g24aei7du4MFs) | [85.1 (67.4)](https://mega.nz/#!QTxCDIbT!eOZxj4dHl0uFnjKEB-J3YBY98blXZvppgWGA3CGa-tk) | [69.7 (37.5)](https://mega.nz/#!ETpiECDa!CCkq4JryztHqgw7spL5zDw0usJpAfEsSd5gPlkMufCc) |
+| osnet_x0_25 | 0.2 | 0.08 | softmax | (256, 128) | `random_flip` | `euclidean` | [91.2 (75.0)](https://mega.nz/#!VWxCgSqY!Q4WaQ3j9D7HMhK3jsbvMuwaZ7yBY80T2Zj5V8JAlAKU) | [82.0 (61.4)](https://mega.nz/#!5TpwnATK!UvU_Asdy_aJ9SNzuvqhEFoemxSSB8vm_Gm8Xe03jqiA) | [61.4 (29.5)](https://mega.nz/#!AWgE3SzD!DngUaNyA7VIqOd2gq10Aty_-ER0CmG0xTJLHLj6_36g) |
+
+
+## Cross-domain ReID
+
+#### Market1501 -> DukeMTMC-reID
+
+
+| Model | # Param (10^6) | GFLOPs | Loss | Input | Transforms | Distance  | Rank-1 | Rank-5 | Rank-10 | Rank-20 | mAP | Download |
+| :--- | :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
+| osnet_ibn_x1_0 | 2.2 | 0.98  | softmax | (256, 128) | `random_flip`, `color_jitter` | `euclidean` | 48.5 | 62.3 | 67.4 | 72.2 | 26.7 | [model](https://mega.nz/#!wXwGxKxK!f8EMk8hBt6AjxU3JIPGMFSMvX7j-Nt5Lp1Gpbqso1Ts) |
+
+
+#### DukeMTMC-reID -> Market1501
+
+
+| Model | # Param (10^6) | GFLOPs | Loss | Input | Transforms | Distance  | Rank-1 | Rank-5 | Rank-10 | Rank-20 | mAP | Download |
+| :--- | :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
+| osnet_ibn_x1_0 | 2.2 | 0.98  | softmax | (256, 128) | `random_flip`, `color_jitter` | `euclidean` | 57.7 | 73.7 | 80.0 | 84.8 | 26.1 | [model](https://mega.nz/#!FD4WEKJS!ZGgI-2IwVuX6re09xylChR03o6Dkjpi6KSebrbS0fAA) |
+
+
+#### MSMT17 -> Market1501, DukeMTMC-reID & CUHK03
+
+
+| Model | # Param (10^6) | GFLOPs | Loss | Input | Transforms | Distance | msmt17 -> market1501 | msmt17 -> dukemtmcreid | msmt17 -> cuhk03 | Download |
+| :--- | :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |
+| osnet_ibn_x1_0 | 2.2 | 0.98  | softmax | (256, 128) | `random_flip`, `color_jitter` | `euclidean` | 60.2 (29.9) | 59.9 (37.4) | 15.1 (14.1) | [model](https://mega.nz/#!0HgmGSwY!RoUlNAOA92epmFetZYPeyl2xOrEkUkEgcKRyluOXchM) |
+
+
+#### MSMT17 (`combineall=True`) -> Market1501, DukeMTMC-reID & CUHK03
+
+
+| Model | # Param (10^6) | GFLOPs | Loss | Input | Transforms | Distance | msmt17 -> market1501 | msmt17 -> dukemtmcreid | msmt17 -> cuhk03 | Download |
+| :--- | :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |
+| resnet50 | 23.5 | 2.7 | softmax | (256, 128) | `random_flip`, `color_jitter` | `euclidean` | 46.3 (22.8) | 52.3 (32.1) | 11.7 (13.1) | [model](https://mega.nz/#!VTpkWSbS!Y8gDnmg7u-sPwnZDhWXrtZNYOj7UYL4QzZkhDf1qWW4) |
+| osnet_x1_0 | 2.2 | 0.98 | softmax | (256, 128) | `random_flip`, `color_jitter` | `euclidean` | 66.6 (37.5) | 66.0 (45.3) | 21.0 (19.9) | [model](https://mega.nz/#!MepG3QRC!Lb-C9d7rdS_YJjGSoJ5cRlzjYcP28P_1Cm5S5WSslW0) |
+| osnet_x0_75 | 1.3 | 0.57 | softmax | (256, 128) | `random_flip`, `color_jitter` | `euclidean` | 63.6 (35.5) | 65.3 (44.5) | 20.0 (19.4) | [model](https://mega.nz/#!tO4WDagL!8Tl6kdJWRXRHQb16GeUHR008tJqW3N7_3fyVMu-LcKM) |
+| osnet_x0_5 | 0.6 | 0.27 | softmax | (256, 128) | `random_flip`, `color_jitter` | `euclidean` | 64.3 (34.9) | 65.2 (43.3) | 19.6 (19.2) | [model](https://mega.nz/#!papSWQhY!IId-QfcHj7nXQ_muUubgv9_n0SsnZzarmb5mQgcMv74) |
+| osnet_x0_25 | 0.2 | 0.08 | softmax | (256, 128) | `random_flip`, `color_jitter` | `euclidean` | 59.9 (31.0) | 61.5 (39.6) | 14.5 (14.5) | [model](https://mega.nz/#!QCoE0Kpa!BITLANumgjiR68TUFteL__N_RIoDKkL0M5Bl3Q8LC3U) |
+| osnet_ibn_x1_0 | 2.2 | 0.98 | softmax | (256, 128) | `random_flip`, `color_jitter` | `euclidean` | 66.5 (37.2) | 67.4 (45.6) | 22.0 (20.8) | [model](https://mega.nz/#!dL4Q2K5B!ZdHQ_X_rs2T-xmggigM5YvzJhmT1orkr6aQ1_fHgunM) |
@@ -0,0 +1,19 @@
+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line.
+SPHINXOPTS    =
+SPHINXBUILD   = sphinx-build
+SOURCEDIR     = .
+BUILDDIR      = _build
+
+# Put it first so that "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)