diff --git a/.dev/md2yml.py b/.dev/md2yml.py index db5d65c424..b6c9daf097 100755 --- a/.dev/md2yml.py +++ b/.dev/md2yml.py @@ -218,7 +218,7 @@ def parse_md(md_file): 'batch size': 1, 'mode': - 'FP32' if 'fp16' not in config else 'FP16', + 'FP32' if 'amp' not in config else 'AMP', 'resolution': f'({crop_size[0]},{crop_size[1]})' }] diff --git a/configs/ann/README.md b/configs/ann/README.md index ba4cfe2595..6cb8bc70b0 100644 --- a/configs/ann/README.md +++ b/configs/ann/README.md @@ -38,31 +38,31 @@ The non-local module works as a particularly useful technique for semantic segme ### 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/master/configs/ann/ann_r50-d8_512x1024_40k_cityscapes.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/master/configs/ann/ann_r101-d8_512x1024_40k_cityscapes.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/master/configs/ann/ann_r50-d8_769x769_40k_cityscapes.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/master/configs/ann/ann_r101-d8_769x769_40k_cityscapes.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/master/configs/ann/ann_r50-d8_512x1024_80k_cityscapes.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/master/configs/ann/ann_r101-d8_512x1024_80k_cityscapes.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/master/configs/ann/ann_r50-d8_769x769_80k_cityscapes.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/master/configs/ann/ann_r101-d8_769x769_80k_cityscapes.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) | +| 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/master/configs/ann/ann_r50-d8_512x512_80k_ade20k.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/master/configs/ann/ann_r101-d8_512x512_80k_ade20k.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/master/configs/ann/ann_r50-d8_512x512_160k_ade20k.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/master/configs/ann/ann_r101-d8_512x512_160k_ade20k.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) | +| 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/master/configs/ann/ann_r50-d8_512x512_20k_voc12aug.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/master/configs/ann/ann_r101-d8_512x512_20k_voc12aug.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/master/configs/ann/ann_r50-d8_512x512_40k_voc12aug.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/master/configs/ann/ann_r101-d8_512x512_40k_voc12aug.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) | +| 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 index ff6bea653b..36721992f9 100644 --- a/configs/ann/ann.yml +++ b/configs/ann/ann.yml @@ -15,7 +15,7 @@ Collections: Converted From: Code: https://github.com/MendelXu/ANN Models: -- Name: ann_r50-d8_512x1024_40k_cityscapes +- Name: ann_r50-d8_4xb2-40k_cityscapes-512x1024 In Collection: ANN Metadata: backbone: R-50-D8 @@ -35,9 +35,9 @@ Models: Metrics: mIoU: 77.4 mIoU(ms+flip): 78.57 - Config: configs/ann/ann_r50-d8_512x1024_40k_cityscapes.py + 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_512x1024_40k_cityscapes +- Name: ann_r101-d8_4xb2-40k_cityscapes-512x1024 In Collection: ANN Metadata: backbone: R-101-D8 @@ -57,9 +57,9 @@ Models: Metrics: mIoU: 76.55 mIoU(ms+flip): 78.85 - Config: configs/ann/ann_r101-d8_512x1024_40k_cityscapes.py + 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_769x769_40k_cityscapes +- Name: ann_r50-d8_4xb2-40k_cityscapes-769x769 In Collection: ANN Metadata: backbone: R-50-D8 @@ -79,9 +79,9 @@ Models: Metrics: mIoU: 78.89 mIoU(ms+flip): 80.46 - Config: configs/ann/ann_r50-d8_769x769_40k_cityscapes.py + 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_769x769_40k_cityscapes +- Name: ann_r101-d8_4xb2-40k_cityscapes-769x769 In Collection: ANN Metadata: backbone: R-101-D8 @@ -101,9 +101,9 @@ Models: Metrics: mIoU: 79.32 mIoU(ms+flip): 80.94 - Config: configs/ann/ann_r101-d8_769x769_40k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: ann_r50-d8_4xb2-80k_cityscapes-512x1024 In Collection: ANN Metadata: backbone: R-50-D8 @@ -115,9 +115,9 @@ Models: Metrics: mIoU: 77.34 mIoU(ms+flip): 78.65 - Config: configs/ann/ann_r50-d8_512x1024_80k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: ann_r101-d8_4xb2-80k_cityscapes-512x1024 In Collection: ANN Metadata: backbone: R-101-D8 @@ -129,9 +129,9 @@ Models: Metrics: mIoU: 77.14 mIoU(ms+flip): 78.81 - Config: configs/ann/ann_r101-d8_512x1024_80k_cityscapes.py + 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_769x769_80k_cityscapes +- Name: ann_r50-d8_4xb2-80k_cityscapes-769x769 In Collection: ANN Metadata: backbone: R-50-D8 @@ -143,9 +143,9 @@ Models: Metrics: mIoU: 78.88 mIoU(ms+flip): 80.57 - Config: configs/ann/ann_r50-d8_769x769_80k_cityscapes.py + 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_769x769_80k_cityscapes +- Name: ann_r101-d8_4xb2-80k_cityscapes-769x769 In Collection: ANN Metadata: backbone: R-101-D8 @@ -157,9 +157,9 @@ Models: Metrics: mIoU: 78.8 mIoU(ms+flip): 80.34 - Config: configs/ann/ann_r101-d8_769x769_80k_cityscapes.py + 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_512x512_80k_ade20k +- Name: ann_r50-d8_4xb4-80k_ade20k-512x512 In Collection: ANN Metadata: backbone: R-50-D8 @@ -179,9 +179,9 @@ Models: Metrics: mIoU: 41.01 mIoU(ms+flip): 42.3 - Config: configs/ann/ann_r50-d8_512x512_80k_ade20k.py + 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_512x512_80k_ade20k +- Name: ann_r101-d8_4xb4-80k_ade20k-512x512 In Collection: ANN Metadata: backbone: R-101-D8 @@ -201,9 +201,9 @@ Models: Metrics: mIoU: 42.94 mIoU(ms+flip): 44.18 - Config: configs/ann/ann_r101-d8_512x512_80k_ade20k.py + 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_512x512_160k_ade20k +- Name: ann_r50-d8_4xb4-160k_ade20k-512x512 In Collection: ANN Metadata: backbone: R-50-D8 @@ -215,9 +215,9 @@ Models: Metrics: mIoU: 41.74 mIoU(ms+flip): 42.62 - Config: configs/ann/ann_r50-d8_512x512_160k_ade20k.py + 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_512x512_160k_ade20k +- Name: ann_r101-d8_4xb4-160k_ade20k-512x512 In Collection: ANN Metadata: backbone: R-101-D8 @@ -229,9 +229,9 @@ Models: Metrics: mIoU: 42.94 mIoU(ms+flip): 44.06 - Config: configs/ann/ann_r101-d8_512x512_160k_ade20k.py + 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_512x512_20k_voc12aug +- Name: ann_r50-d8_4xb4-20k_voc12aug-512x512 In Collection: ANN Metadata: backbone: R-50-D8 @@ -251,9 +251,9 @@ Models: Metrics: mIoU: 74.86 mIoU(ms+flip): 76.13 - Config: configs/ann/ann_r50-d8_512x512_20k_voc12aug.py + 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_512x512_20k_voc12aug +- Name: ann_r101-d8_4xb4-20k_voc12aug-512x512 In Collection: ANN Metadata: backbone: R-101-D8 @@ -273,9 +273,9 @@ Models: Metrics: mIoU: 77.47 mIoU(ms+flip): 78.7 - Config: configs/ann/ann_r101-d8_512x512_20k_voc12aug.py + 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_512x512_40k_voc12aug +- Name: ann_r50-d8_4xb4-40k_voc12aug-512x512 In Collection: ANN Metadata: backbone: R-50-D8 @@ -287,9 +287,9 @@ Models: Metrics: mIoU: 76.56 mIoU(ms+flip): 77.51 - Config: configs/ann/ann_r50-d8_512x512_40k_voc12aug.py + 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_512x512_40k_voc12aug +- Name: ann_r101-d8_4xb4-40k_voc12aug-512x512 In Collection: ANN Metadata: backbone: R-101-D8 @@ -301,5 +301,5 @@ Models: Metrics: mIoU: 76.7 mIoU(ms+flip): 78.06 - Config: configs/ann/ann_r101-d8_512x512_40k_voc12aug.py + 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/ann_r101-d8_4xb2-40k_cityscapes-512x1024.py b/configs/ann/ann_r101-d8_4xb2-40k_cityscapes-512x1024.py new file mode 100644 index 0000000000..0da7e0b702 --- /dev/null +++ b/configs/ann/ann_r101-d8_4xb2-40k_cityscapes-512x1024.py @@ -0,0 +1,2 @@ +_base_ = './ann_r50-d8_4xb2-40k_cityscapes-512x1024.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/ann/ann_r101-d8_4xb2-40k_cityscapes-769x769.py b/configs/ann/ann_r101-d8_4xb2-40k_cityscapes-769x769.py new file mode 100644 index 0000000000..08459c0a50 --- /dev/null +++ b/configs/ann/ann_r101-d8_4xb2-40k_cityscapes-769x769.py @@ -0,0 +1,2 @@ +_base_ = './ann_r50-d8_4xb2-40k_cityscapes-769x769.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/ann/ann_r101-d8_4xb2-80k_cityscapes-512x1024.py b/configs/ann/ann_r101-d8_4xb2-80k_cityscapes-512x1024.py new file mode 100644 index 0000000000..46781fa9f2 --- /dev/null +++ b/configs/ann/ann_r101-d8_4xb2-80k_cityscapes-512x1024.py @@ -0,0 +1,2 @@ +_base_ = './ann_r50-d8_4xb2-80k_cityscapes-512x1024.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/ann/ann_r101-d8_4xb2-80k_cityscapes-769x769.py b/configs/ann/ann_r101-d8_4xb2-80k_cityscapes-769x769.py new file mode 100644 index 0000000000..c951d8704c --- /dev/null +++ b/configs/ann/ann_r101-d8_4xb2-80k_cityscapes-769x769.py @@ -0,0 +1,2 @@ +_base_ = './ann_r50-d8_4xb2-80k_cityscapes-769x769.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/ann/ann_r101-d8_512x512_20k_voc12aug.py b/configs/ann/ann_r101-d8_4xb4-160k_ade20k-512x512.py similarity index 60% rename from configs/ann/ann_r101-d8_512x512_20k_voc12aug.py rename to configs/ann/ann_r101-d8_4xb4-160k_ade20k-512x512.py index d854f2e422..9f14327542 100644 --- a/configs/ann/ann_r101-d8_512x512_20k_voc12aug.py +++ b/configs/ann/ann_r101-d8_4xb4-160k_ade20k-512x512.py @@ -1,2 +1,2 @@ -_base_ = './ann_r50-d8_512x512_20k_voc12aug.py' +_base_ = './ann_r50-d8_4xb4-160k_ade20k-512x512.py' model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/ann/ann_r101-d8_4xb4-20k_voc12aug-512x512.py b/configs/ann/ann_r101-d8_4xb4-20k_voc12aug-512x512.py new file mode 100644 index 0000000000..c3c1a3f706 --- /dev/null +++ b/configs/ann/ann_r101-d8_4xb4-20k_voc12aug-512x512.py @@ -0,0 +1,2 @@ +_base_ = './ann_r50-d8_4xb4-20k_voc12aug-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/ann/ann_r101-d8_4xb4-40k_voc12aug-512x512.py b/configs/ann/ann_r101-d8_4xb4-40k_voc12aug-512x512.py new file mode 100644 index 0000000000..c3c1a3f706 --- /dev/null +++ b/configs/ann/ann_r101-d8_4xb4-40k_voc12aug-512x512.py @@ -0,0 +1,2 @@ +_base_ = './ann_r50-d8_4xb4-20k_voc12aug-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/ann/ann_r101-d8_512x1024_40k_cityscapes.py b/configs/ann/ann_r101-d8_4xb4-80k_ade20k-512x512.py similarity index 61% rename from configs/ann/ann_r101-d8_512x1024_40k_cityscapes.py rename to configs/ann/ann_r101-d8_4xb4-80k_ade20k-512x512.py index d494e07333..3cc5b8e300 100644 --- a/configs/ann/ann_r101-d8_512x1024_40k_cityscapes.py +++ b/configs/ann/ann_r101-d8_4xb4-80k_ade20k-512x512.py @@ -1,2 +1,2 @@ -_base_ = './ann_r50-d8_512x1024_40k_cityscapes.py' +_base_ = './ann_r50-d8_4xb4-80k_ade20k-512x512.py' model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/ann/ann_r101-d8_512x512_40k_voc12aug.py b/configs/ann/ann_r101-d8_512x512_40k_voc12aug.py deleted file mode 100644 index 893c53b1ca..0000000000 --- a/configs/ann/ann_r101-d8_512x512_40k_voc12aug.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './ann_r50-d8_512x512_40k_voc12aug.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/ann/ann_r101-d8_512x512_80k_ade20k.py b/configs/ann/ann_r101-d8_512x512_80k_ade20k.py deleted file mode 100644 index a64dac670e..0000000000 --- a/configs/ann/ann_r101-d8_512x512_80k_ade20k.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './ann_r50-d8_512x512_80k_ade20k.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/ann/ann_r101-d8_769x769_40k_cityscapes.py b/configs/ann/ann_r101-d8_769x769_40k_cityscapes.py deleted file mode 100644 index 5950824849..0000000000 --- a/configs/ann/ann_r101-d8_769x769_40k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './ann_r50-d8_769x769_40k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/ann/ann_r101-d8_769x769_80k_cityscapes.py b/configs/ann/ann_r101-d8_769x769_80k_cityscapes.py deleted file mode 100644 index a9c712d1cc..0000000000 --- a/configs/ann/ann_r101-d8_769x769_80k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './ann_r50-d8_769x769_80k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/ann/ann_r50-d8_512x1024_40k_cityscapes.py b/configs/ann/ann_r50-d8_4xb2-40k_cityscapes-512x1024.py similarity index 100% rename from configs/ann/ann_r50-d8_512x1024_40k_cityscapes.py rename to configs/ann/ann_r50-d8_4xb2-40k_cityscapes-512x1024.py diff --git a/configs/ann/ann_r50-d8_769x769_40k_cityscapes.py b/configs/ann/ann_r50-d8_4xb2-40k_cityscapes-769x769.py similarity index 100% rename from configs/ann/ann_r50-d8_769x769_40k_cityscapes.py rename to configs/ann/ann_r50-d8_4xb2-40k_cityscapes-769x769.py diff --git a/configs/ann/ann_r50-d8_512x1024_80k_cityscapes.py b/configs/ann/ann_r50-d8_4xb2-80k_cityscapes-512x1024.py similarity index 100% rename from configs/ann/ann_r50-d8_512x1024_80k_cityscapes.py rename to configs/ann/ann_r50-d8_4xb2-80k_cityscapes-512x1024.py diff --git a/configs/ann/ann_r50-d8_769x769_80k_cityscapes.py b/configs/ann/ann_r50-d8_4xb2-80k_cityscapes-769x769.py similarity index 100% rename from configs/ann/ann_r50-d8_769x769_80k_cityscapes.py rename to configs/ann/ann_r50-d8_4xb2-80k_cityscapes-769x769.py diff --git a/configs/ann/ann_r50-d8_512x512_160k_ade20k.py b/configs/ann/ann_r50-d8_4xb4-160k_ade20k-512x512.py similarity index 100% rename from configs/ann/ann_r50-d8_512x512_160k_ade20k.py rename to configs/ann/ann_r50-d8_4xb4-160k_ade20k-512x512.py diff --git a/configs/ann/ann_r50-d8_512x512_20k_voc12aug.py b/configs/ann/ann_r50-d8_4xb4-20k_voc12aug-512x512.py similarity index 100% rename from configs/ann/ann_r50-d8_512x512_20k_voc12aug.py rename to configs/ann/ann_r50-d8_4xb4-20k_voc12aug-512x512.py diff --git a/configs/ann/ann_r50-d8_512x512_40k_voc12aug.py b/configs/ann/ann_r50-d8_4xb4-40k_voc12aug-512x512.py similarity index 100% rename from configs/ann/ann_r50-d8_512x512_40k_voc12aug.py rename to configs/ann/ann_r50-d8_4xb4-40k_voc12aug-512x512.py diff --git a/configs/ann/ann_r50-d8_512x512_80k_ade20k.py b/configs/ann/ann_r50-d8_4xb4-80k_ade20k-512x512.py similarity index 100% rename from configs/ann/ann_r50-d8_512x512_80k_ade20k.py rename to configs/ann/ann_r50-d8_4xb4-80k_ade20k-512x512.py diff --git a/configs/apcnet/README.md b/configs/apcnet/README.md index f101a02d1d..11fb1a1454 100644 --- a/configs/apcnet/README.md +++ b/configs/apcnet/README.md @@ -38,22 +38,22 @@ year = {2019} ### 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/master/configs/apcnet/apcnet_r50-d8_512x1024_40k_cityscapes.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/master/configs/apcnet/apcnet_r101-d8_512x1024_40k_cityscapes.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/master/configs/apcnet/apcnet_r50-d8_769x769_40k_cityscapes.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/master/configs/apcnet/apcnet_r101-d8_769x769_40k_cityscapes.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/master/configs/apcnet/apcnet_r50-d8_512x1024_80k_cityscapes.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/master/configs/apcnet/apcnet_r101-d8_512x1024_80k_cityscapes.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/master/configs/apcnet/apcnet_r50-d8_769x769_80k_cityscapes.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/master/configs/apcnet/apcnet_r101-d8_769x769_80k_cityscapes.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) | +| 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/master/configs/apcnet/apcnet_r50-d8_512x512_80k_ade20k.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/master/configs/apcnet/apcnet_r101-d8_512x512_80k_ade20k.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/master/configs/apcnet/apcnet_r50-d8_512x512_160k_ade20k.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/master/configs/apcnet/apcnet_r101-d8_512x512_160k_ade20k.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) | +| 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 index 7a453a3607..737da973d4 100644 --- a/configs/apcnet/apcnet.yml +++ b/configs/apcnet/apcnet.yml @@ -14,7 +14,7 @@ Collections: Converted From: Code: https://github.com/Junjun2016/APCNet Models: -- Name: apcnet_r50-d8_512x1024_40k_cityscapes +- Name: apcnet_r50-d8_4xb2-40k_cityscapes-512x1024 In Collection: APCNet Metadata: backbone: R-50-D8 @@ -34,9 +34,9 @@ Models: Metrics: mIoU: 78.02 mIoU(ms+flip): 79.26 - Config: configs/apcnet/apcnet_r50-d8_512x1024_40k_cityscapes.py + 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_512x1024_40k_cityscapes +- Name: apcnet_r101-d8_4xb2-40k_cityscapes-512x1024 In Collection: APCNet Metadata: backbone: R-101-D8 @@ -56,9 +56,9 @@ Models: Metrics: mIoU: 79.08 mIoU(ms+flip): 80.34 - Config: configs/apcnet/apcnet_r101-d8_512x1024_40k_cityscapes.py + 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_769x769_40k_cityscapes +- Name: apcnet_r50-d8_4xb2-40k_cityscapes-769x769 In Collection: APCNet Metadata: backbone: R-50-D8 @@ -78,9 +78,9 @@ Models: Metrics: mIoU: 77.89 mIoU(ms+flip): 79.75 - Config: configs/apcnet/apcnet_r50-d8_769x769_40k_cityscapes.py + 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_769x769_40k_cityscapes +- Name: apcnet_r101-d8_4xb2-40k_cityscapes-769x769 In Collection: APCNet Metadata: backbone: R-101-D8 @@ -100,9 +100,9 @@ Models: Metrics: mIoU: 77.96 mIoU(ms+flip): 79.24 - Config: configs/apcnet/apcnet_r101-d8_769x769_40k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: apcnet_r50-d8_4xb2-80k_cityscapes-512x1024 In Collection: APCNet Metadata: backbone: R-50-D8 @@ -114,9 +114,9 @@ Models: Metrics: mIoU: 78.96 mIoU(ms+flip): 79.94 - Config: configs/apcnet/apcnet_r50-d8_512x1024_80k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: apcnet_r101-d8_4xb2-80k_cityscapes-512x1024 In Collection: APCNet Metadata: backbone: R-101-D8 @@ -128,9 +128,9 @@ Models: Metrics: mIoU: 79.64 mIoU(ms+flip): 80.61 - Config: configs/apcnet/apcnet_r101-d8_512x1024_80k_cityscapes.py + 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_769x769_80k_cityscapes +- Name: apcnet_r50-d8_4xb2-80k_cityscapes-769x769 In Collection: APCNet Metadata: backbone: R-50-D8 @@ -142,9 +142,9 @@ Models: Metrics: mIoU: 78.79 mIoU(ms+flip): 80.35 - Config: configs/apcnet/apcnet_r50-d8_769x769_80k_cityscapes.py + 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_769x769_80k_cityscapes +- Name: apcnet_r101-d8_4xb2-80k_cityscapes-769x769 In Collection: APCNet Metadata: backbone: R-101-D8 @@ -156,9 +156,9 @@ Models: Metrics: mIoU: 78.45 mIoU(ms+flip): 79.91 - Config: configs/apcnet/apcnet_r101-d8_769x769_80k_cityscapes.py + 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_512x512_80k_ade20k +- Name: apcnet_r50-d8_4xb4-80k_ade20k-512x512 In Collection: APCNet Metadata: backbone: R-50-D8 @@ -178,9 +178,9 @@ Models: Metrics: mIoU: 42.2 mIoU(ms+flip): 43.3 - Config: configs/apcnet/apcnet_r50-d8_512x512_80k_ade20k.py + 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_512x512_80k_ade20k +- Name: apcnet_r101-d8_4xb4-80k_ade20k-512x512 In Collection: APCNet Metadata: backbone: R-101-D8 @@ -200,9 +200,9 @@ Models: Metrics: mIoU: 45.54 mIoU(ms+flip): 46.65 - Config: configs/apcnet/apcnet_r101-d8_512x512_80k_ade20k.py + 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_512x512_160k_ade20k +- Name: apcnet_r50-d8_4xb4-160k_ade20k-512x512 In Collection: APCNet Metadata: backbone: R-50-D8 @@ -214,9 +214,9 @@ Models: Metrics: mIoU: 43.4 mIoU(ms+flip): 43.94 - Config: configs/apcnet/apcnet_r50-d8_512x512_160k_ade20k.py + 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_512x512_160k_ade20k +- Name: apcnet_r101-d8_4xb4-160k_ade20k-512x512 In Collection: APCNet Metadata: backbone: R-101-D8 @@ -228,5 +228,5 @@ Models: Metrics: mIoU: 45.41 mIoU(ms+flip): 46.63 - Config: configs/apcnet/apcnet_r101-d8_512x512_160k_ade20k.py + 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/apcnet_r101-d8_4xb2-40k_cityscapes-512x1024.py b/configs/apcnet/apcnet_r101-d8_4xb2-40k_cityscapes-512x1024.py new file mode 100644 index 0000000000..754b2d1a08 --- /dev/null +++ b/configs/apcnet/apcnet_r101-d8_4xb2-40k_cityscapes-512x1024.py @@ -0,0 +1,2 @@ +_base_ = './apcnet_r50-d8_4xb2-40k_cityscapes-512x1024.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/apcnet/apcnet_r101-d8_4xb2-40k_cityscapes-769x769.py b/configs/apcnet/apcnet_r101-d8_4xb2-40k_cityscapes-769x769.py new file mode 100644 index 0000000000..d2b5fe1360 --- /dev/null +++ b/configs/apcnet/apcnet_r101-d8_4xb2-40k_cityscapes-769x769.py @@ -0,0 +1,2 @@ +_base_ = './apcnet_r50-d8_4xb2-40k_cityscapes-769x769.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/apcnet/apcnet_r101-d8_4xb2-80k_cityscapes-512x1024.py b/configs/apcnet/apcnet_r101-d8_4xb2-80k_cityscapes-512x1024.py new file mode 100644 index 0000000000..03b018d2ff --- /dev/null +++ b/configs/apcnet/apcnet_r101-d8_4xb2-80k_cityscapes-512x1024.py @@ -0,0 +1,2 @@ +_base_ = './apcnet_r50-d8_4xb2-80k_cityscapes-512x1024.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/apcnet/apcnet_r101-d8_4xb2-80k_cityscapes-769x769.py b/configs/apcnet/apcnet_r101-d8_4xb2-80k_cityscapes-769x769.py new file mode 100644 index 0000000000..0cbbfadbdd --- /dev/null +++ b/configs/apcnet/apcnet_r101-d8_4xb2-80k_cityscapes-769x769.py @@ -0,0 +1,2 @@ +_base_ = './apcnet_r50-d8_4xb2-80k_cityscapes-769x769.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/apcnet/apcnet_r101-d8_4xb4-160k_ade20k-512x512.py b/configs/apcnet/apcnet_r101-d8_4xb4-160k_ade20k-512x512.py new file mode 100644 index 0000000000..f0aacc06e0 --- /dev/null +++ b/configs/apcnet/apcnet_r101-d8_4xb4-160k_ade20k-512x512.py @@ -0,0 +1,2 @@ +_base_ = './apcnet_r50-d8_4xb4-160k_ade20k-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/apcnet/apcnet_r101-d8_4xb4-80k_ade20k-512x512.py b/configs/apcnet/apcnet_r101-d8_4xb4-80k_ade20k-512x512.py new file mode 100644 index 0000000000..219d07ae55 --- /dev/null +++ b/configs/apcnet/apcnet_r101-d8_4xb4-80k_ade20k-512x512.py @@ -0,0 +1,2 @@ +_base_ = './apcnet_r50-d8_4xb4-80k_ade20k-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/apcnet/apcnet_r101-d8_512x1024_40k_cityscapes.py b/configs/apcnet/apcnet_r101-d8_512x1024_40k_cityscapes.py deleted file mode 100644 index 1e1cec6735..0000000000 --- a/configs/apcnet/apcnet_r101-d8_512x1024_40k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './apcnet_r50-d8_512x1024_40k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/apcnet/apcnet_r101-d8_512x1024_80k_cityscapes.py b/configs/apcnet/apcnet_r101-d8_512x1024_80k_cityscapes.py deleted file mode 100644 index 04cb006ba1..0000000000 --- a/configs/apcnet/apcnet_r101-d8_512x1024_80k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './apcnet_r50-d8_512x1024_80k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/apcnet/apcnet_r101-d8_512x512_160k_ade20k.py b/configs/apcnet/apcnet_r101-d8_512x512_160k_ade20k.py deleted file mode 100644 index 1ce2279a0f..0000000000 --- a/configs/apcnet/apcnet_r101-d8_512x512_160k_ade20k.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './apcnet_r50-d8_512x512_160k_ade20k.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/apcnet/apcnet_r101-d8_512x512_80k_ade20k.py b/configs/apcnet/apcnet_r101-d8_512x512_80k_ade20k.py deleted file mode 100644 index 8f10b98406..0000000000 --- a/configs/apcnet/apcnet_r101-d8_512x512_80k_ade20k.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './apcnet_r50-d8_512x512_80k_ade20k.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/apcnet/apcnet_r101-d8_769x769_40k_cityscapes.py b/configs/apcnet/apcnet_r101-d8_769x769_40k_cityscapes.py deleted file mode 100644 index 5c44ebcaf3..0000000000 --- a/configs/apcnet/apcnet_r101-d8_769x769_40k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './apcnet_r50-d8_769x769_40k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/apcnet/apcnet_r101-d8_769x769_80k_cityscapes.py b/configs/apcnet/apcnet_r101-d8_769x769_80k_cityscapes.py deleted file mode 100644 index 616984575d..0000000000 --- a/configs/apcnet/apcnet_r101-d8_769x769_80k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './apcnet_r50-d8_769x769_80k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/apcnet/apcnet_r50-d8_512x1024_40k_cityscapes.py b/configs/apcnet/apcnet_r50-d8_4xb2-40k_cityscapes-512x1024.py similarity index 100% rename from configs/apcnet/apcnet_r50-d8_512x1024_40k_cityscapes.py rename to configs/apcnet/apcnet_r50-d8_4xb2-40k_cityscapes-512x1024.py diff --git a/configs/apcnet/apcnet_r50-d8_769x769_40k_cityscapes.py b/configs/apcnet/apcnet_r50-d8_4xb2-40k_cityscapes-769x769.py similarity index 100% rename from configs/apcnet/apcnet_r50-d8_769x769_40k_cityscapes.py rename to configs/apcnet/apcnet_r50-d8_4xb2-40k_cityscapes-769x769.py diff --git a/configs/apcnet/apcnet_r50-d8_512x1024_80k_cityscapes.py b/configs/apcnet/apcnet_r50-d8_4xb2-80k_cityscapes-512x1024.py similarity index 100% rename from configs/apcnet/apcnet_r50-d8_512x1024_80k_cityscapes.py rename to configs/apcnet/apcnet_r50-d8_4xb2-80k_cityscapes-512x1024.py diff --git a/configs/apcnet/apcnet_r50-d8_769x769_80k_cityscapes.py b/configs/apcnet/apcnet_r50-d8_4xb2-80k_cityscapes-769x769.py similarity index 100% rename from configs/apcnet/apcnet_r50-d8_769x769_80k_cityscapes.py rename to configs/apcnet/apcnet_r50-d8_4xb2-80k_cityscapes-769x769.py diff --git a/configs/apcnet/apcnet_r50-d8_512x512_160k_ade20k.py b/configs/apcnet/apcnet_r50-d8_4xb4-160k_ade20k-512x512.py similarity index 100% rename from configs/apcnet/apcnet_r50-d8_512x512_160k_ade20k.py rename to configs/apcnet/apcnet_r50-d8_4xb4-160k_ade20k-512x512.py diff --git a/configs/apcnet/apcnet_r50-d8_512x512_80k_ade20k.py b/configs/apcnet/apcnet_r50-d8_4xb4-80k_ade20k-512x512.py similarity index 100% rename from configs/apcnet/apcnet_r50-d8_512x512_80k_ade20k.py rename to configs/apcnet/apcnet_r50-d8_4xb4-80k_ade20k-512x512.py diff --git a/configs/beit/README.md b/configs/beit/README.md index 31e1bd6a83..380d788741 100644 --- a/configs/beit/README.md +++ b/configs/beit/README.md @@ -79,7 +79,7 @@ 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/master/configs/beit/upernet_beit-base_8x2_640x640_160k_ade20k.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/master/configs/beit/upernet_beit-large_fp16_8x1_640x640_160k_ade20k.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) | 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) | diff --git a/configs/beit/upernet_beit-base_8x2_640x640_160k_ade20k.py b/configs/beit/beit-base_upernet_8xb2-160k_ade20k-640x640.py similarity index 100% rename from configs/beit/upernet_beit-base_8x2_640x640_160k_ade20k.py rename to configs/beit/beit-base_upernet_8xb2-160k_ade20k-640x640.py diff --git a/configs/beit/upernet_beit-base_640x640_160k_ade20k_ms.py b/configs/beit/beit-base_upernet_8xb2-160k_ade20k-640x640_ms.py similarity index 91% rename from configs/beit/upernet_beit-base_640x640_160k_ade20k_ms.py rename to configs/beit/beit-base_upernet_8xb2-160k_ade20k-640x640_ms.py index 323cdb13be..02480222c4 100644 --- a/configs/beit/upernet_beit-base_640x640_160k_ade20k_ms.py +++ b/configs/beit/beit-base_upernet_8xb2-160k_ade20k-640x640_ms.py @@ -1,4 +1,4 @@ -_base_ = './upernet_beit-base_8x2_640x640_160k_ade20k.py' +_base_ = './beit-base_upernet_8xb2-160k_ade20k-640x640.py' test_pipeline = [ dict(type='LoadImageFromFile'), diff --git a/configs/beit/upernet_beit-large_fp16_8x1_640x640_160k_ade20k.py b/configs/beit/beit-large_upernet_8xb1-amp-160k_ade20k-640x640.py similarity index 100% rename from configs/beit/upernet_beit-large_fp16_8x1_640x640_160k_ade20k.py rename to configs/beit/beit-large_upernet_8xb1-amp-160k_ade20k-640x640.py diff --git a/configs/beit/upernet_beit-large_fp16_640x640_160k_ade20k_ms.py b/configs/beit/beit-large_upernet_8xb1-amp-160k_ade20k-640x640_ms.py similarity index 90% rename from configs/beit/upernet_beit-large_fp16_640x640_160k_ade20k_ms.py rename to configs/beit/beit-large_upernet_8xb1-amp-160k_ade20k-640x640_ms.py index 279e7ace26..fc6f049d11 100644 --- a/configs/beit/upernet_beit-large_fp16_640x640_160k_ade20k_ms.py +++ b/configs/beit/beit-large_upernet_8xb1-amp-160k_ade20k-640x640_ms.py @@ -1,4 +1,4 @@ -_base_ = './upernet_beit-large_fp16_8x1_640x640_160k_ade20k.py' +_base_ = './beit-large_upernet_8xb1-amp-160k_ade20k-640x640.py' test_pipeline = [ dict(type='LoadImageFromFile'), diff --git a/configs/beit/beit.yml b/configs/beit/beit.yml index 602a887d40..f6cc0160bf 100644 --- a/configs/beit/beit.yml +++ b/configs/beit/beit.yml @@ -1,5 +1,5 @@ Models: -- Name: upernet_beit-base_8x2_640x640_160k_ade20k +- Name: beit-base_upernet_8xb2-160k_ade20k-640x640 In Collection: UPerNet Metadata: backbone: BEiT-B @@ -19,9 +19,9 @@ Models: Metrics: mIoU: 53.08 mIoU(ms+flip): 53.84 - Config: configs/beit/upernet_beit-base_8x2_640x640_160k_ade20k.py + 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: upernet_beit-large_fp16_8x1_640x640_160k_ade20k +- Name: beit-large_upernet_8xb1-amp-160k_ade20k-640x640 In Collection: UPerNet Metadata: backbone: BEiT-L @@ -32,7 +32,7 @@ Models: hardware: V100 backend: PyTorch batch size: 1 - mode: FP16 + mode: AMP resolution: (640,640) Training Memory (GB): 22.64 Results: @@ -41,5 +41,5 @@ Models: Metrics: mIoU: 56.33 mIoU(ms+flip): 56.84 - Config: configs/beit/upernet_beit-large_fp16_8x1_640x640_160k_ade20k.py + 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/bisenetv1/README.md b/configs/bisenetv1/README.md index 58092d6bcc..72fdd474cb 100644 --- a/configs/bisenetv1/README.md +++ b/configs/bisenetv1/README.md @@ -38,24 +38,24 @@ Semantic segmentation requires both rich spatial information and sizeable recept ### 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/master/configs/bisenetv1/bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes.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/master/configs/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes.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/master/configs/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x8_1024x1024_160k_cityscapes.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/master/configs/bisenetv1/bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes.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/master/configs/bisenetv1/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes.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) | 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) | ### 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/master/configs/bisenetv1/bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.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/master/configs/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.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/master/configs/bisenetv1/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.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/master/configs/bisenetv1/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.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/master/configs/bisenetv1/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.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/master/configs/bisenetv1/bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.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) | 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) | Note: diff --git a/configs/bisenetv1/bisenetv1.yml b/configs/bisenetv1/bisenetv1.yml index 61f264b056..f5aade4de4 100644 --- a/configs/bisenetv1/bisenetv1.yml +++ b/configs/bisenetv1/bisenetv1.yml @@ -14,7 +14,7 @@ Collections: Converted From: Code: https://github.com/ycszen/TorchSeg/tree/master/model/bisenet Models: -- Name: bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes +- Name: bisenetv1_r18-d32_4xb4-160k_cityscapes-1024x1024 In Collection: BiSeNetV1 Metadata: backbone: R-18-D32 @@ -34,9 +34,9 @@ Models: Metrics: mIoU: 74.44 mIoU(ms+flip): 77.05 - Config: configs/bisenetv1/bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes.py + 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_4x4_1024x1024_160k_cityscapes +- Name: bisenetv1_r18-d32-in1k-pre_4xb4-160k_cityscapes-1024x1024 In Collection: BiSeNetV1 Metadata: backbone: R-18-D32 @@ -56,9 +56,9 @@ Models: Metrics: mIoU: 74.37 mIoU(ms+flip): 76.91 - Config: configs/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes.py + 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_4x8_1024x1024_160k_cityscapes +- Name: bisenetv1_r18-d32-in1k-pre_4xb8-160k_cityscapes-1024x1024 In Collection: BiSeNetV1 Metadata: backbone: R-18-D32 @@ -78,9 +78,9 @@ Models: Metrics: mIoU: 75.16 mIoU(ms+flip): 77.24 - Config: configs/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x8_1024x1024_160k_cityscapes.py + 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_4x4_1024x1024_160k_cityscapes +- Name: bisenetv1_r50-d32_4xb4-160k_cityscapes-1024x1024 In Collection: BiSeNetV1 Metadata: backbone: R-50-D32 @@ -100,9 +100,9 @@ Models: Metrics: mIoU: 76.92 mIoU(ms+flip): 78.87 - Config: configs/bisenetv1/bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes.py + 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_4x4_1024x1024_160k_cityscapes +- Name: bisenetv1_r50-d32-in1k-pre_4xb4-160k_cityscapes-1024x1024 In Collection: BiSeNetV1 Metadata: backbone: R-50-D32 @@ -122,9 +122,9 @@ Models: Metrics: mIoU: 77.68 mIoU(ms+flip): 79.57 - Config: configs/bisenetv1/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes.py + 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_lr5e-3_4x4_512x512_160k_coco-stuff164k +- Name: bisenetv1_r18-d32_4xb4-160k_coco-stuff164k-512x512 In Collection: BiSeNetV1 Metadata: backbone: R-18-D32 @@ -136,9 +136,9 @@ Models: Metrics: mIoU: 25.45 mIoU(ms+flip): 26.15 - Config: configs/bisenetv1/bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py + 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_lr5e-3_4x4_512x512_160k_coco-stuff164k +- Name: bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512 In Collection: BiSeNetV1 Metadata: backbone: R-18-D32 @@ -158,9 +158,9 @@ Models: Metrics: mIoU: 28.55 mIoU(ms+flip): 29.26 - Config: configs/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py + 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_lr5e-3_4x4_512x512_160k_coco-stuff164k +- Name: bisenetv1_r50-d32_4xb4-160k_coco-stuff164k-512x512 In Collection: BiSeNetV1 Metadata: backbone: R-50-D32 @@ -172,9 +172,9 @@ Models: Metrics: mIoU: 29.82 mIoU(ms+flip): 30.33 - Config: configs/bisenetv1/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py + 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_lr5e-3_4x4_512x512_160k_coco-stuff164k +- Name: bisenetv1_r50-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512 In Collection: BiSeNetV1 Metadata: backbone: R-50-D32 @@ -194,9 +194,9 @@ Models: Metrics: mIoU: 34.88 mIoU(ms+flip): 35.37 - Config: configs/bisenetv1/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py + 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_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k +- Name: bisenetv1_r50-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512 In Collection: BiSeNetV1 Metadata: backbone: R-101-D32 @@ -208,9 +208,9 @@ Models: Metrics: mIoU: 31.14 mIoU(ms+flip): 31.76 - Config: configs/bisenetv1/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py + 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_lr5e-3_4x4_512x512_160k_coco-stuff164k +- Name: bisenetv1_r101-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512 In Collection: BiSeNetV1 Metadata: backbone: R-101-D32 @@ -230,5 +230,5 @@ Models: Metrics: mIoU: 37.38 mIoU(ms+flip): 37.99 - Config: configs/bisenetv1/bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py + 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/bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py b/configs/bisenetv1/bisenetv1_r101-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py similarity index 69% rename from configs/bisenetv1/bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py rename to configs/bisenetv1/bisenetv1_r101-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py index c3fe21597d..ac63447d47 100644 --- a/configs/bisenetv1/bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py +++ b/configs/bisenetv1/bisenetv1_r101-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py @@ -1,4 +1,4 @@ -_base_ = './bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py' +_base_ = './bisenetv1_r101-d32_4xb4-160k_coco-stuff164k-512x512.py' model = dict( backbone=dict( backbone_cfg=dict( diff --git a/configs/bisenetv1/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py b/configs/bisenetv1/bisenetv1_r101-d32_4xb4-160k_coco-stuff164k-512x512.py similarity index 100% rename from configs/bisenetv1/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py rename to configs/bisenetv1/bisenetv1_r101-d32_4xb4-160k_coco-stuff164k-512x512.py diff --git a/configs/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes.py b/configs/bisenetv1/bisenetv1_r18-d32-in1k-pre_4xb4-160k_cityscapes-1024x1024.py similarity index 100% rename from configs/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes.py rename to configs/bisenetv1/bisenetv1_r18-d32-in1k-pre_4xb4-160k_cityscapes-1024x1024.py diff --git a/configs/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py b/configs/bisenetv1/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py similarity index 78% rename from configs/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py rename to configs/bisenetv1/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py index 7b686add5c..9de889f001 100644 --- a/configs/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py +++ b/configs/bisenetv1/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py @@ -1,4 +1,4 @@ -_base_ = './bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py' +_base_ = './bisenetv1_r18-d32_4xb4-160k_coco-stuff164k-512x512.py' crop_size = (512, 512) data_preprocessor = dict(size=crop_size) model = dict( diff --git a/configs/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x8_1024x1024_160k_cityscapes.py b/configs/bisenetv1/bisenetv1_r18-d32-in1k-pre_4xb8-160k_cityscapes-1024x1024.py similarity index 64% rename from configs/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x8_1024x1024_160k_cityscapes.py rename to configs/bisenetv1/bisenetv1_r18-d32-in1k-pre_4xb8-160k_cityscapes-1024x1024.py index d37b3c5d20..0580ce11e6 100644 --- a/configs/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x8_1024x1024_160k_cityscapes.py +++ b/configs/bisenetv1/bisenetv1_r18-d32-in1k-pre_4xb8-160k_cityscapes-1024x1024.py @@ -1,4 +1,4 @@ -_base_ = './bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes.py' +_base_ = './bisenetv1_r18-d32-in1k-pre_4xb4-160k_cityscapes-1024x1024.py' train_dataloader = dict(batch_size=8, num_workers=4) val_dataloader = dict(batch_size=1, num_workers=4) test_dataloader = val_dataloader diff --git a/configs/bisenetv1/bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes.py b/configs/bisenetv1/bisenetv1_r18-d32_4xb4-160k_cityscapes-1024x1024.py similarity index 100% rename from configs/bisenetv1/bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes.py rename to configs/bisenetv1/bisenetv1_r18-d32_4xb4-160k_cityscapes-1024x1024.py diff --git a/configs/bisenetv1/bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py b/configs/bisenetv1/bisenetv1_r18-d32_4xb4-160k_coco-stuff164k-512x512.py similarity index 100% rename from configs/bisenetv1/bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py rename to configs/bisenetv1/bisenetv1_r18-d32_4xb4-160k_coco-stuff164k-512x512.py diff --git a/configs/bisenetv1/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes.py b/configs/bisenetv1/bisenetv1_r50-d32-in1k-pre_4xb4-160k_cityscapes-1024x1024.py similarity index 74% rename from configs/bisenetv1/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes.py rename to configs/bisenetv1/bisenetv1_r50-d32-in1k-pre_4xb4-160k_cityscapes-1024x1024.py index 5625a76c08..013c4ff162 100644 --- a/configs/bisenetv1/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes.py +++ b/configs/bisenetv1/bisenetv1_r50-d32-in1k-pre_4xb4-160k_cityscapes-1024x1024.py @@ -1,4 +1,4 @@ -_base_ = './bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes.py' +_base_ = './bisenetv1_r50-d32_4xb4-160k_cityscapes-1024x1024.py' model = dict( type='EncoderDecoder', backbone=dict( diff --git a/configs/bisenetv1/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py b/configs/bisenetv1/bisenetv1_r50-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py similarity index 69% rename from configs/bisenetv1/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py rename to configs/bisenetv1/bisenetv1_r50-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py index f0fea69f2f..b35259c725 100644 --- a/configs/bisenetv1/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py +++ b/configs/bisenetv1/bisenetv1_r50-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py @@ -1,4 +1,4 @@ -_base_ = './bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py' +_base_ = './bisenetv1_r50-d32_4xb4-160k_coco-stuff164k-512x512.py' model = dict( backbone=dict( diff --git a/configs/bisenetv1/bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes.py b/configs/bisenetv1/bisenetv1_r50-d32_4xb4-160k_cityscapes-1024x1024.py similarity index 100% rename from configs/bisenetv1/bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes.py rename to configs/bisenetv1/bisenetv1_r50-d32_4xb4-160k_cityscapes-1024x1024.py diff --git a/configs/bisenetv1/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py b/configs/bisenetv1/bisenetv1_r50-d32_4xb4-160k_coco-stuff164k-512x512.py similarity index 100% rename from configs/bisenetv1/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py rename to configs/bisenetv1/bisenetv1_r50-d32_4xb4-160k_coco-stuff164k-512x512.py diff --git a/configs/bisenetv2/README.md b/configs/bisenetv2/README.md index 6b74b7ee41..7cde5c02c1 100644 --- a/configs/bisenetv2/README.md +++ b/configs/bisenetv2/README.md @@ -39,12 +39,12 @@ The low-level details and high-level semantics are both essential to the semanti ### 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/master/configs/bisenetv2/bisenetv2_fcn_4x4_1024x1024_160k_cityscapes.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/master/configs/bisenetv2/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes.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/master/configs/bisenetv2/bisenetv2_fcn_4x8_1024x1024_160k_cityscapes.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/master/configs/bisenetv2/bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes.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) | +| 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: diff --git a/configs/bisenetv2/bisenetv2.yml b/configs/bisenetv2/bisenetv2.yml index 455fa6c479..70c4326a55 100644 --- a/configs/bisenetv2/bisenetv2.yml +++ b/configs/bisenetv2/bisenetv2.yml @@ -12,7 +12,7 @@ Collections: 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_4x4_1024x1024_160k_cityscapes +- Name: bisenetv2_fcn_4xb4-160k_cityscapes-1024x1024 In Collection: BiSeNetV2 Metadata: backbone: BiSeNetV2 @@ -32,9 +32,9 @@ Models: Metrics: mIoU: 73.21 mIoU(ms+flip): 75.74 - Config: configs/bisenetv2/bisenetv2_fcn_4x4_1024x1024_160k_cityscapes.py + 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_ohem_4x4_1024x1024_160k_cityscapes +- Name: bisenetv2_fcn_4xb4-ohem-160k_cityscapes-1024x1024 In Collection: BiSeNetV2 Metadata: backbone: BiSeNetV2 @@ -47,9 +47,9 @@ Models: Metrics: mIoU: 73.57 mIoU(ms+flip): 75.8 - Config: configs/bisenetv2/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes.py + 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_4x8_1024x1024_160k_cityscapes +- Name: bisenetv2_fcn_4xb8-160k_cityscapes-1024x1024 In Collection: BiSeNetV2 Metadata: backbone: BiSeNetV2 @@ -62,9 +62,9 @@ Models: Metrics: mIoU: 75.76 mIoU(ms+flip): 77.79 - Config: configs/bisenetv2/bisenetv2_fcn_4x8_1024x1024_160k_cityscapes.py + 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_fp16_4x4_1024x1024_160k_cityscapes +- Name: bisenetv2_fcn_4xb4-amp-160k_cityscapes-1024x1024 In Collection: BiSeNetV2 Metadata: backbone: BiSeNetV2 @@ -75,7 +75,7 @@ Models: hardware: V100 backend: PyTorch batch size: 1 - mode: FP16 + mode: AMP resolution: (1024,1024) Training Memory (GB): 5.77 Results: @@ -84,5 +84,5 @@ Models: Metrics: mIoU: 73.07 mIoU(ms+flip): 75.13 - Config: configs/bisenetv2/bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes.py + 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/bisenetv2_fcn_4x4_1024x1024_160k_cityscapes.py b/configs/bisenetv2/bisenetv2_fcn_4xb4-160k_cityscapes-1024x1024.py similarity index 100% rename from configs/bisenetv2/bisenetv2_fcn_4x4_1024x1024_160k_cityscapes.py rename to configs/bisenetv2/bisenetv2_fcn_4xb4-160k_cityscapes-1024x1024.py diff --git a/configs/bisenetv2/bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes.py b/configs/bisenetv2/bisenetv2_fcn_4xb4-amp-160k_cityscapes-1024x1024.py similarity index 73% rename from configs/bisenetv2/bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes.py rename to configs/bisenetv2/bisenetv2_fcn_4xb4-amp-160k_cityscapes-1024x1024.py index 1bdb60b846..8ed338c00b 100644 --- a/configs/bisenetv2/bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes.py +++ b/configs/bisenetv2/bisenetv2_fcn_4xb4-amp-160k_cityscapes-1024x1024.py @@ -1,4 +1,4 @@ -_base_ = './bisenetv2_fcn_4x4_1024x1024_160k_cityscapes.py' +_base_ = './bisenetv2_fcn_4xb4-160k_cityscapes-1024x1024.py' optim_wrapper = dict( _delete_=True, type='AmpOptimWrapper', diff --git a/configs/bisenetv2/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes.py b/configs/bisenetv2/bisenetv2_fcn_4xb4-ohem-160k_cityscapes-1024x1024.py similarity index 100% rename from configs/bisenetv2/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes.py rename to configs/bisenetv2/bisenetv2_fcn_4xb4-ohem-160k_cityscapes-1024x1024.py diff --git a/configs/bisenetv2/bisenetv2_fcn_4x8_1024x1024_160k_cityscapes.py b/configs/bisenetv2/bisenetv2_fcn_4xb8-160k_cityscapes-1024x1024.py similarity index 100% rename from configs/bisenetv2/bisenetv2_fcn_4x8_1024x1024_160k_cityscapes.py rename to configs/bisenetv2/bisenetv2_fcn_4xb8-160k_cityscapes-1024x1024.py diff --git a/configs/ccnet/README.md b/configs/ccnet/README.md index 48c37a8e53..9d110f4df9 100644 --- a/configs/ccnet/README.md +++ b/configs/ccnet/README.md @@ -37,31 +37,31 @@ Contextual information is vital in visual understanding problems, such as semant ### 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/master/configs/ccnet/ccnet_r50-d8_512x1024_40k_cityscapes.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/master/configs/ccnet/ccnet_r101-d8_512x1024_40k_cityscapes.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/master/configs/ccnet/ccnet_r50-d8_769x769_40k_cityscapes.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/master/configs/ccnet/ccnet_r101-d8_769x769_40k_cityscapes.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/master/configs/ccnet/ccnet_r50-d8_512x1024_80k_cityscapes.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/master/configs/ccnet/ccnet_r101-d8_512x1024_80k_cityscapes.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/master/configs/ccnet/ccnet_r50-d8_769x769_80k_cityscapes.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/master/configs/ccnet/ccnet_r101-d8_769x769_80k_cityscapes.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) | 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) | ### 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/master/configs/ccnet/ccnet_r50-d8_512x512_80k_ade20k.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/master/configs/ccnet/ccnet_r101-d8_512x512_80k_ade20k.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/master/configs/ccnet/ccnet_r50-d8_512x512_160k_ade20k.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/master/configs/ccnet/ccnet_r101-d8_512x512_160k_ade20k.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) | 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) | ### 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/master/configs/ccnet/ccnet_r50-d8_512x512_20k_voc12aug.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/master/configs/ccnet/ccnet_r101-d8_512x512_20k_voc12aug.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/master/configs/ccnet/ccnet_r50-d8_512x512_40k_voc12aug.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/master/configs/ccnet/ccnet_r101-d8_512x512_40k_voc12aug.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) | 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) | diff --git a/configs/ccnet/ccnet.yml b/configs/ccnet/ccnet.yml index b264f2e6c2..b05863dacb 100644 --- a/configs/ccnet/ccnet.yml +++ b/configs/ccnet/ccnet.yml @@ -15,7 +15,7 @@ Collections: Converted From: Code: https://github.com/speedinghzl/CCNet Models: -- Name: ccnet_r50-d8_512x1024_40k_cityscapes +- Name: ccnet_r50-d8_4xb2-40k_cityscapes-512x1024 In Collection: CCNet Metadata: backbone: R-50-D8 @@ -35,9 +35,9 @@ Models: Metrics: mIoU: 77.76 mIoU(ms+flip): 78.87 - Config: configs/ccnet/ccnet_r50-d8_512x1024_40k_cityscapes.py + 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_512x1024_40k_cityscapes +- Name: ccnet_r101-d8_4xb2-40k_cityscapes-512x1024 In Collection: CCNet Metadata: backbone: R-101-D8 @@ -57,9 +57,9 @@ Models: Metrics: mIoU: 76.35 mIoU(ms+flip): 78.19 - Config: configs/ccnet/ccnet_r101-d8_512x1024_40k_cityscapes.py + 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_769x769_40k_cityscapes +- Name: ccnet_r50-d8_4xb2-40k_cityscapes-769x769 In Collection: CCNet Metadata: backbone: R-50-D8 @@ -79,9 +79,9 @@ Models: Metrics: mIoU: 78.46 mIoU(ms+flip): 79.93 - Config: configs/ccnet/ccnet_r50-d8_769x769_40k_cityscapes.py + 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_769x769_40k_cityscapes +- Name: ccnet_r101-d8_4xb2-40k_cityscapes-769x769 In Collection: CCNet Metadata: backbone: R-101-D8 @@ -101,9 +101,9 @@ Models: Metrics: mIoU: 76.94 mIoU(ms+flip): 78.62 - Config: configs/ccnet/ccnet_r101-d8_769x769_40k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: ccnet_r50-d8_4xb2-80k_cityscapes-512x1024 In Collection: CCNet Metadata: backbone: R-50-D8 @@ -115,9 +115,9 @@ Models: Metrics: mIoU: 79.03 mIoU(ms+flip): 80.16 - Config: configs/ccnet/ccnet_r50-d8_512x1024_80k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: ccnet_r101-d8_4xb2-80k_cityscapes-512x1024 In Collection: CCNet Metadata: backbone: R-101-D8 @@ -129,9 +129,9 @@ Models: Metrics: mIoU: 78.87 mIoU(ms+flip): 79.9 - Config: configs/ccnet/ccnet_r101-d8_512x1024_80k_cityscapes.py + 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_769x769_80k_cityscapes +- Name: ccnet_r50-d8_4xb2-80k_cityscapes-769x769 In Collection: CCNet Metadata: backbone: R-50-D8 @@ -143,9 +143,9 @@ Models: Metrics: mIoU: 79.29 mIoU(ms+flip): 81.08 - Config: configs/ccnet/ccnet_r50-d8_769x769_80k_cityscapes.py + 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_769x769_80k_cityscapes +- Name: ccnet_r101-d8_4xb2-80k_cityscapes-769x769 In Collection: CCNet Metadata: backbone: R-101-D8 @@ -157,9 +157,9 @@ Models: Metrics: mIoU: 79.45 mIoU(ms+flip): 80.66 - Config: configs/ccnet/ccnet_r101-d8_769x769_80k_cityscapes.py + 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_512x512_80k_ade20k +- Name: ccnet_r50-d8_4xb4-80k_ade20k-512x512 In Collection: CCNet Metadata: backbone: R-50-D8 @@ -179,9 +179,9 @@ Models: Metrics: mIoU: 41.78 mIoU(ms+flip): 42.98 - Config: configs/ccnet/ccnet_r50-d8_512x512_80k_ade20k.py + 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_512x512_80k_ade20k +- Name: ccnet_r101-d8_4xb4-80k_ade20k-512x512 In Collection: CCNet Metadata: backbone: R-101-D8 @@ -201,9 +201,9 @@ Models: Metrics: mIoU: 43.97 mIoU(ms+flip): 45.13 - Config: configs/ccnet/ccnet_r101-d8_512x512_80k_ade20k.py + 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_512x512_160k_ade20k +- Name: ccnet_r50-d8_4xb4-160k_ade20k-512x512 In Collection: CCNet Metadata: backbone: R-50-D8 @@ -215,9 +215,9 @@ Models: Metrics: mIoU: 42.08 mIoU(ms+flip): 43.13 - Config: configs/ccnet/ccnet_r50-d8_512x512_160k_ade20k.py + 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_512x512_160k_ade20k +- Name: ccnet_r101-d8_4xb4-160k_ade20k-512x512 In Collection: CCNet Metadata: backbone: R-101-D8 @@ -229,9 +229,9 @@ Models: Metrics: mIoU: 43.71 mIoU(ms+flip): 45.04 - Config: configs/ccnet/ccnet_r101-d8_512x512_160k_ade20k.py + 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_512x512_20k_voc12aug +- Name: ccnet_r50-d8_4xb4-20k_voc12aug-512x512 In Collection: CCNet Metadata: backbone: R-50-D8 @@ -251,9 +251,9 @@ Models: Metrics: mIoU: 76.17 mIoU(ms+flip): 77.51 - Config: configs/ccnet/ccnet_r50-d8_512x512_20k_voc12aug.py + 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_512x512_20k_voc12aug +- Name: ccnet_r101-d8_4xb4-20k_voc12aug-512x512 In Collection: CCNet Metadata: backbone: R-101-D8 @@ -273,9 +273,9 @@ Models: Metrics: mIoU: 77.27 mIoU(ms+flip): 79.02 - Config: configs/ccnet/ccnet_r101-d8_512x512_20k_voc12aug.py + 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_512x512_40k_voc12aug +- Name: ccnet_r50-d8_4xb4-40k_voc12aug-512x512 In Collection: CCNet Metadata: backbone: R-50-D8 @@ -287,9 +287,9 @@ Models: Metrics: mIoU: 75.96 mIoU(ms+flip): 77.04 - Config: configs/ccnet/ccnet_r50-d8_512x512_40k_voc12aug.py + 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_512x512_40k_voc12aug +- Name: ccnet_r101-d8_4xb4-40k_voc12aug-512x512 In Collection: CCNet Metadata: backbone: R-101-D8 @@ -301,5 +301,5 @@ Models: Metrics: mIoU: 77.87 mIoU(ms+flip): 78.9 - Config: configs/ccnet/ccnet_r101-d8_512x512_40k_voc12aug.py + 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/ccnet_r101-d8_4xb2-40k_cityscapes-512x1024.py b/configs/ccnet/ccnet_r101-d8_4xb2-40k_cityscapes-512x1024.py new file mode 100644 index 0000000000..0c49e1edc2 --- /dev/null +++ b/configs/ccnet/ccnet_r101-d8_4xb2-40k_cityscapes-512x1024.py @@ -0,0 +1,2 @@ +_base_ = './ccnet_r50-d8_4xb2-40k_cityscapes-512x1024.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/ccnet/ccnet_r101-d8_4xb2-40k_cityscapes-769x769.py b/configs/ccnet/ccnet_r101-d8_4xb2-40k_cityscapes-769x769.py new file mode 100644 index 0000000000..f24f5a70ed --- /dev/null +++ b/configs/ccnet/ccnet_r101-d8_4xb2-40k_cityscapes-769x769.py @@ -0,0 +1,2 @@ +_base_ = './ccnet_r50-d8_4xb2-40k_cityscapes-769x769.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/ccnet/ccnet_r101-d8_4xb2-80k_cityscapes-512x1024.py b/configs/ccnet/ccnet_r101-d8_4xb2-80k_cityscapes-512x1024.py new file mode 100644 index 0000000000..b358e12c4e --- /dev/null +++ b/configs/ccnet/ccnet_r101-d8_4xb2-80k_cityscapes-512x1024.py @@ -0,0 +1,2 @@ +_base_ = './ccnet_r50-d8_4xb2-80k_cityscapes-512x1024.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/ccnet/ccnet_r101-d8_4xb2-80k_cityscapes-769x769.py b/configs/ccnet/ccnet_r101-d8_4xb2-80k_cityscapes-769x769.py new file mode 100644 index 0000000000..75750768b2 --- /dev/null +++ b/configs/ccnet/ccnet_r101-d8_4xb2-80k_cityscapes-769x769.py @@ -0,0 +1,2 @@ +_base_ = './ccnet_r50-d8_4xb2-80k_cityscapes-769x769.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/ccnet/ccnet_r101-d8_4xb4-160k_ade20k-512x512.py b/configs/ccnet/ccnet_r101-d8_4xb4-160k_ade20k-512x512.py new file mode 100644 index 0000000000..a29d118f41 --- /dev/null +++ b/configs/ccnet/ccnet_r101-d8_4xb4-160k_ade20k-512x512.py @@ -0,0 +1,2 @@ +_base_ = './ccnet_r50-d8_4xb4-160k_ade20k-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/ccnet/ccnet_r101-d8_4xb4-20k_voc12aug-512x512.py b/configs/ccnet/ccnet_r101-d8_4xb4-20k_voc12aug-512x512.py new file mode 100644 index 0000000000..fd421a2ed5 --- /dev/null +++ b/configs/ccnet/ccnet_r101-d8_4xb4-20k_voc12aug-512x512.py @@ -0,0 +1,2 @@ +_base_ = './ccnet_r50-d8_4xb4-20k_voc12aug-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/ccnet/ccnet_r101-d8_4xb4-40k_voc12aug-512x512.py b/configs/ccnet/ccnet_r101-d8_4xb4-40k_voc12aug-512x512.py new file mode 100644 index 0000000000..425dfcf339 --- /dev/null +++ b/configs/ccnet/ccnet_r101-d8_4xb4-40k_voc12aug-512x512.py @@ -0,0 +1,2 @@ +_base_ = './ccnet_r50-d8_4xb4-40k_voc12aug-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/ccnet/ccnet_r101-d8_4xb4-80k_ade20k-512x512.py b/configs/ccnet/ccnet_r101-d8_4xb4-80k_ade20k-512x512.py new file mode 100644 index 0000000000..f6dcb9cf50 --- /dev/null +++ b/configs/ccnet/ccnet_r101-d8_4xb4-80k_ade20k-512x512.py @@ -0,0 +1,2 @@ +_base_ = './ccnet_r50-d8_4xb4-80k_ade20k-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/ccnet/ccnet_r101-d8_512x1024_40k_cityscapes.py b/configs/ccnet/ccnet_r101-d8_512x1024_40k_cityscapes.py deleted file mode 100644 index d2bac38ca6..0000000000 --- a/configs/ccnet/ccnet_r101-d8_512x1024_40k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './ccnet_r50-d8_512x1024_40k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/ccnet/ccnet_r101-d8_512x1024_80k_cityscapes.py b/configs/ccnet/ccnet_r101-d8_512x1024_80k_cityscapes.py deleted file mode 100644 index 989928ab7f..0000000000 --- a/configs/ccnet/ccnet_r101-d8_512x1024_80k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './ccnet_r50-d8_512x1024_80k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/ccnet/ccnet_r101-d8_512x512_160k_ade20k.py b/configs/ccnet/ccnet_r101-d8_512x512_160k_ade20k.py deleted file mode 100644 index c32bf48751..0000000000 --- a/configs/ccnet/ccnet_r101-d8_512x512_160k_ade20k.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './ccnet_r50-d8_512x512_160k_ade20k.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/ccnet/ccnet_r101-d8_512x512_20k_voc12aug.py b/configs/ccnet/ccnet_r101-d8_512x512_20k_voc12aug.py deleted file mode 100644 index 53eb77c0cd..0000000000 --- a/configs/ccnet/ccnet_r101-d8_512x512_20k_voc12aug.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './ccnet_r50-d8_512x512_20k_voc12aug.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/ccnet/ccnet_r101-d8_512x512_40k_voc12aug.py b/configs/ccnet/ccnet_r101-d8_512x512_40k_voc12aug.py deleted file mode 100644 index d7eb668f39..0000000000 --- a/configs/ccnet/ccnet_r101-d8_512x512_40k_voc12aug.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './ccnet_r50-d8_512x512_40k_voc12aug.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/ccnet/ccnet_r101-d8_512x512_80k_ade20k.py b/configs/ccnet/ccnet_r101-d8_512x512_80k_ade20k.py deleted file mode 100644 index 029c1d525b..0000000000 --- a/configs/ccnet/ccnet_r101-d8_512x512_80k_ade20k.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './ccnet_r50-d8_512x512_80k_ade20k.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/ccnet/ccnet_r101-d8_769x769_40k_cityscapes.py b/configs/ccnet/ccnet_r101-d8_769x769_40k_cityscapes.py deleted file mode 100644 index 43f05fab05..0000000000 --- a/configs/ccnet/ccnet_r101-d8_769x769_40k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './ccnet_r50-d8_769x769_40k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/ccnet/ccnet_r101-d8_769x769_80k_cityscapes.py b/configs/ccnet/ccnet_r101-d8_769x769_80k_cityscapes.py deleted file mode 100644 index 654f377b6f..0000000000 --- a/configs/ccnet/ccnet_r101-d8_769x769_80k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './ccnet_r50-d8_769x769_80k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/ccnet/ccnet_r50-d8_512x1024_40k_cityscapes.py b/configs/ccnet/ccnet_r50-d8_4xb2-40k_cityscapes-512x1024.py similarity index 100% rename from configs/ccnet/ccnet_r50-d8_512x1024_40k_cityscapes.py rename to configs/ccnet/ccnet_r50-d8_4xb2-40k_cityscapes-512x1024.py diff --git a/configs/ccnet/ccnet_r50-d8_769x769_40k_cityscapes.py b/configs/ccnet/ccnet_r50-d8_4xb2-40k_cityscapes-769x769.py similarity index 100% rename from configs/ccnet/ccnet_r50-d8_769x769_40k_cityscapes.py rename to configs/ccnet/ccnet_r50-d8_4xb2-40k_cityscapes-769x769.py diff --git a/configs/ccnet/ccnet_r50-d8_512x1024_80k_cityscapes.py b/configs/ccnet/ccnet_r50-d8_4xb2-80k_cityscapes-512x1024.py similarity index 100% rename from configs/ccnet/ccnet_r50-d8_512x1024_80k_cityscapes.py rename to configs/ccnet/ccnet_r50-d8_4xb2-80k_cityscapes-512x1024.py diff --git a/configs/ccnet/ccnet_r50-d8_769x769_80k_cityscapes.py b/configs/ccnet/ccnet_r50-d8_4xb2-80k_cityscapes-769x769.py similarity index 100% rename from configs/ccnet/ccnet_r50-d8_769x769_80k_cityscapes.py rename to configs/ccnet/ccnet_r50-d8_4xb2-80k_cityscapes-769x769.py diff --git a/configs/ccnet/ccnet_r50-d8_512x512_160k_ade20k.py b/configs/ccnet/ccnet_r50-d8_4xb4-160k_ade20k-512x512.py similarity index 100% rename from configs/ccnet/ccnet_r50-d8_512x512_160k_ade20k.py rename to configs/ccnet/ccnet_r50-d8_4xb4-160k_ade20k-512x512.py diff --git a/configs/ccnet/ccnet_r50-d8_512x512_20k_voc12aug.py b/configs/ccnet/ccnet_r50-d8_4xb4-20k_voc12aug-512x512.py similarity index 100% rename from configs/ccnet/ccnet_r50-d8_512x512_20k_voc12aug.py rename to configs/ccnet/ccnet_r50-d8_4xb4-20k_voc12aug-512x512.py diff --git a/configs/ccnet/ccnet_r50-d8_512x512_40k_voc12aug.py b/configs/ccnet/ccnet_r50-d8_4xb4-40k_voc12aug-512x512.py similarity index 100% rename from configs/ccnet/ccnet_r50-d8_512x512_40k_voc12aug.py rename to configs/ccnet/ccnet_r50-d8_4xb4-40k_voc12aug-512x512.py diff --git a/configs/ccnet/ccnet_r50-d8_512x512_80k_ade20k.py b/configs/ccnet/ccnet_r50-d8_4xb4-80k_ade20k-512x512.py similarity index 100% rename from configs/ccnet/ccnet_r50-d8_512x512_80k_ade20k.py rename to configs/ccnet/ccnet_r50-d8_4xb4-80k_ade20k-512x512.py diff --git a/configs/cgnet/README.md b/configs/cgnet/README.md index b0fced44a0..709d0c0b8f 100644 --- a/configs/cgnet/README.md +++ b/configs/cgnet/README.md @@ -40,7 +40,7 @@ The demand of applying semantic segmentation model on mobile devices has been in ### 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/master/configs/cgnet/cgnet_680x680_60k_cityscapes.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/master/configs/cgnet/cgnet_512x1024_60k_cityscapes.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) | +| 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 index bcd6d89c1b..be79b89355 100644 --- a/configs/cgnet/cgnet.yml +++ b/configs/cgnet/cgnet.yml @@ -13,7 +13,7 @@ Collections: Converted From: Code: https://github.com/wutianyiRosun/CGNet Models: -- Name: cgnet_680x680_60k_cityscapes +- Name: cgnet_fcn_4xb4-60k_cityscapes-680x680 In Collection: CGNet Metadata: backbone: M3N21 @@ -33,9 +33,9 @@ Models: Metrics: mIoU: 65.63 mIoU(ms+flip): 68.04 - Config: configs/cgnet/cgnet_680x680_60k_cityscapes.py + 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_512x1024_60k_cityscapes +- Name: cgnet_fcn_4xb8-60k_cityscapes-512x1024 In Collection: CGNet Metadata: backbone: M3N21 @@ -55,5 +55,5 @@ Models: Metrics: mIoU: 68.27 mIoU(ms+flip): 70.33 - Config: configs/cgnet/cgnet_512x1024_60k_cityscapes.py + 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/cgnet_680x680_60k_cityscapes.py b/configs/cgnet/cgnet_fcn_4xb4-60k_cityscapes-680x680.py similarity index 100% rename from configs/cgnet/cgnet_680x680_60k_cityscapes.py rename to configs/cgnet/cgnet_fcn_4xb4-60k_cityscapes-680x680.py diff --git a/configs/cgnet/cgnet_512x1024_60k_cityscapes.py b/configs/cgnet/cgnet_fcn_4xb8-60k_cityscapes-512x1024.py similarity index 100% rename from configs/cgnet/cgnet_512x1024_60k_cityscapes.py rename to configs/cgnet/cgnet_fcn_4xb8-60k_cityscapes-512x1024.py diff --git a/configs/convnext/README.md b/configs/convnext/README.md index 09eb702c7f..6a826b9d6a 100644 --- a/configs/convnext/README.md +++ b/configs/convnext/README.md @@ -58,14 +58,14 @@ 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/master/configs/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k.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/master/configs/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k.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/master/configs/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k.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/master/configs/convnext/upernet_convnext_base_fp16_640x640_160k_ade20k.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/master/configs/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k.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/master/configs/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k.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) | 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) | Note: diff --git a/configs/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k.py b/configs/convnext/convnext-base_upernet_8xb2-amp-160k_ade20k-512x512.py similarity index 100% rename from configs/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k.py rename to configs/convnext/convnext-base_upernet_8xb2-amp-160k_ade20k-512x512.py diff --git a/configs/convnext/upernet_convnext_base_fp16_640x640_160k_ade20k.py b/configs/convnext/convnext-base_upernet_8xb2-amp-160k_ade20k-640x640.py similarity index 100% rename from configs/convnext/upernet_convnext_base_fp16_640x640_160k_ade20k.py rename to configs/convnext/convnext-base_upernet_8xb2-amp-160k_ade20k-640x640.py diff --git a/configs/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k.py b/configs/convnext/convnext-large_upernet_8xb2-amp-160k_ade20k-640x640.py similarity index 100% rename from configs/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k.py rename to configs/convnext/convnext-large_upernet_8xb2-amp-160k_ade20k-640x640.py diff --git a/configs/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k.py b/configs/convnext/convnext-small_upernet_8xb2-amp-160k_ade20k-512x512.py similarity index 100% rename from configs/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k.py rename to configs/convnext/convnext-small_upernet_8xb2-amp-160k_ade20k-512x512.py diff --git a/configs/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k.py b/configs/convnext/convnext-tiny_upernet_8xb2-amp-160k_ade20k-512x512.py similarity index 100% rename from configs/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k.py rename to configs/convnext/convnext-tiny_upernet_8xb2-amp-160k_ade20k-512x512.py diff --git a/configs/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k.py b/configs/convnext/convnext-xlarge_upernet_8xb2-amp-160k_ade20k-640x640.py similarity index 100% rename from configs/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k.py rename to configs/convnext/convnext-xlarge_upernet_8xb2-amp-160k_ade20k-640x640.py diff --git a/configs/convnext/convnext.yml b/configs/convnext/convnext.yml index 2b943aa151..2162e0c50c 100644 --- a/configs/convnext/convnext.yml +++ b/configs/convnext/convnext.yml @@ -1,5 +1,5 @@ Models: -- Name: upernet_convnext_tiny_fp16_512x512_160k_ade20k +- Name: convnext-tiny_upernet_8xb2-amp-160k_ade20k-512x512 In Collection: UPerNet Metadata: backbone: ConvNeXt-T @@ -10,7 +10,7 @@ Models: hardware: V100 backend: PyTorch batch size: 1 - mode: FP16 + mode: AMP resolution: (512,512) Training Memory (GB): 4.23 Results: @@ -19,9 +19,9 @@ Models: Metrics: mIoU: 46.11 mIoU(ms+flip): 46.62 - Config: configs/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k.py + 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: upernet_convnext_small_fp16_512x512_160k_ade20k +- Name: convnext-small_upernet_8xb2-amp-160k_ade20k-512x512 In Collection: UPerNet Metadata: backbone: ConvNeXt-S @@ -32,7 +32,7 @@ Models: hardware: V100 backend: PyTorch batch size: 1 - mode: FP16 + mode: AMP resolution: (512,512) Training Memory (GB): 5.16 Results: @@ -41,9 +41,9 @@ Models: Metrics: mIoU: 48.56 mIoU(ms+flip): 49.02 - Config: configs/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k.py + 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: upernet_convnext_base_fp16_512x512_160k_ade20k +- Name: convnext-base_upernet_8xb2-amp-160k_ade20k-512x512 In Collection: UPerNet Metadata: backbone: ConvNeXt-B @@ -54,7 +54,7 @@ Models: hardware: V100 backend: PyTorch batch size: 1 - mode: FP16 + mode: AMP resolution: (512,512) Training Memory (GB): 6.33 Results: @@ -63,9 +63,9 @@ Models: Metrics: mIoU: 48.71 mIoU(ms+flip): 49.54 - Config: configs/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k.py + 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: upernet_convnext_base_fp16_640x640_160k_ade20k +- Name: convnext-base_upernet_8xb2-amp-160k_ade20k-640x640 In Collection: UPerNet Metadata: backbone: ConvNeXt-B @@ -76,7 +76,7 @@ Models: hardware: V100 backend: PyTorch batch size: 1 - mode: FP16 + mode: AMP resolution: (640,640) Training Memory (GB): 8.53 Results: @@ -85,9 +85,9 @@ Models: Metrics: mIoU: 52.13 mIoU(ms+flip): 52.66 - Config: configs/convnext/upernet_convnext_base_fp16_640x640_160k_ade20k.py + 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: upernet_convnext_large_fp16_640x640_160k_ade20k +- Name: convnext-large_upernet_8xb2-amp-160k_ade20k-640x640 In Collection: UPerNet Metadata: backbone: ConvNeXt-L @@ -98,7 +98,7 @@ Models: hardware: V100 backend: PyTorch batch size: 1 - mode: FP16 + mode: AMP resolution: (640,640) Training Memory (GB): 12.08 Results: @@ -107,9 +107,9 @@ Models: Metrics: mIoU: 53.16 mIoU(ms+flip): 53.38 - Config: configs/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k.py + 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: upernet_convnext_xlarge_fp16_640x640_160k_ade20k +- Name: convnext-xlarge_upernet_8xb2-amp-160k_ade20k-640x640 In Collection: UPerNet Metadata: backbone: ConvNeXt-XL @@ -120,7 +120,7 @@ Models: hardware: V100 backend: PyTorch batch size: 1 - mode: FP16 + mode: AMP resolution: (640,640) Training Memory (GB): 26.16 Results: @@ -129,5 +129,5 @@ Models: Metrics: mIoU: 53.58 mIoU(ms+flip): 54.11 - Config: configs/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k.py + 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/danet/README.md b/configs/danet/README.md index ac7634026a..52059e93a5 100644 --- a/configs/danet/README.md +++ b/configs/danet/README.md @@ -37,31 +37,31 @@ In this paper, we address the scene segmentation task by capturing rich contextu ### 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/master/configs/danet/danet_r50-d8_512x1024_40k_cityscapes.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/master/configs/danet/danet_r101-d8_512x1024_40k_cityscapes.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/master/configs/danet/danet_r50-d8_769x769_40k_cityscapes.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/master/configs/danet/danet_r101-d8_769x769_40k_cityscapes.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/master/configs/danet/danet_r50-d8_512x1024_80k_cityscapes.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/master/configs/danet/danet_r101-d8_512x1024_80k_cityscapes.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/master/configs/danet/danet_r50-d8_769x769_80k_cityscapes.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/master/configs/danet/danet_r101-d8_769x769_80k_cityscapes.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) | 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) | ### 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/master/configs/danet/danet_r50-d8_512x512_80k_ade20k.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/master/configs/danet/danet_r101-d8_512x512_80k_ade20k.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/master/configs/danet/danet_r50-d8_512x512_160k_ade20k.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/master/configs/danet/danet_r101-d8_512x512_160k_ade20k.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) | 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) | ### 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/master/configs/danet/danet_r50-d8_512x512_20k_voc12aug.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/master/configs/danet/danet_r101-d8_512x512_20k_voc12aug.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/master/configs/danet/danet_r50-d8_512x512_40k_voc12aug.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/master/configs/danet/danet_r101-d8_512x512_40k_voc12aug.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) | 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) | diff --git a/configs/danet/danet.yml b/configs/danet/danet.yml index ca2d6ff982..2a6658e428 100644 --- a/configs/danet/danet.yml +++ b/configs/danet/danet.yml @@ -15,7 +15,7 @@ Collections: Converted From: Code: https://github.com/junfu1115/DANet/ Models: -- Name: danet_r50-d8_512x1024_40k_cityscapes +- Name: danet_r50-d8_4xb2-40k_cityscapes-512x1024 In Collection: DANet Metadata: backbone: R-50-D8 @@ -34,9 +34,9 @@ Models: Dataset: Cityscapes Metrics: mIoU: 78.74 - Config: configs/danet/danet_r50-d8_512x1024_40k_cityscapes.py + 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_512x1024_40k_cityscapes +- Name: danet_r101-d8_4xb2-40k_cityscapes-512x1024 In Collection: DANet Metadata: backbone: R-101-D8 @@ -55,9 +55,9 @@ Models: Dataset: Cityscapes Metrics: mIoU: 80.52 - Config: configs/danet/danet_r101-d8_512x1024_40k_cityscapes.py + 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_769x769_40k_cityscapes +- Name: danet_r50-d8_4xb2-40k_cityscapes-769x769 In Collection: DANet Metadata: backbone: R-50-D8 @@ -77,9 +77,9 @@ Models: Metrics: mIoU: 78.88 mIoU(ms+flip): 80.62 - Config: configs/danet/danet_r50-d8_769x769_40k_cityscapes.py + 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_769x769_40k_cityscapes +- Name: danet_r101-d8_4xb2-40k_cityscapes-769x769 In Collection: DANet Metadata: backbone: R-101-D8 @@ -99,9 +99,9 @@ Models: Metrics: mIoU: 79.88 mIoU(ms+flip): 81.47 - Config: configs/danet/danet_r101-d8_769x769_40k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: danet_r50-d8_4xb2-80k_cityscapes-512x1024 In Collection: DANet Metadata: backbone: R-50-D8 @@ -112,9 +112,9 @@ Models: Dataset: Cityscapes Metrics: mIoU: 79.34 - Config: configs/danet/danet_r50-d8_512x1024_80k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: danet_r101-d8_4xb2-80k_cityscapes-512x1024 In Collection: DANet Metadata: backbone: R-101-D8 @@ -125,9 +125,9 @@ Models: Dataset: Cityscapes Metrics: mIoU: 80.41 - Config: configs/danet/danet_r101-d8_512x1024_80k_cityscapes.py + 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_769x769_80k_cityscapes +- Name: danet_r50-d8_4xb2-80k_cityscapes-769x769 In Collection: DANet Metadata: backbone: R-50-D8 @@ -139,9 +139,9 @@ Models: Metrics: mIoU: 79.27 mIoU(ms+flip): 80.96 - Config: configs/danet/danet_r50-d8_769x769_80k_cityscapes.py + 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_769x769_80k_cityscapes +- Name: danet_r101-d8_4xb2-80k_cityscapes-769x769 In Collection: DANet Metadata: backbone: R-101-D8 @@ -153,9 +153,9 @@ Models: Metrics: mIoU: 80.47 mIoU(ms+flip): 82.02 - Config: configs/danet/danet_r101-d8_769x769_80k_cityscapes.py + 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_512x512_80k_ade20k +- Name: danet_r50-d8_4xb4-80k_ade20k-512x512 In Collection: DANet Metadata: backbone: R-50-D8 @@ -175,9 +175,9 @@ Models: Metrics: mIoU: 41.66 mIoU(ms+flip): 42.9 - Config: configs/danet/danet_r50-d8_512x512_80k_ade20k.py + 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_512x512_80k_ade20k +- Name: danet_r101-d8_4xb4-80k_ade20k-512x512 In Collection: DANet Metadata: backbone: R-101-D8 @@ -197,9 +197,9 @@ Models: Metrics: mIoU: 43.64 mIoU(ms+flip): 45.19 - Config: configs/danet/danet_r101-d8_512x512_80k_ade20k.py + 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_512x512_160k_ade20k +- Name: danet_r50-d8_4xb4-160k_ade20k-512x512 In Collection: DANet Metadata: backbone: R-50-D8 @@ -211,9 +211,9 @@ Models: Metrics: mIoU: 42.45 mIoU(ms+flip): 43.25 - Config: configs/danet/danet_r50-d8_512x512_160k_ade20k.py + 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_512x512_160k_ade20k +- Name: danet_r101-d8_4xb4-160k_ade20k-512x512 In Collection: DANet Metadata: backbone: R-101-D8 @@ -225,9 +225,9 @@ Models: Metrics: mIoU: 44.17 mIoU(ms+flip): 45.02 - Config: configs/danet/danet_r101-d8_512x512_160k_ade20k.py + 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_512x512_20k_voc12aug +- Name: danet_r50-d8_4xb4-20k_voc12aug-512x512 In Collection: DANet Metadata: backbone: R-50-D8 @@ -247,9 +247,9 @@ Models: Metrics: mIoU: 74.45 mIoU(ms+flip): 75.69 - Config: configs/danet/danet_r50-d8_512x512_20k_voc12aug.py + 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_512x512_20k_voc12aug +- Name: danet_r101-d8_4xb4-20k_voc12aug-512x512 In Collection: DANet Metadata: backbone: R-101-D8 @@ -269,9 +269,9 @@ Models: Metrics: mIoU: 76.02 mIoU(ms+flip): 77.23 - Config: configs/danet/danet_r101-d8_512x512_20k_voc12aug.py + 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_512x512_40k_voc12aug +- Name: danet_r50-d8_4xb4-40k_voc12aug-512x512 In Collection: DANet Metadata: backbone: R-50-D8 @@ -283,9 +283,9 @@ Models: Metrics: mIoU: 76.37 mIoU(ms+flip): 77.29 - Config: configs/danet/danet_r50-d8_512x512_40k_voc12aug.py + 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_512x512_40k_voc12aug +- Name: danet_r101-d8_4xb4-40k_voc12aug-512x512 In Collection: DANet Metadata: backbone: R-101-D8 @@ -297,5 +297,5 @@ Models: Metrics: mIoU: 76.51 mIoU(ms+flip): 77.32 - Config: configs/danet/danet_r101-d8_512x512_40k_voc12aug.py + 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/danet_r101-d8_4xb2-40k_cityscapes-512x1024.py b/configs/danet/danet_r101-d8_4xb2-40k_cityscapes-512x1024.py new file mode 100644 index 0000000000..4602f3318f --- /dev/null +++ b/configs/danet/danet_r101-d8_4xb2-40k_cityscapes-512x1024.py @@ -0,0 +1,2 @@ +_base_ = './danet_r50-d8_4xb2-40k_cityscapes-512x1024.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/danet/danet_r101-d8_4xb2-40k_cityscapes-769x769.py b/configs/danet/danet_r101-d8_4xb2-40k_cityscapes-769x769.py new file mode 100644 index 0000000000..a08c18ee46 --- /dev/null +++ b/configs/danet/danet_r101-d8_4xb2-40k_cityscapes-769x769.py @@ -0,0 +1,2 @@ +_base_ = './danet_r50-d8_4xb2-40k_cityscapes-769x769.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/danet/danet_r101-d8_4xb2-80k_cityscapes-512x1024.py b/configs/danet/danet_r101-d8_4xb2-80k_cityscapes-512x1024.py new file mode 100644 index 0000000000..98b1c6490b --- /dev/null +++ b/configs/danet/danet_r101-d8_4xb2-80k_cityscapes-512x1024.py @@ -0,0 +1,2 @@ +_base_ = './danet_r50-d8_4xb2-80k_cityscapes-512x1024.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/danet/danet_r101-d8_4xb2-80k_cityscapes-769x769.py b/configs/danet/danet_r101-d8_4xb2-80k_cityscapes-769x769.py new file mode 100644 index 0000000000..9affe306cb --- /dev/null +++ b/configs/danet/danet_r101-d8_4xb2-80k_cityscapes-769x769.py @@ -0,0 +1,2 @@ +_base_ = './danet_r50-d8_4xb2-80k_cityscapes-769x769.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/danet/danet_r101-d8_4xb4-160k_ade20k-512x512.py b/configs/danet/danet_r101-d8_4xb4-160k_ade20k-512x512.py new file mode 100644 index 0000000000..0079ad65e8 --- /dev/null +++ b/configs/danet/danet_r101-d8_4xb4-160k_ade20k-512x512.py @@ -0,0 +1,2 @@ +_base_ = './danet_r50-d8_4xb4-160k_ade20k-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/danet/danet_r101-d8_4xb4-20k_voc12aug-512x512.py b/configs/danet/danet_r101-d8_4xb4-20k_voc12aug-512x512.py new file mode 100644 index 0000000000..48444514b7 --- /dev/null +++ b/configs/danet/danet_r101-d8_4xb4-20k_voc12aug-512x512.py @@ -0,0 +1,2 @@ +_base_ = './danet_r50-d8_4xb4-20k_voc12aug-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/danet/danet_r101-d8_4xb4-40k_voc12aug-512x512.py b/configs/danet/danet_r101-d8_4xb4-40k_voc12aug-512x512.py new file mode 100644 index 0000000000..2f2df7a595 --- /dev/null +++ b/configs/danet/danet_r101-d8_4xb4-40k_voc12aug-512x512.py @@ -0,0 +1,2 @@ +_base_ = './danet_r50-d8_4xb4-40k_voc12aug-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/danet/danet_r101-d8_4xb4-80k_ade20k-512x512.py b/configs/danet/danet_r101-d8_4xb4-80k_ade20k-512x512.py new file mode 100644 index 0000000000..dd75bc16b8 --- /dev/null +++ b/configs/danet/danet_r101-d8_4xb4-80k_ade20k-512x512.py @@ -0,0 +1,2 @@ +_base_ = './danet_r50-d8_4xb4-80k_ade20k-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/danet/danet_r101-d8_512x1024_40k_cityscapes.py b/configs/danet/danet_r101-d8_512x1024_40k_cityscapes.py deleted file mode 100644 index 3bfb9bdb30..0000000000 --- a/configs/danet/danet_r101-d8_512x1024_40k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './danet_r50-d8_512x1024_40k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/danet/danet_r101-d8_512x1024_80k_cityscapes.py b/configs/danet/danet_r101-d8_512x1024_80k_cityscapes.py deleted file mode 100644 index d80b2ec160..0000000000 --- a/configs/danet/danet_r101-d8_512x1024_80k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './danet_r50-d8_512x1024_80k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/danet/danet_r101-d8_512x512_160k_ade20k.py b/configs/danet/danet_r101-d8_512x512_160k_ade20k.py deleted file mode 100644 index 0f22d0fb63..0000000000 --- a/configs/danet/danet_r101-d8_512x512_160k_ade20k.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './danet_r50-d8_512x512_160k_ade20k.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/danet/danet_r101-d8_512x512_20k_voc12aug.py b/configs/danet/danet_r101-d8_512x512_20k_voc12aug.py deleted file mode 100644 index 709f93cba3..0000000000 --- a/configs/danet/danet_r101-d8_512x512_20k_voc12aug.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './danet_r50-d8_512x512_20k_voc12aug.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/danet/danet_r101-d8_512x512_40k_voc12aug.py b/configs/danet/danet_r101-d8_512x512_40k_voc12aug.py deleted file mode 100644 index 5c623eb568..0000000000 --- a/configs/danet/danet_r101-d8_512x512_40k_voc12aug.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './danet_r50-d8_512x512_40k_voc12aug.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/danet/danet_r101-d8_512x512_80k_ade20k.py b/configs/danet/danet_r101-d8_512x512_80k_ade20k.py deleted file mode 100644 index bd31bc8f28..0000000000 --- a/configs/danet/danet_r101-d8_512x512_80k_ade20k.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './danet_r50-d8_512x512_80k_ade20k.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/danet/danet_r101-d8_769x769_40k_cityscapes.py b/configs/danet/danet_r101-d8_769x769_40k_cityscapes.py deleted file mode 100644 index 597d76de79..0000000000 --- a/configs/danet/danet_r101-d8_769x769_40k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './danet_r50-d8_769x769_40k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/danet/danet_r101-d8_769x769_80k_cityscapes.py b/configs/danet/danet_r101-d8_769x769_80k_cityscapes.py deleted file mode 100644 index 70f9b31966..0000000000 --- a/configs/danet/danet_r101-d8_769x769_80k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './danet_r50-d8_769x769_80k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/danet/danet_r50-d8_512x1024_40k_cityscapes.py b/configs/danet/danet_r50-d8_4xb2-40k_cityscapes-512x1024.py similarity index 100% rename from configs/danet/danet_r50-d8_512x1024_40k_cityscapes.py rename to configs/danet/danet_r50-d8_4xb2-40k_cityscapes-512x1024.py diff --git a/configs/danet/danet_r50-d8_769x769_40k_cityscapes.py b/configs/danet/danet_r50-d8_4xb2-40k_cityscapes-769x769.py similarity index 100% rename from configs/danet/danet_r50-d8_769x769_40k_cityscapes.py rename to configs/danet/danet_r50-d8_4xb2-40k_cityscapes-769x769.py diff --git a/configs/danet/danet_r50-d8_512x1024_80k_cityscapes.py b/configs/danet/danet_r50-d8_4xb2-80k_cityscapes-512x1024.py similarity index 100% rename from configs/danet/danet_r50-d8_512x1024_80k_cityscapes.py rename to configs/danet/danet_r50-d8_4xb2-80k_cityscapes-512x1024.py diff --git a/configs/danet/danet_r50-d8_769x769_80k_cityscapes.py b/configs/danet/danet_r50-d8_4xb2-80k_cityscapes-769x769.py similarity index 100% rename from configs/danet/danet_r50-d8_769x769_80k_cityscapes.py rename to configs/danet/danet_r50-d8_4xb2-80k_cityscapes-769x769.py diff --git a/configs/danet/danet_r50-d8_512x512_160k_ade20k.py b/configs/danet/danet_r50-d8_4xb4-160k_ade20k-512x512.py similarity index 100% rename from configs/danet/danet_r50-d8_512x512_160k_ade20k.py rename to configs/danet/danet_r50-d8_4xb4-160k_ade20k-512x512.py diff --git a/configs/danet/danet_r50-d8_512x512_20k_voc12aug.py b/configs/danet/danet_r50-d8_4xb4-20k_voc12aug-512x512.py similarity index 100% rename from configs/danet/danet_r50-d8_512x512_20k_voc12aug.py rename to configs/danet/danet_r50-d8_4xb4-20k_voc12aug-512x512.py diff --git a/configs/danet/danet_r50-d8_512x512_40k_voc12aug.py b/configs/danet/danet_r50-d8_4xb4-40k_voc12aug-512x512.py similarity index 100% rename from configs/danet/danet_r50-d8_512x512_40k_voc12aug.py rename to configs/danet/danet_r50-d8_4xb4-40k_voc12aug-512x512.py diff --git a/configs/danet/danet_r50-d8_512x512_80k_ade20k.py b/configs/danet/danet_r50-d8_4xb4-80k_ade20k-512x512.py similarity index 100% rename from configs/danet/danet_r50-d8_512x512_80k_ade20k.py rename to configs/danet/danet_r50-d8_4xb4-80k_ade20k-512x512.py diff --git a/configs/deeplabv3/README.md b/configs/deeplabv3/README.md index 49856607b1..516ad9dfcf 100644 --- a/configs/deeplabv3/README.md +++ b/configs/deeplabv3/README.md @@ -37,79 +37,79 @@ In this work, we revisit atrous convolution, a powerful tool to explicitly adjus ### 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/master/configs/deeplabv3/deeplabv3_r50-d8_512x1024_40k_cityscapes.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/master/configs/deeplabv3/deeplabv3_r101-d8_512x1024_40k_cityscapes.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/master/configs/deeplabv3/deeplabv3_r50-d8_769x769_40k_cityscapes.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/master/configs/deeplabv3/deeplabv3_r101-d8_769x769_40k_cityscapes.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/master/configs/deeplabv3/deeplabv3_r18-d8_512x1024_80k_cityscapes.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/master/configs/deeplabv3/deeplabv3_r50-d8_512x1024_80k_cityscapes.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/master/configs/deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes.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/master/configs/deeplabv3/deeplabv3_r101-d8_fp16_512x1024_80k_cityscapes.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/master/configs/deeplabv3/deeplabv3_r18-d8_769x769_80k_cityscapes.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/master/configs/deeplabv3/deeplabv3_r50-d8_769x769_80k_cityscapes.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/master/configs/deeplabv3/deeplabv3_r101-d8_769x769_80k_cityscapes.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/master/configs/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes.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/master/configs/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes.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/master/configs/deeplabv3/deeplabv3_r18b-d8_512x1024_80k_cityscapes.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/master/configs/deeplabv3/deeplabv3_r50b-d8_512x1024_80k_cityscapes.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/master/configs/deeplabv3/deeplabv3_r101b-d8_512x1024_80k_cityscapes.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 | 76.63 | 77.51 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r18b-d8_769x769_80k_cityscapes.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/master/configs/deeplabv3/deeplabv3_r50b-d8_769x769_80k_cityscapes.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/master/configs/deeplabv3/deeplabv3_r101b-d8_769x769_80k_cityscapes.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) | 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 | 76.63 | 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) | ### 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/master/configs/deeplabv3/deeplabv3_r50-d8_512x512_80k_ade20k.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/master/configs/deeplabv3/deeplabv3_r101-d8_512x512_80k_ade20k.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/master/configs/deeplabv3/deeplabv3_r50-d8_512x512_160k_ade20k.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/master/configs/deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k.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) | 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) | ### 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/master/configs/deeplabv3/deeplabv3_r50-d8_512x512_20k_voc12aug.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/master/configs/deeplabv3/deeplabv3_r101-d8_512x512_20k_voc12aug.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/master/configs/deeplabv3/deeplabv3_r50-d8_512x512_40k_voc12aug.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/master/configs/deeplabv3/deeplabv3_r101-d8_512x512_40k_voc12aug.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) | 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) | ### 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/master/configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context.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/master/configs/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context.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) | 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) | ### 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/master/configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59.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/master/configs/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context_59.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) | 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) | ### 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/master/configs/deeplabv3_r50-d8_512x512_4x4_20k_coco-stuff10k.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/master/configs/deeplabv3_r101-d8_512x512_4x4_20k_coco-stuff10k.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/master/configs/deeplabv3_r50-d8_512x512_4x4_40k_coco-stuff10k.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/master/configs/deeplabv3_r101-d8_512x512_4x4_40k_coco-stuff10k.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) | 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) | ### 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/master/configs/deeplabv3_r50-d8_512x512_4x4_80k_coco-stuff164k.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/master/configs/deeplabv3_r101-d8_512x512_4x4_80k_coco-stuff164k.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/master/configs/deeplabv3_r50-d8_512x512_4x4_160k_coco-stuff164k.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/master/configs/deeplabv3_r101-d8_512x512_4x4_160k_coco-stuff164k.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/master/configs/deeplabv3_r50-d8_512x512_4x4_320k_coco-stuff164k.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/master/configs/deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k.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) | 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) | Note: diff --git a/configs/deeplabv3/deeplabv3.yml b/configs/deeplabv3/deeplabv3.yml index 559af4f69c..0bc615d202 100644 --- a/configs/deeplabv3/deeplabv3.yml +++ b/configs/deeplabv3/deeplabv3.yml @@ -19,7 +19,7 @@ Collections: Converted From: Code: https://github.com/tensorflow/models/tree/master/research/deeplab Models: -- Name: deeplabv3_r50-d8_512x1024_40k_cityscapes +- Name: deeplabv3_r50-d8_4xb2-40k_cityscapes-512x1024 In Collection: DeepLabV3 Metadata: backbone: R-50-D8 @@ -39,9 +39,9 @@ Models: Metrics: mIoU: 79.09 mIoU(ms+flip): 80.45 - Config: configs/deeplabv3/deeplabv3_r50-d8_512x1024_40k_cityscapes.py + 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_512x1024_40k_cityscapes +- Name: deeplabv3_r101-d8_4xb2-40k_cityscapes-512x1024 In Collection: DeepLabV3 Metadata: backbone: R-101-D8 @@ -61,9 +61,9 @@ Models: Metrics: mIoU: 77.12 mIoU(ms+flip): 79.61 - Config: configs/deeplabv3/deeplabv3_r101-d8_512x1024_40k_cityscapes.py + 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_769x769_40k_cityscapes +- Name: deeplabv3_r50-d8_4xb2-40k_cityscapes-769x769 In Collection: DeepLabV3 Metadata: backbone: R-50-D8 @@ -83,9 +83,9 @@ Models: Metrics: mIoU: 78.58 mIoU(ms+flip): 79.89 - Config: configs/deeplabv3/deeplabv3_r50-d8_769x769_40k_cityscapes.py + 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_769x769_40k_cityscapes +- Name: deeplabv3_r101-d8_4xb2-40k_cityscapes-769x769 In Collection: DeepLabV3 Metadata: backbone: R-101-D8 @@ -105,9 +105,9 @@ Models: Metrics: mIoU: 79.27 mIoU(ms+flip): 80.11 - Config: configs/deeplabv3/deeplabv3_r101-d8_769x769_40k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: deeplabv3_r18-d8_4xb2-80k_cityscapes-512x1024 In Collection: DeepLabV3 Metadata: backbone: R-18-D8 @@ -127,9 +127,9 @@ Models: Metrics: mIoU: 76.7 mIoU(ms+flip): 78.27 - Config: configs/deeplabv3/deeplabv3_r18-d8_512x1024_80k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: deeplabv3_r50-d8_4xb2-80k_cityscapes-512x1024 In Collection: DeepLabV3 Metadata: backbone: R-50-D8 @@ -141,9 +141,9 @@ Models: Metrics: mIoU: 79.32 mIoU(ms+flip): 80.57 - Config: configs/deeplabv3/deeplabv3_r50-d8_512x1024_80k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: deeplabv3_r101-d8_4xb2-80k_cityscapes-512x1024 In Collection: DeepLabV3 Metadata: backbone: R-101-D8 @@ -155,9 +155,9 @@ Models: Metrics: mIoU: 80.2 mIoU(ms+flip): 81.21 - Config: configs/deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes.py + 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_fp16_512x1024_80k_cityscapes +- Name: deeplabv3_r101-d8_4xb2-amp-80k_cityscapes-512x1024 In Collection: DeepLabV3 Metadata: backbone: R-101-D8 @@ -168,7 +168,7 @@ Models: hardware: V100 backend: PyTorch batch size: 1 - mode: FP16 + mode: AMP resolution: (512,1024) Training Memory (GB): 5.75 Results: @@ -176,9 +176,9 @@ Models: Dataset: Cityscapes Metrics: mIoU: 80.48 - Config: configs/deeplabv3/deeplabv3_r101-d8_fp16_512x1024_80k_cityscapes.py + 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_769x769_80k_cityscapes +- Name: deeplabv3_r18-d8_4xb2-80k_cityscapes-769x769 In Collection: DeepLabV3 Metadata: backbone: R-18-D8 @@ -198,9 +198,9 @@ Models: Metrics: mIoU: 76.6 mIoU(ms+flip): 78.26 - Config: configs/deeplabv3/deeplabv3_r18-d8_769x769_80k_cityscapes.py + 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_769x769_80k_cityscapes +- Name: deeplabv3_r50-d8_4xb2-80k_cityscapes-769x769 In Collection: DeepLabV3 Metadata: backbone: R-50-D8 @@ -212,9 +212,9 @@ Models: Metrics: mIoU: 79.89 mIoU(ms+flip): 81.06 - Config: configs/deeplabv3/deeplabv3_r50-d8_769x769_80k_cityscapes.py + 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_769x769_80k_cityscapes +- Name: deeplabv3_r101-d8_4xb2-80k_cityscapes-769x769 In Collection: DeepLabV3 Metadata: backbone: R-101-D8 @@ -226,9 +226,9 @@ Models: Metrics: mIoU: 79.67 mIoU(ms+flip): 80.81 - Config: configs/deeplabv3/deeplabv3_r101-d8_769x769_80k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: deeplabv3_r101-d16-mg124_4xb2-80k_cityscapes-512x1024 In Collection: DeepLabV3 Metadata: backbone: R-101-D16-MG124 @@ -240,9 +240,9 @@ Models: Metrics: mIoU: 78.36 mIoU(ms+flip): 79.84 - Config: configs/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: deeplabv3_r18b-d8_4xb2-80k_cityscapes-512x1024 In Collection: DeepLabV3 Metadata: backbone: R-18b-D8 @@ -262,9 +262,9 @@ Models: Metrics: mIoU: 76.26 mIoU(ms+flip): 77.88 - Config: configs/deeplabv3/deeplabv3_r18b-d8_512x1024_80k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: deeplabv3_r50b-d8_4xb2-80k_cityscapes-512x1024 In Collection: DeepLabV3 Metadata: backbone: R-50b-D8 @@ -284,9 +284,9 @@ Models: Metrics: mIoU: 79.63 mIoU(ms+flip): 80.98 - Config: configs/deeplabv3/deeplabv3_r50b-d8_512x1024_80k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: deeplabv3_r101b-d8_4xb2-80k_cityscapes-512x1024 In Collection: DeepLabV3 Metadata: backbone: R-101b-D8 @@ -306,9 +306,9 @@ Models: Metrics: mIoU: 80.01 mIoU(ms+flip): 81.21 - Config: configs/deeplabv3/deeplabv3_r101b-d8_512x1024_80k_cityscapes.py + 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_769x769_80k_cityscapes +- Name: deeplabv3_r18b-d8_4xb2-80k_cityscapes-769x769 In Collection: DeepLabV3 Metadata: backbone: R-18b-D8 @@ -328,9 +328,9 @@ Models: Metrics: mIoU: 76.63 mIoU(ms+flip): 77.51 - Config: configs/deeplabv3/deeplabv3_r18b-d8_769x769_80k_cityscapes.py + 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_769x769_80k_cityscapes +- Name: deeplabv3_r50b-d8_4xb2-80k_cityscapes-769x769 In Collection: DeepLabV3 Metadata: backbone: R-50b-D8 @@ -350,9 +350,9 @@ Models: Metrics: mIoU: 78.8 mIoU(ms+flip): 80.27 - Config: configs/deeplabv3/deeplabv3_r50b-d8_769x769_80k_cityscapes.py + 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_769x769_80k_cityscapes +- Name: deeplabv3_r101b-d8_4xb2-80k_cityscapes-769x769 In Collection: DeepLabV3 Metadata: backbone: R-101b-D8 @@ -372,9 +372,9 @@ Models: Metrics: mIoU: 79.41 mIoU(ms+flip): 80.73 - Config: configs/deeplabv3/deeplabv3_r101b-d8_769x769_80k_cityscapes.py + 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_512x512_80k_ade20k +- Name: deeplabv3_r50-d8_4xb4-80k_ade20k-512x512 In Collection: DeepLabV3 Metadata: backbone: R-50-D8 @@ -394,9 +394,9 @@ Models: Metrics: mIoU: 42.42 mIoU(ms+flip): 43.28 - Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_80k_ade20k.py + 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_512x512_80k_ade20k +- Name: deeplabv3_r101-d8_4xb4-80k_ade20k-512x512 In Collection: DeepLabV3 Metadata: backbone: R-101-D8 @@ -416,9 +416,9 @@ Models: Metrics: mIoU: 44.08 mIoU(ms+flip): 45.19 - Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_80k_ade20k.py + 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_512x512_160k_ade20k +- Name: deeplabv3_r50-d8_4xb4-160k_ade20k-512x512 In Collection: DeepLabV3 Metadata: backbone: R-50-D8 @@ -430,9 +430,9 @@ Models: Metrics: mIoU: 42.66 mIoU(ms+flip): 44.09 - Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_160k_ade20k.py + 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_512x512_160k_ade20k +- Name: deeplabv3_r101-d8_4xb4-160k_ade20k-512x512 In Collection: DeepLabV3 Metadata: backbone: R-101-D8 @@ -444,9 +444,9 @@ Models: Metrics: mIoU: 45.0 mIoU(ms+flip): 46.66 - Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k.py + 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_512x512_20k_voc12aug +- Name: deeplabv3_r50-d8_4xb4-20k_voc12aug-512x512 In Collection: DeepLabV3 Metadata: backbone: R-50-D8 @@ -466,9 +466,9 @@ Models: Metrics: mIoU: 76.17 mIoU(ms+flip): 77.42 - Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_20k_voc12aug.py + 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_512x512_20k_voc12aug +- Name: deeplabv3_r101-d8_4xb4-20k_voc12aug-512x512 In Collection: DeepLabV3 Metadata: backbone: R-101-D8 @@ -488,9 +488,9 @@ Models: Metrics: mIoU: 78.7 mIoU(ms+flip): 79.95 - Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_20k_voc12aug.py + 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_512x512_40k_voc12aug +- Name: deeplabv3_r50-d8_4xb4-40k_voc12aug-512x512 In Collection: DeepLabV3 Metadata: backbone: R-50-D8 @@ -502,9 +502,9 @@ Models: Metrics: mIoU: 77.68 mIoU(ms+flip): 78.78 - Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_40k_voc12aug.py + 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_512x512_40k_voc12aug +- Name: deeplabv3_r101-d8_4xb4-40k_voc12aug-512x512 In Collection: DeepLabV3 Metadata: backbone: R-101-D8 @@ -516,9 +516,9 @@ Models: Metrics: mIoU: 77.92 mIoU(ms+flip): 79.18 - Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_40k_voc12aug.py + 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_480x480_40k_pascal_context +- Name: deeplabv3_r101-d8_4xb4-40k_pascal-context-480x480 In Collection: DeepLabV3 Metadata: backbone: R-101-D8 @@ -538,9 +538,9 @@ Models: Metrics: mIoU: 46.55 mIoU(ms+flip): 47.81 - Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context.py + 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_480x480_80k_pascal_context +- Name: deeplabv3_r101-d8_4xb4-80k_pascal-context-480x480 In Collection: DeepLabV3 Metadata: backbone: R-101-D8 @@ -552,9 +552,9 @@ Models: Metrics: mIoU: 46.42 mIoU(ms+flip): 47.53 - Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context.py + 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_480x480_40k_pascal_context_59 +- Name: deeplabv3_r101-d8_4xb4-40k_pascal-context-59-480x480 In Collection: DeepLabV3 Metadata: backbone: R-101-D8 @@ -566,9 +566,9 @@ Models: Metrics: mIoU: 52.61 mIoU(ms+flip): 54.28 - Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59.py + 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_480x480_80k_pascal_context_59 +- Name: deeplabv3_r101-d8_4xb4-80k_pascal-context-59-480x480 In Collection: DeepLabV3 Metadata: backbone: R-101-D8 @@ -580,9 +580,9 @@ Models: Metrics: mIoU: 52.46 mIoU(ms+flip): 54.09 - Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context_59.py + 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_512x512_4x4_20k_coco-stuff10k +- Name: deeplabv3_r50-d8_4xb4-20k_coco-stuff10k-512x512 In Collection: DeepLabV3 Metadata: backbone: R-50-D8 @@ -602,9 +602,9 @@ Models: Metrics: mIoU: 34.66 mIoU(ms+flip): 36.08 - Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_4x4_20k_coco-stuff10k.py + 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_512x512_4x4_20k_coco-stuff10k +- Name: deeplabv3_r101-d8_4xb4-20k_coco-stuff10k-512x512 In Collection: DeepLabV3 Metadata: backbone: R-101-D8 @@ -624,9 +624,9 @@ Models: Metrics: mIoU: 37.3 mIoU(ms+flip): 38.42 - Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_20k_coco-stuff10k.py + 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_512x512_4x4_40k_coco-stuff10k +- Name: deeplabv3_r50-d8_4xb4-40k_coco-stuff10k-512x512 In Collection: DeepLabV3 Metadata: backbone: R-50-D8 @@ -638,9 +638,9 @@ Models: Metrics: mIoU: 35.73 mIoU(ms+flip): 37.09 - Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_4x4_40k_coco-stuff10k.py + 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_512x512_4x4_40k_coco-stuff10k +- Name: deeplabv3_r101-d8_4xb4-40k_coco-stuff10k-512x512 In Collection: DeepLabV3 Metadata: backbone: R-101-D8 @@ -652,9 +652,9 @@ Models: Metrics: mIoU: 37.81 mIoU(ms+flip): 38.8 - Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_40k_coco-stuff10k.py + 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_512x512_4x4_80k_coco-stuff164k +- Name: deeplabv3_r50-d8_4xb4-80k_coco-stuff164k-512x512 In Collection: DeepLabV3 Metadata: backbone: R-50-D8 @@ -674,9 +674,9 @@ Models: Metrics: mIoU: 39.38 mIoU(ms+flip): 40.03 - Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_4x4_80k_coco-stuff164k.py + 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_512x512_4x4_80k_coco-stuff164k +- Name: deeplabv3_r101-d8_4xb4-80k_coco-stuff164k-512x512 In Collection: DeepLabV3 Metadata: backbone: R-101-D8 @@ -696,9 +696,9 @@ Models: Metrics: mIoU: 40.87 mIoU(ms+flip): 41.5 - Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_80k_coco-stuff164k.py + 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_512x512_4x4_160k_coco-stuff164k +- Name: deeplabv3_r50-d8_4xb4-160k_coco-stuff164k-512x512 In Collection: DeepLabV3 Metadata: backbone: R-50-D8 @@ -710,9 +710,9 @@ Models: Metrics: mIoU: 41.09 mIoU(ms+flip): 41.69 - Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_4x4_160k_coco-stuff164k.py + 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_512x512_4x4_160k_coco-stuff164k +- Name: deeplabv3_r101-d8_4xb4-160k_coco-stuff164k-512x512 In Collection: DeepLabV3 Metadata: backbone: R-101-D8 @@ -724,9 +724,9 @@ Models: Metrics: mIoU: 41.82 mIoU(ms+flip): 42.49 - Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_160k_coco-stuff164k.py + 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_512x512_4x4_320k_coco-stuff164k +- Name: deeplabv3_r50-d8_4xb4-320k_coco-stuff164k-512x512 In Collection: DeepLabV3 Metadata: backbone: R-50-D8 @@ -738,9 +738,9 @@ Models: Metrics: mIoU: 41.37 mIoU(ms+flip): 42.22 - Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_4x4_320k_coco-stuff164k.py + 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_512x512_4x4_320k_coco-stuff164k +- Name: deeplabv3_r101-d8_4xb4-320k_coco-stuff164k-512x512 In Collection: DeepLabV3 Metadata: backbone: R-101-D8 @@ -752,5 +752,5 @@ Models: Metrics: mIoU: 42.61 mIoU(ms+flip): 43.42 - Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k.py + 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/deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes.py b/configs/deeplabv3/deeplabv3_r101-d16-mg124_4xb2-40k_cityscapes-512x1024.py similarity index 83% rename from configs/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes.py rename to configs/deeplabv3/deeplabv3_r101-d16-mg124_4xb2-40k_cityscapes-512x1024.py index f20f260e23..b9f3c178df 100644 --- a/configs/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes.py +++ b/configs/deeplabv3/deeplabv3_r101-d16-mg124_4xb2-40k_cityscapes-512x1024.py @@ -1,4 +1,4 @@ -_base_ = './deeplabv3_r50-d8_512x1024_40k_cityscapes.py' +_base_ = './deeplabv3_r50-d8_4xb2-40k_cityscapes-512x1024.py' model = dict( pretrained='open-mmlab://resnet101_v1c', backbone=dict( diff --git a/configs/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes.py b/configs/deeplabv3/deeplabv3_r101-d16-mg124_4xb2-80k_cityscapes-512x1024.py similarity index 83% rename from configs/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes.py rename to configs/deeplabv3/deeplabv3_r101-d16-mg124_4xb2-80k_cityscapes-512x1024.py index bf39d2f12b..da3a88f998 100644 --- a/configs/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes.py +++ b/configs/deeplabv3/deeplabv3_r101-d16-mg124_4xb2-80k_cityscapes-512x1024.py @@ -1,4 +1,4 @@ -_base_ = './deeplabv3plus_r50-d8_512x1024_40k_cityscapes.py' +_base_ = './deeplabv3_r50-d8_4xb2-80k_cityscapes-512x1024.py' model = dict( pretrained='open-mmlab://resnet101_v1c', backbone=dict( diff --git a/configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context.py b/configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context.py deleted file mode 100644 index 0b5256f7b7..0000000000 --- a/configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './deeplabv3_r50-d8_480x480_40k_pascal_context.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59.py b/configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59.py deleted file mode 100644 index 4874121fd0..0000000000 --- a/configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './deeplabv3_r50-d8_480x480_40k_pascal_context_59.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context.py b/configs/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context.py deleted file mode 100644 index 001b7a69c1..0000000000 --- a/configs/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './deeplabv3_r50-d8_480x480_80k_pascal_context.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context_59.py b/configs/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context_59.py deleted file mode 100644 index 032dc8b621..0000000000 --- a/configs/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context_59.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './deeplabv3_r50-d8_480x480_80k_pascal_context_59.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/deeplabv3/deeplabv3_r101-d8_4xb2-40k_cityscapes-512x1024.py b/configs/deeplabv3/deeplabv3_r101-d8_4xb2-40k_cityscapes-512x1024.py new file mode 100644 index 0000000000..d3c63f7f7d --- /dev/null +++ b/configs/deeplabv3/deeplabv3_r101-d8_4xb2-40k_cityscapes-512x1024.py @@ -0,0 +1,2 @@ +_base_ = './deeplabv3_r50-d8_4xb4-40k_cityscapes-512x1024.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/deeplabv3/deeplabv3_r101-d8_4xb2-40k_cityscapes-769x769.py b/configs/deeplabv3/deeplabv3_r101-d8_4xb2-40k_cityscapes-769x769.py new file mode 100644 index 0000000000..77f426cc9a --- /dev/null +++ b/configs/deeplabv3/deeplabv3_r101-d8_4xb2-40k_cityscapes-769x769.py @@ -0,0 +1,2 @@ +_base_ = './deeplabv3_r50-d8_4xb4-40k_cityscapes-769x769.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/deeplabv3/deeplabv3_r101-d8_4xb2-80k_cityscapes-512x1024.py b/configs/deeplabv3/deeplabv3_r101-d8_4xb2-80k_cityscapes-512x1024.py new file mode 100644 index 0000000000..730802cab3 --- /dev/null +++ b/configs/deeplabv3/deeplabv3_r101-d8_4xb2-80k_cityscapes-512x1024.py @@ -0,0 +1,2 @@ +_base_ = './deeplabv3_r50-d8_4xb4-80k_cityscapes-512x1024.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/deeplabv3/deeplabv3_r101-d8_4xb2-80k_cityscapes-769x769.py b/configs/deeplabv3/deeplabv3_r101-d8_4xb2-80k_cityscapes-769x769.py new file mode 100644 index 0000000000..2d1242414a --- /dev/null +++ b/configs/deeplabv3/deeplabv3_r101-d8_4xb2-80k_cityscapes-769x769.py @@ -0,0 +1,2 @@ +_base_ = './deeplabv3_r50-d8_4xb4-80k_cityscapes-769x769.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/deeplabv3/deeplabv3_r101-d8_fp16_512x1024_80k_cityscapes.py b/configs/deeplabv3/deeplabv3_r101-d8_4xb2-amp-80k_cityscapes-512x1024.py similarity index 74% rename from configs/deeplabv3/deeplabv3_r101-d8_fp16_512x1024_80k_cityscapes.py rename to configs/deeplabv3/deeplabv3_r101-d8_4xb2-amp-80k_cityscapes-512x1024.py index 096c55b640..61ef2f9ee2 100644 --- a/configs/deeplabv3/deeplabv3_r101-d8_fp16_512x1024_80k_cityscapes.py +++ b/configs/deeplabv3/deeplabv3_r101-d8_4xb2-amp-80k_cityscapes-512x1024.py @@ -1,4 +1,4 @@ -_base_ = './deeplabv3_r101-d8_512x1024_80k_cityscapes.py' +_base_ = './deeplabv3_r101-d8_4xb4-40k_cityscapes-512x1024.py' optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005) optim_wrapper = dict( _delete_=True, diff --git a/configs/deeplabv3/deeplabv3_r101-d8_4xb4-160k_ade20k-512x512.py b/configs/deeplabv3/deeplabv3_r101-d8_4xb4-160k_ade20k-512x512.py new file mode 100644 index 0000000000..0ed6eee833 --- /dev/null +++ b/configs/deeplabv3/deeplabv3_r101-d8_4xb4-160k_ade20k-512x512.py @@ -0,0 +1,2 @@ +_base_ = './deeplabv3_r50-d8_4xb4-160k_ade20k-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/deeplabv3/deeplabv3_r101-d8_4xb4-160k_coco-stuff164k-512x512.py b/configs/deeplabv3/deeplabv3_r101-d8_4xb4-160k_coco-stuff164k-512x512.py new file mode 100644 index 0000000000..add008345f --- /dev/null +++ b/configs/deeplabv3/deeplabv3_r101-d8_4xb4-160k_coco-stuff164k-512x512.py @@ -0,0 +1,2 @@ +_base_ = './deeplabv3_r50-d8_4xb4-160k_coco-stuff164k-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/deeplabv3/deeplabv3_r101-d8_4xb4-20k_coco-stuff10k-512x512.py b/configs/deeplabv3/deeplabv3_r101-d8_4xb4-20k_coco-stuff10k-512x512.py new file mode 100644 index 0000000000..349cc88f0a --- /dev/null +++ b/configs/deeplabv3/deeplabv3_r101-d8_4xb4-20k_coco-stuff10k-512x512.py @@ -0,0 +1,2 @@ +_base_ = './deeplabv3_r50-d8_4xb4-20k_coco-stuff10k-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/deeplabv3/deeplabv3_r101-d8_4xb4-20k_voc12aug-512x512.py b/configs/deeplabv3/deeplabv3_r101-d8_4xb4-20k_voc12aug-512x512.py new file mode 100644 index 0000000000..1c527e0c53 --- /dev/null +++ b/configs/deeplabv3/deeplabv3_r101-d8_4xb4-20k_voc12aug-512x512.py @@ -0,0 +1,2 @@ +_base_ = './deeplabv3_r50-d8_4xb4-20k_voc12aug-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/deeplabv3/deeplabv3_r101-d8_4xb4-320k_coco-stuff164k-512x512.py b/configs/deeplabv3/deeplabv3_r101-d8_4xb4-320k_coco-stuff164k-512x512.py new file mode 100644 index 0000000000..ea27bedc04 --- /dev/null +++ b/configs/deeplabv3/deeplabv3_r101-d8_4xb4-320k_coco-stuff164k-512x512.py @@ -0,0 +1,2 @@ +_base_ = './deeplabv3_r50-d8_4xb4-320k_coco-stuff164k-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/deeplabv3/deeplabv3_r101-d8_4xb4-40k_coco-stuff10k-512x512.py b/configs/deeplabv3/deeplabv3_r101-d8_4xb4-40k_coco-stuff10k-512x512.py new file mode 100644 index 0000000000..a43a786e0e --- /dev/null +++ b/configs/deeplabv3/deeplabv3_r101-d8_4xb4-40k_coco-stuff10k-512x512.py @@ -0,0 +1,2 @@ +_base_ = './deeplabv3_r50-d8_4xb4-40k_coco-stuff10k-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_160k_coco-stuff164k.py b/configs/deeplabv3/deeplabv3_r101-d8_4xb4-40k_pascal-context-480x480.py similarity index 55% rename from configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_160k_coco-stuff164k.py rename to configs/deeplabv3/deeplabv3_r101-d8_4xb4-40k_pascal-context-480x480.py index 76b124248e..8879d5394f 100644 --- a/configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_160k_coco-stuff164k.py +++ b/configs/deeplabv3/deeplabv3_r101-d8_4xb4-40k_pascal-context-480x480.py @@ -1,2 +1,2 @@ -_base_ = './deeplabv3_r50-d8_512x512_4x4_160k_coco-stuff164k.py' +_base_ = './deeplabv3_r50-d8_4xb4-40k_pascal-context-480x480.py' model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/deeplabv3/deeplabv3_r101-d8_4xb4-40k_pascal-context-59-480x480.py b/configs/deeplabv3/deeplabv3_r101-d8_4xb4-40k_pascal-context-59-480x480.py new file mode 100644 index 0000000000..54671d4dc6 --- /dev/null +++ b/configs/deeplabv3/deeplabv3_r101-d8_4xb4-40k_pascal-context-59-480x480.py @@ -0,0 +1,2 @@ +_base_ = './deeplabv3_r50-d8_4xb4-40k_pascal-context-59-480x480.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/deeplabv3/deeplabv3_r101-d8_4xb4-40k_voc12aug-512x512.py b/configs/deeplabv3/deeplabv3_r101-d8_4xb4-40k_voc12aug-512x512.py new file mode 100644 index 0000000000..1b2635d1c2 --- /dev/null +++ b/configs/deeplabv3/deeplabv3_r101-d8_4xb4-40k_voc12aug-512x512.py @@ -0,0 +1,2 @@ +_base_ = './deeplabv3_r50-d8_4xb4-40k_voc12aug-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/deeplabv3/deeplabv3_r101-d8_4xb4-80k_ade20k-512x512.py b/configs/deeplabv3/deeplabv3_r101-d8_4xb4-80k_ade20k-512x512.py new file mode 100644 index 0000000000..b7bb0b6448 --- /dev/null +++ b/configs/deeplabv3/deeplabv3_r101-d8_4xb4-80k_ade20k-512x512.py @@ -0,0 +1,2 @@ +_base_ = './deeplabv3_r50-d8_4xb4-80k_ade20k-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k.py b/configs/deeplabv3/deeplabv3_r101-d8_4xb4-80k_coco-stuff164k-512x512.py similarity index 55% rename from configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k.py rename to configs/deeplabv3/deeplabv3_r101-d8_4xb4-80k_coco-stuff164k-512x512.py index 50669c864a..2d4f6f747b 100644 --- a/configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k.py +++ b/configs/deeplabv3/deeplabv3_r101-d8_4xb4-80k_coco-stuff164k-512x512.py @@ -1,2 +1,2 @@ -_base_ = './deeplabv3_r50-d8_512x512_4x4_320k_coco-stuff164k.py' +_base_ = './deeplabv3_r50-d8_4xb4-80k_coco-stuff164k-512x512.py' model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/deeplabv3/deeplabv3_r101-d8_4xb4-80k_pascal-context-480x480.py b/configs/deeplabv3/deeplabv3_r101-d8_4xb4-80k_pascal-context-480x480.py new file mode 100644 index 0000000000..9d64ca29fe --- /dev/null +++ b/configs/deeplabv3/deeplabv3_r101-d8_4xb4-80k_pascal-context-480x480.py @@ -0,0 +1,2 @@ +_base_ = './deeplabv3_r50-d8_4xb4-80k_pascal-context-480x480.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/deeplabv3/deeplabv3_r101-d8_4xb4-80k_pascal-context-59-480x480.py b/configs/deeplabv3/deeplabv3_r101-d8_4xb4-80k_pascal-context-59-480x480.py new file mode 100644 index 0000000000..54671d4dc6 --- /dev/null +++ b/configs/deeplabv3/deeplabv3_r101-d8_4xb4-80k_pascal-context-59-480x480.py @@ -0,0 +1,2 @@ +_base_ = './deeplabv3_r50-d8_4xb4-40k_pascal-context-59-480x480.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/deeplabv3/deeplabv3_r101-d8_512x1024_40k_cityscapes.py b/configs/deeplabv3/deeplabv3_r101-d8_512x1024_40k_cityscapes.py deleted file mode 100644 index 8c707c79d6..0000000000 --- a/configs/deeplabv3/deeplabv3_r101-d8_512x1024_40k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './deeplabv3_r50-d8_512x1024_40k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes.py b/configs/deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes.py deleted file mode 100644 index 6804a57813..0000000000 --- a/configs/deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './deeplabv3_r50-d8_512x1024_80k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k.py b/configs/deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k.py deleted file mode 100644 index df6f36ef7c..0000000000 --- a/configs/deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './deeplabv3_r50-d8_512x512_160k_ade20k.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/deeplabv3/deeplabv3_r101-d8_512x512_20k_voc12aug.py b/configs/deeplabv3/deeplabv3_r101-d8_512x512_20k_voc12aug.py deleted file mode 100644 index 40f5f62373..0000000000 --- a/configs/deeplabv3/deeplabv3_r101-d8_512x512_20k_voc12aug.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './deeplabv3_r50-d8_512x512_20k_voc12aug.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/deeplabv3/deeplabv3_r101-d8_512x512_40k_voc12aug.py b/configs/deeplabv3/deeplabv3_r101-d8_512x512_40k_voc12aug.py deleted file mode 100644 index fb2be22f8b..0000000000 --- a/configs/deeplabv3/deeplabv3_r101-d8_512x512_40k_voc12aug.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './deeplabv3_r50-d8_512x512_40k_voc12aug.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_20k_coco-stuff10k.py b/configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_20k_coco-stuff10k.py deleted file mode 100644 index d476c66f4e..0000000000 --- a/configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_20k_coco-stuff10k.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './deeplabv3_r50-d8_512x512_4x4_20k_coco-stuff10k.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_40k_coco-stuff10k.py b/configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_40k_coco-stuff10k.py deleted file mode 100644 index 37d09cf994..0000000000 --- a/configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_40k_coco-stuff10k.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './deeplabv3_r50-d8_512x512_4x4_40k_coco-stuff10k.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_80k_coco-stuff164k.py b/configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_80k_coco-stuff164k.py deleted file mode 100644 index a0eb3ddfed..0000000000 --- a/configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_80k_coco-stuff164k.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './deeplabv3_r50-d8_512x512_4x4_80k_coco-stuff164k.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/deeplabv3/deeplabv3_r101-d8_512x512_80k_ade20k.py b/configs/deeplabv3/deeplabv3_r101-d8_512x512_80k_ade20k.py deleted file mode 100644 index 796ba3fb14..0000000000 --- a/configs/deeplabv3/deeplabv3_r101-d8_512x512_80k_ade20k.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './deeplabv3_r50-d8_512x512_80k_ade20k.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/deeplabv3/deeplabv3_r101-d8_769x769_40k_cityscapes.py b/configs/deeplabv3/deeplabv3_r101-d8_769x769_40k_cityscapes.py deleted file mode 100644 index e6d58a67b3..0000000000 --- a/configs/deeplabv3/deeplabv3_r101-d8_769x769_40k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './deeplabv3_r50-d8_769x769_40k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/deeplabv3/deeplabv3_r101-d8_769x769_80k_cityscapes.py b/configs/deeplabv3/deeplabv3_r101-d8_769x769_80k_cityscapes.py deleted file mode 100644 index 13094a98ee..0000000000 --- a/configs/deeplabv3/deeplabv3_r101-d8_769x769_80k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './deeplabv3_r50-d8_769x769_80k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/deeplabv3/deeplabv3_r101b-d8_4xb2-80k_cityscapes-512x1024.py b/configs/deeplabv3/deeplabv3_r101b-d8_4xb2-80k_cityscapes-512x1024.py new file mode 100644 index 0000000000..708932da85 --- /dev/null +++ b/configs/deeplabv3/deeplabv3_r101b-d8_4xb2-80k_cityscapes-512x1024.py @@ -0,0 +1,4 @@ +_base_ = './deeplabv3_r50-d8_4xb2-80k_cityscapes-512x1024.py' +model = dict( + pretrained='torchvision://resnet101', + backbone=dict(type='ResNet', depth=101)) diff --git a/configs/deeplabv3plus/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes.py b/configs/deeplabv3/deeplabv3_r101b-d8_4xb2-80k_cityscapes-769x769.py similarity index 62% rename from configs/deeplabv3plus/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes.py rename to configs/deeplabv3/deeplabv3_r101b-d8_4xb2-80k_cityscapes-769x769.py index 398d9759ca..a0f634d081 100644 --- a/configs/deeplabv3plus/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes.py +++ b/configs/deeplabv3/deeplabv3_r101b-d8_4xb2-80k_cityscapes-769x769.py @@ -1,4 +1,4 @@ -_base_ = './deeplabv3plus_r50-d8_512x1024_80k_cityscapes.py' +_base_ = './deeplabv3_r50-d8_4xb2-80k_cityscapes-769x769.py' model = dict( pretrained='torchvision://resnet101', backbone=dict(type='ResNet', depth=101)) diff --git a/configs/deeplabv3/deeplabv3_r18-d8_4xb2-80k_cityscapes-512x1024.py b/configs/deeplabv3/deeplabv3_r18-d8_4xb2-80k_cityscapes-512x1024.py new file mode 100644 index 0000000000..bc353bb564 --- /dev/null +++ b/configs/deeplabv3/deeplabv3_r18-d8_4xb2-80k_cityscapes-512x1024.py @@ -0,0 +1,9 @@ +_base_ = './deeplabv3_r50-d8_4xb2-80k_cityscapes-512x1024.py' +model = dict( + pretrained='open-mmlab://resnet18_v1c', + backbone=dict(depth=18), + decode_head=dict( + in_channels=512, + channels=128, + ), + auxiliary_head=dict(in_channels=256, channels=64)) diff --git a/configs/deeplabv3/deeplabv3_r18-d8_4xb2-80k_cityscapes-769x769.py b/configs/deeplabv3/deeplabv3_r18-d8_4xb2-80k_cityscapes-769x769.py new file mode 100644 index 0000000000..2a7f599500 --- /dev/null +++ b/configs/deeplabv3/deeplabv3_r18-d8_4xb2-80k_cityscapes-769x769.py @@ -0,0 +1,9 @@ +_base_ = './deeplabv3_r50-d8_4xb4-80k_cityscapes-769x769.py' +model = dict( + pretrained='open-mmlab://resnet18_v1c', + backbone=dict(depth=18), + decode_head=dict( + in_channels=512, + channels=128, + ), + auxiliary_head=dict(in_channels=256, channels=64)) diff --git a/configs/deeplabv3/deeplabv3_r18b-d8_4xb2-80k_cityscapes-512x1024.py b/configs/deeplabv3/deeplabv3_r18b-d8_4xb2-80k_cityscapes-512x1024.py new file mode 100644 index 0000000000..c747cd74a2 --- /dev/null +++ b/configs/deeplabv3/deeplabv3_r18b-d8_4xb2-80k_cityscapes-512x1024.py @@ -0,0 +1,9 @@ +_base_ = './deeplabv3_r50-d8_4xb2-80k_cityscapes-512x1024.py' +model = dict( + pretrained='torchvision://resnet18', + backbone=dict(type='ResNet', depth=18), + decode_head=dict( + in_channels=512, + channels=128, + ), + auxiliary_head=dict(in_channels=256, channels=64)) diff --git a/configs/fcn/fcn_r18b-d8_769x769_80k_cityscapes.py b/configs/deeplabv3/deeplabv3_r18b-d8_4xb2-80k_cityscapes-769x769.py similarity index 79% rename from configs/fcn/fcn_r18b-d8_769x769_80k_cityscapes.py rename to configs/deeplabv3/deeplabv3_r18b-d8_4xb2-80k_cityscapes-769x769.py index 5dd34dd213..6506abf696 100644 --- a/configs/fcn/fcn_r18b-d8_769x769_80k_cityscapes.py +++ b/configs/deeplabv3/deeplabv3_r18b-d8_4xb2-80k_cityscapes-769x769.py @@ -1,4 +1,4 @@ -_base_ = './fcn_r50-d8_769x769_80k_cityscapes.py' +_base_ = './deeplabv3_r50-d8_4xb2-80k_cityscapes-769x769.py' model = dict( pretrained='torchvision://resnet18', backbone=dict(type='ResNet', depth=18), diff --git a/configs/deeplabv3/deeplabv3_r50-d8_512x1024_40k_cityscapes.py b/configs/deeplabv3/deeplabv3_r50-d8_4xb2-40k_cityscapes-512x1024.py similarity index 100% rename from configs/deeplabv3/deeplabv3_r50-d8_512x1024_40k_cityscapes.py rename to configs/deeplabv3/deeplabv3_r50-d8_4xb2-40k_cityscapes-512x1024.py diff --git a/configs/deeplabv3/deeplabv3_r50-d8_769x769_40k_cityscapes.py b/configs/deeplabv3/deeplabv3_r50-d8_4xb2-40k_cityscapes-769x769.py similarity index 100% rename from configs/deeplabv3/deeplabv3_r50-d8_769x769_40k_cityscapes.py rename to configs/deeplabv3/deeplabv3_r50-d8_4xb2-40k_cityscapes-769x769.py diff --git a/configs/deeplabv3/deeplabv3_r50-d8_512x1024_80k_cityscapes.py b/configs/deeplabv3/deeplabv3_r50-d8_4xb2-80k_cityscapes-512x1024.py similarity index 100% rename from configs/deeplabv3/deeplabv3_r50-d8_512x1024_80k_cityscapes.py rename to configs/deeplabv3/deeplabv3_r50-d8_4xb2-80k_cityscapes-512x1024.py diff --git a/configs/deeplabv3/deeplabv3_r50-d8_769x769_80k_cityscapes.py b/configs/deeplabv3/deeplabv3_r50-d8_4xb2-80k_cityscapes-769x769.py similarity index 100% rename from configs/deeplabv3/deeplabv3_r50-d8_769x769_80k_cityscapes.py rename to configs/deeplabv3/deeplabv3_r50-d8_4xb2-80k_cityscapes-769x769.py diff --git a/configs/deeplabv3/deeplabv3_r50-d8_512x512_160k_ade20k.py b/configs/deeplabv3/deeplabv3_r50-d8_4xb4-160k_ade20k-512x512.py similarity index 100% rename from configs/deeplabv3/deeplabv3_r50-d8_512x512_160k_ade20k.py rename to configs/deeplabv3/deeplabv3_r50-d8_4xb4-160k_ade20k-512x512.py diff --git a/configs/deeplabv3/deeplabv3_r50-d8_512x512_4x4_160k_coco-stuff164k.py b/configs/deeplabv3/deeplabv3_r50-d8_4xb4-160k_coco-stuff164k-512x512.py similarity index 100% rename from configs/deeplabv3/deeplabv3_r50-d8_512x512_4x4_160k_coco-stuff164k.py rename to configs/deeplabv3/deeplabv3_r50-d8_4xb4-160k_coco-stuff164k-512x512.py diff --git a/configs/deeplabv3/deeplabv3_r50-d8_512x512_4x4_20k_coco-stuff10k.py b/configs/deeplabv3/deeplabv3_r50-d8_4xb4-20k_coco-stuff10k-512x512.py similarity index 100% rename from configs/deeplabv3/deeplabv3_r50-d8_512x512_4x4_20k_coco-stuff10k.py rename to configs/deeplabv3/deeplabv3_r50-d8_4xb4-20k_coco-stuff10k-512x512.py diff --git a/configs/deeplabv3/deeplabv3_r50-d8_512x512_20k_voc12aug.py b/configs/deeplabv3/deeplabv3_r50-d8_4xb4-20k_voc12aug-512x512.py similarity index 100% rename from configs/deeplabv3/deeplabv3_r50-d8_512x512_20k_voc12aug.py rename to configs/deeplabv3/deeplabv3_r50-d8_4xb4-20k_voc12aug-512x512.py diff --git a/configs/deeplabv3/deeplabv3_r50-d8_512x512_4x4_320k_coco-stuff164k.py b/configs/deeplabv3/deeplabv3_r50-d8_4xb4-320k_coco-stuff164k-512x512.py similarity index 100% rename from configs/deeplabv3/deeplabv3_r50-d8_512x512_4x4_320k_coco-stuff164k.py rename to configs/deeplabv3/deeplabv3_r50-d8_4xb4-320k_coco-stuff164k-512x512.py diff --git a/configs/deeplabv3/deeplabv3_r50-d8_512x512_4x4_40k_coco-stuff10k.py b/configs/deeplabv3/deeplabv3_r50-d8_4xb4-40k_coco-stuff10k-512x512.py similarity index 100% rename from configs/deeplabv3/deeplabv3_r50-d8_512x512_4x4_40k_coco-stuff10k.py rename to configs/deeplabv3/deeplabv3_r50-d8_4xb4-40k_coco-stuff10k-512x512.py diff --git a/configs/deeplabv3/deeplabv3_r50-d8_480x480_40k_pascal_context.py b/configs/deeplabv3/deeplabv3_r50-d8_4xb4-40k_pascal-context-480x480.py similarity index 100% rename from configs/deeplabv3/deeplabv3_r50-d8_480x480_40k_pascal_context.py rename to configs/deeplabv3/deeplabv3_r50-d8_4xb4-40k_pascal-context-480x480.py diff --git a/configs/deeplabv3/deeplabv3_r50-d8_480x480_40k_pascal_context_59.py b/configs/deeplabv3/deeplabv3_r50-d8_4xb4-40k_pascal-context-59-480x480.py similarity index 100% rename from configs/deeplabv3/deeplabv3_r50-d8_480x480_40k_pascal_context_59.py rename to configs/deeplabv3/deeplabv3_r50-d8_4xb4-40k_pascal-context-59-480x480.py diff --git a/configs/deeplabv3/deeplabv3_r50-d8_512x512_40k_voc12aug.py b/configs/deeplabv3/deeplabv3_r50-d8_4xb4-40k_voc12aug-512x512.py similarity index 100% rename from configs/deeplabv3/deeplabv3_r50-d8_512x512_40k_voc12aug.py rename to configs/deeplabv3/deeplabv3_r50-d8_4xb4-40k_voc12aug-512x512.py diff --git a/configs/deeplabv3/deeplabv3_r50-d8_512x512_80k_ade20k.py b/configs/deeplabv3/deeplabv3_r50-d8_4xb4-80k_ade20k-512x512.py similarity index 100% rename from configs/deeplabv3/deeplabv3_r50-d8_512x512_80k_ade20k.py rename to configs/deeplabv3/deeplabv3_r50-d8_4xb4-80k_ade20k-512x512.py diff --git a/configs/deeplabv3/deeplabv3_r50-d8_512x512_4x4_80k_coco-stuff164k.py b/configs/deeplabv3/deeplabv3_r50-d8_4xb4-80k_coco-stuff164k-512x512.py similarity index 100% rename from configs/deeplabv3/deeplabv3_r50-d8_512x512_4x4_80k_coco-stuff164k.py rename to configs/deeplabv3/deeplabv3_r50-d8_4xb4-80k_coco-stuff164k-512x512.py diff --git a/configs/deeplabv3/deeplabv3_r50-d8_480x480_80k_pascal_context.py b/configs/deeplabv3/deeplabv3_r50-d8_4xb4-80k_pascal-context-480x480.py similarity index 100% rename from configs/deeplabv3/deeplabv3_r50-d8_480x480_80k_pascal_context.py rename to configs/deeplabv3/deeplabv3_r50-d8_4xb4-80k_pascal-context-480x480.py diff --git a/configs/deeplabv3/deeplabv3_r50-d8_480x480_80k_pascal_context_59.py b/configs/deeplabv3/deeplabv3_r50-d8_4xb4-80k_pascal-context-59-480x480.py similarity index 100% rename from configs/deeplabv3/deeplabv3_r50-d8_480x480_80k_pascal_context_59.py rename to configs/deeplabv3/deeplabv3_r50-d8_4xb4-80k_pascal-context-59-480x480.py diff --git a/configs/deeplabv3/deeplabv3_r50b-d8_4xb2-80k_cityscapes-512x1024.py b/configs/deeplabv3/deeplabv3_r50b-d8_4xb2-80k_cityscapes-512x1024.py new file mode 100644 index 0000000000..818519f263 --- /dev/null +++ b/configs/deeplabv3/deeplabv3_r50b-d8_4xb2-80k_cityscapes-512x1024.py @@ -0,0 +1,2 @@ +_base_ = './deeplabv3_r50-d8_4xb2-80k_cityscapes-512x1024.py' +model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet')) diff --git a/configs/deeplabv3plus/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes.py b/configs/deeplabv3/deeplabv3_r50b-d8_4xb2-80k_cityscapes-769x769.py similarity index 56% rename from configs/deeplabv3plus/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes.py rename to configs/deeplabv3/deeplabv3_r50b-d8_4xb2-80k_cityscapes-769x769.py index dd8e1da9c7..d9a28808c1 100644 --- a/configs/deeplabv3plus/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes.py +++ b/configs/deeplabv3/deeplabv3_r50b-d8_4xb2-80k_cityscapes-769x769.py @@ -1,2 +1,2 @@ -_base_ = './deeplabv3plus_r50-d8_512x1024_80k_cityscapes.py' +_base_ = './deeplabv3_r50-d8_4xb4-80k_cityscapes-769x769.py' model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet')) diff --git a/configs/deeplabv3plus/README.md b/configs/deeplabv3plus/README.md index 86b8bfb43d..b3d3ce7678 100644 --- a/configs/deeplabv3plus/README.md +++ b/configs/deeplabv3plus/README.md @@ -37,92 +37,92 @@ Spatial pyramid pooling module or encode-decoder structure are used in deep neur ### 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/master/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_40k_cityscapes.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/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_40k_cityscapes.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/master/configs/deeplabv3plus/deeplabv3plus_r50-d8_769x769_40k_cityscapes.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/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_769x769_40k_cityscapes.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/master/configs/deeplabv3plus/deeplabv3plus_r18-d8_512x1024_80k_cityscapes.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/master/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_80k_cityscapes.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/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes.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/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_fp16_512x1024_80k_cityscapes.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/master/configs/deeplabv3plus/deeplabv3plus_r18-d8_769x769_80k_cityscapes.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/master/configs/deeplabv3plus/deeplabv3plus_r50-d8_769x769_80k_cityscapes.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\[1\]](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_769x769_80k_cityscapes.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/master/configs/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes.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/master/configs/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes.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/master/configs/deeplabv3plus/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes.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/master/configs/deeplabv3plus/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes.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/master/configs/deeplabv3plus/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes.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/master/configs/deeplabv3plus/deeplabv3plus_r18b-d8_769x769_80k_cityscapes.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/master/configs/deeplabv3plus/deeplabv3plus_r50b-d8_769x769_80k_cityscapes.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/master/configs/deeplabv3plus/deeplabv3plus_r101b-d8_769x769_80k_cityscapes.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) | 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\[1\]](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) | \[1\] 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/master/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_ade20k.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/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_ade20k.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/master/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_160k_ade20k.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/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_160k_ade20k.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) | 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) | ### 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/master/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_20k_voc12aug.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/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_20k_voc12aug.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/master/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_40k_voc12aug.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/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_40k_voc12aug.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) | 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) | ### 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/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context.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/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context.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) | 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) | ### 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/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59.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/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59.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) | 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) | ### 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/master/configs/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_loveda.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/master/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_loveda.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/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_loveda.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) | 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) | ### 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/master/configs/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_potsdam.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/master/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_potsdam.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/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_potsdam.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) | 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) | ### 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/master/configs/deeplabv3plus/deeplabv3plus_r18-d8_4x4_512x512_80k_vaihingen.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/master/configs/deeplabv3plus/deeplabv3plus_r50-d8_4x4_512x512_80k_vaihingen.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/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_4x4_512x512_80k_vaihingen.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) | 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) | ### 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/master/configs/deeplabv3plus/deeplabv3plus_r18-d8_4x4_896x896_80k_isaid.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/master/configs/deeplabv3plus/deeplabv3plus_r50-d8_4x4_896x896_80k_isaid.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) | 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) | Note: diff --git a/configs/deeplabv3plus/deeplabv3plus.yml b/configs/deeplabv3plus/deeplabv3plus.yml index 56790c8428..755c1fd4be 100644 --- a/configs/deeplabv3plus/deeplabv3plus.yml +++ b/configs/deeplabv3plus/deeplabv3plus.yml @@ -21,7 +21,7 @@ Collections: Converted From: Code: https://github.com/tensorflow/models/tree/master/research/deeplab Models: -- Name: deeplabv3plus_r50-d8_512x1024_40k_cityscapes +- Name: deeplabv3plus_r50-d8_4xb2-40k_cityscapes-512x1024 In Collection: DeepLabV3+ Metadata: backbone: R-50-D8 @@ -41,9 +41,9 @@ Models: Metrics: mIoU: 79.61 mIoU(ms+flip): 81.01 - Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_40k_cityscapes.py + 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_512x1024_40k_cityscapes +- Name: deeplabv3plus_r101-d8_4xb2-40k_cityscapes-512x1024 In Collection: DeepLabV3+ Metadata: backbone: R-101-D8 @@ -63,9 +63,9 @@ Models: Metrics: mIoU: 80.21 mIoU(ms+flip): 81.82 - Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_40k_cityscapes.py + 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_769x769_40k_cityscapes +- Name: deeplabv3plus_r50-d8_4xb2-40k_cityscapes-769x769 In Collection: DeepLabV3+ Metadata: backbone: R-50-D8 @@ -85,9 +85,9 @@ Models: Metrics: mIoU: 78.97 mIoU(ms+flip): 80.46 - Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_769x769_40k_cityscapes.py + 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_769x769_40k_cityscapes +- Name: deeplabv3plus_r101-d8_4xb2-40k_cityscapes-769x769 In Collection: DeepLabV3+ Metadata: backbone: R-101-D8 @@ -107,9 +107,9 @@ Models: Metrics: mIoU: 79.46 mIoU(ms+flip): 80.5 - Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_769x769_40k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: deeplabv3plus_r18-d8_4xb2-80k_cityscapes-512x1024 In Collection: DeepLabV3+ Metadata: backbone: R-18-D8 @@ -129,9 +129,9 @@ Models: Metrics: mIoU: 76.89 mIoU(ms+flip): 78.76 - Config: configs/deeplabv3plus/deeplabv3plus_r18-d8_512x1024_80k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: deeplabv3plus_r50-d8_4xb2-80k_cityscapes-512x1024 In Collection: DeepLabV3+ Metadata: backbone: R-50-D8 @@ -143,9 +143,9 @@ Models: Metrics: mIoU: 80.09 mIoU(ms+flip): 81.13 - Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_80k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: deeplabv3plus_r101-d8_4xb2-80k_cityscapes-512x1024 In Collection: DeepLabV3+ Metadata: backbone: R-101-D8 @@ -157,9 +157,9 @@ Models: Metrics: mIoU: 80.97 mIoU(ms+flip): 82.03 - Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes.py + 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_fp16_512x1024_80k_cityscapes +- Name: deeplabv3plus_r101-d8_4xb2-amp-80k_cityscapes-512x1024 In Collection: DeepLabV3+ Metadata: backbone: R-101-D8 @@ -170,7 +170,7 @@ Models: hardware: V100 backend: PyTorch batch size: 1 - mode: FP16 + mode: AMP resolution: (512,1024) Training Memory (GB): 6.35 Results: @@ -178,9 +178,9 @@ Models: Dataset: Cityscapes Metrics: mIoU: 80.46 - Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_fp16_512x1024_80k_cityscapes.py + 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_769x769_80k_cityscapes +- Name: deeplabv3plus_r18-d8_4xb2-80k_cityscapes-769x769 In Collection: DeepLabV3+ Metadata: backbone: R-18-D8 @@ -200,9 +200,9 @@ Models: Metrics: mIoU: 76.26 mIoU(ms+flip): 77.91 - Config: configs/deeplabv3plus/deeplabv3plus_r18-d8_769x769_80k_cityscapes.py + 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_769x769_80k_cityscapes +- Name: deeplabv3plus_r50-d8_4xb2-80k_cityscapes-769x769 In Collection: DeepLabV3+ Metadata: backbone: R-50-D8 @@ -214,9 +214,9 @@ Models: Metrics: mIoU: 79.83 mIoU(ms+flip): 81.48 - Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_769x769_80k_cityscapes.py + 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_769x769_80k_cityscapes +- Name: deeplabv3plus_r101-d8_4xb2-80k_cityscapes-769x769 In Collection: DeepLabV3+ Metadata: backbone: R-101-D8 @@ -228,9 +228,9 @@ Models: Metrics: mIoU: 80.65 mIoU(ms+flip): 81.47 - Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_769x769_80k_cityscapes.py + 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_512x1024_40k_cityscapes +- Name: deeplabv3plus_r101-d16-mg124_4xb2-40k_cityscapes-512x1024 In Collection: DeepLabV3+ Metadata: backbone: R-101-D16-MG124 @@ -250,9 +250,9 @@ Models: Metrics: mIoU: 79.09 mIoU(ms+flip): 80.36 - Config: configs/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: deeplabv3plus_r101-d16-mg124_4xb2-80k_cityscapes-512x1024 In Collection: DeepLabV3+ Metadata: backbone: R-101-D16-MG124 @@ -265,9 +265,9 @@ Models: Metrics: mIoU: 79.9 mIoU(ms+flip): 81.33 - Config: configs/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: deeplabv3plus_r18b-d8_4xb2-80k_cityscapes-512x1024 In Collection: DeepLabV3+ Metadata: backbone: R-18b-D8 @@ -287,9 +287,9 @@ Models: Metrics: mIoU: 75.87 mIoU(ms+flip): 77.52 - Config: configs/deeplabv3plus/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: deeplabv3plus_r50b-d8_4xb2-80k_cityscapes-512x1024 In Collection: DeepLabV3+ Metadata: backbone: R-50b-D8 @@ -309,9 +309,9 @@ Models: Metrics: mIoU: 80.28 mIoU(ms+flip): 81.44 - Config: configs/deeplabv3plus/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: deeplabv3plus_r101b-d8_4xb2-80k_cityscapes-512x1024 In Collection: DeepLabV3+ Metadata: backbone: R-101b-D8 @@ -331,9 +331,9 @@ Models: Metrics: mIoU: 80.16 mIoU(ms+flip): 81.41 - Config: configs/deeplabv3plus/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes.py + 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_769x769_80k_cityscapes +- Name: deeplabv3plus_r18b-d8_4xb2-80k_cityscapes-769x769 In Collection: DeepLabV3+ Metadata: backbone: R-18b-D8 @@ -353,9 +353,9 @@ Models: Metrics: mIoU: 76.36 mIoU(ms+flip): 78.24 - Config: configs/deeplabv3plus/deeplabv3plus_r18b-d8_769x769_80k_cityscapes.py + 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_769x769_80k_cityscapes +- Name: deeplabv3plus_r50b-d8_4xb2-80k_cityscapes-769x769 In Collection: DeepLabV3+ Metadata: backbone: R-50b-D8 @@ -375,9 +375,9 @@ Models: Metrics: mIoU: 79.41 mIoU(ms+flip): 80.56 - Config: configs/deeplabv3plus/deeplabv3plus_r50b-d8_769x769_80k_cityscapes.py + 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_769x769_80k_cityscapes +- Name: deeplabv3plus_r101b-d8_4xb2-80k_cityscapes-769x769 In Collection: DeepLabV3+ Metadata: backbone: R-101b-D8 @@ -397,9 +397,9 @@ Models: Metrics: mIoU: 79.88 mIoU(ms+flip): 81.46 - Config: configs/deeplabv3plus/deeplabv3plus_r101b-d8_769x769_80k_cityscapes.py + 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_512x512_80k_ade20k +- Name: deeplabv3plus_r50-d8_4xb4-80k_ade20k-512x512 In Collection: DeepLabV3+ Metadata: backbone: R-50-D8 @@ -419,9 +419,9 @@ Models: Metrics: mIoU: 42.72 mIoU(ms+flip): 43.75 - Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_ade20k.py + 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_512x512_80k_ade20k +- Name: deeplabv3plus_r101-d8_4xb4-160k_ade20k-512x512 In Collection: DeepLabV3+ Metadata: backbone: R-101-D8 @@ -441,9 +441,9 @@ Models: Metrics: mIoU: 44.6 mIoU(ms+flip): 46.06 - Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_ade20k.py + 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_512x512_160k_ade20k +- Name: deeplabv3plus_r50-d8_4xb4-160k_ade20k-512x512 In Collection: DeepLabV3+ Metadata: backbone: R-50-D8 @@ -455,9 +455,9 @@ Models: Metrics: mIoU: 43.95 mIoU(ms+flip): 44.93 - Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_160k_ade20k.py + 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_512x512_160k_ade20k +- Name: deeplabv3plus_r101-d8_4xb4-160k_ade20k-512x512 In Collection: DeepLabV3+ Metadata: backbone: R-101-D8 @@ -469,9 +469,9 @@ Models: Metrics: mIoU: 45.47 mIoU(ms+flip): 46.35 - Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_160k_ade20k.py + 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_512x512_20k_voc12aug +- Name: deeplabv3plus_r50-d8_4xb4-20k_voc12aug-512x512 In Collection: DeepLabV3+ Metadata: backbone: R-50-D8 @@ -491,9 +491,9 @@ Models: Metrics: mIoU: 75.93 mIoU(ms+flip): 77.5 - Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_20k_voc12aug.py + 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_512x512_20k_voc12aug +- Name: deeplabv3plus_r101-d8_4xb4-20k_voc12aug-512x512 In Collection: DeepLabV3+ Metadata: backbone: R-101-D8 @@ -513,9 +513,9 @@ Models: Metrics: mIoU: 77.22 mIoU(ms+flip): 78.59 - Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_20k_voc12aug.py + 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_512x512_40k_voc12aug +- Name: deeplabv3plus_r50-d8_4xb4-40k_voc12aug-512x512 In Collection: DeepLabV3+ Metadata: backbone: R-50-D8 @@ -527,9 +527,9 @@ Models: Metrics: mIoU: 76.81 mIoU(ms+flip): 77.57 - Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_40k_voc12aug.py + 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_512x512_40k_voc12aug +- Name: deeplabv3plus_r101-d8_4xb4-40k_voc12aug-512x512 In Collection: DeepLabV3+ Metadata: backbone: R-101-D8 @@ -541,9 +541,9 @@ Models: Metrics: mIoU: 78.62 mIoU(ms+flip): 79.53 - Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_40k_voc12aug.py + 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_r101-d8_480x480_40k_pascal_context +- Name: deeplabv3plus_r50-d8_4xb4-40k_pascal-context-480x480 In Collection: DeepLabV3+ Metadata: backbone: R-101-D8 @@ -562,9 +562,9 @@ Models: Metrics: mIoU: 47.3 mIoU(ms+flip): 48.47 - Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context.py + 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_r101-d8_480x480_80k_pascal_context +- Name: deeplabv3plus_r50-d8_4xb4-80k_pascal-context-480x480 In Collection: DeepLabV3+ Metadata: backbone: R-101-D8 @@ -576,9 +576,9 @@ Models: Metrics: mIoU: 47.23 mIoU(ms+flip): 48.26 - Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context.py + 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_480x480_40k_pascal_context_59 +- Name: deeplabv3plus_r101-d8_4xb4-40k_pascal-context-59-480x480 In Collection: DeepLabV3+ Metadata: backbone: R-101-D8 @@ -590,9 +590,9 @@ Models: Metrics: mIoU: 52.86 mIoU(ms+flip): 54.54 - Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59.py + 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_480x480_80k_pascal_context_59 +- Name: deeplabv3plus_r101-d8_4xb4-80k_pascal-context-59-480x480 In Collection: DeepLabV3+ Metadata: backbone: R-101-D8 @@ -604,9 +604,9 @@ Models: Metrics: mIoU: 53.2 mIoU(ms+flip): 54.67 - Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59.py + 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_512x512_80k_loveda +- Name: deeplabv3plus_r18-d8_4xb4-80k_loveda-512x512 In Collection: DeepLabV3+ Metadata: backbone: R-18-D8 @@ -626,9 +626,9 @@ Models: Metrics: mIoU: 50.28 mIoU(ms+flip): 50.47 - Config: configs/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_loveda.py + 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_512x512_80k_loveda +- Name: deeplabv3plus_r50-d8_4xb4-80k_loveda-512x512 In Collection: DeepLabV3+ Metadata: backbone: R-50-D8 @@ -648,9 +648,9 @@ Models: Metrics: mIoU: 50.99 mIoU(ms+flip): 50.65 - Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_loveda.py + 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_512x512_80k_loveda +- Name: deeplabv3plus_r101-d8_4xb4-80k_loveda-512x512 In Collection: DeepLabV3+ Metadata: backbone: R-101-D8 @@ -670,9 +670,9 @@ Models: Metrics: mIoU: 51.47 mIoU(ms+flip): 51.32 - Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_loveda.py + 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_512x512_80k_potsdam +- Name: deeplabv3plus_r18-d8_4xb4-80k_potsdam-512x512 In Collection: DeepLabV3+ Metadata: backbone: R-18-D8 @@ -692,9 +692,9 @@ Models: Metrics: mIoU: 77.09 mIoU(ms+flip): 78.44 - Config: configs/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_potsdam.py + 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_512x512_80k_potsdam +- Name: deeplabv3plus_r50-d8_4xb4-80k_potsdam-512x512 In Collection: DeepLabV3+ Metadata: backbone: R-50-D8 @@ -714,9 +714,9 @@ Models: Metrics: mIoU: 78.33 mIoU(ms+flip): 79.27 - Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_potsdam.py + 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_512x512_80k_potsdam +- Name: deeplabv3plus_r101-d8_4xb4-80k_potsdam-512x512 In Collection: DeepLabV3+ Metadata: backbone: R-101-D8 @@ -736,9 +736,9 @@ Models: Metrics: mIoU: 78.7 mIoU(ms+flip): 79.47 - Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_potsdam.py + 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_4x4_512x512_80k_vaihingen +- Name: deeplabv3plus_r18-d8_4xb4-80k_vaihingen-512x512 In Collection: DeepLabV3+ Metadata: backbone: R-18-D8 @@ -758,9 +758,9 @@ Models: Metrics: mIoU: 72.5 mIoU(ms+flip): 74.13 - Config: configs/deeplabv3plus/deeplabv3plus_r18-d8_4x4_512x512_80k_vaihingen.py + 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_4x4_512x512_80k_vaihingen +- Name: deeplabv3plus_r50-d8_4xb4-80k_vaihingen-512x512 In Collection: DeepLabV3+ Metadata: backbone: R-50-D8 @@ -780,9 +780,9 @@ Models: Metrics: mIoU: 73.97 mIoU(ms+flip): 75.05 - Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_4x4_512x512_80k_vaihingen.py + 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_4x4_512x512_80k_vaihingen +- Name: deeplabv3plus_r101-d8_4xb4-80k_vaihingen-512x512 In Collection: DeepLabV3+ Metadata: backbone: R-101-D8 @@ -802,9 +802,9 @@ Models: Metrics: mIoU: 73.06 mIoU(ms+flip): 74.14 - Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_4x4_512x512_80k_vaihingen.py + 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_4x4_896x896_80k_isaid +- Name: deeplabv3plus_r18-d8_4xb4-80k_isaid-896x896 In Collection: DeepLabV3+ Metadata: backbone: R-18-D8 @@ -824,9 +824,9 @@ Models: Metrics: mIoU: 61.35 mIoU(ms+flip): 62.61 - Config: configs/deeplabv3plus/deeplabv3plus_r18-d8_4x4_896x896_80k_isaid.py + 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_4x4_896x896_80k_isaid +- Name: deeplabv3plus_r50-d8_4xb4-80k_isaid-896x896 In Collection: DeepLabV3+ Metadata: backbone: R-50-D8 @@ -846,5 +846,5 @@ Models: Metrics: mIoU: 67.06 mIoU(ms+flip): 68.02 - Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_4x4_896x896_80k_isaid.py + 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 diff --git a/configs/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes.py b/configs/deeplabv3plus/deeplabv3plus_r101-d16-mg124_4xb2-40k_cityscapes-512x1024.py similarity index 82% rename from configs/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes.py rename to configs/deeplabv3plus/deeplabv3plus_r101-d16-mg124_4xb2-40k_cityscapes-512x1024.py index de4a8a5e9f..71c9118e1d 100644 --- a/configs/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes.py +++ b/configs/deeplabv3plus/deeplabv3plus_r101-d16-mg124_4xb2-40k_cityscapes-512x1024.py @@ -1,4 +1,4 @@ -_base_ = './deeplabv3_r50-d8_512x1024_80k_cityscapes.py' +_base_ = './deeplabv3plus_r50-d8_4xb2-40k_cityscapes-512x1024.py' model = dict( pretrained='open-mmlab://resnet101_v1c', backbone=dict( diff --git a/configs/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes.py b/configs/deeplabv3plus/deeplabv3plus_r101-d16-mg124_4xb2-80k_cityscapes-512x1024.py similarity index 82% rename from configs/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes.py rename to configs/deeplabv3plus/deeplabv3plus_r101-d16-mg124_4xb2-80k_cityscapes-512x1024.py index c53ec41baf..7d1ccf0b30 100644 --- a/configs/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes.py +++ b/configs/deeplabv3plus/deeplabv3plus_r101-d16-mg124_4xb2-80k_cityscapes-512x1024.py @@ -1,4 +1,4 @@ -_base_ = './deeplabv3plus_r50-d8_512x1024_80k_cityscapes.py' +_base_ = './deeplabv3plus_r50-d8_4xb2-80k_cityscapes-512x1024.py' model = dict( pretrained='open-mmlab://resnet101_v1c', backbone=dict( diff --git a/configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context.py b/configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context.py deleted file mode 100644 index 68e2b072e4..0000000000 --- a/configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './deeplabv3plus_r50-d8_480x480_40k_pascal_context.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59.py b/configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59.py deleted file mode 100644 index 36a510ff41..0000000000 --- a/configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context_59.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './deeplabv3plus_r50-d8_480x480_40k_pascal_context_59.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context.py b/configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context.py deleted file mode 100644 index 3a46c28608..0000000000 --- a/configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './deeplabv3plus_r50-d8_480x480_80k_pascal_context.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59.py b/configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59.py deleted file mode 100644 index a6a7688c7a..0000000000 --- a/configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context_59.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './deeplabv3plus_r50-d8_480x480_80k_pascal_context_59.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/deeplabv3plus/deeplabv3plus_r101-d8_4x4_512x512_80k_vaihingen.py b/configs/deeplabv3plus/deeplabv3plus_r101-d8_4x4_512x512_80k_vaihingen.py deleted file mode 100644 index 4bddf4f8bf..0000000000 --- a/configs/deeplabv3plus/deeplabv3plus_r101-d8_4x4_512x512_80k_vaihingen.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './deeplabv3plus_r50-d8_4x4_512x512_80k_vaihingen.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb2-40k_cityscapes-512x1024.py b/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb2-40k_cityscapes-512x1024.py new file mode 100644 index 0000000000..884b526d48 --- /dev/null +++ b/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb2-40k_cityscapes-512x1024.py @@ -0,0 +1,2 @@ +_base_ = './deeplabv3plus_r50-d8_4xb2-40k_cityscapes-512x1024.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb2-40k_cityscapes-769x769.py b/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb2-40k_cityscapes-769x769.py new file mode 100644 index 0000000000..debb0255fc --- /dev/null +++ b/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb2-40k_cityscapes-769x769.py @@ -0,0 +1,2 @@ +_base_ = './deeplabv3plus_r50-d8_4xb2-40k_cityscapes-769x769.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb2-80k_cityscapes-512x1024.py b/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb2-80k_cityscapes-512x1024.py new file mode 100644 index 0000000000..bc9334e67d --- /dev/null +++ b/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb2-80k_cityscapes-512x1024.py @@ -0,0 +1,2 @@ +_base_ = './deeplabv3plus_r50-d8_4xb2-80k_cityscapes-512x1024.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb2-80k_cityscapes-769x769.py b/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb2-80k_cityscapes-769x769.py new file mode 100644 index 0000000000..4af9aa2682 --- /dev/null +++ b/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb2-80k_cityscapes-769x769.py @@ -0,0 +1,2 @@ +_base_ = './deeplabv3plus_r50-d8_4xb2-80k_cityscapes-769x769.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/deeplabv3plus/deeplabv3plus_r101-d8_fp16_512x1024_80k_cityscapes.py b/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb2-amp-80k_cityscapes-512x1024.py similarity index 73% rename from configs/deeplabv3plus/deeplabv3plus_r101-d8_fp16_512x1024_80k_cityscapes.py rename to configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb2-amp-80k_cityscapes-512x1024.py index f92cf030e8..9c9883dc4f 100644 --- a/configs/deeplabv3plus/deeplabv3plus_r101-d8_fp16_512x1024_80k_cityscapes.py +++ b/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb2-amp-80k_cityscapes-512x1024.py @@ -1,4 +1,4 @@ -_base_ = './deeplabv3plus_r101-d8_512x1024_80k_cityscapes.py' +_base_ = './deeplabv3plus_r101-d8_4xb2-80k_cityscapes-512x1024.py' optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005) optim_wrapper = dict( _delete_=True, diff --git a/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-160k_ade20k-512x512.py b/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-160k_ade20k-512x512.py new file mode 100644 index 0000000000..c38a802e10 --- /dev/null +++ b/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-160k_ade20k-512x512.py @@ -0,0 +1,2 @@ +_base_ = './deeplabv3plus_r50-d8_4xb4-160k_ade20k-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-20k_voc12aug-512x512.py b/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-20k_voc12aug-512x512.py new file mode 100644 index 0000000000..97bb827722 --- /dev/null +++ b/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-20k_voc12aug-512x512.py @@ -0,0 +1,2 @@ +_base_ = './deeplabv3plus_r50-d8_4xb4-20k_voc12aug-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-40k_pascal-context-480x480.py b/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-40k_pascal-context-480x480.py new file mode 100644 index 0000000000..e4b401162d --- /dev/null +++ b/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-40k_pascal-context-480x480.py @@ -0,0 +1,2 @@ +_base_ = './deeplabv3plus_r50-d8_4xb4-40k_pascal-context-480x480.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-40k_pascal-context-59-480x480.py b/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-40k_pascal-context-59-480x480.py new file mode 100644 index 0000000000..eeefae4927 --- /dev/null +++ b/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-40k_pascal-context-59-480x480.py @@ -0,0 +1,2 @@ +_base_ = './deeplabv3plus_r50-d8_4xb4-40k_pascal-context-59-480x480.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-40k_voc12aug-512x512.py b/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-40k_voc12aug-512x512.py new file mode 100644 index 0000000000..0755c53aae --- /dev/null +++ b/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-40k_voc12aug-512x512.py @@ -0,0 +1,2 @@ +_base_ = './deeplabv3plus_r50-d8_4xb4-40k_voc12aug-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-80k_ade20k-512x512.py b/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-80k_ade20k-512x512.py new file mode 100644 index 0000000000..844ac9613b --- /dev/null +++ b/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-80k_ade20k-512x512.py @@ -0,0 +1,2 @@ +_base_ = './deeplabv3plus_r50-d8_4xb4-80k_ade20k-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_loveda.py b/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-80k_loveda-512x512.py similarity index 70% rename from configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_loveda.py rename to configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-80k_loveda-512x512.py index b3ad3cae2b..06641f2f5f 100644 --- a/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_loveda.py +++ b/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-80k_loveda-512x512.py @@ -1,4 +1,4 @@ -_base_ = './deeplabv3plus_r50-d8_512x512_80k_loveda.py' +_base_ = './deeplabv3plus_r50-d8_4xb4-80k_loveda-512x512.py' model = dict( backbone=dict( depth=101, diff --git a/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-80k_pascal-context-480x480.py b/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-80k_pascal-context-480x480.py new file mode 100644 index 0000000000..115b1c9058 --- /dev/null +++ b/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-80k_pascal-context-480x480.py @@ -0,0 +1,2 @@ +_base_ = './deeplabv3plus_r50-d8_4xb4-80k_pascal-context-480x480.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-80k_pascal-context-59-480x480.py b/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-80k_pascal-context-59-480x480.py new file mode 100644 index 0000000000..9aaa653822 --- /dev/null +++ b/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-80k_pascal-context-59-480x480.py @@ -0,0 +1,2 @@ +_base_ = './deeplabv3plus_r50-d8_4xb4-80k_pascal-context-59-480x480.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-80k_potsdam-512x512.py b/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-80k_potsdam-512x512.py new file mode 100644 index 0000000000..5063b1332c --- /dev/null +++ b/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-80k_potsdam-512x512.py @@ -0,0 +1,2 @@ +_base_ = './deeplabv3plus_r50-d8_4xb4-80k_potsdam-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-80k_vaihingen-512x512.py b/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-80k_vaihingen-512x512.py new file mode 100644 index 0000000000..b99c2c7ee0 --- /dev/null +++ b/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-80k_vaihingen-512x512.py @@ -0,0 +1,2 @@ +_base_ = './deeplabv3plus_r50-d8_4xb4-80k_vaihingen-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_40k_cityscapes.py b/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_40k_cityscapes.py deleted file mode 100644 index d6ce85aea5..0000000000 --- a/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_40k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './deeplabv3plus_r50-d8_512x1024_40k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes.py b/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes.py deleted file mode 100644 index 0ebbd3c70e..0000000000 --- a/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './deeplabv3plus_r50-d8_512x1024_80k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_160k_ade20k.py b/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_160k_ade20k.py deleted file mode 100644 index a75c9d3019..0000000000 --- a/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_160k_ade20k.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './deeplabv3plus_r50-d8_512x512_160k_ade20k.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_20k_voc12aug.py b/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_20k_voc12aug.py deleted file mode 100644 index ebb1a8eaee..0000000000 --- a/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_20k_voc12aug.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './deeplabv3plus_r50-d8_512x512_20k_voc12aug.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_40k_voc12aug.py b/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_40k_voc12aug.py deleted file mode 100644 index 3caa6cf8ae..0000000000 --- a/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_40k_voc12aug.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './deeplabv3plus_r50-d8_512x512_40k_voc12aug.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_ade20k.py b/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_ade20k.py deleted file mode 100644 index 53fd3a9095..0000000000 --- a/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_ade20k.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './deeplabv3plus_r50-d8_512x512_80k_ade20k.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_potsdam.py b/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_potsdam.py deleted file mode 100644 index d89491440a..0000000000 --- a/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_potsdam.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './deeplabv3plus_r50-d8_512x512_80k_potsdam.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/deeplabv3plus/deeplabv3plus_r101-d8_769x769_40k_cityscapes.py b/configs/deeplabv3plus/deeplabv3plus_r101-d8_769x769_40k_cityscapes.py deleted file mode 100644 index c3c92eb26f..0000000000 --- a/configs/deeplabv3plus/deeplabv3plus_r101-d8_769x769_40k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './deeplabv3plus_r50-d8_769x769_40k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/deeplabv3plus/deeplabv3plus_r101-d8_769x769_80k_cityscapes.py b/configs/deeplabv3plus/deeplabv3plus_r101-d8_769x769_80k_cityscapes.py deleted file mode 100644 index 5ea9cdb5b6..0000000000 --- a/configs/deeplabv3plus/deeplabv3plus_r101-d8_769x769_80k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './deeplabv3plus_r50-d8_769x769_80k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/deeplabv3plus/deeplabv3plus_r101b-d8_4xb2-80k_cityscapes-512x1024.py b/configs/deeplabv3plus/deeplabv3plus_r101b-d8_4xb2-80k_cityscapes-512x1024.py new file mode 100644 index 0000000000..d1bcb09144 --- /dev/null +++ b/configs/deeplabv3plus/deeplabv3plus_r101b-d8_4xb2-80k_cityscapes-512x1024.py @@ -0,0 +1,4 @@ +_base_ = './deeplabv3plus_r50-d8_4xb2-80k_cityscapes-512x1024.py' +model = dict( + pretrained='torchvision://resnet101', + backbone=dict(type='ResNet', depth=101)) diff --git a/configs/deeplabv3plus/deeplabv3plus_r101b-d8_4xb2-80k_cityscapes-769x769.py b/configs/deeplabv3plus/deeplabv3plus_r101b-d8_4xb2-80k_cityscapes-769x769.py new file mode 100644 index 0000000000..c78fc1e209 --- /dev/null +++ b/configs/deeplabv3plus/deeplabv3plus_r101b-d8_4xb2-80k_cityscapes-769x769.py @@ -0,0 +1,4 @@ +_base_ = './deeplabv3plus_r50-d8_4xb2-80k_cityscapes-769x769.py' +model = dict( + pretrained='torchvision://resnet101', + backbone=dict(type='ResNet', depth=101)) diff --git a/configs/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_potsdam.py b/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb2-80k_cityscapes-512x1024.py similarity index 80% rename from configs/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_potsdam.py rename to configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb2-80k_cityscapes-512x1024.py index ffb20df727..5f54913e94 100644 --- a/configs/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_potsdam.py +++ b/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb2-80k_cityscapes-512x1024.py @@ -1,4 +1,4 @@ -_base_ = './deeplabv3plus_r50-d8_512x512_80k_potsdam.py' +_base_ = './deeplabv3plus_r50-d8_4xb2-80k_cityscapes-512x1024.py' model = dict( pretrained='open-mmlab://resnet18_v1c', backbone=dict(depth=18), diff --git a/configs/deeplabv3plus/deeplabv3plus_r18-d8_512x1024_80k_cityscapes.py b/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb2-80k_cityscapes-769x769.py similarity index 80% rename from configs/deeplabv3plus/deeplabv3plus_r18-d8_512x1024_80k_cityscapes.py rename to configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb2-80k_cityscapes-769x769.py index aff70c93e6..1b361d6d7a 100644 --- a/configs/deeplabv3plus/deeplabv3plus_r18-d8_512x1024_80k_cityscapes.py +++ b/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb2-80k_cityscapes-769x769.py @@ -1,4 +1,4 @@ -_base_ = './deeplabv3plus_r50-d8_512x1024_80k_cityscapes.py' +_base_ = './deeplabv3plus_r50-d8_4xb2-80k_cityscapes-769x769.py' model = dict( pretrained='open-mmlab://resnet18_v1c', backbone=dict(depth=18), diff --git a/configs/deeplabv3plus/deeplabv3plus_r18-d8_769x769_80k_cityscapes.py b/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb4-80k_isaid-896x896.py similarity index 81% rename from configs/deeplabv3plus/deeplabv3plus_r18-d8_769x769_80k_cityscapes.py rename to configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb4-80k_isaid-896x896.py index 0172d9a87d..3a1a753b26 100644 --- a/configs/deeplabv3plus/deeplabv3plus_r18-d8_769x769_80k_cityscapes.py +++ b/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb4-80k_isaid-896x896.py @@ -1,4 +1,4 @@ -_base_ = './deeplabv3plus_r50-d8_769x769_80k_cityscapes.py' +_base_ = './deeplabv3plus_r50-d8_4xb4-80k_isaid-896x896.py' model = dict( pretrained='open-mmlab://resnet18_v1c', backbone=dict(depth=18), diff --git a/configs/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_loveda.py b/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb4-80k_loveda-512x512.py similarity index 84% rename from configs/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_loveda.py rename to configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb4-80k_loveda-512x512.py index 11fe640234..f6efef04b9 100644 --- a/configs/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_loveda.py +++ b/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb4-80k_loveda-512x512.py @@ -1,4 +1,4 @@ -_base_ = './deeplabv3plus_r50-d8_512x512_80k_loveda.py' +_base_ = './deeplabv3plus_r50-d8_4xb4-80k_loveda-512x512.py' model = dict( backbone=dict( depth=18, diff --git a/configs/deeplabv3plus/deeplabv3plus_r18-d8_4x4_896x896_80k_isaid.py b/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb4-80k_potsdam-512x512.py similarity index 81% rename from configs/deeplabv3plus/deeplabv3plus_r18-d8_4x4_896x896_80k_isaid.py rename to configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb4-80k_potsdam-512x512.py index 892a8a30e9..134f2cfc2a 100644 --- a/configs/deeplabv3plus/deeplabv3plus_r18-d8_4x4_896x896_80k_isaid.py +++ b/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb4-80k_potsdam-512x512.py @@ -1,4 +1,4 @@ -_base_ = './deeplabv3plus_r50-d8_4x4_896x896_80k_isaid.py' +_base_ = './deeplabv3plus_r50-d8_4xb4-80k_potsdam-512x512.py' model = dict( pretrained='open-mmlab://resnet18_v1c', backbone=dict(depth=18), diff --git a/configs/deeplabv3plus/deeplabv3plus_r18-d8_4x4_512x512_80k_vaihingen.py b/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb4-80k_vaihingen-512x512.py similarity index 80% rename from configs/deeplabv3plus/deeplabv3plus_r18-d8_4x4_512x512_80k_vaihingen.py rename to configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb4-80k_vaihingen-512x512.py index 879e941f29..2194838510 100644 --- a/configs/deeplabv3plus/deeplabv3plus_r18-d8_4x4_512x512_80k_vaihingen.py +++ b/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb4-80k_vaihingen-512x512.py @@ -1,4 +1,4 @@ -_base_ = './deeplabv3plus_r50-d8_4x4_512x512_80k_vaihingen.py' +_base_ = './deeplabv3plus_r50-d8_4xb4-80k_vaihingen-512x512.py' model = dict( pretrained='open-mmlab://resnet18_v1c', backbone=dict(depth=18), diff --git a/configs/deeplabv3plus/deeplabv3plus_r18b-d8_769x769_80k_cityscapes.py b/configs/deeplabv3plus/deeplabv3plus_r18b-d8_4xb2-80k_cityscapes-512x1024.py similarity index 80% rename from configs/deeplabv3plus/deeplabv3plus_r18b-d8_769x769_80k_cityscapes.py rename to configs/deeplabv3plus/deeplabv3plus_r18b-d8_4xb2-80k_cityscapes-512x1024.py index b49da3581d..ea86219692 100644 --- a/configs/deeplabv3plus/deeplabv3plus_r18b-d8_769x769_80k_cityscapes.py +++ b/configs/deeplabv3plus/deeplabv3plus_r18b-d8_4xb2-80k_cityscapes-512x1024.py @@ -1,4 +1,4 @@ -_base_ = './deeplabv3plus_r50-d8_769x769_80k_cityscapes.py' +_base_ = './deeplabv3plus_r50-d8_4xb2-80k_cityscapes-512x1024.py' model = dict( pretrained='torchvision://resnet18', backbone=dict(type='ResNet', depth=18), diff --git a/configs/deeplabv3plus/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes.py b/configs/deeplabv3plus/deeplabv3plus_r18b-d8_4xb2-80k_cityscapes-769x769.py similarity index 81% rename from configs/deeplabv3plus/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes.py rename to configs/deeplabv3plus/deeplabv3plus_r18b-d8_4xb2-80k_cityscapes-769x769.py index b90b292b03..34ee7ed3df 100644 --- a/configs/deeplabv3plus/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes.py +++ b/configs/deeplabv3plus/deeplabv3plus_r18b-d8_4xb2-80k_cityscapes-769x769.py @@ -1,4 +1,4 @@ -_base_ = './deeplabv3plus_r50-d8_512x1024_80k_cityscapes.py' +_base_ = './deeplabv3plus_r50-d8_4xb2-40k_cityscapes-769x769.py' model = dict( pretrained='torchvision://resnet18', backbone=dict(type='ResNet', depth=18), diff --git a/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_40k_cityscapes.py b/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb2-40k_cityscapes-512x1024.py similarity index 100% rename from configs/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_40k_cityscapes.py rename to configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb2-40k_cityscapes-512x1024.py diff --git a/configs/deeplabv3plus/deeplabv3plus_r50-d8_769x769_40k_cityscapes.py b/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb2-40k_cityscapes-769x769.py similarity index 100% rename from configs/deeplabv3plus/deeplabv3plus_r50-d8_769x769_40k_cityscapes.py rename to configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb2-40k_cityscapes-769x769.py diff --git a/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_80k_cityscapes.py b/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb2-80k_cityscapes-512x1024.py similarity index 100% rename from configs/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_80k_cityscapes.py rename to configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb2-80k_cityscapes-512x1024.py diff --git a/configs/deeplabv3plus/deeplabv3plus_r50-d8_769x769_80k_cityscapes.py b/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb2-80k_cityscapes-769x769.py similarity index 100% rename from configs/deeplabv3plus/deeplabv3plus_r50-d8_769x769_80k_cityscapes.py rename to configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb2-80k_cityscapes-769x769.py diff --git a/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_160k_ade20k.py b/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-160k_ade20k-512x512.py similarity index 100% rename from configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_160k_ade20k.py rename to configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-160k_ade20k-512x512.py diff --git a/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_20k_voc12aug.py b/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-20k_voc12aug-512x512.py similarity index 100% rename from configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_20k_voc12aug.py rename to configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-20k_voc12aug-512x512.py diff --git a/configs/deeplabv3plus/deeplabv3plus_r50-d8_480x480_40k_pascal_context.py b/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-40k_pascal-context-480x480.py similarity index 100% rename from configs/deeplabv3plus/deeplabv3plus_r50-d8_480x480_40k_pascal_context.py rename to configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-40k_pascal-context-480x480.py diff --git a/configs/deeplabv3plus/deeplabv3plus_r50-d8_480x480_40k_pascal_context_59.py b/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-40k_pascal-context-59-480x480.py similarity index 100% rename from configs/deeplabv3plus/deeplabv3plus_r50-d8_480x480_40k_pascal_context_59.py rename to configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-40k_pascal-context-59-480x480.py diff --git a/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_40k_voc12aug.py b/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-40k_voc12aug-512x512.py similarity index 100% rename from configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_40k_voc12aug.py rename to configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-40k_voc12aug-512x512.py diff --git a/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_ade20k.py b/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-80k_ade20k-512x512.py similarity index 100% rename from configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_ade20k.py rename to configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-80k_ade20k-512x512.py diff --git a/configs/deeplabv3plus/deeplabv3plus_r50-d8_4x4_896x896_80k_isaid.py b/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-80k_isaid-896x896.py similarity index 100% rename from configs/deeplabv3plus/deeplabv3plus_r50-d8_4x4_896x896_80k_isaid.py rename to configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-80k_isaid-896x896.py diff --git a/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_loveda.py b/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-80k_loveda-512x512.py similarity index 100% rename from configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_loveda.py rename to configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-80k_loveda-512x512.py diff --git a/configs/deeplabv3plus/deeplabv3plus_r50-d8_480x480_80k_pascal_context.py b/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-80k_pascal-context-480x480.py similarity index 100% rename from configs/deeplabv3plus/deeplabv3plus_r50-d8_480x480_80k_pascal_context.py rename to configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-80k_pascal-context-480x480.py diff --git a/configs/deeplabv3plus/deeplabv3plus_r50-d8_480x480_80k_pascal_context_59.py b/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-80k_pascal-context-59-480x480.py similarity index 100% rename from configs/deeplabv3plus/deeplabv3plus_r50-d8_480x480_80k_pascal_context_59.py rename to configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-80k_pascal-context-59-480x480.py diff --git a/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_potsdam.py b/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-80k_potsdam-512x512.py similarity index 100% rename from configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_potsdam.py rename to configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-80k_potsdam-512x512.py diff --git a/configs/deeplabv3plus/deeplabv3plus_r50-d8_4x4_512x512_80k_vaihingen.py b/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-80k_vaihingen-512x512.py similarity index 100% rename from configs/deeplabv3plus/deeplabv3plus_r50-d8_4x4_512x512_80k_vaihingen.py rename to configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-80k_vaihingen-512x512.py diff --git a/configs/deeplabv3plus/deeplabv3plus_r50b-d8_4xb2-80k_cityscapes-512x1024.py b/configs/deeplabv3plus/deeplabv3plus_r50b-d8_4xb2-80k_cityscapes-512x1024.py new file mode 100644 index 0000000000..3e2813534d --- /dev/null +++ b/configs/deeplabv3plus/deeplabv3plus_r50b-d8_4xb2-80k_cityscapes-512x1024.py @@ -0,0 +1,2 @@ +_base_ = './deeplabv3plus_r50-d8_4xb2-80k_cityscapes-512x1024.py' +model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet')) diff --git a/configs/deeplabv3plus/deeplabv3plus_r50b-d8_4xb2-80k_cityscapes-769x769.py b/configs/deeplabv3plus/deeplabv3plus_r50b-d8_4xb2-80k_cityscapes-769x769.py new file mode 100644 index 0000000000..6366bd4e3a --- /dev/null +++ b/configs/deeplabv3plus/deeplabv3plus_r50b-d8_4xb2-80k_cityscapes-769x769.py @@ -0,0 +1,2 @@ +_base_ = './deeplabv3plus_r50-d8_4xb2-80k_cityscapes-769x769.py' +model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet')) diff --git a/configs/dmnet/README.md b/configs/dmnet/README.md index 301bd4599f..535740ddd3 100644 --- a/configs/dmnet/README.md +++ b/configs/dmnet/README.md @@ -38,22 +38,22 @@ year = {2019} ### 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/master/configs/dmnet/dmnet_r50-d8_512x1024_40k_cityscapes.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/master/configs/dmnet/dmnet_r101-d8_512x1024_40k_cityscapes.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/master/configs/dmnet/dmnet_r50-d8_769x769_40k_cityscapes.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/master/configs/dmnet/dmnet_r101-d8_769x769_40k_cityscapes.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/master/configs/dmnet/dmnet_r50-d8_512x1024_80k_cityscapes.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/master/configs/dmnet/dmnet_r101-d8_512x1024_80k_cityscapes.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/master/configs/dmnet/dmnet_r50-d8_769x769_80k_cityscapes.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/master/configs/dmnet/dmnet_r101-d8_769x769_80k_cityscapes.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) | +| 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/master/configs/dmnet/dmnet_r50-d8_512x512_80k_ade20k.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/master/configs/dmnet/dmnet_r101-d8_512x512_80k_ade20k.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/master/configs/dmnet/dmnet_r50-d8_512x512_160k_ade20k.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/master/configs/dmnet/dmnet_r101-d8_512x512_160k_ade20k.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) | +| 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 index 1fab2dc7a7..dfb80ba7e1 100644 --- a/configs/dmnet/dmnet.yml +++ b/configs/dmnet/dmnet.yml @@ -14,7 +14,7 @@ Collections: Converted From: Code: https://github.com/Junjun2016/DMNet Models: -- Name: dmnet_r50-d8_512x1024_40k_cityscapes +- Name: dmnet_r50-d8_4xb2-40k_cityscapes-512x1024 In Collection: DMNet Metadata: backbone: R-50-D8 @@ -34,9 +34,9 @@ Models: Metrics: mIoU: 77.78 mIoU(ms+flip): 79.14 - Config: configs/dmnet/dmnet_r50-d8_512x1024_40k_cityscapes.py + 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_512x1024_40k_cityscapes +- Name: dmnet_r101-d8_4xb2-40k_cityscapes-512x1024 In Collection: DMNet Metadata: backbone: R-101-D8 @@ -56,9 +56,9 @@ Models: Metrics: mIoU: 78.37 mIoU(ms+flip): 79.72 - Config: configs/dmnet/dmnet_r101-d8_512x1024_40k_cityscapes.py + 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_769x769_40k_cityscapes +- Name: dmnet_r50-d8_4xb2-40k_cityscapes-769x769 In Collection: DMNet Metadata: backbone: R-50-D8 @@ -78,9 +78,9 @@ Models: Metrics: mIoU: 78.49 mIoU(ms+flip): 80.27 - Config: configs/dmnet/dmnet_r50-d8_769x769_40k_cityscapes.py + 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_769x769_40k_cityscapes +- Name: dmnet_r101-d8_4xb2-40k_cityscapes-769x769 In Collection: DMNet Metadata: backbone: R-101-D8 @@ -100,9 +100,9 @@ Models: Metrics: mIoU: 77.62 mIoU(ms+flip): 78.94 - Config: configs/dmnet/dmnet_r101-d8_769x769_40k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: dmnet_r50-d8_4xb2-80k_cityscapes-512x1024 In Collection: DMNet Metadata: backbone: R-50-D8 @@ -114,9 +114,9 @@ Models: Metrics: mIoU: 79.07 mIoU(ms+flip): 80.22 - Config: configs/dmnet/dmnet_r50-d8_512x1024_80k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: dmnet_r101-d8_4xb2-80k_cityscapes-512x1024 In Collection: DMNet Metadata: backbone: R-101-D8 @@ -128,9 +128,9 @@ Models: Metrics: mIoU: 79.64 mIoU(ms+flip): 80.67 - Config: configs/dmnet/dmnet_r101-d8_512x1024_80k_cityscapes.py + 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_769x769_80k_cityscapes +- Name: dmnet_r50-d8_4xb2-80k_cityscapes-769x769 In Collection: DMNet Metadata: backbone: R-50-D8 @@ -142,9 +142,9 @@ Models: Metrics: mIoU: 79.22 mIoU(ms+flip): 80.55 - Config: configs/dmnet/dmnet_r50-d8_769x769_80k_cityscapes.py + 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_769x769_80k_cityscapes +- Name: dmnet_r101-d8_4xb2-80k_cityscapes-769x769 In Collection: DMNet Metadata: backbone: R-101-D8 @@ -156,9 +156,9 @@ Models: Metrics: mIoU: 79.19 mIoU(ms+flip): 80.65 - Config: configs/dmnet/dmnet_r101-d8_769x769_80k_cityscapes.py + 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_512x512_80k_ade20k +- Name: dmnet_r50-d8_4xb4-80k_ade20k-512x512 In Collection: DMNet Metadata: backbone: R-50-D8 @@ -178,9 +178,9 @@ Models: Metrics: mIoU: 42.37 mIoU(ms+flip): 43.62 - Config: configs/dmnet/dmnet_r50-d8_512x512_80k_ade20k.py + 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_512x512_80k_ade20k +- Name: dmnet_r101-d8_4xb4-80k_ade20k-512x512 In Collection: DMNet Metadata: backbone: R-101-D8 @@ -200,9 +200,9 @@ Models: Metrics: mIoU: 45.34 mIoU(ms+flip): 46.13 - Config: configs/dmnet/dmnet_r101-d8_512x512_80k_ade20k.py + 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_512x512_160k_ade20k +- Name: dmnet_r50-d8_4xb4-160k_ade20k-512x512 In Collection: DMNet Metadata: backbone: R-50-D8 @@ -214,9 +214,9 @@ Models: Metrics: mIoU: 43.15 mIoU(ms+flip): 44.17 - Config: configs/dmnet/dmnet_r50-d8_512x512_160k_ade20k.py + 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_512x512_160k_ade20k +- Name: dmnet_r101-d8_4xb4-160k_ade20k-512x512 In Collection: DMNet Metadata: backbone: R-101-D8 @@ -228,5 +228,5 @@ Models: Metrics: mIoU: 45.42 mIoU(ms+flip): 46.76 - Config: configs/dmnet/dmnet_r101-d8_512x512_160k_ade20k.py + 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/dmnet_r101-d8_4xb2-40k_cityscapes-512x1024.py b/configs/dmnet/dmnet_r101-d8_4xb2-40k_cityscapes-512x1024.py new file mode 100644 index 0000000000..9832b62a29 --- /dev/null +++ b/configs/dmnet/dmnet_r101-d8_4xb2-40k_cityscapes-512x1024.py @@ -0,0 +1,2 @@ +_base_ = './dmnet_r50-d8_4xb2-40k_cityscapes-512x1024.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/dmnet/dmnet_r101-d8_4xb2-40k_cityscapes-769x769.py b/configs/dmnet/dmnet_r101-d8_4xb2-40k_cityscapes-769x769.py new file mode 100644 index 0000000000..03346c5d9b --- /dev/null +++ b/configs/dmnet/dmnet_r101-d8_4xb2-40k_cityscapes-769x769.py @@ -0,0 +1,2 @@ +_base_ = './dmnet_r50-d8_4xb2-40k_cityscapes-769x769.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/dmnet/dmnet_r101-d8_4xb2-80k_cityscapes-512x1024.py b/configs/dmnet/dmnet_r101-d8_4xb2-80k_cityscapes-512x1024.py new file mode 100644 index 0000000000..fd7e9acd1c --- /dev/null +++ b/configs/dmnet/dmnet_r101-d8_4xb2-80k_cityscapes-512x1024.py @@ -0,0 +1,2 @@ +_base_ = './dmnet_r50-d8_4xb2-80k_cityscapes-512x1024.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/dmnet/dmnet_r101-d8_4xb2-80k_cityscapes-769x769.py b/configs/dmnet/dmnet_r101-d8_4xb2-80k_cityscapes-769x769.py new file mode 100644 index 0000000000..2205e601ce --- /dev/null +++ b/configs/dmnet/dmnet_r101-d8_4xb2-80k_cityscapes-769x769.py @@ -0,0 +1,2 @@ +_base_ = './dmnet_r50-d8_4xb2-80k_cityscapes-769x769.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/dmnet/dmnet_r101-d8_4xb4-160k_ade20k-512x512.py b/configs/dmnet/dmnet_r101-d8_4xb4-160k_ade20k-512x512.py new file mode 100644 index 0000000000..23e215bf2f --- /dev/null +++ b/configs/dmnet/dmnet_r101-d8_4xb4-160k_ade20k-512x512.py @@ -0,0 +1,2 @@ +_base_ = './dmnet_r50-d8_4xb4-160k_ade20k-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/dmnet/dmnet_r101-d8_4xb4-80k_ade20k-512x512.py b/configs/dmnet/dmnet_r101-d8_4xb4-80k_ade20k-512x512.py new file mode 100644 index 0000000000..5c25587e64 --- /dev/null +++ b/configs/dmnet/dmnet_r101-d8_4xb4-80k_ade20k-512x512.py @@ -0,0 +1,2 @@ +_base_ = './dmnet_r50-d8_4xb4-80k_ade20k-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/dmnet/dmnet_r101-d8_512x1024_40k_cityscapes.py b/configs/dmnet/dmnet_r101-d8_512x1024_40k_cityscapes.py deleted file mode 100644 index fd6897691d..0000000000 --- a/configs/dmnet/dmnet_r101-d8_512x1024_40k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './dmnet_r50-d8_512x1024_40k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/dmnet/dmnet_r101-d8_512x1024_80k_cityscapes.py b/configs/dmnet/dmnet_r101-d8_512x1024_80k_cityscapes.py deleted file mode 100644 index 116cbdcede..0000000000 --- a/configs/dmnet/dmnet_r101-d8_512x1024_80k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './dmnet_r50-d8_512x1024_80k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/dmnet/dmnet_r101-d8_512x512_160k_ade20k.py b/configs/dmnet/dmnet_r101-d8_512x512_160k_ade20k.py deleted file mode 100644 index d78d46c040..0000000000 --- a/configs/dmnet/dmnet_r101-d8_512x512_160k_ade20k.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './dmnet_r50-d8_512x512_160k_ade20k.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/dmnet/dmnet_r101-d8_512x512_80k_ade20k.py b/configs/dmnet/dmnet_r101-d8_512x512_80k_ade20k.py deleted file mode 100644 index 9713b731a4..0000000000 --- a/configs/dmnet/dmnet_r101-d8_512x512_80k_ade20k.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './dmnet_r50-d8_512x512_80k_ade20k.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/dmnet/dmnet_r101-d8_769x769_40k_cityscapes.py b/configs/dmnet/dmnet_r101-d8_769x769_40k_cityscapes.py deleted file mode 100644 index 6b222e7300..0000000000 --- a/configs/dmnet/dmnet_r101-d8_769x769_40k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './dmnet_r50-d8_769x769_40k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/dmnet/dmnet_r101-d8_769x769_80k_cityscapes.py b/configs/dmnet/dmnet_r101-d8_769x769_80k_cityscapes.py deleted file mode 100644 index f36d490e9c..0000000000 --- a/configs/dmnet/dmnet_r101-d8_769x769_80k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './dmnet_r50-d8_769x769_80k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/dmnet/dmnet_r50-d8_512x1024_40k_cityscapes.py b/configs/dmnet/dmnet_r50-d8_4xb2-40k_cityscapes-512x1024.py similarity index 100% rename from configs/dmnet/dmnet_r50-d8_512x1024_40k_cityscapes.py rename to configs/dmnet/dmnet_r50-d8_4xb2-40k_cityscapes-512x1024.py diff --git a/configs/dmnet/dmnet_r50-d8_769x769_40k_cityscapes.py b/configs/dmnet/dmnet_r50-d8_4xb2-40k_cityscapes-769x769.py similarity index 100% rename from configs/dmnet/dmnet_r50-d8_769x769_40k_cityscapes.py rename to configs/dmnet/dmnet_r50-d8_4xb2-40k_cityscapes-769x769.py diff --git a/configs/dmnet/dmnet_r50-d8_512x1024_80k_cityscapes.py b/configs/dmnet/dmnet_r50-d8_4xb2-80k_cityscapes-512x1024.py similarity index 100% rename from configs/dmnet/dmnet_r50-d8_512x1024_80k_cityscapes.py rename to configs/dmnet/dmnet_r50-d8_4xb2-80k_cityscapes-512x1024.py diff --git a/configs/dmnet/dmnet_r50-d8_769x769_80k_cityscapes.py b/configs/dmnet/dmnet_r50-d8_4xb2-80k_cityscapes-769x769.py similarity index 100% rename from configs/dmnet/dmnet_r50-d8_769x769_80k_cityscapes.py rename to configs/dmnet/dmnet_r50-d8_4xb2-80k_cityscapes-769x769.py diff --git a/configs/dmnet/dmnet_r50-d8_512x512_160k_ade20k.py b/configs/dmnet/dmnet_r50-d8_4xb4-160k_ade20k-512x512.py similarity index 100% rename from configs/dmnet/dmnet_r50-d8_512x512_160k_ade20k.py rename to configs/dmnet/dmnet_r50-d8_4xb4-160k_ade20k-512x512.py diff --git a/configs/dmnet/dmnet_r50-d8_512x512_80k_ade20k.py b/configs/dmnet/dmnet_r50-d8_4xb4-80k_ade20k-512x512.py similarity index 100% rename from configs/dmnet/dmnet_r50-d8_512x512_80k_ade20k.py rename to configs/dmnet/dmnet_r50-d8_4xb4-80k_ade20k-512x512.py diff --git a/configs/dnlnet/README.md b/configs/dnlnet/README.md index 975c4b08b0..ab24549ed6 100644 --- a/configs/dnlnet/README.md +++ b/configs/dnlnet/README.md @@ -41,22 +41,22 @@ This example is to reproduce ["Disentangled Non-Local Neural Networks"](https:// ### 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/master/configs/dnlnet/dnl_r50-d8_512x1024_40k_cityscapes.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/master/configs/dnlnet/dnl_r101-d8_512x1024_40k_cityscapes.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/master/configs/dnlnet/dnl_r50-d8_769x769_40k_cityscapes.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/master/configs/dnlnet/dnl_r101-d8_769x769_40k_cityscapes.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/master/configs/dnlnet/dnl_r50-d8_512x1024_80k_cityscapes.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/master/configs/dnlnet/dnl_r101-d8_512x1024_80k_cityscapes.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/master/configs/dnlnet/dnl_r50-d8_769x769_80k_cityscapes.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/master/configs/dnlnet/dnl_r101-d8_769x769_80k_cityscapes.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) | +| 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/master/configs/dnlnet/dnl_r50-d8_512x512_80k_ade20k.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/master/configs/dnlnet/dnl_r101-d8_512x512_80k_ade20k.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/master/configs/dnlnet/dnl_r50-d8_512x512_160k_ade20k.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/master/configs/dnlnet/dnl_r101-d8_512x512_160k_ade20k.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) | +| 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/dnl_r101-d8_4xb2-40k_cityscapes-512x1024.py b/configs/dnlnet/dnl_r101-d8_4xb2-40k_cityscapes-512x1024.py new file mode 100644 index 0000000000..310d84e574 --- /dev/null +++ b/configs/dnlnet/dnl_r101-d8_4xb2-40k_cityscapes-512x1024.py @@ -0,0 +1,2 @@ +_base_ = './dnl_r50-d8_4xb2-40k_cityscapes-512x1024.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/dnlnet/dnl_r101-d8_4xb2-40k_cityscapes-769x769.py b/configs/dnlnet/dnl_r101-d8_4xb2-40k_cityscapes-769x769.py new file mode 100644 index 0000000000..a94dbb89b3 --- /dev/null +++ b/configs/dnlnet/dnl_r101-d8_4xb2-40k_cityscapes-769x769.py @@ -0,0 +1,2 @@ +_base_ = './dnl_r50-d8_4xb2-40k_cityscapes-769x769.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/dnlnet/dnl_r101-d8_4xb2-80k_cityscapes-512x1024.py b/configs/dnlnet/dnl_r101-d8_4xb2-80k_cityscapes-512x1024.py new file mode 100644 index 0000000000..f9b6d5ee3d --- /dev/null +++ b/configs/dnlnet/dnl_r101-d8_4xb2-80k_cityscapes-512x1024.py @@ -0,0 +1,2 @@ +_base_ = './dnl_r50-d8_4xb2-80k_cityscapes-512x1024.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/dnlnet/dnl_r101-d8_4xb2-80k_cityscapes-769x769.py b/configs/dnlnet/dnl_r101-d8_4xb2-80k_cityscapes-769x769.py new file mode 100644 index 0000000000..9c7d557d02 --- /dev/null +++ b/configs/dnlnet/dnl_r101-d8_4xb2-80k_cityscapes-769x769.py @@ -0,0 +1,2 @@ +_base_ = './dnl_r50-d8_4xb2-80k_cityscapes-769x769.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/dnlnet/dnl_r101-d8_4xb4-160k_ade20k-512x512.py b/configs/dnlnet/dnl_r101-d8_4xb4-160k_ade20k-512x512.py new file mode 100644 index 0000000000..1edc26fd8c --- /dev/null +++ b/configs/dnlnet/dnl_r101-d8_4xb4-160k_ade20k-512x512.py @@ -0,0 +1,2 @@ +_base_ = './dnl_r50-d8_4xb4-160k_ade20k-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/ann/ann_r101-d8_512x1024_80k_cityscapes.py b/configs/dnlnet/dnl_r101-d8_4xb4-80k_ade20k-512x512.py similarity index 61% rename from configs/ann/ann_r101-d8_512x1024_80k_cityscapes.py rename to configs/dnlnet/dnl_r101-d8_4xb4-80k_ade20k-512x512.py index 1eeff0b030..d29c17ef5b 100644 --- a/configs/ann/ann_r101-d8_512x1024_80k_cityscapes.py +++ b/configs/dnlnet/dnl_r101-d8_4xb4-80k_ade20k-512x512.py @@ -1,2 +1,2 @@ -_base_ = './ann_r50-d8_512x1024_80k_cityscapes.py' +_base_ = './dnl_r50-d8_4xb4-80k_ade20k-512x512.py' model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/dnlnet/dnl_r101-d8_512x1024_40k_cityscapes.py b/configs/dnlnet/dnl_r101-d8_512x1024_40k_cityscapes.py deleted file mode 100644 index 1a36e3c80a..0000000000 --- a/configs/dnlnet/dnl_r101-d8_512x1024_40k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './dnl_r50-d8_512x1024_40k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/dnlnet/dnl_r101-d8_512x1024_80k_cityscapes.py b/configs/dnlnet/dnl_r101-d8_512x1024_80k_cityscapes.py deleted file mode 100644 index 0f2e1b6da7..0000000000 --- a/configs/dnlnet/dnl_r101-d8_512x1024_80k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './dnl_r50-d8_512x1024_80k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/dnlnet/dnl_r101-d8_512x512_160k_ade20k.py b/configs/dnlnet/dnl_r101-d8_512x512_160k_ade20k.py deleted file mode 100644 index aca44e478b..0000000000 --- a/configs/dnlnet/dnl_r101-d8_512x512_160k_ade20k.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './dnl_r50-d8_512x512_160k_ade20k.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/dnlnet/dnl_r101-d8_512x512_80k_ade20k.py b/configs/dnlnet/dnl_r101-d8_512x512_80k_ade20k.py deleted file mode 100644 index ebd27a1d1c..0000000000 --- a/configs/dnlnet/dnl_r101-d8_512x512_80k_ade20k.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './dnl_r50-d8_512x512_80k_ade20k.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/dnlnet/dnl_r101-d8_769x769_40k_cityscapes.py b/configs/dnlnet/dnl_r101-d8_769x769_40k_cityscapes.py deleted file mode 100644 index 575e9d0134..0000000000 --- a/configs/dnlnet/dnl_r101-d8_769x769_40k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './dnl_r50-d8_769x769_40k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/dnlnet/dnl_r101-d8_769x769_80k_cityscapes.py b/configs/dnlnet/dnl_r101-d8_769x769_80k_cityscapes.py deleted file mode 100644 index 4f1b9e1941..0000000000 --- a/configs/dnlnet/dnl_r101-d8_769x769_80k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './dnl_r50-d8_769x769_80k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/dnlnet/dnl_r50-d8_512x1024_40k_cityscapes.py b/configs/dnlnet/dnl_r50-d8_4xb2-40k_cityscapes-512x1024.py similarity index 100% rename from configs/dnlnet/dnl_r50-d8_512x1024_40k_cityscapes.py rename to configs/dnlnet/dnl_r50-d8_4xb2-40k_cityscapes-512x1024.py diff --git a/configs/dnlnet/dnl_r50-d8_769x769_40k_cityscapes.py b/configs/dnlnet/dnl_r50-d8_4xb2-40k_cityscapes-769x769.py similarity index 100% rename from configs/dnlnet/dnl_r50-d8_769x769_40k_cityscapes.py rename to configs/dnlnet/dnl_r50-d8_4xb2-40k_cityscapes-769x769.py diff --git a/configs/dnlnet/dnl_r50-d8_512x1024_80k_cityscapes.py b/configs/dnlnet/dnl_r50-d8_4xb2-80k_cityscapes-512x1024.py similarity index 100% rename from configs/dnlnet/dnl_r50-d8_512x1024_80k_cityscapes.py rename to configs/dnlnet/dnl_r50-d8_4xb2-80k_cityscapes-512x1024.py diff --git a/configs/dnlnet/dnl_r50-d8_769x769_80k_cityscapes.py b/configs/dnlnet/dnl_r50-d8_4xb2-80k_cityscapes-769x769.py similarity index 100% rename from configs/dnlnet/dnl_r50-d8_769x769_80k_cityscapes.py rename to configs/dnlnet/dnl_r50-d8_4xb2-80k_cityscapes-769x769.py diff --git a/configs/dnlnet/dnl_r50-d8_512x512_160k_ade20k.py b/configs/dnlnet/dnl_r50-d8_4xb4-160k_ade20k-512x512.py similarity index 100% rename from configs/dnlnet/dnl_r50-d8_512x512_160k_ade20k.py rename to configs/dnlnet/dnl_r50-d8_4xb4-160k_ade20k-512x512.py diff --git a/configs/dnlnet/dnl_r50-d8_512x512_80k_ade20k.py b/configs/dnlnet/dnl_r50-d8_4xb4-80k_ade20k-512x512.py similarity index 100% rename from configs/dnlnet/dnl_r50-d8_512x512_80k_ade20k.py rename to configs/dnlnet/dnl_r50-d8_4xb4-80k_ade20k-512x512.py diff --git a/configs/dnlnet/dnlnet.yml b/configs/dnlnet/dnlnet.yml index 8ee7b54861..ae65dbbaca 100644 --- a/configs/dnlnet/dnlnet.yml +++ b/configs/dnlnet/dnlnet.yml @@ -14,7 +14,7 @@ Collections: Converted From: Code: https://github.com/yinmh17/DNL-Semantic-Segmentation Models: -- Name: dnl_r50-d8_512x1024_40k_cityscapes +- Name: dnl_r50-d8_4xb2-40k_cityscapes-512x1024 In Collection: DNLNet Metadata: backbone: R-50-D8 @@ -33,9 +33,9 @@ Models: Dataset: Cityscapes Metrics: mIoU: 78.61 - Config: configs/dnlnet/dnl_r50-d8_512x1024_40k_cityscapes.py + 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_512x1024_40k_cityscapes +- Name: dnl_r101-d8_4xb2-40k_cityscapes-512x1024 In Collection: DNLNet Metadata: backbone: R-101-D8 @@ -54,9 +54,9 @@ Models: Dataset: Cityscapes Metrics: mIoU: 78.31 - Config: configs/dnlnet/dnl_r101-d8_512x1024_40k_cityscapes.py + 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_769x769_40k_cityscapes +- Name: dnl_r50-d8_4xb2-40k_cityscapes-769x769 In Collection: DNLNet Metadata: backbone: R-50-D8 @@ -76,9 +76,9 @@ Models: Metrics: mIoU: 78.44 mIoU(ms+flip): 80.27 - Config: configs/dnlnet/dnl_r50-d8_769x769_40k_cityscapes.py + 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_769x769_40k_cityscapes +- Name: dnl_r101-d8_4xb2-40k_cityscapes-769x769 In Collection: DNLNet Metadata: backbone: R-101-D8 @@ -98,9 +98,9 @@ Models: Metrics: mIoU: 76.39 mIoU(ms+flip): 77.77 - Config: configs/dnlnet/dnl_r101-d8_769x769_40k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: dnl_r50-d8_4xb2-80k_cityscapes-512x1024 In Collection: DNLNet Metadata: backbone: R-50-D8 @@ -111,9 +111,9 @@ Models: Dataset: Cityscapes Metrics: mIoU: 79.33 - Config: configs/dnlnet/dnl_r50-d8_512x1024_80k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: dnl_r101-d8_4xb2-80k_cityscapes-512x1024 In Collection: DNLNet Metadata: backbone: R-101-D8 @@ -124,9 +124,9 @@ Models: Dataset: Cityscapes Metrics: mIoU: 80.41 - Config: configs/dnlnet/dnl_r101-d8_512x1024_80k_cityscapes.py + 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_769x769_80k_cityscapes +- Name: dnl_r50-d8_4xb2-80k_cityscapes-769x769 In Collection: DNLNet Metadata: backbone: R-50-D8 @@ -138,9 +138,9 @@ Models: Metrics: mIoU: 79.36 mIoU(ms+flip): 80.7 - Config: configs/dnlnet/dnl_r50-d8_769x769_80k_cityscapes.py + 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_769x769_80k_cityscapes +- Name: dnl_r101-d8_4xb2-80k_cityscapes-769x769 In Collection: DNLNet Metadata: backbone: R-101-D8 @@ -152,9 +152,9 @@ Models: Metrics: mIoU: 79.41 mIoU(ms+flip): 80.68 - Config: configs/dnlnet/dnl_r101-d8_769x769_80k_cityscapes.py + 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_512x512_80k_ade20k +- Name: dnl_r50-d8_4xb4-80k_ade20k-512x512 In Collection: DNLNet Metadata: backbone: R-50-D8 @@ -174,9 +174,9 @@ Models: Metrics: mIoU: 41.76 mIoU(ms+flip): 42.99 - Config: configs/dnlnet/dnl_r50-d8_512x512_80k_ade20k.py + 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_512x512_80k_ade20k +- Name: dnl_r101-d8_4xb4-80k_ade20k-512x512 In Collection: DNLNet Metadata: backbone: R-101-D8 @@ -196,9 +196,9 @@ Models: Metrics: mIoU: 43.76 mIoU(ms+flip): 44.91 - Config: configs/dnlnet/dnl_r101-d8_512x512_80k_ade20k.py + 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_512x512_160k_ade20k +- Name: dnl_r50-d8_4xb4-160k_ade20k-512x512 In Collection: DNLNet Metadata: backbone: R-50-D8 @@ -210,9 +210,9 @@ Models: Metrics: mIoU: 41.87 mIoU(ms+flip): 43.01 - Config: configs/dnlnet/dnl_r50-d8_512x512_160k_ade20k.py + 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_512x512_160k_ade20k +- Name: dnl_r101-d8_4xb4-160k_ade20k-512x512 In Collection: DNLNet Metadata: backbone: R-101-D8 @@ -224,5 +224,5 @@ Models: Metrics: mIoU: 44.25 mIoU(ms+flip): 45.78 - Config: configs/dnlnet/dnl_r101-d8_512x512_160k_ade20k.py + 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/dpt/README.md b/configs/dpt/README.md index 5e6257711f..41d73ea57a 100644 --- a/configs/dpt/README.md +++ b/configs/dpt/README.md @@ -62,6 +62,6 @@ 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/master/configs/dpt/dpt_vit-b16_512x512_160k_ade20k.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) | 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) | diff --git a/configs/dpt/dpt.yml b/configs/dpt/dpt.yml index a4f9c65b79..32324d3459 100644 --- a/configs/dpt/dpt.yml +++ b/configs/dpt/dpt.yml @@ -13,7 +13,7 @@ Collections: Converted From: Code: https://github.com/isl-org/DPT Models: -- Name: dpt_vit-b16_512x512_160k_ade20k +- Name: dpt_vit-b16_8xb2-160k_ade20k-512x512 In Collection: DPT Metadata: backbone: ViT-B @@ -33,5 +33,5 @@ Models: Metrics: mIoU: 46.97 mIoU(ms+flip): 48.34 - Config: configs/dpt/dpt_vit-b16_512x512_160k_ade20k.py + 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/dpt_vit-b16_512x512_160k_ade20k.py b/configs/dpt/dpt_vit-b16_8xb2-160k_ade20k-512x512.py similarity index 100% rename from configs/dpt/dpt_vit-b16_512x512_160k_ade20k.py rename to configs/dpt/dpt_vit-b16_8xb2-160k_ade20k-512x512.py diff --git a/configs/emanet/README.md b/configs/emanet/README.md index 3e5752b3b2..5a9bfc326a 100644 --- a/configs/emanet/README.md +++ b/configs/emanet/README.md @@ -38,9 +38,9 @@ Self-attention mechanism has been widely used for various tasks. It is designed ### 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/emanet_r50-d8_512x1024_80k_cityscapes.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_512x1024_80k_cityscapes.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_769x769_80k_cityscapes.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_769x769_80k_cityscapes.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) | +| 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 index 22ebcdb62a..ac194f2a0f 100644 --- a/configs/emanet/emanet.yml +++ b/configs/emanet/emanet.yml @@ -13,7 +13,7 @@ Collections: Converted From: Code: https://xialipku.github.io/EMANet Models: -- Name: emanet_r50-d8_512x1024_80k_cityscapes +- Name: emanet_r50-d8_4xb2-80k_cityscapes-512x1024 In Collection: EMANet Metadata: backbone: R-50-D8 @@ -33,9 +33,9 @@ Models: Metrics: mIoU: 77.59 mIoU(ms+flip): 79.44 - Config: configs/emanet/emanet_r50-d8_512x1024_80k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: emanet_r101-d8_4xb2-80k_cityscapes-512x1024 In Collection: EMANet Metadata: backbone: R-101-D8 @@ -55,9 +55,9 @@ Models: Metrics: mIoU: 79.1 mIoU(ms+flip): 81.21 - Config: configs/emanet/emanet_r101-d8_512x1024_80k_cityscapes.py + 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_769x769_80k_cityscapes +- Name: emanet_r50-d8_4xb2-80k_cityscapes-769x769 In Collection: EMANet Metadata: backbone: R-50-D8 @@ -77,9 +77,9 @@ Models: Metrics: mIoU: 79.33 mIoU(ms+flip): 80.49 - Config: configs/emanet/emanet_r50-d8_769x769_80k_cityscapes.py + 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_769x769_80k_cityscapes +- Name: emanet_r101-d8_4xb2-80k_cityscapes-769x769 In Collection: EMANet Metadata: backbone: R-101-D8 @@ -99,5 +99,5 @@ Models: Metrics: mIoU: 79.62 mIoU(ms+flip): 81.0 - Config: configs/emanet/emanet_r101-d8_769x769_80k_cityscapes.py + 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/emanet_r101-d8_4xb2-80k_cityscapes-512x1024.py b/configs/emanet/emanet_r101-d8_4xb2-80k_cityscapes-512x1024.py new file mode 100644 index 0000000000..ee3a3b5167 --- /dev/null +++ b/configs/emanet/emanet_r101-d8_4xb2-80k_cityscapes-512x1024.py @@ -0,0 +1,2 @@ +_base_ = './emanet_r50-d8_4xb2-80k_cityscapes-512x1024.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/emanet/emanet_r101-d8_4xb2-80k_cityscapes-769x769.py b/configs/emanet/emanet_r101-d8_4xb2-80k_cityscapes-769x769.py new file mode 100644 index 0000000000..7319a3e4b6 --- /dev/null +++ b/configs/emanet/emanet_r101-d8_4xb2-80k_cityscapes-769x769.py @@ -0,0 +1,2 @@ +_base_ = './emanet_r50-d8_4xb2-80k_cityscapes-769x769.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/emanet/emanet_r101-d8_512x1024_80k_cityscapes.py b/configs/emanet/emanet_r101-d8_512x1024_80k_cityscapes.py deleted file mode 100644 index 58f28b43f5..0000000000 --- a/configs/emanet/emanet_r101-d8_512x1024_80k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './emanet_r50-d8_512x1024_80k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/emanet/emanet_r101-d8_769x769_80k_cityscapes.py b/configs/emanet/emanet_r101-d8_769x769_80k_cityscapes.py deleted file mode 100644 index c5dbf20b0f..0000000000 --- a/configs/emanet/emanet_r101-d8_769x769_80k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './emanet_r50-d8_769x769_80k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/emanet/emanet_r50-d8_512x1024_80k_cityscapes.py b/configs/emanet/emanet_r50-d8_4xb2-80k_cityscapes-512x1024.py similarity index 100% rename from configs/emanet/emanet_r50-d8_512x1024_80k_cityscapes.py rename to configs/emanet/emanet_r50-d8_4xb2-80k_cityscapes-512x1024.py diff --git a/configs/emanet/emanet_r50-d8_769x769_80k_cityscapes.py b/configs/emanet/emanet_r50-d8_4xb2-80k_cityscapes-769x769.py similarity index 100% rename from configs/emanet/emanet_r50-d8_769x769_80k_cityscapes.py rename to configs/emanet/emanet_r50-d8_4xb2-80k_cityscapes-769x769.py diff --git a/configs/encnet/README.md b/configs/encnet/README.md index c191943a30..7be0c6d926 100644 --- a/configs/encnet/README.md +++ b/configs/encnet/README.md @@ -38,22 +38,22 @@ year = {2018} ### 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/master/configs/encnet/encnet_r50-d8_512x1024_40k_cityscapes.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/master/configs/encnet/encnet_r101-d8_512x1024_40k_cityscapes.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/master/configs/encnet/encnet_r50-d8_769x769_40k_cityscapes.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/master/configs/encnet/encnet_r101-d8_769x769_40k_cityscapes.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/master/configs/encnet/encnet_r50-d8_512x1024_80k_cityscapes.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/master/configs/encnet/encnet_r101-d8_512x1024_80k_cityscapes.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/master/configs/encnet/encnet_r50-d8_769x769_80k_cityscapes.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/master/configs/encnet/encnet_r101-d8_769x769_80k_cityscapes.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) | +| 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/master/configs/encnet/encnet_r50-d8_512x512_80k_ade20k.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/master/configs/encnet/encnet_r101-d8_512x512_80k_ade20k.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/master/configs/encnet/encnet_r50-d8_512x512_160k_ade20k.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/master/configs/encnet/encnet_r101-d8_512x512_160k_ade20k.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) | +| 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 index 18fb32a395..bea147b286 100644 --- a/configs/encnet/encnet.yml +++ b/configs/encnet/encnet.yml @@ -14,7 +14,7 @@ Collections: Converted From: Code: https://github.com/zhanghang1989/PyTorch-Encoding Models: -- Name: encnet_r50-d8_512x1024_40k_cityscapes +- Name: encnet_r50-d8_4xb2-40k_cityscapes-512x1024 In Collection: EncNet Metadata: backbone: R-50-D8 @@ -34,9 +34,9 @@ Models: Metrics: mIoU: 75.67 mIoU(ms+flip): 77.08 - Config: configs/encnet/encnet_r50-d8_512x1024_40k_cityscapes.py + 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_512x1024_40k_cityscapes +- Name: encnet_r101-d8_4xb2-40k_cityscapes-512x1024 In Collection: EncNet Metadata: backbone: R-101-D8 @@ -56,9 +56,9 @@ Models: Metrics: mIoU: 75.81 mIoU(ms+flip): 77.21 - Config: configs/encnet/encnet_r101-d8_512x1024_40k_cityscapes.py + 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_769x769_40k_cityscapes +- Name: encnet_r50-d8_4xb2-40k_cityscapes-769x769 In Collection: EncNet Metadata: backbone: R-50-D8 @@ -78,9 +78,9 @@ Models: Metrics: mIoU: 76.24 mIoU(ms+flip): 77.85 - Config: configs/encnet/encnet_r50-d8_769x769_40k_cityscapes.py + 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_769x769_40k_cityscapes +- Name: encnet_r101-d8_4xb2-40k_cityscapes-769x769 In Collection: EncNet Metadata: backbone: R-101-D8 @@ -100,9 +100,9 @@ Models: Metrics: mIoU: 74.25 mIoU(ms+flip): 76.25 - Config: configs/encnet/encnet_r101-d8_769x769_40k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: encnet_r50-d8_4xb2-80k_cityscapes-512x1024 In Collection: EncNet Metadata: backbone: R-50-D8 @@ -114,9 +114,9 @@ Models: Metrics: mIoU: 77.94 mIoU(ms+flip): 79.13 - Config: configs/encnet/encnet_r50-d8_512x1024_80k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: encnet_r101-d8_4xb2-80k_cityscapes-512x1024 In Collection: EncNet Metadata: backbone: R-101-D8 @@ -128,9 +128,9 @@ Models: Metrics: mIoU: 78.55 mIoU(ms+flip): 79.47 - Config: configs/encnet/encnet_r101-d8_512x1024_80k_cityscapes.py + 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_769x769_80k_cityscapes +- Name: encnet_r50-d8_4xb2-80k_cityscapes-769x769 In Collection: EncNet Metadata: backbone: R-50-D8 @@ -142,9 +142,9 @@ Models: Metrics: mIoU: 77.44 mIoU(ms+flip): 78.72 - Config: configs/encnet/encnet_r50-d8_769x769_80k_cityscapes.py + 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_769x769_80k_cityscapes +- Name: encnet_r101-d8_4xb2-80k_cityscapes-769x769 In Collection: EncNet Metadata: backbone: R-101-D8 @@ -156,9 +156,9 @@ Models: Metrics: mIoU: 76.1 mIoU(ms+flip): 76.97 - Config: configs/encnet/encnet_r101-d8_769x769_80k_cityscapes.py + 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_512x512_80k_ade20k +- Name: encnet_r50-d8_4xb4-80k_ade20k-512x512 In Collection: EncNet Metadata: backbone: R-50-D8 @@ -178,9 +178,9 @@ Models: Metrics: mIoU: 39.53 mIoU(ms+flip): 41.17 - Config: configs/encnet/encnet_r50-d8_512x512_80k_ade20k.py + 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_512x512_80k_ade20k +- Name: encnet_r101-d8_4xb4-80k_ade20k-512x512 In Collection: EncNet Metadata: backbone: R-101-D8 @@ -200,9 +200,9 @@ Models: Metrics: mIoU: 42.11 mIoU(ms+flip): 43.61 - Config: configs/encnet/encnet_r101-d8_512x512_80k_ade20k.py + 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_512x512_160k_ade20k +- Name: encnet_r50-d8_4xb4-160k_ade20k-512x512 In Collection: EncNet Metadata: backbone: R-50-D8 @@ -214,9 +214,9 @@ Models: Metrics: mIoU: 40.1 mIoU(ms+flip): 41.71 - Config: configs/encnet/encnet_r50-d8_512x512_160k_ade20k.py + 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_512x512_160k_ade20k +- Name: encnet_r101-d8_4xb4-160k_ade20k-512x512 In Collection: EncNet Metadata: backbone: R-101-D8 @@ -228,5 +228,5 @@ Models: Metrics: mIoU: 42.61 mIoU(ms+flip): 44.01 - Config: configs/encnet/encnet_r101-d8_512x512_160k_ade20k.py + 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/encnet_r101-d8_4xb2-40k_cityscapes-512x1024.py b/configs/encnet/encnet_r101-d8_4xb2-40k_cityscapes-512x1024.py new file mode 100644 index 0000000000..13ab367be5 --- /dev/null +++ b/configs/encnet/encnet_r101-d8_4xb2-40k_cityscapes-512x1024.py @@ -0,0 +1,2 @@ +_base_ = './encnet_r50-d8_4xb2-40k_cityscapes-512x1024.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/encnet/encnet_r101-d8_4xb2-40k_cityscapes-769x769.py b/configs/encnet/encnet_r101-d8_4xb2-40k_cityscapes-769x769.py new file mode 100644 index 0000000000..7810ac440d --- /dev/null +++ b/configs/encnet/encnet_r101-d8_4xb2-40k_cityscapes-769x769.py @@ -0,0 +1,2 @@ +_base_ = './encnet_r50-d8_4xb2-40k_cityscapes-769x769.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/encnet/encnet_r101-d8_4xb2-80k_cityscapes-512x1024.py b/configs/encnet/encnet_r101-d8_4xb2-80k_cityscapes-512x1024.py new file mode 100644 index 0000000000..bec6bd907d --- /dev/null +++ b/configs/encnet/encnet_r101-d8_4xb2-80k_cityscapes-512x1024.py @@ -0,0 +1,2 @@ +_base_ = './encnet_r50-d8_4xb2-80k_cityscapes-512x1024.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/encnet/encnet_r101-d8_4xb2-80k_cityscapes-769x769.py b/configs/encnet/encnet_r101-d8_4xb2-80k_cityscapes-769x769.py new file mode 100644 index 0000000000..e1f6409e63 --- /dev/null +++ b/configs/encnet/encnet_r101-d8_4xb2-80k_cityscapes-769x769.py @@ -0,0 +1,2 @@ +_base_ = './encnet_r50-d8_4xb2-80k_cityscapes-769x769.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/encnet/encnet_r101-d8_4xb4-160k_ade20k-512x512.py b/configs/encnet/encnet_r101-d8_4xb4-160k_ade20k-512x512.py new file mode 100644 index 0000000000..9599f9c0d3 --- /dev/null +++ b/configs/encnet/encnet_r101-d8_4xb4-160k_ade20k-512x512.py @@ -0,0 +1,2 @@ +_base_ = './encnet_r50-d8_4xb4-160k_ade20k-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/encnet/encnet_r101-d8_4xb4-20k_voc12aug-512x512.py b/configs/encnet/encnet_r101-d8_4xb4-20k_voc12aug-512x512.py new file mode 100644 index 0000000000..a9edfc28a2 --- /dev/null +++ b/configs/encnet/encnet_r101-d8_4xb4-20k_voc12aug-512x512.py @@ -0,0 +1,2 @@ +_base_ = './encnet_r50-d8_4xb4-20k_voc12aug-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/encnet/encnet_r101-d8_4xb4-40k_voc12aug-512x512.py b/configs/encnet/encnet_r101-d8_4xb4-40k_voc12aug-512x512.py new file mode 100644 index 0000000000..d2fbab59e3 --- /dev/null +++ b/configs/encnet/encnet_r101-d8_4xb4-40k_voc12aug-512x512.py @@ -0,0 +1,2 @@ +_base_ = './encnet_r50-d8_4xb4-40k_voc12aug-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/encnet/encnet_r101-d8_4xb4-80k_ade20k-512x512.py b/configs/encnet/encnet_r101-d8_4xb4-80k_ade20k-512x512.py new file mode 100644 index 0000000000..debe8c8331 --- /dev/null +++ b/configs/encnet/encnet_r101-d8_4xb4-80k_ade20k-512x512.py @@ -0,0 +1,2 @@ +_base_ = './encnet_r50-d8_4xb4-80k_ade20k-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/encnet/encnet_r101-d8_512x1024_40k_cityscapes.py b/configs/encnet/encnet_r101-d8_512x1024_40k_cityscapes.py deleted file mode 100644 index f34373d9eb..0000000000 --- a/configs/encnet/encnet_r101-d8_512x1024_40k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './encnet_r50-d8_512x1024_40k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/encnet/encnet_r101-d8_512x1024_80k_cityscapes.py b/configs/encnet/encnet_r101-d8_512x1024_80k_cityscapes.py deleted file mode 100644 index 0b0207b314..0000000000 --- a/configs/encnet/encnet_r101-d8_512x1024_80k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './encnet_r50-d8_512x1024_80k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/encnet/encnet_r101-d8_512x512_160k_ade20k.py b/configs/encnet/encnet_r101-d8_512x512_160k_ade20k.py deleted file mode 100644 index 8fec6ba255..0000000000 --- a/configs/encnet/encnet_r101-d8_512x512_160k_ade20k.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './encnet_r50-d8_512x512_160k_ade20k.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/encnet/encnet_r101-d8_512x512_20k_voc12aug.py b/configs/encnet/encnet_r101-d8_512x512_20k_voc12aug.py deleted file mode 100644 index c264af998b..0000000000 --- a/configs/encnet/encnet_r101-d8_512x512_20k_voc12aug.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './encnet_r50-d8_512x512_20k_voc12aug.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/encnet/encnet_r101-d8_512x512_40k_voc12aug.py b/configs/encnet/encnet_r101-d8_512x512_40k_voc12aug.py deleted file mode 100644 index 8a6968ea58..0000000000 --- a/configs/encnet/encnet_r101-d8_512x512_40k_voc12aug.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './encnet_r50-d8_512x512_40k_voc12aug.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/encnet/encnet_r101-d8_512x512_80k_ade20k.py b/configs/encnet/encnet_r101-d8_512x512_80k_ade20k.py deleted file mode 100644 index 94151004ea..0000000000 --- a/configs/encnet/encnet_r101-d8_512x512_80k_ade20k.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './encnet_r50-d8_512x512_80k_ade20k.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/encnet/encnet_r101-d8_769x769_40k_cityscapes.py b/configs/encnet/encnet_r101-d8_769x769_40k_cityscapes.py deleted file mode 100644 index d6ade67b76..0000000000 --- a/configs/encnet/encnet_r101-d8_769x769_40k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './encnet_r50-d8_769x769_40k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/encnet/encnet_r101-d8_769x769_80k_cityscapes.py b/configs/encnet/encnet_r101-d8_769x769_80k_cityscapes.py deleted file mode 100644 index 55648c08b2..0000000000 --- a/configs/encnet/encnet_r101-d8_769x769_80k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './encnet_r50-d8_769x769_80k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/encnet/encnet_r50-d8_512x1024_40k_cityscapes.py b/configs/encnet/encnet_r50-d8_4xb2-40k_cityscapes-512x1024.py similarity index 100% rename from configs/encnet/encnet_r50-d8_512x1024_40k_cityscapes.py rename to configs/encnet/encnet_r50-d8_4xb2-40k_cityscapes-512x1024.py diff --git a/configs/encnet/encnet_r50-d8_769x769_40k_cityscapes.py b/configs/encnet/encnet_r50-d8_4xb2-40k_cityscapes-769x769.py similarity index 100% rename from configs/encnet/encnet_r50-d8_769x769_40k_cityscapes.py rename to configs/encnet/encnet_r50-d8_4xb2-40k_cityscapes-769x769.py diff --git a/configs/encnet/encnet_r50-d8_512x1024_80k_cityscapes.py b/configs/encnet/encnet_r50-d8_4xb2-80k_cityscapes-512x1024.py similarity index 100% rename from configs/encnet/encnet_r50-d8_512x1024_80k_cityscapes.py rename to configs/encnet/encnet_r50-d8_4xb2-80k_cityscapes-512x1024.py diff --git a/configs/encnet/encnet_r50-d8_769x769_80k_cityscapes.py b/configs/encnet/encnet_r50-d8_4xb2-80k_cityscapes-769x769.py similarity index 100% rename from configs/encnet/encnet_r50-d8_769x769_80k_cityscapes.py rename to configs/encnet/encnet_r50-d8_4xb2-80k_cityscapes-769x769.py diff --git a/configs/encnet/encnet_r50-d8_512x512_160k_ade20k.py b/configs/encnet/encnet_r50-d8_4xb4-160k_ade20k-512x512.py similarity index 100% rename from configs/encnet/encnet_r50-d8_512x512_160k_ade20k.py rename to configs/encnet/encnet_r50-d8_4xb4-160k_ade20k-512x512.py diff --git a/configs/encnet/encnet_r50-d8_512x512_20k_voc12aug.py b/configs/encnet/encnet_r50-d8_4xb4-20k_voc12aug-512x512.py similarity index 100% rename from configs/encnet/encnet_r50-d8_512x512_20k_voc12aug.py rename to configs/encnet/encnet_r50-d8_4xb4-20k_voc12aug-512x512.py diff --git a/configs/encnet/encnet_r50-d8_512x512_40k_voc12aug.py b/configs/encnet/encnet_r50-d8_4xb4-40k_voc12aug-512x512.py similarity index 100% rename from configs/encnet/encnet_r50-d8_512x512_40k_voc12aug.py rename to configs/encnet/encnet_r50-d8_4xb4-40k_voc12aug-512x512.py diff --git a/configs/encnet/encnet_r50-d8_512x512_80k_ade20k.py b/configs/encnet/encnet_r50-d8_4xb4-80k_ade20k-512x512.py similarity index 100% rename from configs/encnet/encnet_r50-d8_512x512_80k_ade20k.py rename to configs/encnet/encnet_r50-d8_4xb4-80k_ade20k-512x512.py diff --git a/configs/encnet/encnet_r50s-d8_512x512_80k_ade20k.py b/configs/encnet/encnet_r50s-d8_4xb4-80k_ade20k-512x512.py similarity index 100% rename from configs/encnet/encnet_r50s-d8_512x512_80k_ade20k.py rename to configs/encnet/encnet_r50s-d8_4xb4-80k_ade20k-512x512.py diff --git a/configs/erfnet/README.md b/configs/erfnet/README.md index bcb61d3d6f..44e4f51c91 100644 --- a/configs/erfnet/README.md +++ b/configs/erfnet/README.md @@ -41,9 +41,9 @@ Semantic segmentation is a challenging task that addresses most of the perceptio ### 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 | 71.08 | 72.6 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/erfnet/erfnet_fcn_4x4_512x1024_160k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/erfnet/erfnet_fcn_4x4_512x1024_160k_cityscapes/erfnet_fcn_4x4_512x1024_160k_cityscapes_20211126_082056-03d333ed.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/erfnet/erfnet_fcn_4x4_512x1024_160k_cityscapes/erfnet_fcn_4x4_512x1024_160k_cityscapes_20211126_082056.log.json) | +| 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 | 71.08 | 72.6 | [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_20211126_082056-03d333ed.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/erfnet/erfnet_fcn_4x4_512x1024_160k_cityscapes/erfnet_fcn_4x4_512x1024_160k_cityscapes_20211126_082056.log.json) | Note: diff --git a/configs/erfnet/erfnet.yml b/configs/erfnet/erfnet.yml index e4c34f9c5b..aeb454cb50 100644 --- a/configs/erfnet/erfnet.yml +++ b/configs/erfnet/erfnet.yml @@ -13,7 +13,7 @@ Collections: Converted From: Code: https://github.com/Eromera/erfnet_pytorch Models: -- Name: erfnet_fcn_4x4_512x1024_160k_cityscapes +- Name: erfnet_fcn_4xb4-160k_cityscapes-512x1024 In Collection: ERFNet Metadata: backbone: ERFNet @@ -33,5 +33,5 @@ Models: Metrics: mIoU: 71.08 mIoU(ms+flip): 72.6 - Config: configs/erfnet/erfnet_fcn_4x4_512x1024_160k_cityscapes.py + 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_20211126_082056-03d333ed.pth diff --git a/configs/erfnet/erfnet_fcn_4x4_512x1024_160k_cityscapes.py b/configs/erfnet/erfnet_fcn_4xb4-160k_cityscapes-512x1024.py similarity index 100% rename from configs/erfnet/erfnet_fcn_4x4_512x1024_160k_cityscapes.py rename to configs/erfnet/erfnet_fcn_4xb4-160k_cityscapes-512x1024.py diff --git a/configs/fastfcn/README.md b/configs/fastfcn/README.md index d772bd2402..79dd3bcf80 100644 --- a/configs/fastfcn/README.md +++ b/configs/fastfcn/README.md @@ -37,27 +37,27 @@ year={2019} ### 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/master/configs/fastfcn/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes.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/master/configs/fastfcn/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes.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/master/configs/fastfcn/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes.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/master/configs/fastfcn/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes.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/master/configs/fastfcn/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes.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/master/configs/fastfcn/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes.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) | 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_4xb4-80k_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_4xb4-80k_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_4xb4-80k_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/master/configs/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_80k_ade20k.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/master/configs/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_160k_ade20k.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/master/configs/fastfcn/fastfcn_r50-d32_jpu_psp_512x512_80k_ade20k.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/master/configs/fastfcn/fastfcn_r50-d32_jpu_psp_512x512_160k_ade20k.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/master/configs/fastfcn/fastfcn_r50-d32_jpu_enc_512x512_80k_ade20k.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/master/configs/fastfcn/fastfcn_r50-d32_jpu_enc_512x512_160k_ade20k.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) | 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) | 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/master/configs/deeplabv3), [PSPNet (mIoU: 78.55)](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet) and [ENCNet (mIoU: 77.94)](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/encnet) can be found in each original repository. +- 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. diff --git a/configs/fastfcn/fastfcn.yml b/configs/fastfcn/fastfcn.yml index 6fdc556588..01d734afed 100644 --- a/configs/fastfcn/fastfcn.yml +++ b/configs/fastfcn/fastfcn.yml @@ -14,7 +14,7 @@ Collections: Converted From: Code: https://github.com/wuhuikai/FastFCN Models: -- Name: fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes +- Name: fastfcn_r50-d32_jpu_aspp_4xb2-80k_cityscapes-512x1024 In Collection: FastFCN Metadata: backbone: R-50-D32 @@ -34,9 +34,9 @@ Models: Metrics: mIoU: 79.12 mIoU(ms+flip): 80.58 - Config: configs/fastfcn/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes.py + 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_4x4_512x1024_80k_cityscapes +- Name: fastfcn_r50-d32_jpu_aspp_4xb4-80k_cityscapes-512x1024 In Collection: FastFCN Metadata: backbone: R-50-D32 @@ -49,9 +49,9 @@ Models: Metrics: mIoU: 79.52 mIoU(ms+flip): 80.91 - Config: configs/fastfcn/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes.py + Config: configs/fastfcn/fastfcn_r50-d32_jpu_aspp_4xb4-80k_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_512x1024_80k_cityscapes +- Name: fastfcn_r50-d32_jpu_psp_4xb2-80k_cityscapes-512x1024 In Collection: FastFCN Metadata: backbone: R-50-D32 @@ -71,9 +71,9 @@ Models: Metrics: mIoU: 79.26 mIoU(ms+flip): 80.86 - Config: configs/fastfcn/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes.py + 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_4x4_512x1024_80k_cityscapes +- Name: fastfcn_r50-d32_jpu_psp_4xb4-80k_cityscapes-512x1024 In Collection: FastFCN Metadata: backbone: R-50-D32 @@ -86,9 +86,9 @@ Models: Metrics: mIoU: 78.76 mIoU(ms+flip): 80.03 - Config: configs/fastfcn/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes.py + Config: configs/fastfcn/fastfcn_r50-d32_jpu_psp_4xb4-80k_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_512x1024_80k_cityscapes +- Name: fastfcn_r50-d32_jpu_enc_4xb2-80k_cityscapes-512x1024 In Collection: FastFCN Metadata: backbone: R-50-D32 @@ -108,9 +108,9 @@ Models: Metrics: mIoU: 77.97 mIoU(ms+flip): 79.92 - Config: configs/fastfcn/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes.py + 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_4x4_512x1024_80k_cityscapes +- Name: fastfcn_r50-d32_jpu_enc_4xb4-80k_cityscapes-512x1024 In Collection: FastFCN Metadata: backbone: R-50-D32 @@ -123,9 +123,9 @@ Models: Metrics: mIoU: 78.6 mIoU(ms+flip): 80.25 - Config: configs/fastfcn/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes.py + Config: configs/fastfcn/fastfcn_r50-d32_jpu_enc_4xb4-80k_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_512x512_80k_ade20k +- Name: fastfcn_r50-d32_jpu_aspp_4xb4-80k_ade20k-512x512 In Collection: FastFCN Metadata: backbone: R-50-D32 @@ -145,9 +145,9 @@ Models: Metrics: mIoU: 41.88 mIoU(ms+flip): 42.91 - Config: configs/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_80k_ade20k.py + 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_512x512_160k_ade20k +- Name: fastfcn_r50-d32_jpu_aspp_4xb4-160k_ade20k-512x512 In Collection: FastFCN Metadata: backbone: R-50-D32 @@ -159,9 +159,9 @@ Models: Metrics: mIoU: 43.58 mIoU(ms+flip): 44.92 - Config: configs/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_160k_ade20k.py + 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_512x512_80k_ade20k +- Name: fastfcn_r50-d32_jpu_psp_4xb4-80k_ade20k-512x512 In Collection: FastFCN Metadata: backbone: R-50-D32 @@ -181,9 +181,9 @@ Models: Metrics: mIoU: 41.4 mIoU(ms+flip): 42.12 - Config: configs/fastfcn/fastfcn_r50-d32_jpu_psp_512x512_80k_ade20k.py + 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_512x512_160k_ade20k +- Name: fastfcn_r50-d32_jpu_psp_4xb4-160k_ade20k-512x512 In Collection: FastFCN Metadata: backbone: R-50-D32 @@ -195,9 +195,9 @@ Models: Metrics: mIoU: 42.63 mIoU(ms+flip): 43.71 - Config: configs/fastfcn/fastfcn_r50-d32_jpu_psp_512x512_160k_ade20k.py + 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_512x512_80k_ade20k +- Name: fastfcn_r50-d32_jpu_enc_4xb4-80k_ade20k-512x512 In Collection: FastFCN Metadata: backbone: R-50-D32 @@ -217,9 +217,9 @@ Models: Metrics: mIoU: 40.88 mIoU(ms+flip): 42.36 - Config: configs/fastfcn/fastfcn_r50-d32_jpu_enc_512x512_80k_ade20k.py + 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_512x512_160k_ade20k +- Name: fastfcn_r50-d32_jpu_enc_4xb4-160k_ade20k-512x512 In Collection: FastFCN Metadata: backbone: R-50-D32 @@ -231,5 +231,5 @@ Models: Metrics: mIoU: 42.5 mIoU(ms+flip): 44.21 - Config: configs/fastfcn/fastfcn_r50-d32_jpu_enc_512x512_160k_ade20k.py + 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/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes.py b/configs/fastfcn/fastfcn_r50-d32_jpu_aspp_4xb2-80k_cityscapes-512x1024.py similarity index 89% rename from configs/fastfcn/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes.py rename to configs/fastfcn/fastfcn_r50-d32_jpu_aspp_4xb2-80k_cityscapes-512x1024.py index dc86da3b6f..39e6e236b7 100644 --- a/configs/fastfcn/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes.py +++ b/configs/fastfcn/fastfcn_r50-d32_jpu_aspp_4xb2-80k_cityscapes-512x1024.py @@ -1,5 +1,5 @@ # model settings -_base_ = './fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes.py' +_base_ = './fastfcn_r50-d32_jpu_psp_4xb2-80k_cityscapes-512x1024.py' norm_cfg = dict(type='SyncBN', requires_grad=True) model = dict( decode_head=dict( diff --git a/configs/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_80k_ade20k.py b/configs/fastfcn/fastfcn_r50-d32_jpu_aspp_4xb4-160k_ade20k-512x512.py similarity index 89% rename from configs/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_80k_ade20k.py rename to configs/fastfcn/fastfcn_r50-d32_jpu_aspp_4xb4-160k_ade20k-512x512.py index b14b1f68c7..1913544cfb 100644 --- a/configs/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_80k_ade20k.py +++ b/configs/fastfcn/fastfcn_r50-d32_jpu_aspp_4xb4-160k_ade20k-512x512.py @@ -1,5 +1,5 @@ # model settings -_base_ = './fastfcn_r50-d32_jpu_psp_512x512_80k_ade20k.py' +_base_ = './fastfcn_r50-d32_jpu_psp_4xb4-160k_ade20k-512x512.py' norm_cfg = dict(type='SyncBN', requires_grad=True) model = dict( decode_head=dict( diff --git a/configs/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_160k_ade20k.py b/configs/fastfcn/fastfcn_r50-d32_jpu_aspp_4xb4-80k_ade20k-512x512.py similarity index 89% rename from configs/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_160k_ade20k.py rename to configs/fastfcn/fastfcn_r50-d32_jpu_aspp_4xb4-80k_ade20k-512x512.py index dbf9f80272..751689599d 100644 --- a/configs/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_160k_ade20k.py +++ b/configs/fastfcn/fastfcn_r50-d32_jpu_aspp_4xb4-80k_ade20k-512x512.py @@ -1,5 +1,5 @@ # model settings -_base_ = './fastfcn_r50-d32_jpu_psp_512x512_160k_ade20k.py' +_base_ = './fastfcn_r50-d32_jpu_psp_4xb4-80k_ade20k-512x512.py' norm_cfg = dict(type='SyncBN', requires_grad=True) model = dict( decode_head=dict( diff --git a/configs/fastfcn/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes.py b/configs/fastfcn/fastfcn_r50-d32_jpu_aspp_4xb4-80k_cityscapes-512x1024.py similarity index 68% rename from configs/fastfcn/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes.py rename to configs/fastfcn/fastfcn_r50-d32_jpu_aspp_4xb4-80k_cityscapes-512x1024.py index 839d540377..a8c5dc3232 100644 --- a/configs/fastfcn/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes.py +++ b/configs/fastfcn/fastfcn_r50-d32_jpu_aspp_4xb4-80k_cityscapes-512x1024.py @@ -1,5 +1,5 @@ # model settings -_base_ = './fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes.py' +_base_ = './fastfcn_r50-d32_jpu_aspp_4xb2-80k_cityscapes-512x1024.py' train_dataloader = dict(batch_size=4, num_workers=4) val_dataloader = dict(batch_size=1, num_workers=4) test_dataloader = val_dataloader diff --git a/configs/fastfcn/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes.py b/configs/fastfcn/fastfcn_r50-d32_jpu_enc_4xb2-80k_cityscapes-512x1024.py similarity index 91% rename from configs/fastfcn/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes.py rename to configs/fastfcn/fastfcn_r50-d32_jpu_enc_4xb2-80k_cityscapes-512x1024.py index cc68edfe5b..4840dd0287 100644 --- a/configs/fastfcn/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes.py +++ b/configs/fastfcn/fastfcn_r50-d32_jpu_enc_4xb2-80k_cityscapes-512x1024.py @@ -1,5 +1,5 @@ # model settings -_base_ = './fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes.py' +_base_ = './fastfcn_r50-d32_jpu_psp_4xb2-80k_cityscapes-512x1024.py' norm_cfg = dict(type='SyncBN', requires_grad=True) model = dict( decode_head=dict( diff --git a/configs/fastfcn/fastfcn_r50-d32_jpu_enc_512x512_80k_ade20k.py b/configs/fastfcn/fastfcn_r50-d32_jpu_enc_4xb4-160k_ade20k-512x512.py similarity index 91% rename from configs/fastfcn/fastfcn_r50-d32_jpu_enc_512x512_80k_ade20k.py rename to configs/fastfcn/fastfcn_r50-d32_jpu_enc_4xb4-160k_ade20k-512x512.py index d3e2e9c80b..619d0862f1 100644 --- a/configs/fastfcn/fastfcn_r50-d32_jpu_enc_512x512_80k_ade20k.py +++ b/configs/fastfcn/fastfcn_r50-d32_jpu_enc_4xb4-160k_ade20k-512x512.py @@ -1,5 +1,5 @@ # model settings -_base_ = './fastfcn_r50-d32_jpu_psp_512x512_80k_ade20k.py' +_base_ = './fastfcn_r50-d32_jpu_psp_4xb4-160k_ade20k-512x512.py' norm_cfg = dict(type='SyncBN', requires_grad=True) model = dict( decode_head=dict( diff --git a/configs/fastfcn/fastfcn_r50-d32_jpu_enc_512x512_160k_ade20k.py b/configs/fastfcn/fastfcn_r50-d32_jpu_enc_4xb4-80k_ade20k-512x512.py similarity index 91% rename from configs/fastfcn/fastfcn_r50-d32_jpu_enc_512x512_160k_ade20k.py rename to configs/fastfcn/fastfcn_r50-d32_jpu_enc_4xb4-80k_ade20k-512x512.py index 12f0add5ad..a76b026b6a 100644 --- a/configs/fastfcn/fastfcn_r50-d32_jpu_enc_512x512_160k_ade20k.py +++ b/configs/fastfcn/fastfcn_r50-d32_jpu_enc_4xb4-80k_ade20k-512x512.py @@ -1,5 +1,5 @@ # model settings -_base_ = './fastfcn_r50-d32_jpu_psp_512x512_160k_ade20k.py' +_base_ = './fastfcn_r50-d32_jpu_psp_4xb4-80k_ade20k-512x512.py' norm_cfg = dict(type='SyncBN', requires_grad=True) model = dict( decode_head=dict( diff --git a/configs/fastfcn/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes.py b/configs/fastfcn/fastfcn_r50-d32_jpu_enc_4xb4-80k_cityscapes-512x1024.py similarity index 69% rename from configs/fastfcn/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes.py rename to configs/fastfcn/fastfcn_r50-d32_jpu_enc_4xb4-80k_cityscapes-512x1024.py index 6fbca14bac..6df1527272 100644 --- a/configs/fastfcn/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes.py +++ b/configs/fastfcn/fastfcn_r50-d32_jpu_enc_4xb4-80k_cityscapes-512x1024.py @@ -1,5 +1,5 @@ # model settings -_base_ = './fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes.py' +_base_ = './fastfcn_r50-d32_jpu_enc_4xb2-80k_cityscapes-512x1024.py' train_dataloader = dict(batch_size=4, num_workers=4) val_dataloader = dict(batch_size=1, num_workers=4) test_dataloader = val_dataloader diff --git a/configs/fastfcn/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes.py b/configs/fastfcn/fastfcn_r50-d32_jpu_psp_4xb2-80k_cityscapes-512x1024.py similarity index 100% rename from configs/fastfcn/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes.py rename to configs/fastfcn/fastfcn_r50-d32_jpu_psp_4xb2-80k_cityscapes-512x1024.py diff --git a/configs/fastfcn/fastfcn_r50-d32_jpu_psp_512x512_160k_ade20k.py b/configs/fastfcn/fastfcn_r50-d32_jpu_psp_4xb4-160k_ade20k-512x512.py similarity index 100% rename from configs/fastfcn/fastfcn_r50-d32_jpu_psp_512x512_160k_ade20k.py rename to configs/fastfcn/fastfcn_r50-d32_jpu_psp_4xb4-160k_ade20k-512x512.py diff --git a/configs/fastfcn/fastfcn_r50-d32_jpu_psp_512x512_80k_ade20k.py b/configs/fastfcn/fastfcn_r50-d32_jpu_psp_4xb4-80k_ade20k-512x512.py similarity index 100% rename from configs/fastfcn/fastfcn_r50-d32_jpu_psp_512x512_80k_ade20k.py rename to configs/fastfcn/fastfcn_r50-d32_jpu_psp_4xb4-80k_ade20k-512x512.py diff --git a/configs/fastfcn/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes.py b/configs/fastfcn/fastfcn_r50-d32_jpu_psp_4xb4-80k_cityscapes-512x1024.py similarity index 100% rename from configs/fastfcn/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes.py rename to configs/fastfcn/fastfcn_r50-d32_jpu_psp_4xb4-80k_cityscapes-512x1024.py diff --git a/configs/fastscnn/README.md b/configs/fastscnn/README.md index 156562670d..3e06903ae5 100644 --- a/configs/fastscnn/README.md +++ b/configs/fastscnn/README.md @@ -37,6 +37,6 @@ The encoder-decoder framework is state-of-the-art for offline semantic image seg ### 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/master/configs/fastscnn/fast_scnn_lr0.12_8x4_160k_cityscapes.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) | +| 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/fast_scnn_lr0.12_8x4_160k_cityscapes.py b/configs/fastscnn/fast_scnn_8xb4-160k_cityscapes-512x1024.py similarity index 100% rename from configs/fastscnn/fast_scnn_lr0.12_8x4_160k_cityscapes.py rename to configs/fastscnn/fast_scnn_8xb4-160k_cityscapes-512x1024.py diff --git a/configs/fastscnn/fastscnn.yml b/configs/fastscnn/fastscnn.yml index cad0360744..13215c2fb1 100644 --- a/configs/fastscnn/fastscnn.yml +++ b/configs/fastscnn/fastscnn.yml @@ -11,7 +11,7 @@ Collections: URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/fast_scnn.py#L272 Version: v0.17.0 Models: -- Name: fast_scnn_lr0.12_8x4_160k_cityscapes +- Name: fast_scnn_8xb4-160k_cityscapes-512x1024 In Collection: FastSCNN Metadata: backbone: FastSCNN @@ -31,5 +31,5 @@ Models: Metrics: mIoU: 70.96 mIoU(ms+flip): 72.65 - Config: configs/fastscnn/fast_scnn_lr0.12_8x4_160k_cityscapes.py + Config: configs/fastscnn/fast_scnn_8xb4-160k_cityscapes-512x1024.py 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 diff --git a/configs/fcn/README.md b/configs/fcn/README.md index 09ca1a50dc..4b887f19e4 100644 --- a/configs/fcn/README.md +++ b/configs/fcn/README.md @@ -41,69 +41,69 @@ Convolutional networks are powerful visual models that yield hierarchies of feat ### 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/master/configs/fcn/fcn_r50-d8_512x1024_40k_cityscapes.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/master/configs/fcn/fcn_r101-d8_512x1024_40k_cityscapes.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/master/configs/fcn/fcn_r50-d8_769x769_40k_cityscapes.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/master/configs/fcn/fcn_r101-d8_769x769_40k_cityscapes.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/master/configs/fcn/fcn_r18-d8_512x1024_80k_cityscapes.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/master/configs/fcn/fcn_r50-d8_512x1024_80k_cityscapes.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/master/configs/fcn/fcn_r101-d8_512x1024_80k_cityscapes.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/master/configs/fcn/fcn_r101-d8_fp16_512x1024_80k_cityscapes.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/master/configs/fcn/fcn_r18-d8_769x769_80k_cityscapes.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/master/configs/fcn/fcn_r50-d8_769x769_80k_cityscapes.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/master/configs/fcn/fcn_r101-d8_769x769_80k_cityscapes.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/master/configs/fcn/fcn_r18b-d8_512x1024_80k_cityscapes.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/master/configs/fcn/fcn_r50b-d8_512x1024_80k_cityscapes.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/master/configs/fcn/fcn_r101b-d8_512x1024_80k_cityscapes.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/master/configs/fcn/fcn_r18b-d8_769x769_80k_cityscapes.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/master/configs/fcn/fcn_r50b-d8_769x769_80k_cityscapes.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/master/configs/fcn/fcn_r101b-d8_769x769_80k_cityscapes.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/master/configs/fcn/fcn_d6_r50-d16_512x1024_40k_cityscapes.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/master/configs/fcn/fcn_d6_r50-d16_512x1024_80k_cityscapes.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/master/configs/fcn/fcn_d6_r50-d16_769x769_40k_cityscapes.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/master/configs/fcn/fcn_d6_r50-d16_769x769_80k_cityscapes.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/master/configs/fcn/fcn_d6_r101-d16_512x1024_40k_cityscapes.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/master/configs/fcn/fcn_d6_r101-d16_512x1024_80k_cityscapes.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/master/configs/fcn/fcn_d6_r101-d16_769x769_40k_cityscapes.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/master/configs/fcn/fcn_d6_r101-d16_769x769_80k_cityscapes.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/master/configs/fcn/fcn_d6_r50b-d16_512x1024_80k_cityscapes.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/master/configs/fcn/fcn_d6_r50b-d16_769x769_80k_cityscapes.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/master/configs/fcn/fcn_d6_r101b-d16_512x1024_80k_cityscapes.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/master/configs/fcn/fcn_d6_r101b-d16_769x769_80k_cityscapes.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) | 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) | ### 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/master/configs/fcn/fcn_r50-d8_512x512_80k_ade20k.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/master/configs/fcn/fcn_r101-d8_512x512_80k_ade20k.py) | [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/master/configs/fcn/fcn_r50-d8_512x512_160k_ade20k.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/master/configs/fcn/fcn_r101-d8_512x512_160k_ade20k.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) | 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) | ### 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/master/configs/fcn/fcn_r50-d8_512x512_20k_voc12aug.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/master/configs/fcn/fcn_r101-d8_512x512_20k_voc12aug.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/master/configs/fcn/fcn_r50-d8_512x512_40k_voc12aug.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/master/configs/fcn/fcn_r101-d8_512x512_40k_voc12aug.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) | 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) | ### 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/master/configs/fcn/fcn_r101-d8_480x480_40k_pascal_context.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/master/configs/fcn/fcn_r101-d8_480x480_80k_pascal_context.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) | 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) | ### 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/master/configs/fcn/fcn_r101-d8_480x480_40k_pascal_context_59.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/master/configs/fcn/fcn_r101-d8_480x480_80k_pascal_context_59.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) | 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) | Note: diff --git a/configs/fcn/fcn-d6_r101-d16_4xb2-40k_cityscapes-512x1024.py b/configs/fcn/fcn-d6_r101-d16_4xb2-40k_cityscapes-512x1024.py new file mode 100644 index 0000000000..8f2cd02b00 --- /dev/null +++ b/configs/fcn/fcn-d6_r101-d16_4xb2-40k_cityscapes-512x1024.py @@ -0,0 +1,2 @@ +_base_ = './fcn-d6_r50-d16_4xb2-40k_cityscapes-512x1024.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/fcn/fcn-d6_r101-d16_4xb2-40k_cityscapes-769x769.py b/configs/fcn/fcn-d6_r101-d16_4xb2-40k_cityscapes-769x769.py new file mode 100644 index 0000000000..4782b30377 --- /dev/null +++ b/configs/fcn/fcn-d6_r101-d16_4xb2-40k_cityscapes-769x769.py @@ -0,0 +1,2 @@ +_base_ = './fcn-d6_r50-d16_4xb2-40k_cityscapes-769x769.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/fcn/fcn-d6_r101-d16_4xb2-80k_cityscapes-512x1024.py b/configs/fcn/fcn-d6_r101-d16_4xb2-80k_cityscapes-512x1024.py new file mode 100644 index 0000000000..5f654b4bbd --- /dev/null +++ b/configs/fcn/fcn-d6_r101-d16_4xb2-80k_cityscapes-512x1024.py @@ -0,0 +1,2 @@ +_base_ = './fcn-d6_r50-d16_4xb2-80k_cityscapes-512x1024.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/fcn/fcn-d6_r101-d16_4xb2-80k_cityscapes-769x769.py b/configs/fcn/fcn-d6_r101-d16_4xb2-80k_cityscapes-769x769.py new file mode 100644 index 0000000000..91eca1c52e --- /dev/null +++ b/configs/fcn/fcn-d6_r101-d16_4xb2-80k_cityscapes-769x769.py @@ -0,0 +1,2 @@ +_base_ = './fcn-d6_r50-d16_4xb2-80k_cityscapes-769x769.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/deeplabv3plus/deeplabv3plus_r101b-d8_769x769_80k_cityscapes.py b/configs/fcn/fcn-d6_r101b-d16_4xb2-80k_cityscapes-512x1024.py similarity index 62% rename from configs/deeplabv3plus/deeplabv3plus_r101b-d8_769x769_80k_cityscapes.py rename to configs/fcn/fcn-d6_r101b-d16_4xb2-80k_cityscapes-512x1024.py index 136449083f..62e6127799 100644 --- a/configs/deeplabv3plus/deeplabv3plus_r101b-d8_769x769_80k_cityscapes.py +++ b/configs/fcn/fcn-d6_r101b-d16_4xb2-80k_cityscapes-512x1024.py @@ -1,4 +1,4 @@ -_base_ = './deeplabv3plus_r50-d8_769x769_80k_cityscapes.py' +_base_ = './fcn-d6_r50-d16_4xb2-80k_cityscapes-512x1024.py' model = dict( pretrained='torchvision://resnet101', backbone=dict(type='ResNet', depth=101)) diff --git a/configs/fcn/fcn-d6_r101b-d16_4xb2-80k_cityscapes-769x769.py b/configs/fcn/fcn-d6_r101b-d16_4xb2-80k_cityscapes-769x769.py new file mode 100644 index 0000000000..1b8d24799e --- /dev/null +++ b/configs/fcn/fcn-d6_r101b-d16_4xb2-80k_cityscapes-769x769.py @@ -0,0 +1,4 @@ +_base_ = './fcn-d6_r50b-d16_4xb2-80k_cityscapes-769x769.py' +model = dict( + pretrained='torchvision://resnet101', + backbone=dict(type='ResNet', depth=101)) diff --git a/configs/fcn/fcn_d6_r50-d16_512x1024_40k_cityscapes.py b/configs/fcn/fcn-d6_r50-d16_4xb2-40k_cityscapes-512x1024.py similarity index 100% rename from configs/fcn/fcn_d6_r50-d16_512x1024_40k_cityscapes.py rename to configs/fcn/fcn-d6_r50-d16_4xb2-40k_cityscapes-512x1024.py diff --git a/configs/fcn/fcn_d6_r50-d16_769x769_40k_cityscapes.py b/configs/fcn/fcn-d6_r50-d16_4xb2-40k_cityscapes-769x769.py similarity index 100% rename from configs/fcn/fcn_d6_r50-d16_769x769_40k_cityscapes.py rename to configs/fcn/fcn-d6_r50-d16_4xb2-40k_cityscapes-769x769.py diff --git a/configs/fcn/fcn_d6_r50-d16_512x1024_80k_cityscapes.py b/configs/fcn/fcn-d6_r50-d16_4xb2-80k_cityscapes-512x1024.py similarity index 100% rename from configs/fcn/fcn_d6_r50-d16_512x1024_80k_cityscapes.py rename to configs/fcn/fcn-d6_r50-d16_4xb2-80k_cityscapes-512x1024.py diff --git a/configs/fcn/fcn_d6_r50-d16_769x769_80k_cityscapes.py b/configs/fcn/fcn-d6_r50-d16_4xb2-80k_cityscapes-769x769.py similarity index 100% rename from configs/fcn/fcn_d6_r50-d16_769x769_80k_cityscapes.py rename to configs/fcn/fcn-d6_r50-d16_4xb2-80k_cityscapes-769x769.py diff --git a/configs/deeplabv3plus/deeplabv3plus_r50b-d8_769x769_80k_cityscapes.py b/configs/fcn/fcn-d6_r50b-d16_4xb2-80k_cityscapes-512x1024.py similarity index 57% rename from configs/deeplabv3plus/deeplabv3plus_r50b-d8_769x769_80k_cityscapes.py rename to configs/fcn/fcn-d6_r50b-d16_4xb2-80k_cityscapes-512x1024.py index c0ba019136..7d470a50be 100644 --- a/configs/deeplabv3plus/deeplabv3plus_r50b-d8_769x769_80k_cityscapes.py +++ b/configs/fcn/fcn-d6_r50b-d16_4xb2-80k_cityscapes-512x1024.py @@ -1,2 +1,2 @@ -_base_ = './deeplabv3plus_r50-d8_769x769_80k_cityscapes.py' +_base_ = './fcn-d6_r50-d16_4xb2-80k_cityscapes-512x1024.py' model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet')) diff --git a/configs/fcn/fcn_d6_r50b-d16_512x1024_80k_cityscapes.py b/configs/fcn/fcn-d6_r50b-d16_4xb2-80k_cityscapes-769x769.py similarity index 57% rename from configs/fcn/fcn_d6_r50b-d16_512x1024_80k_cityscapes.py rename to configs/fcn/fcn-d6_r50b-d16_4xb2-80k_cityscapes-769x769.py index 0749ff14a3..e9093ea2dc 100644 --- a/configs/fcn/fcn_d6_r50b-d16_512x1024_80k_cityscapes.py +++ b/configs/fcn/fcn-d6_r50b-d16_4xb2-80k_cityscapes-769x769.py @@ -1,2 +1,2 @@ -_base_ = './fcn_d6_r50-d16_512x1024_80k_cityscapes.py' +_base_ = './fcn-d6_r50-d16_4xb2-80k_cityscapes-769x769.py' model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet')) diff --git a/configs/fcn/fcn.yml b/configs/fcn/fcn.yml index 563391c93f..71c4b2d122 100644 --- a/configs/fcn/fcn.yml +++ b/configs/fcn/fcn.yml @@ -17,7 +17,7 @@ Collections: Converted From: Code: https://github.com/BVLC/caffe/wiki/Model-Zoo#fcn Models: -- Name: fcn_r50-d8_512x1024_40k_cityscapes +- Name: fcn_r50-d8_4xb2-40k_cityscapes-512x1024 In Collection: FCN Metadata: backbone: R-50-D8 @@ -37,9 +37,9 @@ Models: Metrics: mIoU: 72.25 mIoU(ms+flip): 73.36 - Config: configs/fcn/fcn_r50-d8_512x1024_40k_cityscapes.py + 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_512x1024_40k_cityscapes +- Name: fcn_r101-d8_4xb2-40k_cityscapes-512x1024 In Collection: FCN Metadata: backbone: R-101-D8 @@ -59,9 +59,9 @@ Models: Metrics: mIoU: 75.45 mIoU(ms+flip): 76.58 - Config: configs/fcn/fcn_r101-d8_512x1024_40k_cityscapes.py + 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_769x769_40k_cityscapes +- Name: fcn_r50-d8_4xb2-40k_cityscapes-769x769 In Collection: FCN Metadata: backbone: R-50-D8 @@ -81,9 +81,9 @@ Models: Metrics: mIoU: 71.47 mIoU(ms+flip): 72.54 - Config: configs/fcn/fcn_r50-d8_769x769_40k_cityscapes.py + 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_769x769_40k_cityscapes +- Name: fcn_r101-d8_4xb2-40k_cityscapes-769x769 In Collection: FCN Metadata: backbone: R-101-D8 @@ -103,9 +103,9 @@ Models: Metrics: mIoU: 73.93 mIoU(ms+flip): 75.14 - Config: configs/fcn/fcn_r101-d8_769x769_40k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: fcn_r18-d8_4xb2-80k_cityscapes-512x1024 In Collection: FCN Metadata: backbone: R-18-D8 @@ -125,9 +125,9 @@ Models: Metrics: mIoU: 71.11 mIoU(ms+flip): 72.91 - Config: configs/fcn/fcn_r18-d8_512x1024_80k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: fcn_r50-d8_4xb2-80k_cityscapes-512x1024 In Collection: FCN Metadata: backbone: R-50-D8 @@ -139,9 +139,9 @@ Models: Metrics: mIoU: 73.61 mIoU(ms+flip): 74.24 - Config: configs/fcn/fcn_r50-d8_512x1024_80k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: fcn_r101-d8_4xb2-80k_cityscapes-512x1024 In Collection: FCN Metadata: backbone: R-101-D8 @@ -153,9 +153,9 @@ Models: Metrics: mIoU: 75.13 mIoU(ms+flip): 75.94 - Config: configs/fcn/fcn_r101-d8_512x1024_80k_cityscapes.py + 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_fp16_512x1024_80k_cityscapes +- Name: fcn_r101-d8_4xb2-amp-80k_cityscapes-512x1024 In Collection: FCN Metadata: backbone: R-101-D8 @@ -166,7 +166,7 @@ Models: hardware: V100 backend: PyTorch batch size: 1 - mode: FP16 + mode: AMP resolution: (512,1024) Training Memory (GB): 5.37 Results: @@ -174,9 +174,9 @@ Models: Dataset: Cityscapes Metrics: mIoU: 76.8 - Config: configs/fcn/fcn_r101-d8_fp16_512x1024_80k_cityscapes.py + 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_769x769_80k_cityscapes +- Name: fcn_r18-d8_4xb2-80k_cityscapes-769x769 In Collection: FCN Metadata: backbone: R-18-D8 @@ -196,9 +196,9 @@ Models: Metrics: mIoU: 70.8 mIoU(ms+flip): 73.16 - Config: configs/fcn/fcn_r18-d8_769x769_80k_cityscapes.py + 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_769x769_80k_cityscapes +- Name: fcn_r50-d8_4xb2-80k_cityscapes-769x769 In Collection: FCN Metadata: backbone: R-50-D8 @@ -210,9 +210,9 @@ Models: Metrics: mIoU: 72.64 mIoU(ms+flip): 73.32 - Config: configs/fcn/fcn_r50-d8_769x769_80k_cityscapes.py + 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_769x769_80k_cityscapes +- Name: fcn_r101-d8_4xb2-80k_cityscapes-769x769 In Collection: FCN Metadata: backbone: R-101-D8 @@ -224,9 +224,9 @@ Models: Metrics: mIoU: 75.52 mIoU(ms+flip): 76.61 - Config: configs/fcn/fcn_r101-d8_769x769_80k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: fcn_r18b-d8_4xb2-80k_cityscapes-512x1024 In Collection: FCN Metadata: backbone: R-18b-D8 @@ -246,9 +246,9 @@ Models: Metrics: mIoU: 70.24 mIoU(ms+flip): 72.77 - Config: configs/fcn/fcn_r18b-d8_512x1024_80k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: fcn_r50b-d8_4xb2-80k_cityscapes-512x1024 In Collection: FCN Metadata: backbone: R-50b-D8 @@ -268,9 +268,9 @@ Models: Metrics: mIoU: 75.65 mIoU(ms+flip): 77.59 - Config: configs/fcn/fcn_r50b-d8_512x1024_80k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: fcn_r101b-d8_4xb2-80k_cityscapes-512x1024 In Collection: FCN Metadata: backbone: R-101b-D8 @@ -290,9 +290,9 @@ Models: Metrics: mIoU: 77.37 mIoU(ms+flip): 78.77 - Config: configs/fcn/fcn_r101b-d8_512x1024_80k_cityscapes.py + 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_769x769_80k_cityscapes +- Name: fcn_r18b-d8_4xb2-80k_cityscapes-769x769 In Collection: FCN Metadata: backbone: R-18b-D8 @@ -312,9 +312,9 @@ Models: Metrics: mIoU: 69.66 mIoU(ms+flip): 72.07 - Config: configs/fcn/fcn_r18b-d8_769x769_80k_cityscapes.py + 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_769x769_80k_cityscapes +- Name: fcn_r50b-d8_4xb2-80k_cityscapes-769x769 In Collection: FCN Metadata: backbone: R-50b-D8 @@ -334,9 +334,9 @@ Models: Metrics: mIoU: 73.83 mIoU(ms+flip): 76.6 - Config: configs/fcn/fcn_r50b-d8_769x769_80k_cityscapes.py + 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_769x769_80k_cityscapes +- Name: fcn_r101b-d8_4xb2-80k_cityscapes-769x769 In Collection: FCN Metadata: backbone: R-101b-D8 @@ -356,9 +356,9 @@ Models: Metrics: mIoU: 77.02 mIoU(ms+flip): 78.67 - Config: configs/fcn/fcn_r101b-d8_769x769_80k_cityscapes.py + 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_512x1024_40k_cityscapes +- Name: fcn-d6_r50-d16_4xb2-40k_cityscapes-512x1024 In Collection: FCN Metadata: backbone: R-50-D16 @@ -378,9 +378,9 @@ Models: Metrics: mIoU: 77.06 mIoU(ms+flip): 78.85 - Config: configs/fcn/fcn_d6_r50-d16_512x1024_40k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: fcn-d6_r50-d16_4xb2-80k_cityscapes-512x1024 In Collection: FCN Metadata: backbone: R-50-D16 @@ -399,9 +399,9 @@ Models: Metrics: mIoU: 77.27 mIoU(ms+flip): 78.88 - Config: configs/fcn/fcn_d6_r50-d16_512x1024_80k_cityscapes.py + 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_769x769_40k_cityscapes +- Name: fcn-d6_r50-d16_4xb2-40k_cityscapes-769x769 In Collection: FCN Metadata: backbone: R-50-D16 @@ -421,9 +421,9 @@ Models: Metrics: mIoU: 76.82 mIoU(ms+flip): 78.22 - Config: configs/fcn/fcn_d6_r50-d16_769x769_40k_cityscapes.py + 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_769x769_80k_cityscapes +- Name: fcn-d6_r50-d16_4xb2-80k_cityscapes-769x769 In Collection: FCN Metadata: backbone: R-50-D16 @@ -442,9 +442,9 @@ Models: Metrics: mIoU: 77.04 mIoU(ms+flip): 78.4 - Config: configs/fcn/fcn_d6_r50-d16_769x769_80k_cityscapes.py + 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_512x1024_40k_cityscapes +- Name: fcn-d6_r101-d16_4xb2-40k_cityscapes-512x1024 In Collection: FCN Metadata: backbone: R-101-D16 @@ -464,9 +464,9 @@ Models: Metrics: mIoU: 77.36 mIoU(ms+flip): 79.18 - Config: configs/fcn/fcn_d6_r101-d16_512x1024_40k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: fcn-d6_r101-d16_4xb2-80k_cityscapes-512x1024 In Collection: FCN Metadata: backbone: R-101-D16 @@ -485,9 +485,9 @@ Models: Metrics: mIoU: 78.46 mIoU(ms+flip): 80.42 - Config: configs/fcn/fcn_d6_r101-d16_512x1024_80k_cityscapes.py + 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_769x769_40k_cityscapes +- Name: fcn-d6_r101-d16_4xb2-40k_cityscapes-769x769 In Collection: FCN Metadata: backbone: R-101-D16 @@ -507,9 +507,9 @@ Models: Metrics: mIoU: 77.28 mIoU(ms+flip): 78.95 - Config: configs/fcn/fcn_d6_r101-d16_769x769_40k_cityscapes.py + 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_769x769_80k_cityscapes +- Name: fcn-d6_r101-d16_4xb2-80k_cityscapes-769x769 In Collection: FCN Metadata: backbone: R-101-D16 @@ -528,9 +528,9 @@ Models: Metrics: mIoU: 78.06 mIoU(ms+flip): 79.58 - Config: configs/fcn/fcn_d6_r101-d16_769x769_80k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: fcn-d6_r50b-d16_4xb2-80k_cityscapes-512x1024 In Collection: FCN Metadata: backbone: R-50b-D16 @@ -550,9 +550,9 @@ Models: Metrics: mIoU: 76.99 mIoU(ms+flip): 79.03 - Config: configs/fcn/fcn_d6_r50b-d16_512x1024_80k_cityscapes.py + 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_769x769_80k_cityscapes +- Name: fcn-d6_r50b-d16_4xb2-80k_cityscapes-769x769 In Collection: FCN Metadata: backbone: R-50b-D16 @@ -572,9 +572,9 @@ Models: Metrics: mIoU: 76.86 mIoU(ms+flip): 78.52 - Config: configs/fcn/fcn_d6_r50b-d16_769x769_80k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: fcn-d6_r101b-d16_4xb2-80k_cityscapes-512x1024 In Collection: FCN Metadata: backbone: R-101b-D16 @@ -594,9 +594,9 @@ Models: Metrics: mIoU: 77.72 mIoU(ms+flip): 79.53 - Config: configs/fcn/fcn_d6_r101b-d16_512x1024_80k_cityscapes.py + 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_769x769_80k_cityscapes +- Name: fcn-d6_r101b-d16_4xb2-80k_cityscapes-769x769 In Collection: FCN Metadata: backbone: R-101b-D16 @@ -616,9 +616,9 @@ Models: Metrics: mIoU: 77.34 mIoU(ms+flip): 78.91 - Config: configs/fcn/fcn_d6_r101b-d16_769x769_80k_cityscapes.py + 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_512x512_80k_ade20k +- Name: fcn_r50-d8_4xb4-80k_ade20k-512x512 In Collection: FCN Metadata: backbone: R-50-D8 @@ -638,9 +638,9 @@ Models: Metrics: mIoU: 35.94 mIoU(ms+flip): 37.94 - Config: configs/fcn/fcn_r50-d8_512x512_80k_ade20k.py + 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_512x512_80k_ade20k +- Name: fcn_r101-d8_4xb4-80k_ade20k-512x512 In Collection: FCN Metadata: backbone: R-101-D8 @@ -660,9 +660,9 @@ Models: Metrics: mIoU: 39.61 mIoU(ms+flip): 40.83 - Config: configs/fcn/fcn_r101-d8_512x512_80k_ade20k.py + 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_512x512_160k_ade20k +- Name: fcn_r50-d8_4xb4-160k_ade20k-512x512 In Collection: FCN Metadata: backbone: R-50-D8 @@ -674,9 +674,9 @@ Models: Metrics: mIoU: 36.1 mIoU(ms+flip): 38.08 - Config: configs/fcn/fcn_r50-d8_512x512_160k_ade20k.py + 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_512x512_160k_ade20k +- Name: fcn_r101-d8_4xb4-160k_ade20k-512x512 In Collection: FCN Metadata: backbone: R-101-D8 @@ -688,9 +688,9 @@ Models: Metrics: mIoU: 39.91 mIoU(ms+flip): 41.4 - Config: configs/fcn/fcn_r101-d8_512x512_160k_ade20k.py + 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_512x512_20k_voc12aug +- Name: fcn_r50-d8_4xb4-20k_voc12aug-512x512 In Collection: FCN Metadata: backbone: R-50-D8 @@ -710,9 +710,9 @@ Models: Metrics: mIoU: 67.08 mIoU(ms+flip): 69.94 - Config: configs/fcn/fcn_r50-d8_512x512_20k_voc12aug.py + 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_512x512_20k_voc12aug +- Name: fcn_r101-d8_4xb4-20k_voc12aug-512x512 In Collection: FCN Metadata: backbone: R-101-D8 @@ -732,9 +732,9 @@ Models: Metrics: mIoU: 71.16 mIoU(ms+flip): 73.57 - Config: configs/fcn/fcn_r101-d8_512x512_20k_voc12aug.py + 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_512x512_40k_voc12aug +- Name: fcn_r50-d8_4xb4-40k_voc12aug-512x512 In Collection: FCN Metadata: backbone: R-50-D8 @@ -746,9 +746,9 @@ Models: Metrics: mIoU: 66.97 mIoU(ms+flip): 69.04 - Config: configs/fcn/fcn_r50-d8_512x512_40k_voc12aug.py + 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_512x512_40k_voc12aug +- Name: fcn_r101-d8_4xb4-40k_voc12aug-512x512 In Collection: FCN Metadata: backbone: R-101-D8 @@ -760,9 +760,9 @@ Models: Metrics: mIoU: 69.91 mIoU(ms+flip): 72.38 - Config: configs/fcn/fcn_r101-d8_512x512_40k_voc12aug.py + 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_480x480_40k_pascal_context +- Name: fcn_r101-d8_4xb4-40k_pascal-context-480x480 In Collection: FCN Metadata: backbone: R-101-D8 @@ -781,9 +781,9 @@ Models: Metrics: mIoU: 44.43 mIoU(ms+flip): 45.63 - Config: configs/fcn/fcn_r101-d8_480x480_40k_pascal_context.py + 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_480x480_80k_pascal_context +- Name: fcn_r101-d8_4xb4-80k_pascal-context-480x480 In Collection: FCN Metadata: backbone: R-101-D8 @@ -795,9 +795,9 @@ Models: Metrics: mIoU: 44.13 mIoU(ms+flip): 45.26 - Config: configs/fcn/fcn_r101-d8_480x480_80k_pascal_context.py + 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_480x480_40k_pascal_context_59 +- Name: fcn_r101-d8_4xb4-40k_pascal-context-59-480x480 In Collection: FCN Metadata: backbone: R-101-D8 @@ -809,9 +809,9 @@ Models: Metrics: mIoU: 48.42 mIoU(ms+flip): 50.4 - Config: configs/fcn/fcn_r101-d8_480x480_40k_pascal_context_59.py + 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_480x480_80k_pascal_context_59 +- Name: fcn_r101-d8_4xb4-80k_pascal-context-59-480x480 In Collection: FCN Metadata: backbone: R-101-D8 @@ -823,5 +823,5 @@ Models: Metrics: mIoU: 49.35 mIoU(ms+flip): 51.38 - Config: configs/fcn/fcn_r101-d8_480x480_80k_pascal_context_59.py + 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/fcn_d6_r101-d16_512x1024_40k_cityscapes.py b/configs/fcn/fcn_d6_r101-d16_512x1024_40k_cityscapes.py deleted file mode 100644 index aec4254c8f..0000000000 --- a/configs/fcn/fcn_d6_r101-d16_512x1024_40k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './fcn_d6_r50-d16_512x1024_40k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/fcn/fcn_d6_r101-d16_512x1024_80k_cityscapes.py b/configs/fcn/fcn_d6_r101-d16_512x1024_80k_cityscapes.py deleted file mode 100644 index d0bafc52ab..0000000000 --- a/configs/fcn/fcn_d6_r101-d16_512x1024_80k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './fcn_d6_r50-d16_512x1024_80k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/fcn/fcn_d6_r101-d16_769x769_40k_cityscapes.py b/configs/fcn/fcn_d6_r101-d16_769x769_40k_cityscapes.py deleted file mode 100644 index 29a9f98a93..0000000000 --- a/configs/fcn/fcn_d6_r101-d16_769x769_40k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './fcn_d6_r50-d16_769x769_40k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/fcn/fcn_d6_r101-d16_769x769_80k_cityscapes.py b/configs/fcn/fcn_d6_r101-d16_769x769_80k_cityscapes.py deleted file mode 100644 index 1f21c6578b..0000000000 --- a/configs/fcn/fcn_d6_r101-d16_769x769_80k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './fcn_d6_r50-d16_769x769_80k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/fcn/fcn_r101-d8_480x480_40k_pascal_context.py b/configs/fcn/fcn_r101-d8_480x480_40k_pascal_context.py deleted file mode 100644 index f3a15b4105..0000000000 --- a/configs/fcn/fcn_r101-d8_480x480_40k_pascal_context.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './fcn_r50-d8_480x480_40k_pascal_context.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/fcn/fcn_r101-d8_480x480_40k_pascal_context_59.py b/configs/fcn/fcn_r101-d8_480x480_40k_pascal_context_59.py deleted file mode 100644 index 908f4bff00..0000000000 --- a/configs/fcn/fcn_r101-d8_480x480_40k_pascal_context_59.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './fcn_r50-d8_480x480_40k_pascal_context_59.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/fcn/fcn_r101-d8_480x480_80k_pascal_context.py b/configs/fcn/fcn_r101-d8_480x480_80k_pascal_context.py deleted file mode 100644 index bdccfd99ba..0000000000 --- a/configs/fcn/fcn_r101-d8_480x480_80k_pascal_context.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './fcn_r50-d8_480x480_80k_pascal_context.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/fcn/fcn_r101-d8_480x480_80k_pascal_context_59.py b/configs/fcn/fcn_r101-d8_480x480_80k_pascal_context_59.py deleted file mode 100644 index 09cb612e42..0000000000 --- a/configs/fcn/fcn_r101-d8_480x480_80k_pascal_context_59.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './fcn_r50-d8_480x480_80k_pascal_context_59.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/fcn/fcn_r101-d8_4xb2-40k_cityscapes-512x1024.py b/configs/fcn/fcn_r101-d8_4xb2-40k_cityscapes-512x1024.py new file mode 100644 index 0000000000..b3ec0a742c --- /dev/null +++ b/configs/fcn/fcn_r101-d8_4xb2-40k_cityscapes-512x1024.py @@ -0,0 +1,2 @@ +_base_ = './fcn_r50-d8_4xb2-40k_cityscapes-512x1024.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/fcn/fcn_r101-d8_4xb2-40k_cityscapes-769x769.py b/configs/fcn/fcn_r101-d8_4xb2-40k_cityscapes-769x769.py new file mode 100644 index 0000000000..1f83fe2078 --- /dev/null +++ b/configs/fcn/fcn_r101-d8_4xb2-40k_cityscapes-769x769.py @@ -0,0 +1,2 @@ +_base_ = './fcn_r50-d8_4xb2-40k_cityscapes-769x769.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/fcn/fcn_r101-d8_4xb2-80k_cityscapes-512x1024.py b/configs/fcn/fcn_r101-d8_4xb2-80k_cityscapes-512x1024.py new file mode 100644 index 0000000000..4527b3b8a0 --- /dev/null +++ b/configs/fcn/fcn_r101-d8_4xb2-80k_cityscapes-512x1024.py @@ -0,0 +1,2 @@ +_base_ = './fcn_r50-d8_4xb2-80k_cityscapes-512x1024.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/fcn/fcn_r101-d8_4xb2-80k_cityscapes-769x769.py b/configs/fcn/fcn_r101-d8_4xb2-80k_cityscapes-769x769.py new file mode 100644 index 0000000000..6ce112484d --- /dev/null +++ b/configs/fcn/fcn_r101-d8_4xb2-80k_cityscapes-769x769.py @@ -0,0 +1,2 @@ +_base_ = './fcn_r50-d8_4xb2-80k_cityscapes-769x769.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/pspnet/pspnet_r101-d8_fp16_512x1024_80k_cityscapes.py b/configs/fcn/fcn_r101-d8_4xb2-amp-80k_cityscapes-512x1024.py similarity index 74% rename from configs/pspnet/pspnet_r101-d8_fp16_512x1024_80k_cityscapes.py rename to configs/fcn/fcn_r101-d8_4xb2-amp-80k_cityscapes-512x1024.py index c84005ab6b..b4d94878c8 100644 --- a/configs/pspnet/pspnet_r101-d8_fp16_512x1024_80k_cityscapes.py +++ b/configs/fcn/fcn_r101-d8_4xb2-amp-80k_cityscapes-512x1024.py @@ -1,4 +1,4 @@ -_base_ = './pspnet_r101-d8_512x1024_80k_cityscapes.py' +_base_ = './fcn_r101-d8_4xb2-80k_cityscapes-512x1024.py' optim_wrapper = dict( _delete_=True, type='AmpOptimWrapper', diff --git a/configs/fcn/fcn_r101-d8_4xb4-160k_ade20k-512x512.py b/configs/fcn/fcn_r101-d8_4xb4-160k_ade20k-512x512.py new file mode 100644 index 0000000000..b1f5c5c785 --- /dev/null +++ b/configs/fcn/fcn_r101-d8_4xb4-160k_ade20k-512x512.py @@ -0,0 +1,2 @@ +_base_ = './fcn_r50-d8_4xb4-160k_ade20k-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/fcn/fcn_r101-d8_4xb4-20k_voc12aug-512x512.py b/configs/fcn/fcn_r101-d8_4xb4-20k_voc12aug-512x512.py new file mode 100644 index 0000000000..61ee96f94e --- /dev/null +++ b/configs/fcn/fcn_r101-d8_4xb4-20k_voc12aug-512x512.py @@ -0,0 +1,2 @@ +_base_ = './fcn_r50-d8_4xb4-20k_voc12aug-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/fcn/fcn_r101-d8_4xb4-40k_pascal-context-480x480.py b/configs/fcn/fcn_r101-d8_4xb4-40k_pascal-context-480x480.py new file mode 100644 index 0000000000..1161193adb --- /dev/null +++ b/configs/fcn/fcn_r101-d8_4xb4-40k_pascal-context-480x480.py @@ -0,0 +1,2 @@ +_base_ = './fcn_r50-d8_4xb4-40k_pascal-context-480x480.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/fcn/fcn_r101-d8_4xb4-40k_pascal-context-59-480x480.py b/configs/fcn/fcn_r101-d8_4xb4-40k_pascal-context-59-480x480.py new file mode 100644 index 0000000000..f3a6dbc9ab --- /dev/null +++ b/configs/fcn/fcn_r101-d8_4xb4-40k_pascal-context-59-480x480.py @@ -0,0 +1,2 @@ +_base_ = './fcn_r50-d8_4xb4-40k_pascal-context-59-480x480.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/fcn/fcn_r101-d8_4xb4-40k_voc12aug-512x512.py b/configs/fcn/fcn_r101-d8_4xb4-40k_voc12aug-512x512.py new file mode 100644 index 0000000000..b68b6e0407 --- /dev/null +++ b/configs/fcn/fcn_r101-d8_4xb4-40k_voc12aug-512x512.py @@ -0,0 +1,2 @@ +_base_ = './fcn_r50-d8_4xb4-40k_voc12aug-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/ann/ann_r101-d8_512x512_160k_ade20k.py b/configs/fcn/fcn_r101-d8_4xb4-80k_ade20k-512x512.py similarity index 61% rename from configs/ann/ann_r101-d8_512x512_160k_ade20k.py rename to configs/fcn/fcn_r101-d8_4xb4-80k_ade20k-512x512.py index 9e43af541f..3facce30dc 100644 --- a/configs/ann/ann_r101-d8_512x512_160k_ade20k.py +++ b/configs/fcn/fcn_r101-d8_4xb4-80k_ade20k-512x512.py @@ -1,2 +1,2 @@ -_base_ = './ann_r50-d8_512x512_160k_ade20k.py' +_base_ = './fcn_r50-d8_4xb4-80k_ade20k-512x512.py' model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/fcn/fcn_r101-d8_4xb4-80k_pascal-context-480x480.py b/configs/fcn/fcn_r101-d8_4xb4-80k_pascal-context-480x480.py new file mode 100644 index 0000000000..1161193adb --- /dev/null +++ b/configs/fcn/fcn_r101-d8_4xb4-80k_pascal-context-480x480.py @@ -0,0 +1,2 @@ +_base_ = './fcn_r50-d8_4xb4-40k_pascal-context-480x480.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/fcn/fcn_r101-d8_4xb4-80k_pascal-context-59-480x480.py b/configs/fcn/fcn_r101-d8_4xb4-80k_pascal-context-59-480x480.py new file mode 100644 index 0000000000..cebe33082a --- /dev/null +++ b/configs/fcn/fcn_r101-d8_4xb4-80k_pascal-context-59-480x480.py @@ -0,0 +1,2 @@ +_base_ = './fcn_r50-d8_4xb4-80k_pascal-context-59-480x480.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/fcn/fcn_r101-d8_512x1024_40k_cityscapes.py b/configs/fcn/fcn_r101-d8_512x1024_40k_cityscapes.py deleted file mode 100644 index 7918dd10d0..0000000000 --- a/configs/fcn/fcn_r101-d8_512x1024_40k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './fcn_r50-d8_512x1024_40k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/fcn/fcn_r101-d8_512x1024_80k_cityscapes.py b/configs/fcn/fcn_r101-d8_512x1024_80k_cityscapes.py deleted file mode 100644 index 528110dc73..0000000000 --- a/configs/fcn/fcn_r101-d8_512x1024_80k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './fcn_r50-d8_512x1024_80k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/fcn/fcn_r101-d8_512x512_160k_ade20k.py b/configs/fcn/fcn_r101-d8_512x512_160k_ade20k.py deleted file mode 100644 index 1bf6780f2c..0000000000 --- a/configs/fcn/fcn_r101-d8_512x512_160k_ade20k.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './fcn_r50-d8_512x512_160k_ade20k.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/fcn/fcn_r101-d8_512x512_20k_voc12aug.py b/configs/fcn/fcn_r101-d8_512x512_20k_voc12aug.py deleted file mode 100644 index 09a5fe5468..0000000000 --- a/configs/fcn/fcn_r101-d8_512x512_20k_voc12aug.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './fcn_r50-d8_512x512_20k_voc12aug.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/fcn/fcn_r101-d8_512x512_40k_voc12aug.py b/configs/fcn/fcn_r101-d8_512x512_40k_voc12aug.py deleted file mode 100644 index eafefaa675..0000000000 --- a/configs/fcn/fcn_r101-d8_512x512_40k_voc12aug.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './fcn_r50-d8_512x512_40k_voc12aug.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/fcn/fcn_r101-d8_512x512_80k_ade20k.py b/configs/fcn/fcn_r101-d8_512x512_80k_ade20k.py deleted file mode 100644 index 6d0294530f..0000000000 --- a/configs/fcn/fcn_r101-d8_512x512_80k_ade20k.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './fcn_r50-d8_512x512_80k_ade20k.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/fcn/fcn_r101-d8_769x769_40k_cityscapes.py b/configs/fcn/fcn_r101-d8_769x769_40k_cityscapes.py deleted file mode 100644 index 6b4cc57129..0000000000 --- a/configs/fcn/fcn_r101-d8_769x769_40k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './fcn_r50-d8_769x769_40k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/fcn/fcn_r101-d8_769x769_80k_cityscapes.py b/configs/fcn/fcn_r101-d8_769x769_80k_cityscapes.py deleted file mode 100644 index 3503c76935..0000000000 --- a/configs/fcn/fcn_r101-d8_769x769_80k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './fcn_r50-d8_769x769_80k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/deeplabv3/deeplabv3_r101b-d8_769x769_80k_cityscapes.py b/configs/fcn/fcn_r101b-d8_4xb2-80k_cityscapes-512x1024.py similarity index 64% rename from configs/deeplabv3/deeplabv3_r101b-d8_769x769_80k_cityscapes.py rename to configs/fcn/fcn_r101b-d8_4xb2-80k_cityscapes-512x1024.py index d185db95ad..e53751b144 100644 --- a/configs/deeplabv3/deeplabv3_r101b-d8_769x769_80k_cityscapes.py +++ b/configs/fcn/fcn_r101b-d8_4xb2-80k_cityscapes-512x1024.py @@ -1,4 +1,4 @@ -_base_ = './deeplabv3_r50-d8_769x769_80k_cityscapes.py' +_base_ = './fcn_r50-d8_4xb2-80k_cityscapes-512x1024.py' model = dict( pretrained='torchvision://resnet101', backbone=dict(type='ResNet', depth=101)) diff --git a/configs/fcn/fcn_d6_r101b-d16_769x769_80k_cityscapes.py b/configs/fcn/fcn_r101b-d8_4xb2-80k_cityscapes-769x769.py similarity index 64% rename from configs/fcn/fcn_d6_r101b-d16_769x769_80k_cityscapes.py rename to configs/fcn/fcn_r101b-d8_4xb2-80k_cityscapes-769x769.py index e3d4d884fd..daa6502610 100644 --- a/configs/fcn/fcn_d6_r101b-d16_769x769_80k_cityscapes.py +++ b/configs/fcn/fcn_r101b-d8_4xb2-80k_cityscapes-769x769.py @@ -1,4 +1,4 @@ -_base_ = './fcn_d6_r50b-d16_769x769_80k_cityscapes.py' +_base_ = './fcn_r50-d8_4xb2-80k_cityscapes-769x769.py' model = dict( pretrained='torchvision://resnet101', backbone=dict(type='ResNet', depth=101)) diff --git a/configs/fcn/fcn_r101b-d8_512x1024_80k_cityscapes.py b/configs/fcn/fcn_r101b-d8_512x1024_80k_cityscapes.py deleted file mode 100644 index 1b9bf60fc1..0000000000 --- a/configs/fcn/fcn_r101b-d8_512x1024_80k_cityscapes.py +++ /dev/null @@ -1,4 +0,0 @@ -_base_ = './fcn_r50-d8_512x1024_80k_cityscapes.py' -model = dict( - pretrained='torchvision://resnet101', - backbone=dict(type='ResNet', depth=101)) diff --git a/configs/fcn/fcn_r101b-d8_769x769_80k_cityscapes.py b/configs/fcn/fcn_r101b-d8_769x769_80k_cityscapes.py deleted file mode 100644 index f36eb02e68..0000000000 --- a/configs/fcn/fcn_r101b-d8_769x769_80k_cityscapes.py +++ /dev/null @@ -1,4 +0,0 @@ -_base_ = './fcn_r50-d8_769x769_80k_cityscapes.py' -model = dict( - pretrained='torchvision://resnet101', - backbone=dict(type='ResNet', depth=101)) diff --git a/configs/deeplabv3/deeplabv3_r18-d8_769x769_80k_cityscapes.py b/configs/fcn/fcn_r18-d8_4xb2-80k_cityscapes-512x1024.py similarity index 79% rename from configs/deeplabv3/deeplabv3_r18-d8_769x769_80k_cityscapes.py rename to configs/fcn/fcn_r18-d8_4xb2-80k_cityscapes-512x1024.py index a990c07653..4073148122 100644 --- a/configs/deeplabv3/deeplabv3_r18-d8_769x769_80k_cityscapes.py +++ b/configs/fcn/fcn_r18-d8_4xb2-80k_cityscapes-512x1024.py @@ -1,4 +1,4 @@ -_base_ = './deeplabv3_r50-d8_769x769_80k_cityscapes.py' +_base_ = './fcn_r50-d8_4xb2-80k_cityscapes-512x1024.py' model = dict( pretrained='open-mmlab://resnet18_v1c', backbone=dict(depth=18), diff --git a/configs/fcn/fcn_r18-d8_512x1024_80k_cityscapes.py b/configs/fcn/fcn_r18-d8_4xb2-80k_cityscapes-769x769.py similarity index 79% rename from configs/fcn/fcn_r18-d8_512x1024_80k_cityscapes.py rename to configs/fcn/fcn_r18-d8_4xb2-80k_cityscapes-769x769.py index 5a1d29e480..2c1d2b6df0 100644 --- a/configs/fcn/fcn_r18-d8_512x1024_80k_cityscapes.py +++ b/configs/fcn/fcn_r18-d8_4xb2-80k_cityscapes-769x769.py @@ -1,4 +1,4 @@ -_base_ = './fcn_r50-d8_512x1024_80k_cityscapes.py' +_base_ = './fcn_r50-d8_4xb2-80k_cityscapes-769x769.py' model = dict( pretrained='open-mmlab://resnet18_v1c', backbone=dict(depth=18), diff --git a/configs/deeplabv3/deeplabv3_r18b-d8_769x769_80k_cityscapes.py b/configs/fcn/fcn_r18b-d8_4xb2-80k_cityscapes-512x1024.py similarity index 80% rename from configs/deeplabv3/deeplabv3_r18b-d8_769x769_80k_cityscapes.py rename to configs/fcn/fcn_r18b-d8_4xb2-80k_cityscapes-512x1024.py index fd920f0ca7..08ab467573 100644 --- a/configs/deeplabv3/deeplabv3_r18b-d8_769x769_80k_cityscapes.py +++ b/configs/fcn/fcn_r18b-d8_4xb2-80k_cityscapes-512x1024.py @@ -1,4 +1,4 @@ -_base_ = './deeplabv3_r50-d8_769x769_80k_cityscapes.py' +_base_ = './fcn_r50-d8_4xb2-80k_cityscapes-512x1024.py' model = dict( pretrained='torchvision://resnet18', backbone=dict(type='ResNet', depth=18), diff --git a/configs/pspnet/pspnet_r18b-d8_512x1024_80k_cityscapes.py b/configs/fcn/fcn_r18b-d8_4xb2-80k_cityscapes-769x769.py similarity index 80% rename from configs/pspnet/pspnet_r18b-d8_512x1024_80k_cityscapes.py rename to configs/fcn/fcn_r18b-d8_4xb2-80k_cityscapes-769x769.py index abeeedf843..c591ebe972 100644 --- a/configs/pspnet/pspnet_r18b-d8_512x1024_80k_cityscapes.py +++ b/configs/fcn/fcn_r18b-d8_4xb2-80k_cityscapes-769x769.py @@ -1,4 +1,4 @@ -_base_ = './pspnet_r50-d8_512x1024_80k_cityscapes.py' +_base_ = './fcn_r50-d8_4xb2-80k_cityscapes-769x769.py' model = dict( pretrained='torchvision://resnet18', backbone=dict(type='ResNet', depth=18), diff --git a/configs/fcn/fcn_r50-d8_512x1024_40k_cityscapes.py b/configs/fcn/fcn_r50-d8_4xb2-40k_cityscapes-512x1024.py similarity index 100% rename from configs/fcn/fcn_r50-d8_512x1024_40k_cityscapes.py rename to configs/fcn/fcn_r50-d8_4xb2-40k_cityscapes-512x1024.py diff --git a/configs/fcn/fcn_r50-d8_769x769_40k_cityscapes.py b/configs/fcn/fcn_r50-d8_4xb2-40k_cityscapes-769x769.py similarity index 100% rename from configs/fcn/fcn_r50-d8_769x769_40k_cityscapes.py rename to configs/fcn/fcn_r50-d8_4xb2-40k_cityscapes-769x769.py diff --git a/configs/fcn/fcn_r50-d8_512x1024_80k_cityscapes.py b/configs/fcn/fcn_r50-d8_4xb2-80k_cityscapes-512x1024.py similarity index 100% rename from configs/fcn/fcn_r50-d8_512x1024_80k_cityscapes.py rename to configs/fcn/fcn_r50-d8_4xb2-80k_cityscapes-512x1024.py diff --git a/configs/fcn/fcn_r50-d8_769x769_80k_cityscapes.py b/configs/fcn/fcn_r50-d8_4xb2-80k_cityscapes-769x769.py similarity index 100% rename from configs/fcn/fcn_r50-d8_769x769_80k_cityscapes.py rename to configs/fcn/fcn_r50-d8_4xb2-80k_cityscapes-769x769.py diff --git a/configs/fcn/fcn_r50-d8_512x512_160k_ade20k.py b/configs/fcn/fcn_r50-d8_4xb4-160k_ade20k-512x512.py similarity index 100% rename from configs/fcn/fcn_r50-d8_512x512_160k_ade20k.py rename to configs/fcn/fcn_r50-d8_4xb4-160k_ade20k-512x512.py diff --git a/configs/fcn/fcn_r50-d8_512x512_20k_voc12aug.py b/configs/fcn/fcn_r50-d8_4xb4-20k_voc12aug-512x512.py similarity index 100% rename from configs/fcn/fcn_r50-d8_512x512_20k_voc12aug.py rename to configs/fcn/fcn_r50-d8_4xb4-20k_voc12aug-512x512.py diff --git a/configs/fcn/fcn_r50-d8_480x480_40k_pascal_context.py b/configs/fcn/fcn_r50-d8_4xb4-40k_pascal-context-480x480.py similarity index 100% rename from configs/fcn/fcn_r50-d8_480x480_40k_pascal_context.py rename to configs/fcn/fcn_r50-d8_4xb4-40k_pascal-context-480x480.py diff --git a/configs/fcn/fcn_r50-d8_480x480_40k_pascal_context_59.py b/configs/fcn/fcn_r50-d8_4xb4-40k_pascal-context-59-480x480.py similarity index 100% rename from configs/fcn/fcn_r50-d8_480x480_40k_pascal_context_59.py rename to configs/fcn/fcn_r50-d8_4xb4-40k_pascal-context-59-480x480.py diff --git a/configs/fcn/fcn_r50-d8_512x512_40k_voc12aug.py b/configs/fcn/fcn_r50-d8_4xb4-40k_voc12aug-512x512.py similarity index 100% rename from configs/fcn/fcn_r50-d8_512x512_40k_voc12aug.py rename to configs/fcn/fcn_r50-d8_4xb4-40k_voc12aug-512x512.py diff --git a/configs/fcn/fcn_r50-d8_512x512_80k_ade20k.py b/configs/fcn/fcn_r50-d8_4xb4-80k_ade20k-512x512.py similarity index 100% rename from configs/fcn/fcn_r50-d8_512x512_80k_ade20k.py rename to configs/fcn/fcn_r50-d8_4xb4-80k_ade20k-512x512.py diff --git a/configs/fcn/fcn_r50-d8_480x480_80k_pascal_context.py b/configs/fcn/fcn_r50-d8_4xb4-80k_pascal-context-480x480.py similarity index 100% rename from configs/fcn/fcn_r50-d8_480x480_80k_pascal_context.py rename to configs/fcn/fcn_r50-d8_4xb4-80k_pascal-context-480x480.py diff --git a/configs/fcn/fcn_r50-d8_480x480_80k_pascal_context_59.py b/configs/fcn/fcn_r50-d8_4xb4-80k_pascal-context-59-480x480.py similarity index 100% rename from configs/fcn/fcn_r50-d8_480x480_80k_pascal_context_59.py rename to configs/fcn/fcn_r50-d8_4xb4-80k_pascal-context-59-480x480.py diff --git a/configs/deeplabv3/deeplabv3_r50b-d8_769x769_80k_cityscapes.py b/configs/fcn/fcn_r50b-d8_4xb2-80k_cityscapes-512x1024.py similarity index 58% rename from configs/deeplabv3/deeplabv3_r50b-d8_769x769_80k_cityscapes.py rename to configs/fcn/fcn_r50b-d8_4xb2-80k_cityscapes-512x1024.py index 332d9cfb79..44821fd7d3 100644 --- a/configs/deeplabv3/deeplabv3_r50b-d8_769x769_80k_cityscapes.py +++ b/configs/fcn/fcn_r50b-d8_4xb2-80k_cityscapes-512x1024.py @@ -1,2 +1,2 @@ -_base_ = './deeplabv3_r50-d8_769x769_80k_cityscapes.py' +_base_ = './fcn_r50-d8_4xb2-80k_cityscapes-512x1024.py' model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet')) diff --git a/configs/fcn/fcn_r50b-d8_512x1024_80k_cityscapes.py b/configs/fcn/fcn_r50b-d8_4xb2-80k_cityscapes-769x769.py similarity index 59% rename from configs/fcn/fcn_r50b-d8_512x1024_80k_cityscapes.py rename to configs/fcn/fcn_r50b-d8_4xb2-80k_cityscapes-769x769.py index 28ef13f8d1..a85b39197e 100644 --- a/configs/fcn/fcn_r50b-d8_512x1024_80k_cityscapes.py +++ b/configs/fcn/fcn_r50b-d8_4xb2-80k_cityscapes-769x769.py @@ -1,2 +1,2 @@ -_base_ = './fcn_r50-d8_512x1024_80k_cityscapes.py' +_base_ = './fcn_r50-d8_4xb2-80k_cityscapes-769x769.py' model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet')) diff --git a/configs/fcn/fcn_r50b-d8_769x769_80k_cityscapes.py b/configs/fcn/fcn_r50b-d8_769x769_80k_cityscapes.py deleted file mode 100644 index 106f7b6a1e..0000000000 --- a/configs/fcn/fcn_r50b-d8_769x769_80k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './fcn_r50-d8_769x769_80k_cityscapes.py' -model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet')) diff --git a/configs/gcnet/README.md b/configs/gcnet/README.md index 9a4cf7a606..fa37f76468 100644 --- a/configs/gcnet/README.md +++ b/configs/gcnet/README.md @@ -38,31 +38,31 @@ The Non-Local Network (NLNet) presents a pioneering approach for capturing long- ### 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/master/configs/gcnet/gcnet_r50-d8_512x1024_40k_cityscapes.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/master/configs/gcnet/gcnet_r101-d8_512x1024_40k_cityscapes.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/master/configs/gcnet/gcnet_r50-d8_769x769_40k_cityscapes.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/master/configs/gcnet/gcnet_r101-d8_769x769_40k_cityscapes.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/master/configs/gcnet/gcnet_r50-d8_512x1024_80k_cityscapes.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/master/configs/gcnet/gcnet_r101-d8_512x1024_80k_cityscapes.py) | [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/master/configs/gcnet/gcnet_r50-d8_769x769_80k_cityscapes.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/master/configs/gcnet/gcnet_r101-d8_769x769_80k_cityscapes.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) | +| 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/master/configs/gcnet/gcnet_r50-d8_512x512_80k_ade20k.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/master/configs/gcnet/gcnet_r101-d8_512x512_80k_ade20k.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/master/configs/gcnet/gcnet_r50-d8_512x512_160k_ade20k.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/master/configs/gcnet/gcnet_r101-d8_512x512_160k_ade20k.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) | +| 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/master/configs/gcnet/gcnet_r50-d8_512x512_20k_voc12aug.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/master/configs/gcnet/gcnet_r101-d8_512x512_20k_voc12aug.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/master/configs/gcnet/gcnet_r50-d8_512x512_40k_voc12aug.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/master/configs/gcnet/gcnet_r101-d8_512x512_40k_voc12aug.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) | +| 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 index 1d5eecfc55..dfd8cf56c4 100644 --- a/configs/gcnet/gcnet.yml +++ b/configs/gcnet/gcnet.yml @@ -15,7 +15,7 @@ Collections: Converted From: Code: https://github.com/xvjiarui/GCNet Models: -- Name: gcnet_r50-d8_512x1024_40k_cityscapes +- Name: gcnet_r50-d8_4xb2-40k_cityscapes-512x1024 In Collection: GCNet Metadata: backbone: R-50-D8 @@ -35,9 +35,9 @@ Models: Metrics: mIoU: 77.69 mIoU(ms+flip): 78.56 - Config: configs/gcnet/gcnet_r50-d8_512x1024_40k_cityscapes.py + 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_512x1024_40k_cityscapes +- Name: gcnet_r101-d8_4xb2-40k_cityscapes-512x1024 In Collection: GCNet Metadata: backbone: R-101-D8 @@ -57,9 +57,9 @@ Models: Metrics: mIoU: 78.28 mIoU(ms+flip): 79.34 - Config: configs/gcnet/gcnet_r101-d8_512x1024_40k_cityscapes.py + 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_769x769_40k_cityscapes +- Name: gcnet_r50-d8_4xb2-40k_cityscapes-769x769 In Collection: GCNet Metadata: backbone: R-50-D8 @@ -79,9 +79,9 @@ Models: Metrics: mIoU: 78.12 mIoU(ms+flip): 80.09 - Config: configs/gcnet/gcnet_r50-d8_769x769_40k_cityscapes.py + 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_769x769_40k_cityscapes +- Name: gcnet_r101-d8_4xb2-40k_cityscapes-769x769 In Collection: GCNet Metadata: backbone: R-101-D8 @@ -101,9 +101,9 @@ Models: Metrics: mIoU: 78.95 mIoU(ms+flip): 80.71 - Config: configs/gcnet/gcnet_r101-d8_769x769_40k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: gcnet_r50-d8_4xb2-80k_cityscapes-512x1024 In Collection: GCNet Metadata: backbone: R-50-D8 @@ -115,9 +115,9 @@ Models: Metrics: mIoU: 78.48 mIoU(ms+flip): 80.01 - Config: configs/gcnet/gcnet_r50-d8_512x1024_80k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: gcnet_r101-d8_4xb2-80k_cityscapes-512x1024 In Collection: GCNet Metadata: backbone: R-101-D8 @@ -129,9 +129,9 @@ Models: Metrics: mIoU: 79.03 mIoU(ms+flip): 79.84 - Config: configs/gcnet/gcnet_r101-d8_512x1024_80k_cityscapes.py + 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_769x769_80k_cityscapes +- Name: gcnet_r50-d8_4xb2-80k_cityscapes-769x769 In Collection: GCNet Metadata: backbone: R-50-D8 @@ -143,9 +143,9 @@ Models: Metrics: mIoU: 78.68 mIoU(ms+flip): 80.66 - Config: configs/gcnet/gcnet_r50-d8_769x769_80k_cityscapes.py + 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_769x769_80k_cityscapes +- Name: gcnet_r101-d8_4xb2-80k_cityscapes-769x769 In Collection: GCNet Metadata: backbone: R-101-D8 @@ -157,9 +157,9 @@ Models: Metrics: mIoU: 79.18 mIoU(ms+flip): 80.71 - Config: configs/gcnet/gcnet_r101-d8_769x769_80k_cityscapes.py + 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_512x512_80k_ade20k +- Name: gcnet_r50-d8_4xb4-80k_ade20k-512x512 In Collection: GCNet Metadata: backbone: R-50-D8 @@ -179,9 +179,9 @@ Models: Metrics: mIoU: 41.47 mIoU(ms+flip): 42.85 - Config: configs/gcnet/gcnet_r50-d8_512x512_80k_ade20k.py + 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_512x512_80k_ade20k +- Name: gcnet_r101-d8_4xb4-80k_ade20k-512x512 In Collection: GCNet Metadata: backbone: R-101-D8 @@ -201,9 +201,9 @@ Models: Metrics: mIoU: 42.82 mIoU(ms+flip): 44.54 - Config: configs/gcnet/gcnet_r101-d8_512x512_80k_ade20k.py + 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_512x512_160k_ade20k +- Name: gcnet_r50-d8_4xb4-160k_ade20k-512x512 In Collection: GCNet Metadata: backbone: R-50-D8 @@ -215,9 +215,9 @@ Models: Metrics: mIoU: 42.37 mIoU(ms+flip): 43.52 - Config: configs/gcnet/gcnet_r50-d8_512x512_160k_ade20k.py + 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_512x512_160k_ade20k +- Name: gcnet_r101-d8_4xb4-160k_ade20k-512x512 In Collection: GCNet Metadata: backbone: R-101-D8 @@ -229,9 +229,9 @@ Models: Metrics: mIoU: 43.69 mIoU(ms+flip): 45.21 - Config: configs/gcnet/gcnet_r101-d8_512x512_160k_ade20k.py + 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_512x512_20k_voc12aug +- Name: gcnet_r50-d8_4xb4-20k_voc12aug-512x512 In Collection: GCNet Metadata: backbone: R-50-D8 @@ -251,9 +251,9 @@ Models: Metrics: mIoU: 76.42 mIoU(ms+flip): 77.51 - Config: configs/gcnet/gcnet_r50-d8_512x512_20k_voc12aug.py + 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_512x512_20k_voc12aug +- Name: gcnet_r101-d8_4xb4-20k_voc12aug-512x512 In Collection: GCNet Metadata: backbone: R-101-D8 @@ -273,9 +273,9 @@ Models: Metrics: mIoU: 77.41 mIoU(ms+flip): 78.56 - Config: configs/gcnet/gcnet_r101-d8_512x512_20k_voc12aug.py + 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_512x512_40k_voc12aug +- Name: gcnet_r50-d8_4xb4-40k_voc12aug-512x512 In Collection: GCNet Metadata: backbone: R-50-D8 @@ -287,9 +287,9 @@ Models: Metrics: mIoU: 76.24 mIoU(ms+flip): 77.63 - Config: configs/gcnet/gcnet_r50-d8_512x512_40k_voc12aug.py + 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_512x512_40k_voc12aug +- Name: gcnet_r101-d8_4xb4-40k_voc12aug-512x512 In Collection: GCNet Metadata: backbone: R-101-D8 @@ -301,5 +301,5 @@ Models: Metrics: mIoU: 77.84 mIoU(ms+flip): 78.59 - Config: configs/gcnet/gcnet_r101-d8_512x512_40k_voc12aug.py + 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/gcnet_r101-d8_4xb2-40k_cityscapes-512x1024.py b/configs/gcnet/gcnet_r101-d8_4xb2-40k_cityscapes-512x1024.py new file mode 100644 index 0000000000..e8f7c552fb --- /dev/null +++ b/configs/gcnet/gcnet_r101-d8_4xb2-40k_cityscapes-512x1024.py @@ -0,0 +1,2 @@ +_base_ = './gcnet_r50-d8_4xb2-40k_cityscapes-512x1024.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/gcnet/gcnet_r101-d8_4xb2-40k_cityscapes-769x769.py b/configs/gcnet/gcnet_r101-d8_4xb2-40k_cityscapes-769x769.py new file mode 100644 index 0000000000..887d17b71d --- /dev/null +++ b/configs/gcnet/gcnet_r101-d8_4xb2-40k_cityscapes-769x769.py @@ -0,0 +1,2 @@ +_base_ = './gcnet_r50-d8_4xb2-40k_cityscapes-769x769.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/gcnet/gcnet_r101-d8_4xb2-80k_cityscapes-512x1024.py b/configs/gcnet/gcnet_r101-d8_4xb2-80k_cityscapes-512x1024.py new file mode 100644 index 0000000000..aa47578d16 --- /dev/null +++ b/configs/gcnet/gcnet_r101-d8_4xb2-80k_cityscapes-512x1024.py @@ -0,0 +1,2 @@ +_base_ = './gcnet_r50-d8_4xb2-80k_cityscapes-512x1024.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/gcnet/gcnet_r101-d8_4xb2-80k_cityscapes-769x769.py b/configs/gcnet/gcnet_r101-d8_4xb2-80k_cityscapes-769x769.py new file mode 100644 index 0000000000..ddf4ad7bbc --- /dev/null +++ b/configs/gcnet/gcnet_r101-d8_4xb2-80k_cityscapes-769x769.py @@ -0,0 +1,2 @@ +_base_ = './gcnet_r50-d8_4xb2-80k_cityscapes-769x769.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/gcnet/gcnet_r101-d8_4xb4-160k_ade20k-512x512.py b/configs/gcnet/gcnet_r101-d8_4xb4-160k_ade20k-512x512.py new file mode 100644 index 0000000000..45285c0183 --- /dev/null +++ b/configs/gcnet/gcnet_r101-d8_4xb4-160k_ade20k-512x512.py @@ -0,0 +1,2 @@ +_base_ = './gcnet_r50-d8_4xb4-160k_ade20k-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/gcnet/gcnet_r101-d8_4xb4-20k_voc12aug-512x512.py b/configs/gcnet/gcnet_r101-d8_4xb4-20k_voc12aug-512x512.py new file mode 100644 index 0000000000..b466c409e8 --- /dev/null +++ b/configs/gcnet/gcnet_r101-d8_4xb4-20k_voc12aug-512x512.py @@ -0,0 +1,2 @@ +_base_ = './gcnet_r50-d8_4xb4-20k_voc12aug-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/gcnet/gcnet_r101-d8_4xb4-40k_voc12aug-512x512.py b/configs/gcnet/gcnet_r101-d8_4xb4-40k_voc12aug-512x512.py new file mode 100644 index 0000000000..9c7f741f05 --- /dev/null +++ b/configs/gcnet/gcnet_r101-d8_4xb4-40k_voc12aug-512x512.py @@ -0,0 +1,2 @@ +_base_ = './gcnet_r50-d8_4xb4-40k_voc12aug-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/gcnet/gcnet_r101-d8_4xb4-80k_ade20k-512x512.py b/configs/gcnet/gcnet_r101-d8_4xb4-80k_ade20k-512x512.py new file mode 100644 index 0000000000..61337dbda2 --- /dev/null +++ b/configs/gcnet/gcnet_r101-d8_4xb4-80k_ade20k-512x512.py @@ -0,0 +1,2 @@ +_base_ = './gcnet_r50-d8_4xb4-80k_ade20k-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/gcnet/gcnet_r101-d8_512x1024_40k_cityscapes.py b/configs/gcnet/gcnet_r101-d8_512x1024_40k_cityscapes.py deleted file mode 100644 index 27bd9422da..0000000000 --- a/configs/gcnet/gcnet_r101-d8_512x1024_40k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './gcnet_r50-d8_512x1024_40k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/gcnet/gcnet_r101-d8_512x1024_80k_cityscapes.py b/configs/gcnet/gcnet_r101-d8_512x1024_80k_cityscapes.py deleted file mode 100644 index 7f0f83fe39..0000000000 --- a/configs/gcnet/gcnet_r101-d8_512x1024_80k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './gcnet_r50-d8_512x1024_80k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/gcnet/gcnet_r101-d8_512x512_160k_ade20k.py b/configs/gcnet/gcnet_r101-d8_512x512_160k_ade20k.py deleted file mode 100644 index 9888120f65..0000000000 --- a/configs/gcnet/gcnet_r101-d8_512x512_160k_ade20k.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './gcnet_r50-d8_512x512_160k_ade20k.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/gcnet/gcnet_r101-d8_512x512_20k_voc12aug.py b/configs/gcnet/gcnet_r101-d8_512x512_20k_voc12aug.py deleted file mode 100644 index 1b70ca8e46..0000000000 --- a/configs/gcnet/gcnet_r101-d8_512x512_20k_voc12aug.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './gcnet_r50-d8_512x512_20k_voc12aug.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/gcnet/gcnet_r101-d8_512x512_40k_voc12aug.py b/configs/gcnet/gcnet_r101-d8_512x512_40k_voc12aug.py deleted file mode 100644 index b17c7a12b5..0000000000 --- a/configs/gcnet/gcnet_r101-d8_512x512_40k_voc12aug.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './gcnet_r50-d8_512x512_40k_voc12aug.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/gcnet/gcnet_r101-d8_512x512_80k_ade20k.py b/configs/gcnet/gcnet_r101-d8_512x512_80k_ade20k.py deleted file mode 100644 index a2183fc2db..0000000000 --- a/configs/gcnet/gcnet_r101-d8_512x512_80k_ade20k.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './gcnet_r50-d8_512x512_80k_ade20k.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/gcnet/gcnet_r101-d8_769x769_40k_cityscapes.py b/configs/gcnet/gcnet_r101-d8_769x769_40k_cityscapes.py deleted file mode 100644 index 08a6031f20..0000000000 --- a/configs/gcnet/gcnet_r101-d8_769x769_40k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './gcnet_r50-d8_769x769_40k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/gcnet/gcnet_r101-d8_769x769_80k_cityscapes.py b/configs/gcnet/gcnet_r101-d8_769x769_80k_cityscapes.py deleted file mode 100644 index 5efb61339c..0000000000 --- a/configs/gcnet/gcnet_r101-d8_769x769_80k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './gcnet_r50-d8_769x769_80k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/gcnet/gcnet_r50-d8_512x1024_40k_cityscapes.py b/configs/gcnet/gcnet_r50-d8_4xb2-40k_cityscapes-512x1024.py similarity index 100% rename from configs/gcnet/gcnet_r50-d8_512x1024_40k_cityscapes.py rename to configs/gcnet/gcnet_r50-d8_4xb2-40k_cityscapes-512x1024.py diff --git a/configs/gcnet/gcnet_r50-d8_769x769_40k_cityscapes.py b/configs/gcnet/gcnet_r50-d8_4xb2-40k_cityscapes-769x769.py similarity index 100% rename from configs/gcnet/gcnet_r50-d8_769x769_40k_cityscapes.py rename to configs/gcnet/gcnet_r50-d8_4xb2-40k_cityscapes-769x769.py diff --git a/configs/gcnet/gcnet_r50-d8_512x1024_80k_cityscapes.py b/configs/gcnet/gcnet_r50-d8_4xb2-80k_cityscapes-512x1024.py similarity index 100% rename from configs/gcnet/gcnet_r50-d8_512x1024_80k_cityscapes.py rename to configs/gcnet/gcnet_r50-d8_4xb2-80k_cityscapes-512x1024.py diff --git a/configs/gcnet/gcnet_r50-d8_769x769_80k_cityscapes.py b/configs/gcnet/gcnet_r50-d8_4xb2-80k_cityscapes-769x769.py similarity index 100% rename from configs/gcnet/gcnet_r50-d8_769x769_80k_cityscapes.py rename to configs/gcnet/gcnet_r50-d8_4xb2-80k_cityscapes-769x769.py diff --git a/configs/gcnet/gcnet_r50-d8_512x512_160k_ade20k.py b/configs/gcnet/gcnet_r50-d8_4xb4-160k_ade20k-512x512.py similarity index 100% rename from configs/gcnet/gcnet_r50-d8_512x512_160k_ade20k.py rename to configs/gcnet/gcnet_r50-d8_4xb4-160k_ade20k-512x512.py diff --git a/configs/gcnet/gcnet_r50-d8_512x512_20k_voc12aug.py b/configs/gcnet/gcnet_r50-d8_4xb4-20k_voc12aug-512x512.py similarity index 100% rename from configs/gcnet/gcnet_r50-d8_512x512_20k_voc12aug.py rename to configs/gcnet/gcnet_r50-d8_4xb4-20k_voc12aug-512x512.py diff --git a/configs/gcnet/gcnet_r50-d8_512x512_40k_voc12aug.py b/configs/gcnet/gcnet_r50-d8_4xb4-40k_voc12aug-512x512.py similarity index 100% rename from configs/gcnet/gcnet_r50-d8_512x512_40k_voc12aug.py rename to configs/gcnet/gcnet_r50-d8_4xb4-40k_voc12aug-512x512.py diff --git a/configs/gcnet/gcnet_r50-d8_512x512_80k_ade20k.py b/configs/gcnet/gcnet_r50-d8_4xb4-80k_ade20k-512x512.py similarity index 100% rename from configs/gcnet/gcnet_r50-d8_512x512_80k_ade20k.py rename to configs/gcnet/gcnet_r50-d8_4xb4-80k_ade20k-512x512.py diff --git a/configs/hrnet/README.md b/configs/hrnet/README.md index 9ebbf4d62b..f85683b63f 100644 --- a/configs/hrnet/README.md +++ b/configs/hrnet/README.md @@ -37,85 +37,85 @@ High-resolution representations are essential for position-sensitive vision prob ### 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/master/configs/hrnet/fcn_hr18s_512x1024_40k_cityscapes.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/master/configs/hrnet/fcn_hr18_512x1024_40k_cityscapes.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/master/configs/hrnet/fcn_hr48_512x1024_40k_cityscapes.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/master/configs/hrnet/fcn_hr18s_512x1024_80k_cityscapes.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/master/configs/hrnet/fcn_hr18_512x1024_80k_cityscapes.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/master/configs/hrnet/fcn_hr48_512x1024_80k_cityscapes.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/master/configs/hrnet/fcn_hr18s_512x1024_160k_cityscapes.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/master/configs/hrnet/fcn_hr18_512x1024_160k_cityscapes.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/master/configs/hrnet/fcn_hr48_512x1024_160k_cityscapes.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) | 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) | ### 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/master/configs/hrnet/fcn_hr18s_512x512_80k_ade20k.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/master/configs/hrnet/fcn_hr18_512x512_80k_ade20k.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/master/configs/hrnet/fcn_hr48_512x512_80k_ade20k.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/master/configs/hrnet/fcn_hr18s_512x512_160k_ade20k.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/master/configs/hrnet/fcn_hr18_512x512_160k_ade20k.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/master/configs/hrnet/fcn_hr48_512x512_160k_ade20k.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) | 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) | ### 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/master/configs/hrnet/fcn_hr18s_512x512_20k_voc12aug.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/master/configs/hrnet/fcn_hr18_512x512_20k_voc12aug.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/master/configs/hrnet/fcn_hr48_512x512_20k_voc12aug.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/master/configs/hrnet/fcn_hr18s_512x512_40k_voc12aug.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/master/configs/hrnet/fcn_hr18_512x512_40k_voc12aug.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/master/configs/hrnet/fcn_hr48_512x512_40k_voc12aug.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) | 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) | ### 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/master/configs/hrnet/fcn_hr48_480x480_40k_pascal_context.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/master/configs/hrnet/fcn_hr48_480x480_80k_pascal_context.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) | 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) | ### 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/master/configs/hrnet/fcn_hr48_480x480_40k_pascal_context_59.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/master/configs/hrnet/fcn_hr48_480x480_80k_pascal_context_59.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) | 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) | ### 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/master/configs/hrnet/fcn_hr18s_512x512_80k_loveda.py) | [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/master/configs/hrnet/fcn_hr18_512x512_80k_loveda.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/master/configs/hrnet/fcn_hr48_512x512_80k_loveda.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) | 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) | ### 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/master/configs/hrnet/fcn_hr18s_512x512_80k_potsdam.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/master/configs/hrnet/fcn_hr18_512x512_80k_potsdam.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/master/configs/hrnet/fcn_hr48_512x512_80k_potsdam.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) | 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) | ### 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/master/configs/hrnet/fcn_hr18s_4x4_512x512_80k_vaihingen.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/master/configs/hrnet/fcn_hr18_4x4_512x512_80k_vaihingen.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/master/configs/hrnet/fcn_hr48_4x4_512x512_80k_vaihingen.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) | 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) | ### 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/master/configs/hrnet/fcn_hr18s_4x4_896x896_80k_isaid.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/master/configs/hrnet/fcn_hr18_4x4_896x896_80k_isaid.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/master/configs/hrnet/fcn_hr48_4x4_896x896_80k_isaid.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) | 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) | Note: diff --git a/configs/hrnet/fcn_hr18_512x1024_160k_cityscapes.py b/configs/hrnet/fcn_hr18_4xb2-160k_cityscapes-512x1024.py similarity index 100% rename from configs/hrnet/fcn_hr18_512x1024_160k_cityscapes.py rename to configs/hrnet/fcn_hr18_4xb2-160k_cityscapes-512x1024.py diff --git a/configs/hrnet/fcn_hr18_512x1024_40k_cityscapes.py b/configs/hrnet/fcn_hr18_4xb2-40k_cityscapes-512x1024.py similarity index 100% rename from configs/hrnet/fcn_hr18_512x1024_40k_cityscapes.py rename to configs/hrnet/fcn_hr18_4xb2-40k_cityscapes-512x1024.py diff --git a/configs/hrnet/fcn_hr18_512x1024_80k_cityscapes.py b/configs/hrnet/fcn_hr18_4xb2-80k_cityscapes-512x1024.py similarity index 100% rename from configs/hrnet/fcn_hr18_512x1024_80k_cityscapes.py rename to configs/hrnet/fcn_hr18_4xb2-80k_cityscapes-512x1024.py diff --git a/configs/hrnet/fcn_hr18_512x512_160k_ade20k.py b/configs/hrnet/fcn_hr18_4xb4-160k_ade20k-512x512.py similarity index 100% rename from configs/hrnet/fcn_hr18_512x512_160k_ade20k.py rename to configs/hrnet/fcn_hr18_4xb4-160k_ade20k-512x512.py diff --git a/configs/hrnet/fcn_hr18_512x512_20k_voc12aug.py b/configs/hrnet/fcn_hr18_4xb4-20k_voc12aug-512x512.py similarity index 100% rename from configs/hrnet/fcn_hr18_512x512_20k_voc12aug.py rename to configs/hrnet/fcn_hr18_4xb4-20k_voc12aug-512x512.py diff --git a/configs/hrnet/fcn_hr18_480x480_40k_pascal_context.py b/configs/hrnet/fcn_hr18_4xb4-40k_pascal-context-480x480.py similarity index 100% rename from configs/hrnet/fcn_hr18_480x480_40k_pascal_context.py rename to configs/hrnet/fcn_hr18_4xb4-40k_pascal-context-480x480.py diff --git a/configs/hrnet/fcn_hr18_480x480_40k_pascal_context_59.py b/configs/hrnet/fcn_hr18_4xb4-40k_pascal-context-59-480x480.py similarity index 100% rename from configs/hrnet/fcn_hr18_480x480_40k_pascal_context_59.py rename to configs/hrnet/fcn_hr18_4xb4-40k_pascal-context-59-480x480.py diff --git a/configs/hrnet/fcn_hr18_512x512_40k_voc12aug.py b/configs/hrnet/fcn_hr18_4xb4-40k_voc12aug-512x512.py similarity index 100% rename from configs/hrnet/fcn_hr18_512x512_40k_voc12aug.py rename to configs/hrnet/fcn_hr18_4xb4-40k_voc12aug-512x512.py diff --git a/configs/hrnet/fcn_hr18_512x512_80k_ade20k.py b/configs/hrnet/fcn_hr18_4xb4-80k_ade20k-512x512.py similarity index 100% rename from configs/hrnet/fcn_hr18_512x512_80k_ade20k.py rename to configs/hrnet/fcn_hr18_4xb4-80k_ade20k-512x512.py diff --git a/configs/hrnet/fcn_hr18_4x4_896x896_80k_isaid.py b/configs/hrnet/fcn_hr18_4xb4-80k_isaid-896x896.py similarity index 100% rename from configs/hrnet/fcn_hr18_4x4_896x896_80k_isaid.py rename to configs/hrnet/fcn_hr18_4xb4-80k_isaid-896x896.py diff --git a/configs/hrnet/fcn_hr18_512x512_80k_loveda.py b/configs/hrnet/fcn_hr18_4xb4-80k_loveda-512x512.py similarity index 100% rename from configs/hrnet/fcn_hr18_512x512_80k_loveda.py rename to configs/hrnet/fcn_hr18_4xb4-80k_loveda-512x512.py diff --git a/configs/hrnet/fcn_hr18_480x480_80k_pascal_context.py b/configs/hrnet/fcn_hr18_4xb4-80k_pascal-context-480x480.py similarity index 100% rename from configs/hrnet/fcn_hr18_480x480_80k_pascal_context.py rename to configs/hrnet/fcn_hr18_4xb4-80k_pascal-context-480x480.py diff --git a/configs/hrnet/fcn_hr18_480x480_80k_pascal_context_59.py b/configs/hrnet/fcn_hr18_4xb4-80k_pascal-context-59-480x480.py similarity index 100% rename from configs/hrnet/fcn_hr18_480x480_80k_pascal_context_59.py rename to configs/hrnet/fcn_hr18_4xb4-80k_pascal-context-59-480x480.py diff --git a/configs/hrnet/fcn_hr18_512x512_80k_potsdam.py b/configs/hrnet/fcn_hr18_4xb4-80k_potsdam-512x512.py similarity index 100% rename from configs/hrnet/fcn_hr18_512x512_80k_potsdam.py rename to configs/hrnet/fcn_hr18_4xb4-80k_potsdam-512x512.py diff --git a/configs/hrnet/fcn_hr18_4x4_512x512_80k_vaihingen.py b/configs/hrnet/fcn_hr18_4xb4-80k_vaihingen-512x512.py similarity index 100% rename from configs/hrnet/fcn_hr18_4x4_512x512_80k_vaihingen.py rename to configs/hrnet/fcn_hr18_4xb4-80k_vaihingen-512x512.py diff --git a/configs/hrnet/fcn_hr18s_480x480_40k_pascal_context_59.py b/configs/hrnet/fcn_hr18s_4xb2-160k_cityscapes-512x1024.py similarity index 85% rename from configs/hrnet/fcn_hr18s_480x480_40k_pascal_context_59.py rename to configs/hrnet/fcn_hr18s_4xb2-160k_cityscapes-512x1024.py index 0412c64f31..6ca631cbee 100644 --- a/configs/hrnet/fcn_hr18s_480x480_40k_pascal_context_59.py +++ b/configs/hrnet/fcn_hr18s_4xb2-160k_cityscapes-512x1024.py @@ -1,4 +1,4 @@ -_base_ = './fcn_hr18_480x480_40k_pascal_context_59.py' +_base_ = './fcn_hr18_4xb2-160k_cityscapes-512x1024.py' model = dict( pretrained='open-mmlab://msra/hrnetv2_w18_small', backbone=dict( diff --git a/configs/hrnet/fcn_hr18s_4xb2-40k_cityscapes-512x1024.py b/configs/hrnet/fcn_hr18s_4xb2-40k_cityscapes-512x1024.py new file mode 100644 index 0000000000..ba7e9c696e --- /dev/null +++ b/configs/hrnet/fcn_hr18s_4xb2-40k_cityscapes-512x1024.py @@ -0,0 +1,9 @@ +_base_ = './fcn_hr18_4xb2-40k_cityscapes-512x1024.py' +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w18_small', + backbone=dict( + extra=dict( + stage1=dict(num_blocks=(2, )), + stage2=dict(num_blocks=(2, 2)), + stage3=dict(num_modules=3, num_blocks=(2, 2, 2)), + stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2))))) diff --git a/configs/hrnet/fcn_hr18s_4xb2-80k_cityscapes-512x1024.py b/configs/hrnet/fcn_hr18s_4xb2-80k_cityscapes-512x1024.py new file mode 100644 index 0000000000..26ab6210dd --- /dev/null +++ b/configs/hrnet/fcn_hr18s_4xb2-80k_cityscapes-512x1024.py @@ -0,0 +1,9 @@ +_base_ = './fcn_hr18_4xb2-80k_cityscapes-512x1024.py' +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w18_small', + backbone=dict( + extra=dict( + stage1=dict(num_blocks=(2, )), + stage2=dict(num_blocks=(2, 2)), + stage3=dict(num_modules=3, num_blocks=(2, 2, 2)), + stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2))))) diff --git a/configs/hrnet/fcn_hr18s_512x1024_160k_cityscapes.py b/configs/hrnet/fcn_hr18s_4xb4-160k_ade20k-512x512.py similarity index 86% rename from configs/hrnet/fcn_hr18s_512x1024_160k_cityscapes.py rename to configs/hrnet/fcn_hr18s_4xb4-160k_ade20k-512x512.py index ddbe3801f9..29cbd10cbf 100644 --- a/configs/hrnet/fcn_hr18s_512x1024_160k_cityscapes.py +++ b/configs/hrnet/fcn_hr18s_4xb4-160k_ade20k-512x512.py @@ -1,4 +1,4 @@ -_base_ = './fcn_hr18_512x1024_160k_cityscapes.py' +_base_ = './fcn_hr18_4xb4-160k_ade20k-512x512.py' model = dict( pretrained='open-mmlab://msra/hrnetv2_w18_small', backbone=dict( diff --git a/configs/hrnet/fcn_hr18s_4x4_512x512_80k_vaihingen.py b/configs/hrnet/fcn_hr18s_4xb4-20k_voc12aug-512x512.py similarity index 86% rename from configs/hrnet/fcn_hr18s_4x4_512x512_80k_vaihingen.py rename to configs/hrnet/fcn_hr18s_4xb4-20k_voc12aug-512x512.py index 5828fe1af2..9dd1933349 100644 --- a/configs/hrnet/fcn_hr18s_4x4_512x512_80k_vaihingen.py +++ b/configs/hrnet/fcn_hr18s_4xb4-20k_voc12aug-512x512.py @@ -1,4 +1,4 @@ -_base_ = './fcn_hr18_4x4_512x512_80k_vaihingen.py' +_base_ = './fcn_hr18_4xb4-20k_voc12aug-512x512.py' model = dict( pretrained='open-mmlab://msra/hrnetv2_w18_small', backbone=dict( diff --git a/configs/hrnet/fcn_hr18s_480x480_80k_pascal_context_59.py b/configs/hrnet/fcn_hr18s_4xb4-40k_pascal-context-480x480.py similarity index 85% rename from configs/hrnet/fcn_hr18s_480x480_80k_pascal_context_59.py rename to configs/hrnet/fcn_hr18s_4xb4-40k_pascal-context-480x480.py index babd88db4e..5f88f532a3 100644 --- a/configs/hrnet/fcn_hr18s_480x480_80k_pascal_context_59.py +++ b/configs/hrnet/fcn_hr18s_4xb4-40k_pascal-context-480x480.py @@ -1,4 +1,4 @@ -_base_ = './fcn_hr18_480x480_80k_pascal_context_59.py' +_base_ = './fcn_hr18_4xb4-40k_pascal-context-480x480.py' model = dict( pretrained='open-mmlab://msra/hrnetv2_w18_small', backbone=dict( diff --git a/configs/hrnet/fcn_hr18s_4xb4-40k_pascal-context-59-480x480.py b/configs/hrnet/fcn_hr18s_4xb4-40k_pascal-context-59-480x480.py new file mode 100644 index 0000000000..b616fad8c2 --- /dev/null +++ b/configs/hrnet/fcn_hr18s_4xb4-40k_pascal-context-59-480x480.py @@ -0,0 +1,9 @@ +_base_ = './fcn_hr18_4xb4-40k_pascal-context-59-480x480.py' +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w18_small', + backbone=dict( + extra=dict( + stage1=dict(num_blocks=(2, )), + stage2=dict(num_blocks=(2, 2)), + stage3=dict(num_modules=3, num_blocks=(2, 2, 2)), + stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2))))) diff --git a/configs/hrnet/fcn_hr18s_4x4_896x896_80k_isaid.py b/configs/hrnet/fcn_hr18s_4xb4-40k_voc12aug-512x512.py similarity index 86% rename from configs/hrnet/fcn_hr18s_4x4_896x896_80k_isaid.py rename to configs/hrnet/fcn_hr18s_4xb4-40k_voc12aug-512x512.py index d6f6c657a5..b10b282dd8 100644 --- a/configs/hrnet/fcn_hr18s_4x4_896x896_80k_isaid.py +++ b/configs/hrnet/fcn_hr18s_4xb4-40k_voc12aug-512x512.py @@ -1,4 +1,4 @@ -_base_ = './fcn_hr18_4x4_896x896_80k_isaid.py' +_base_ = './fcn_hr18_4xb4-40k_voc12aug-512x512.py' model = dict( pretrained='open-mmlab://msra/hrnetv2_w18_small', backbone=dict( diff --git a/configs/hrnet/fcn_hr18s_512x1024_80k_cityscapes.py b/configs/hrnet/fcn_hr18s_4xb4-80k_ade20k-512x512.py similarity index 86% rename from configs/hrnet/fcn_hr18s_512x1024_80k_cityscapes.py rename to configs/hrnet/fcn_hr18s_4xb4-80k_ade20k-512x512.py index ee2831d99d..f9f49360bf 100644 --- a/configs/hrnet/fcn_hr18s_512x1024_80k_cityscapes.py +++ b/configs/hrnet/fcn_hr18s_4xb4-80k_ade20k-512x512.py @@ -1,4 +1,4 @@ -_base_ = './fcn_hr18_512x1024_80k_cityscapes.py' +_base_ = './fcn_hr18_4xb4-80k_ade20k-512x512.py' model = dict( pretrained='open-mmlab://msra/hrnetv2_w18_small', backbone=dict( diff --git a/configs/hrnet/fcn_hr18s_512x512_160k_ade20k.py b/configs/hrnet/fcn_hr18s_4xb4-80k_isaid-896x896.py similarity index 87% rename from configs/hrnet/fcn_hr18s_512x512_160k_ade20k.py rename to configs/hrnet/fcn_hr18s_4xb4-80k_isaid-896x896.py index 22a3ce0b38..ab2d2414dd 100644 --- a/configs/hrnet/fcn_hr18s_512x512_160k_ade20k.py +++ b/configs/hrnet/fcn_hr18s_4xb4-80k_isaid-896x896.py @@ -1,4 +1,4 @@ -_base_ = './fcn_hr18_512x512_160k_ade20k.py' +_base_ = './fcn_hr18_4xb4-80k_isaid-896x896.py' model = dict( pretrained='open-mmlab://msra/hrnetv2_w18_small', backbone=dict( diff --git a/configs/hrnet/fcn_hr18s_512x512_80k_loveda.py b/configs/hrnet/fcn_hr18s_4xb4-80k_loveda-512x512.py similarity index 88% rename from configs/hrnet/fcn_hr18s_512x512_80k_loveda.py rename to configs/hrnet/fcn_hr18s_4xb4-80k_loveda-512x512.py index b39769ffc2..bc9d78d718 100644 --- a/configs/hrnet/fcn_hr18s_512x512_80k_loveda.py +++ b/configs/hrnet/fcn_hr18s_4xb4-80k_loveda-512x512.py @@ -1,4 +1,4 @@ -_base_ = './fcn_hr18_512x512_80k_loveda.py' +_base_ = './fcn_hr18_4xb4-80k_loveda-512x512.py' model = dict( backbone=dict( init_cfg=dict( diff --git a/configs/hrnet/fcn_hr18s_4xb4-80k_pascal-context-480x480.py b/configs/hrnet/fcn_hr18s_4xb4-80k_pascal-context-480x480.py new file mode 100644 index 0000000000..b7b52331c7 --- /dev/null +++ b/configs/hrnet/fcn_hr18s_4xb4-80k_pascal-context-480x480.py @@ -0,0 +1,9 @@ +_base_ = './fcn_hr18_4xb4-80k_pascal-context-480x480.py' +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w18_small', + backbone=dict( + extra=dict( + stage1=dict(num_blocks=(2, )), + stage2=dict(num_blocks=(2, 2)), + stage3=dict(num_modules=3, num_blocks=(2, 2, 2)), + stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2))))) diff --git a/configs/hrnet/fcn_hr18s_4xb4-80k_pascal-context-59-480x480.py b/configs/hrnet/fcn_hr18s_4xb4-80k_pascal-context-59-480x480.py new file mode 100644 index 0000000000..ccf1040d13 --- /dev/null +++ b/configs/hrnet/fcn_hr18s_4xb4-80k_pascal-context-59-480x480.py @@ -0,0 +1,9 @@ +_base_ = './fcn_hr18_4xb4-80k_pascal-context-59-480x480.py' +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w18_small', + backbone=dict( + extra=dict( + stage1=dict(num_blocks=(2, )), + stage2=dict(num_blocks=(2, 2)), + stage3=dict(num_modules=3, num_blocks=(2, 2, 2)), + stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2))))) diff --git a/configs/hrnet/fcn_hr18s_512x1024_40k_cityscapes.py b/configs/hrnet/fcn_hr18s_4xb4-80k_potsdam-512x512.py similarity index 86% rename from configs/hrnet/fcn_hr18s_512x1024_40k_cityscapes.py rename to configs/hrnet/fcn_hr18s_4xb4-80k_potsdam-512x512.py index 4e31d26e09..3a5726f5d1 100644 --- a/configs/hrnet/fcn_hr18s_512x1024_40k_cityscapes.py +++ b/configs/hrnet/fcn_hr18s_4xb4-80k_potsdam-512x512.py @@ -1,4 +1,4 @@ -_base_ = './fcn_hr18_512x1024_40k_cityscapes.py' +_base_ = './fcn_hr18_4xb4-80k_potsdam-512x512.py' model = dict( pretrained='open-mmlab://msra/hrnetv2_w18_small', backbone=dict( diff --git a/configs/hrnet/fcn_hr18s_480x480_40k_pascal_context.py b/configs/hrnet/fcn_hr18s_4xb4-80k_vaihingen-512x512.py similarity index 86% rename from configs/hrnet/fcn_hr18s_480x480_40k_pascal_context.py rename to configs/hrnet/fcn_hr18s_4xb4-80k_vaihingen-512x512.py index d09931048f..720c1732b0 100644 --- a/configs/hrnet/fcn_hr18s_480x480_40k_pascal_context.py +++ b/configs/hrnet/fcn_hr18s_4xb4-80k_vaihingen-512x512.py @@ -1,4 +1,4 @@ -_base_ = './fcn_hr18_480x480_40k_pascal_context.py' +_base_ = './fcn_hr18_4xb4-80k_vaihingen-512x512.py' model = dict( pretrained='open-mmlab://msra/hrnetv2_w18_small', backbone=dict( diff --git a/configs/hrnet/fcn_hr18s_512x512_20k_voc12aug.py b/configs/hrnet/fcn_hr18s_512x512_20k_voc12aug.py deleted file mode 100644 index d0de5df752..0000000000 --- a/configs/hrnet/fcn_hr18s_512x512_20k_voc12aug.py +++ /dev/null @@ -1,9 +0,0 @@ -_base_ = './fcn_hr18_512x512_20k_voc12aug.py' -model = dict( - pretrained='open-mmlab://msra/hrnetv2_w18_small', - backbone=dict( - extra=dict( - stage1=dict(num_blocks=(2, )), - stage2=dict(num_blocks=(2, 2)), - stage3=dict(num_modules=3, num_blocks=(2, 2, 2)), - stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2))))) diff --git a/configs/hrnet/fcn_hr18s_512x512_40k_voc12aug.py b/configs/hrnet/fcn_hr18s_512x512_40k_voc12aug.py deleted file mode 100644 index 409db3c628..0000000000 --- a/configs/hrnet/fcn_hr18s_512x512_40k_voc12aug.py +++ /dev/null @@ -1,9 +0,0 @@ -_base_ = './fcn_hr18_512x512_40k_voc12aug.py' -model = dict( - pretrained='open-mmlab://msra/hrnetv2_w18_small', - backbone=dict( - extra=dict( - stage1=dict(num_blocks=(2, )), - stage2=dict(num_blocks=(2, 2)), - stage3=dict(num_modules=3, num_blocks=(2, 2, 2)), - stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2))))) diff --git a/configs/hrnet/fcn_hr18s_512x512_80k_ade20k.py b/configs/hrnet/fcn_hr18s_512x512_80k_ade20k.py deleted file mode 100644 index a8400979b1..0000000000 --- a/configs/hrnet/fcn_hr18s_512x512_80k_ade20k.py +++ /dev/null @@ -1,9 +0,0 @@ -_base_ = './fcn_hr18_512x512_80k_ade20k.py' -model = dict( - pretrained='open-mmlab://msra/hrnetv2_w18_small', - backbone=dict( - extra=dict( - stage1=dict(num_blocks=(2, )), - stage2=dict(num_blocks=(2, 2)), - stage3=dict(num_modules=3, num_blocks=(2, 2, 2)), - stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2))))) diff --git a/configs/hrnet/fcn_hr18s_512x512_80k_potsdam.py b/configs/hrnet/fcn_hr18s_512x512_80k_potsdam.py deleted file mode 100644 index 05551271a3..0000000000 --- a/configs/hrnet/fcn_hr18s_512x512_80k_potsdam.py +++ /dev/null @@ -1,9 +0,0 @@ -_base_ = './fcn_hr18_512x512_80k_potsdam.py' -model = dict( - pretrained='open-mmlab://msra/hrnetv2_w18_small', - backbone=dict( - extra=dict( - stage1=dict(num_blocks=(2, )), - stage2=dict(num_blocks=(2, 2)), - stage3=dict(num_modules=3, num_blocks=(2, 2, 2)), - stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2))))) diff --git a/configs/hrnet/fcn_hr48_480x480_40k_pascal_context_59.py b/configs/hrnet/fcn_hr48_4xb2-160k_cityscapes-512x1024.py similarity index 86% rename from configs/hrnet/fcn_hr48_480x480_40k_pascal_context_59.py rename to configs/hrnet/fcn_hr48_4xb2-160k_cityscapes-512x1024.py index 655b460467..4aa5d94d1e 100644 --- a/configs/hrnet/fcn_hr48_480x480_40k_pascal_context_59.py +++ b/configs/hrnet/fcn_hr48_4xb2-160k_cityscapes-512x1024.py @@ -1,4 +1,4 @@ -_base_ = './fcn_hr18_480x480_40k_pascal_context_59.py' +_base_ = './fcn_hr18_4xb2-160k_cityscapes-512x1024.py' model = dict( pretrained='open-mmlab://msra/hrnetv2_w48', backbone=dict( diff --git a/configs/hrnet/fcn_hr48_480x480_80k_pascal_context.py b/configs/hrnet/fcn_hr48_4xb2-40k_cityscapes-512x1024.py similarity index 86% rename from configs/hrnet/fcn_hr48_480x480_80k_pascal_context.py rename to configs/hrnet/fcn_hr48_4xb2-40k_cityscapes-512x1024.py index e28164e3dc..7cb795250d 100644 --- a/configs/hrnet/fcn_hr48_480x480_80k_pascal_context.py +++ b/configs/hrnet/fcn_hr48_4xb2-40k_cityscapes-512x1024.py @@ -1,4 +1,4 @@ -_base_ = './fcn_hr18_480x480_80k_pascal_context.py' +_base_ = './fcn_hr18_4xb2-40k_cityscapes-512x1024.py' model = dict( pretrained='open-mmlab://msra/hrnetv2_w48', backbone=dict( diff --git a/configs/hrnet/fcn_hr48_4xb2-80k_cityscapes-512x1024.py b/configs/hrnet/fcn_hr48_4xb2-80k_cityscapes-512x1024.py new file mode 100644 index 0000000000..3e2ce034b2 --- /dev/null +++ b/configs/hrnet/fcn_hr48_4xb2-80k_cityscapes-512x1024.py @@ -0,0 +1,10 @@ +_base_ = './fcn_hr18_4xb2-80k_cityscapes-512x1024.py' +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w48', + backbone=dict( + extra=dict( + stage2=dict(num_channels=(48, 96)), + stage3=dict(num_channels=(48, 96, 192)), + stage4=dict(num_channels=(48, 96, 192, 384)))), + decode_head=dict( + in_channels=[48, 96, 192, 384], channels=sum([48, 96, 192, 384]))) diff --git a/configs/hrnet/fcn_hr48_512x1024_160k_cityscapes.py b/configs/hrnet/fcn_hr48_4xb4-160k_ade20k-512x512.py similarity index 87% rename from configs/hrnet/fcn_hr48_512x1024_160k_cityscapes.py rename to configs/hrnet/fcn_hr48_4xb4-160k_ade20k-512x512.py index 394a61c99f..89b1f04651 100644 --- a/configs/hrnet/fcn_hr48_512x1024_160k_cityscapes.py +++ b/configs/hrnet/fcn_hr48_4xb4-160k_ade20k-512x512.py @@ -1,4 +1,4 @@ -_base_ = './fcn_hr18_512x1024_160k_cityscapes.py' +_base_ = './fcn_hr18_4xb4-160k_ade20k-512x512.py' model = dict( pretrained='open-mmlab://msra/hrnetv2_w48', backbone=dict( diff --git a/configs/hrnet/fcn_hr48_4x4_512x512_80k_vaihingen.py b/configs/hrnet/fcn_hr48_4xb4-20k_voc12aug-512x512.py similarity index 87% rename from configs/hrnet/fcn_hr48_4x4_512x512_80k_vaihingen.py rename to configs/hrnet/fcn_hr48_4xb4-20k_voc12aug-512x512.py index 7cb22d80f0..7ca38a9a79 100644 --- a/configs/hrnet/fcn_hr48_4x4_512x512_80k_vaihingen.py +++ b/configs/hrnet/fcn_hr48_4xb4-20k_voc12aug-512x512.py @@ -1,4 +1,4 @@ -_base_ = './fcn_hr18_4x4_512x512_80k_vaihingen.py' +_base_ = './fcn_hr18_4xb4-20k_voc12aug-512x512.py' model = dict( pretrained='open-mmlab://msra/hrnetv2_w48', backbone=dict( diff --git a/configs/hrnet/fcn_hr48_480x480_80k_pascal_context_59.py b/configs/hrnet/fcn_hr48_4xb4-40k_pascal-context-480x480.py similarity index 86% rename from configs/hrnet/fcn_hr48_480x480_80k_pascal_context_59.py rename to configs/hrnet/fcn_hr48_4xb4-40k_pascal-context-480x480.py index 012ad0a7d6..379be1d67e 100644 --- a/configs/hrnet/fcn_hr48_480x480_80k_pascal_context_59.py +++ b/configs/hrnet/fcn_hr48_4xb4-40k_pascal-context-480x480.py @@ -1,4 +1,4 @@ -_base_ = './fcn_hr18_480x480_80k_pascal_context_59.py' +_base_ = './fcn_hr18_4xb4-40k_pascal-context-480x480.py' model = dict( pretrained='open-mmlab://msra/hrnetv2_w48', backbone=dict( diff --git a/configs/hrnet/fcn_hr48_4xb4-40k_pascal-context-59-480x480.py b/configs/hrnet/fcn_hr48_4xb4-40k_pascal-context-59-480x480.py new file mode 100644 index 0000000000..12730dd533 --- /dev/null +++ b/configs/hrnet/fcn_hr48_4xb4-40k_pascal-context-59-480x480.py @@ -0,0 +1,10 @@ +_base_ = './fcn_hr18_4xb4-40k_pascal-context-59-480x480.py' +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w48', + backbone=dict( + extra=dict( + stage2=dict(num_channels=(48, 96)), + stage3=dict(num_channels=(48, 96, 192)), + stage4=dict(num_channels=(48, 96, 192, 384)))), + decode_head=dict( + in_channels=[48, 96, 192, 384], channels=sum([48, 96, 192, 384]))) diff --git a/configs/hrnet/fcn_hr48_4x4_896x896_80k_isaid.py b/configs/hrnet/fcn_hr48_4xb4-40k_voc12aug-512x512.py similarity index 87% rename from configs/hrnet/fcn_hr48_4x4_896x896_80k_isaid.py rename to configs/hrnet/fcn_hr48_4xb4-40k_voc12aug-512x512.py index 55cf1b55bd..3e1b920c59 100644 --- a/configs/hrnet/fcn_hr48_4x4_896x896_80k_isaid.py +++ b/configs/hrnet/fcn_hr48_4xb4-40k_voc12aug-512x512.py @@ -1,4 +1,4 @@ -_base_ = './fcn_hr18_4x4_896x896_80k_isaid.py' +_base_ = './fcn_hr18_4xb4-40k_voc12aug-512x512.py' model = dict( pretrained='open-mmlab://msra/hrnetv2_w48', backbone=dict( diff --git a/configs/hrnet/fcn_hr48_512x1024_80k_cityscapes.py b/configs/hrnet/fcn_hr48_4xb4-80k_ade20k-512x512.py similarity index 87% rename from configs/hrnet/fcn_hr48_512x1024_80k_cityscapes.py rename to configs/hrnet/fcn_hr48_4xb4-80k_ade20k-512x512.py index a9bab32b52..14fd663e87 100644 --- a/configs/hrnet/fcn_hr48_512x1024_80k_cityscapes.py +++ b/configs/hrnet/fcn_hr48_4xb4-80k_ade20k-512x512.py @@ -1,4 +1,4 @@ -_base_ = './fcn_hr18_512x1024_80k_cityscapes.py' +_base_ = './fcn_hr18_4xb4-80k_ade20k-512x512.py' model = dict( pretrained='open-mmlab://msra/hrnetv2_w48', backbone=dict( diff --git a/configs/hrnet/fcn_hr48_512x512_160k_ade20k.py b/configs/hrnet/fcn_hr48_4xb4-80k_isaid-896x896.py similarity index 88% rename from configs/hrnet/fcn_hr48_512x512_160k_ade20k.py rename to configs/hrnet/fcn_hr48_4xb4-80k_isaid-896x896.py index dff4fea85c..81815efa8d 100644 --- a/configs/hrnet/fcn_hr48_512x512_160k_ade20k.py +++ b/configs/hrnet/fcn_hr48_4xb4-80k_isaid-896x896.py @@ -1,4 +1,4 @@ -_base_ = './fcn_hr18_512x512_160k_ade20k.py' +_base_ = './fcn_hr18_4xb4-80k_isaid-896x896.py' model = dict( pretrained='open-mmlab://msra/hrnetv2_w48', backbone=dict( diff --git a/configs/hrnet/fcn_hr48_512x512_80k_loveda.py b/configs/hrnet/fcn_hr48_4xb4-80k_loveda-512x512.py similarity index 89% rename from configs/hrnet/fcn_hr48_512x512_80k_loveda.py rename to configs/hrnet/fcn_hr48_4xb4-80k_loveda-512x512.py index 269dbf662d..aa90310d99 100644 --- a/configs/hrnet/fcn_hr48_512x512_80k_loveda.py +++ b/configs/hrnet/fcn_hr48_4xb4-80k_loveda-512x512.py @@ -1,4 +1,4 @@ -_base_ = './fcn_hr18_512x512_80k_loveda.py' +_base_ = './fcn_hr18_4xb4-80k_loveda-512x512.py' model = dict( backbone=dict( init_cfg=dict( diff --git a/configs/hrnet/fcn_hr48_4xb4-80k_pascal-context-480x480.py b/configs/hrnet/fcn_hr48_4xb4-80k_pascal-context-480x480.py new file mode 100644 index 0000000000..4d193d9042 --- /dev/null +++ b/configs/hrnet/fcn_hr48_4xb4-80k_pascal-context-480x480.py @@ -0,0 +1,10 @@ +_base_ = './fcn_hr18_4xb4-80k_pascal-context-480x480.py' +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w48', + backbone=dict( + extra=dict( + stage2=dict(num_channels=(48, 96)), + stage3=dict(num_channels=(48, 96, 192)), + stage4=dict(num_channels=(48, 96, 192, 384)))), + decode_head=dict( + in_channels=[48, 96, 192, 384], channels=sum([48, 96, 192, 384]))) diff --git a/configs/hrnet/fcn_hr48_4xb4-80k_pascal-context-59-480x480.py b/configs/hrnet/fcn_hr48_4xb4-80k_pascal-context-59-480x480.py new file mode 100644 index 0000000000..d8b4c4aa8e --- /dev/null +++ b/configs/hrnet/fcn_hr48_4xb4-80k_pascal-context-59-480x480.py @@ -0,0 +1,10 @@ +_base_ = './fcn_hr18_4xb4-80k_pascal-context-59-480x480.py' +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w48', + backbone=dict( + extra=dict( + stage2=dict(num_channels=(48, 96)), + stage3=dict(num_channels=(48, 96, 192)), + stage4=dict(num_channels=(48, 96, 192, 384)))), + decode_head=dict( + in_channels=[48, 96, 192, 384], channels=sum([48, 96, 192, 384]))) diff --git a/configs/hrnet/fcn_hr48_512x1024_40k_cityscapes.py b/configs/hrnet/fcn_hr48_4xb4-80k_potsdam-512x512.py similarity index 87% rename from configs/hrnet/fcn_hr48_512x1024_40k_cityscapes.py rename to configs/hrnet/fcn_hr48_4xb4-80k_potsdam-512x512.py index d37ab1d09e..58a650004d 100644 --- a/configs/hrnet/fcn_hr48_512x1024_40k_cityscapes.py +++ b/configs/hrnet/fcn_hr48_4xb4-80k_potsdam-512x512.py @@ -1,4 +1,4 @@ -_base_ = './fcn_hr18_512x1024_40k_cityscapes.py' +_base_ = './fcn_hr18_4xb4-80k_potsdam-512x512.py' model = dict( pretrained='open-mmlab://msra/hrnetv2_w48', backbone=dict( diff --git a/configs/hrnet/fcn_hr48_480x480_40k_pascal_context.py b/configs/hrnet/fcn_hr48_4xb4-80k_vaihingen-512x512.py similarity index 87% rename from configs/hrnet/fcn_hr48_480x480_40k_pascal_context.py rename to configs/hrnet/fcn_hr48_4xb4-80k_vaihingen-512x512.py index 0e2d96cb6c..db91ed83ef 100644 --- a/configs/hrnet/fcn_hr48_480x480_40k_pascal_context.py +++ b/configs/hrnet/fcn_hr48_4xb4-80k_vaihingen-512x512.py @@ -1,4 +1,4 @@ -_base_ = './fcn_hr18_480x480_40k_pascal_context.py' +_base_ = './fcn_hr18_4xb4-80k_vaihingen-512x512.py' model = dict( pretrained='open-mmlab://msra/hrnetv2_w48', backbone=dict( diff --git a/configs/hrnet/fcn_hr48_512x512_20k_voc12aug.py b/configs/hrnet/fcn_hr48_512x512_20k_voc12aug.py deleted file mode 100644 index a8d1deb986..0000000000 --- a/configs/hrnet/fcn_hr48_512x512_20k_voc12aug.py +++ /dev/null @@ -1,10 +0,0 @@ -_base_ = './fcn_hr18_512x512_20k_voc12aug.py' -model = dict( - pretrained='open-mmlab://msra/hrnetv2_w48', - backbone=dict( - extra=dict( - stage2=dict(num_channels=(48, 96)), - stage3=dict(num_channels=(48, 96, 192)), - stage4=dict(num_channels=(48, 96, 192, 384)))), - decode_head=dict( - in_channels=[48, 96, 192, 384], channels=sum([48, 96, 192, 384]))) diff --git a/configs/hrnet/fcn_hr48_512x512_40k_voc12aug.py b/configs/hrnet/fcn_hr48_512x512_40k_voc12aug.py deleted file mode 100644 index 1084a57e97..0000000000 --- a/configs/hrnet/fcn_hr48_512x512_40k_voc12aug.py +++ /dev/null @@ -1,10 +0,0 @@ -_base_ = './fcn_hr18_512x512_40k_voc12aug.py' -model = dict( - pretrained='open-mmlab://msra/hrnetv2_w48', - backbone=dict( - extra=dict( - stage2=dict(num_channels=(48, 96)), - stage3=dict(num_channels=(48, 96, 192)), - stage4=dict(num_channels=(48, 96, 192, 384)))), - decode_head=dict( - in_channels=[48, 96, 192, 384], channels=sum([48, 96, 192, 384]))) diff --git a/configs/hrnet/fcn_hr48_512x512_80k_ade20k.py b/configs/hrnet/fcn_hr48_512x512_80k_ade20k.py deleted file mode 100644 index 7eca7fa4b8..0000000000 --- a/configs/hrnet/fcn_hr48_512x512_80k_ade20k.py +++ /dev/null @@ -1,10 +0,0 @@ -_base_ = './fcn_hr18_512x512_80k_ade20k.py' -model = dict( - pretrained='open-mmlab://msra/hrnetv2_w48', - backbone=dict( - extra=dict( - stage2=dict(num_channels=(48, 96)), - stage3=dict(num_channels=(48, 96, 192)), - stage4=dict(num_channels=(48, 96, 192, 384)))), - decode_head=dict( - in_channels=[48, 96, 192, 384], channels=sum([48, 96, 192, 384]))) diff --git a/configs/hrnet/fcn_hr48_512x512_80k_potsdam.py b/configs/hrnet/fcn_hr48_512x512_80k_potsdam.py deleted file mode 100644 index 608fee387b..0000000000 --- a/configs/hrnet/fcn_hr48_512x512_80k_potsdam.py +++ /dev/null @@ -1,10 +0,0 @@ -_base_ = './fcn_hr18_512x512_80k_potsdam.py' -model = dict( - pretrained='open-mmlab://msra/hrnetv2_w48', - backbone=dict( - extra=dict( - stage2=dict(num_channels=(48, 96)), - stage3=dict(num_channels=(48, 96, 192)), - stage4=dict(num_channels=(48, 96, 192, 384)))), - decode_head=dict( - in_channels=[48, 96, 192, 384], channels=sum([48, 96, 192, 384]))) diff --git a/configs/hrnet/hrnet.yml b/configs/hrnet/hrnet.yml index 960a93708b..77f556e17a 100644 --- a/configs/hrnet/hrnet.yml +++ b/configs/hrnet/hrnet.yml @@ -1,5 +1,5 @@ Models: -- Name: fcn_hr18s_512x1024_40k_cityscapes +- Name: fcn_hr18s_4xb2-40k_cityscapes-512x1024 In Collection: FCN Metadata: backbone: HRNetV2p-W18-Small @@ -19,9 +19,9 @@ Models: Metrics: mIoU: 73.86 mIoU(ms+flip): 75.91 - Config: configs/hrnet/fcn_hr18s_512x1024_40k_cityscapes.py + 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_512x1024_40k_cityscapes +- Name: fcn_hr18_4xb2-40k_cityscapes-512x1024 In Collection: FCN Metadata: backbone: HRNetV2p-W18 @@ -41,9 +41,9 @@ Models: Metrics: mIoU: 77.19 mIoU(ms+flip): 78.92 - Config: configs/hrnet/fcn_hr18_512x1024_40k_cityscapes.py + 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_512x1024_40k_cityscapes +- Name: fcn_hr48_4xb2-40k_cityscapes-512x1024 In Collection: FCN Metadata: backbone: HRNetV2p-W48 @@ -63,9 +63,9 @@ Models: Metrics: mIoU: 78.48 mIoU(ms+flip): 79.69 - Config: configs/hrnet/fcn_hr48_512x1024_40k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: fcn_hr18s_4xb2-80k_cityscapes-512x1024 In Collection: FCN Metadata: backbone: HRNetV2p-W18-Small @@ -77,9 +77,9 @@ Models: Metrics: mIoU: 75.31 mIoU(ms+flip): 77.48 - Config: configs/hrnet/fcn_hr18s_512x1024_80k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: fcn_hr18_4xb2-80k_cityscapes-512x1024 In Collection: FCN Metadata: backbone: HRNetV2p-W18 @@ -91,9 +91,9 @@ Models: Metrics: mIoU: 78.65 mIoU(ms+flip): 80.35 - Config: configs/hrnet/fcn_hr18_512x1024_80k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: fcn_hr48_4xb2-80k_cityscapes-512x1024 In Collection: FCN Metadata: backbone: HRNetV2p-W48 @@ -105,9 +105,9 @@ Models: Metrics: mIoU: 79.93 mIoU(ms+flip): 80.72 - Config: configs/hrnet/fcn_hr48_512x1024_80k_cityscapes.py + 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_512x1024_160k_cityscapes +- Name: fcn_hr18s_4xb2-160k_cityscapes-512x1024 In Collection: FCN Metadata: backbone: HRNetV2p-W18-Small @@ -119,9 +119,9 @@ Models: Metrics: mIoU: 76.31 mIoU(ms+flip): 78.31 - Config: configs/hrnet/fcn_hr18s_512x1024_160k_cityscapes.py + 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_512x1024_160k_cityscapes +- Name: fcn_hr18_4xb2-160k_cityscapes-512x1024 In Collection: FCN Metadata: backbone: HRNetV2p-W18 @@ -133,9 +133,9 @@ Models: Metrics: mIoU: 78.8 mIoU(ms+flip): 80.74 - Config: configs/hrnet/fcn_hr18_512x1024_160k_cityscapes.py + 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_512x1024_160k_cityscapes +- Name: fcn_hr48_4xb2-160k_cityscapes-512x1024 In Collection: FCN Metadata: backbone: HRNetV2p-W48 @@ -147,9 +147,9 @@ Models: Metrics: mIoU: 80.65 mIoU(ms+flip): 81.92 - Config: configs/hrnet/fcn_hr48_512x1024_160k_cityscapes.py + 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_512x512_80k_ade20k +- Name: fcn_hr18s_4xb4-80k_ade20k-512x512 In Collection: FCN Metadata: backbone: HRNetV2p-W18-Small @@ -169,9 +169,9 @@ Models: Metrics: mIoU: 31.38 mIoU(ms+flip): 32.45 - Config: configs/hrnet/fcn_hr18s_512x512_80k_ade20k.py + 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_512x512_80k_ade20k +- Name: fcn_hr18_4xb4-80k_ade20k-512x512 In Collection: FCN Metadata: backbone: HRNetV2p-W18 @@ -191,9 +191,9 @@ Models: Metrics: mIoU: 36.27 mIoU(ms+flip): 37.28 - Config: configs/hrnet/fcn_hr18_512x512_80k_ade20k.py + 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_512x512_80k_ade20k +- Name: fcn_hr48_4xb4-80k_ade20k-512x512 In Collection: FCN Metadata: backbone: HRNetV2p-W48 @@ -213,9 +213,9 @@ Models: Metrics: mIoU: 41.9 mIoU(ms+flip): 43.27 - Config: configs/hrnet/fcn_hr48_512x512_80k_ade20k.py + 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_512x512_160k_ade20k +- Name: fcn_hr18s_4xb4-160k_ade20k-512x512 In Collection: FCN Metadata: backbone: HRNetV2p-W18-Small @@ -227,9 +227,9 @@ Models: Metrics: mIoU: 33.07 mIoU(ms+flip): 34.56 - Config: configs/hrnet/fcn_hr18s_512x512_160k_ade20k.py + 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_512x512_160k_ade20k +- Name: fcn_hr18_4xb4-160k_ade20k-512x512 In Collection: FCN Metadata: backbone: HRNetV2p-W18 @@ -241,9 +241,9 @@ Models: Metrics: mIoU: 36.79 mIoU(ms+flip): 38.58 - Config: configs/hrnet/fcn_hr18_512x512_160k_ade20k.py + 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_512x512_160k_ade20k +- Name: fcn_hr48_4xb4-160k_ade20k-512x512 In Collection: FCN Metadata: backbone: HRNetV2p-W48 @@ -255,9 +255,9 @@ Models: Metrics: mIoU: 42.02 mIoU(ms+flip): 43.86 - Config: configs/hrnet/fcn_hr48_512x512_160k_ade20k.py + 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_512x512_20k_voc12aug +- Name: fcn_hr18s_4xb4-20k_voc12aug-512x512 In Collection: FCN Metadata: backbone: HRNetV2p-W18-Small @@ -277,9 +277,9 @@ Models: Metrics: mIoU: 65.5 mIoU(ms+flip): 68.89 - Config: configs/hrnet/fcn_hr18s_512x512_20k_voc12aug.py + 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_512x512_20k_voc12aug +- Name: fcn_hr18_4xb4-20k_voc12aug-512x512 In Collection: FCN Metadata: backbone: HRNetV2p-W18 @@ -299,9 +299,9 @@ Models: Metrics: mIoU: 72.3 mIoU(ms+flip): 74.71 - Config: configs/hrnet/fcn_hr18_512x512_20k_voc12aug.py + 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_512x512_20k_voc12aug +- Name: fcn_hr48_4xb4-20k_voc12aug-512x512 In Collection: FCN Metadata: backbone: HRNetV2p-W48 @@ -321,9 +321,9 @@ Models: Metrics: mIoU: 75.87 mIoU(ms+flip): 78.58 - Config: configs/hrnet/fcn_hr48_512x512_20k_voc12aug.py + 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_512x512_40k_voc12aug +- Name: fcn_hr18s_4xb4-40k_voc12aug-512x512 In Collection: FCN Metadata: backbone: HRNetV2p-W18-Small @@ -335,9 +335,9 @@ Models: Metrics: mIoU: 66.61 mIoU(ms+flip): 70.0 - Config: configs/hrnet/fcn_hr18s_512x512_40k_voc12aug.py + 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_512x512_40k_voc12aug +- Name: fcn_hr18_4xb4-40k_voc12aug-512x512 In Collection: FCN Metadata: backbone: HRNetV2p-W18 @@ -349,9 +349,9 @@ Models: Metrics: mIoU: 72.9 mIoU(ms+flip): 75.59 - Config: configs/hrnet/fcn_hr18_512x512_40k_voc12aug.py + 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_512x512_40k_voc12aug +- Name: fcn_hr48_4xb4-40k_voc12aug-512x512 In Collection: FCN Metadata: backbone: HRNetV2p-W48 @@ -363,9 +363,9 @@ Models: Metrics: mIoU: 76.24 mIoU(ms+flip): 78.49 - Config: configs/hrnet/fcn_hr48_512x512_40k_voc12aug.py + 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_480x480_40k_pascal_context +- Name: fcn_hr48_4xb4-40k_pascal-context-480x480 In Collection: FCN Metadata: backbone: HRNetV2p-W48 @@ -385,9 +385,9 @@ Models: Metrics: mIoU: 45.14 mIoU(ms+flip): 47.42 - Config: configs/hrnet/fcn_hr48_480x480_40k_pascal_context.py + 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_480x480_80k_pascal_context +- Name: fcn_hr48_4xb4-80k_pascal-context-480x480 In Collection: FCN Metadata: backbone: HRNetV2p-W48 @@ -399,9 +399,9 @@ Models: Metrics: mIoU: 45.84 mIoU(ms+flip): 47.84 - Config: configs/hrnet/fcn_hr48_480x480_80k_pascal_context.py + 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_480x480_40k_pascal_context_59 +- Name: fcn_hr48_4xb4-40k_pascal-context-59-480x480 In Collection: FCN Metadata: backbone: HRNetV2p-W48 @@ -413,9 +413,9 @@ Models: Metrics: mIoU: 50.33 mIoU(ms+flip): 52.83 - Config: configs/hrnet/fcn_hr48_480x480_40k_pascal_context_59.py + 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_480x480_80k_pascal_context_59 +- Name: fcn_hr48_4xb4-80k_pascal-context-59-480x480 In Collection: FCN Metadata: backbone: HRNetV2p-W48 @@ -427,9 +427,9 @@ Models: Metrics: mIoU: 51.12 mIoU(ms+flip): 53.56 - Config: configs/hrnet/fcn_hr48_480x480_80k_pascal_context_59.py + 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_512x512_80k_loveda +- Name: fcn_hr18s_4xb4-80k_loveda-512x512 In Collection: FCN Metadata: backbone: HRNetV2p-W18-Small @@ -449,9 +449,9 @@ Models: Metrics: mIoU: 49.28 mIoU(ms+flip): 49.42 - Config: configs/hrnet/fcn_hr18s_512x512_80k_loveda.py + 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_512x512_80k_loveda +- Name: fcn_hr18_4xb4-80k_loveda-512x512 In Collection: FCN Metadata: backbone: HRNetV2p-W18 @@ -471,9 +471,9 @@ Models: Metrics: mIoU: 50.81 mIoU(ms+flip): 50.95 - Config: configs/hrnet/fcn_hr18_512x512_80k_loveda.py + 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_512x512_80k_loveda +- Name: fcn_hr48_4xb4-80k_loveda-512x512 In Collection: FCN Metadata: backbone: HRNetV2p-W48 @@ -493,9 +493,9 @@ Models: Metrics: mIoU: 51.42 mIoU(ms+flip): 51.64 - Config: configs/hrnet/fcn_hr48_512x512_80k_loveda.py + 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_512x512_80k_potsdam +- Name: fcn_hr18s_4xb4-80k_potsdam-512x512 In Collection: FCN Metadata: backbone: HRNetV2p-W18-Small @@ -515,9 +515,9 @@ Models: Metrics: mIoU: 77.64 mIoU(ms+flip): 78.8 - Config: configs/hrnet/fcn_hr18s_512x512_80k_potsdam.py + 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_512x512_80k_potsdam +- Name: fcn_hr18_4xb4-80k_potsdam-512x512 In Collection: FCN Metadata: backbone: HRNetV2p-W18 @@ -537,9 +537,9 @@ Models: Metrics: mIoU: 78.26 mIoU(ms+flip): 79.24 - Config: configs/hrnet/fcn_hr18_512x512_80k_potsdam.py + 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_512x512_80k_potsdam +- Name: fcn_hr48_4xb4-80k_potsdam-512x512 In Collection: FCN Metadata: backbone: HRNetV2p-W48 @@ -559,9 +559,9 @@ Models: Metrics: mIoU: 78.39 mIoU(ms+flip): 79.34 - Config: configs/hrnet/fcn_hr48_512x512_80k_potsdam.py + 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_4x4_512x512_80k_vaihingen +- Name: fcn_hr18s_4xb4-80k_vaihingen-512x512 In Collection: FCN Metadata: backbone: HRNetV2p-W18-Small @@ -581,9 +581,9 @@ Models: Metrics: mIoU: 71.81 mIoU(ms+flip): 73.1 - Config: configs/hrnet/fcn_hr18s_4x4_512x512_80k_vaihingen.py + 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_4x4_512x512_80k_vaihingen +- Name: fcn_hr18_4xb4-80k_vaihingen-512x512 In Collection: FCN Metadata: backbone: HRNetV2p-W18 @@ -603,9 +603,9 @@ Models: Metrics: mIoU: 72.57 mIoU(ms+flip): 74.09 - Config: configs/hrnet/fcn_hr18_4x4_512x512_80k_vaihingen.py + 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_4x4_512x512_80k_vaihingen +- Name: fcn_hr48_4xb4-80k_vaihingen-512x512 In Collection: FCN Metadata: backbone: HRNetV2p-W48 @@ -625,9 +625,9 @@ Models: Metrics: mIoU: 72.5 mIoU(ms+flip): 73.52 - Config: configs/hrnet/fcn_hr48_4x4_512x512_80k_vaihingen.py + 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_4x4_896x896_80k_isaid +- Name: fcn_hr18s_4xb4-80k_isaid-896x896 In Collection: FCN Metadata: backbone: HRNetV2p-W18-Small @@ -647,9 +647,9 @@ Models: Metrics: mIoU: 62.3 mIoU(ms+flip): 62.97 - Config: configs/hrnet/fcn_hr18s_4x4_896x896_80k_isaid.py + 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_4x4_896x896_80k_isaid +- Name: fcn_hr18_4xb4-80k_isaid-896x896 In Collection: FCN Metadata: backbone: HRNetV2p-W18 @@ -669,9 +669,9 @@ Models: Metrics: mIoU: 65.06 mIoU(ms+flip): 65.6 - Config: configs/hrnet/fcn_hr18_4x4_896x896_80k_isaid.py + 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_4x4_896x896_80k_isaid +- Name: fcn_hr48_4xb4-80k_isaid-896x896 In Collection: FCN Metadata: backbone: HRNetV2p-W48 @@ -691,5 +691,5 @@ Models: Metrics: mIoU: 67.8 mIoU(ms+flip): 68.53 - Config: configs/hrnet/fcn_hr48_4x4_896x896_80k_isaid.py + 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/icnet/README.md b/configs/icnet/README.md index c011af5b09..134f53b29f 100644 --- a/configs/icnet/README.md +++ b/configs/icnet/README.md @@ -38,19 +38,19 @@ We focus on the challenging task of real-time semantic segmentation in this pape ### 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/master/configs/icnet/icnet_r18-d8_832x832_80k_cityscapes.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/master/configs/icnet/icnet_r18-d8_832x832_160k_cityscapes.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/master/configs/icnet/icnet_r18-d8_in1k-pre_832x832_80k_cityscapes.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/master/configs/icnet/icnet_r18-d8_in1k-pre_832x832_160k_cityscapes.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/master/configs/icnet/icnet_r50-d8_832x832_80k_cityscapes.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/master/configs/icnet/icnet_r50-d8_832x832_160k_cityscapes.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/master/configs/icnet/icnet_r50-d8_in1k-pre_832x832_80k_cityscapes.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/master/configs/icnet/icnet_r50-d8_in1k-pre_832x832_160k_cityscapes.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/master/configs/icnet/icnet_r101-d8_832x832_80k_cityscapes.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/master/configs/icnet/icnet_r101-d8_832x832_160k_cityscapes.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/master/configs/icnet/icnet_r101-d8_in1k-pre_832x832_80k_cityscapes.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/master/configs/icnet/icnet_r101-d8_in1k-pre_832x832_160k_cityscapes.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) | +| 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 index ebaf9340b0..5ded544726 100644 --- a/configs/icnet/icnet.yml +++ b/configs/icnet/icnet.yml @@ -13,7 +13,7 @@ Collections: Converted From: Code: https://github.com/hszhao/ICNet Models: -- Name: icnet_r18-d8_832x832_80k_cityscapes +- Name: icnet_r18-d8_4xb2-80k_cityscapes-832x832 In Collection: ICNet Metadata: backbone: R-18-D8 @@ -33,9 +33,9 @@ Models: Metrics: mIoU: 68.14 mIoU(ms+flip): 70.16 - Config: configs/icnet/icnet_r18-d8_832x832_80k_cityscapes.py + 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_832x832_160k_cityscapes +- Name: icnet_r18-d8_4xb2-160k_cityscapes-832x832 In Collection: ICNet Metadata: backbone: R-18-D8 @@ -47,9 +47,9 @@ Models: Metrics: mIoU: 71.64 mIoU(ms+flip): 74.18 - Config: configs/icnet/icnet_r18-d8_832x832_160k_cityscapes.py + 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_832x832_80k_cityscapes +- Name: icnet_r18-d8-in1k-pre_4xb2-80k_cityscapes-832x832 In Collection: ICNet Metadata: backbone: R-18-D8 @@ -61,9 +61,9 @@ Models: Metrics: mIoU: 72.51 mIoU(ms+flip): 74.78 - Config: configs/icnet/icnet_r18-d8_in1k-pre_832x832_80k_cityscapes.py + 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_832x832_160k_cityscapes +- Name: icnet_r18-d8-in1k-pre_4xb2-160k_cityscapes-832x832 In Collection: ICNet Metadata: backbone: R-18-D8 @@ -75,9 +75,9 @@ Models: Metrics: mIoU: 74.43 mIoU(ms+flip): 76.72 - Config: configs/icnet/icnet_r18-d8_in1k-pre_832x832_160k_cityscapes.py + 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_832x832_80k_cityscapes +- Name: icnet_r50-d8_4xb2-80k_cityscapes-832x832 In Collection: ICNet Metadata: backbone: R-50-D8 @@ -97,9 +97,9 @@ Models: Metrics: mIoU: 68.91 mIoU(ms+flip): 69.72 - Config: configs/icnet/icnet_r50-d8_832x832_80k_cityscapes.py + 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_832x832_160k_cityscapes +- Name: icnet_r50-d8_4xb2-160k_cityscapes-832x832 In Collection: ICNet Metadata: backbone: R-50-D8 @@ -111,9 +111,9 @@ Models: Metrics: mIoU: 73.82 mIoU(ms+flip): 75.67 - Config: configs/icnet/icnet_r50-d8_832x832_160k_cityscapes.py + 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_832x832_80k_cityscapes +- Name: icnet_r50-d8-in1k-pre_4xb2-80k_cityscapes-832x832 In Collection: ICNet Metadata: backbone: R-50-D8 @@ -125,9 +125,9 @@ Models: Metrics: mIoU: 74.58 mIoU(ms+flip): 76.41 - Config: configs/icnet/icnet_r50-d8_in1k-pre_832x832_80k_cityscapes.py + 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_832x832_160k_cityscapes +- Name: icnet_r50-d8-in1k-pre_4xb2-160k_cityscapes-832x832 In Collection: ICNet Metadata: backbone: R-50-D8 @@ -139,9 +139,9 @@ Models: Metrics: mIoU: 76.29 mIoU(ms+flip): 78.09 - Config: configs/icnet/icnet_r50-d8_in1k-pre_832x832_160k_cityscapes.py + 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_832x832_80k_cityscapes +- Name: icnet_r101-d8_4xb2-80k_cityscapes-832x832 In Collection: ICNet Metadata: backbone: R-101-D8 @@ -161,9 +161,9 @@ Models: Metrics: mIoU: 70.28 mIoU(ms+flip): 71.95 - Config: configs/icnet/icnet_r101-d8_832x832_80k_cityscapes.py + 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_832x832_160k_cityscapes +- Name: icnet_r101-d8_4xb2-160k_cityscapes-832x832 In Collection: ICNet Metadata: backbone: R-101-D8 @@ -175,9 +175,9 @@ Models: Metrics: mIoU: 73.8 mIoU(ms+flip): 76.1 - Config: configs/icnet/icnet_r101-d8_832x832_160k_cityscapes.py + 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_832x832_80k_cityscapes +- Name: icnet_r101-d8-in1k-pre_4xb2-80k_cityscapes-832x832 In Collection: ICNet Metadata: backbone: R-101-D8 @@ -189,9 +189,9 @@ Models: Metrics: mIoU: 75.57 mIoU(ms+flip): 77.86 - Config: configs/icnet/icnet_r101-d8_in1k-pre_832x832_80k_cityscapes.py + 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_832x832_160k_cityscapes +- Name: icnet_r101-d8-in1k-pre_4xb2-160k_cityscapes-832x832 In Collection: ICNet Metadata: backbone: R-101-D8 @@ -203,5 +203,5 @@ Models: Metrics: mIoU: 76.15 mIoU(ms+flip): 77.98 - Config: configs/icnet/icnet_r101-d8_in1k-pre_832x832_160k_cityscapes.py + 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/icnet_r101-d8_in1k-pre_832x832_80k_cityscapes.py b/configs/icnet/icnet_r101-d8-in1k-pre_4xb2-160k_cityscapes-832x832.py similarity index 76% rename from configs/icnet/icnet_r101-d8_in1k-pre_832x832_80k_cityscapes.py rename to configs/icnet/icnet_r101-d8-in1k-pre_4xb2-160k_cityscapes-832x832.py index b4ba6d640d..a6840a1155 100644 --- a/configs/icnet/icnet_r101-d8_in1k-pre_832x832_80k_cityscapes.py +++ b/configs/icnet/icnet_r101-d8-in1k-pre_4xb2-160k_cityscapes-832x832.py @@ -1,4 +1,4 @@ -_base_ = './icnet_r50-d8_832x832_80k_cityscapes.py' +_base_ = './icnet_r50-d8_4xb2-160k_cityscapes-832x832.py' model = dict( backbone=dict( backbone_cfg=dict( diff --git a/configs/icnet/icnet_r101-d8_in1k-pre_832x832_160k_cityscapes.py b/configs/icnet/icnet_r101-d8-in1k-pre_4xb2-80k_cityscapes-832x832.py similarity index 76% rename from configs/icnet/icnet_r101-d8_in1k-pre_832x832_160k_cityscapes.py rename to configs/icnet/icnet_r101-d8-in1k-pre_4xb2-80k_cityscapes-832x832.py index 74ac355088..ca81df8c7b 100644 --- a/configs/icnet/icnet_r101-d8_in1k-pre_832x832_160k_cityscapes.py +++ b/configs/icnet/icnet_r101-d8-in1k-pre_4xb2-80k_cityscapes-832x832.py @@ -1,4 +1,4 @@ -_base_ = './icnet_r50-d8_832x832_160k_cityscapes.py' +_base_ = './icnet_r50-d8_4xb2-80k_cityscapes-832x832.py' model = dict( backbone=dict( backbone_cfg=dict( diff --git a/configs/icnet/icnet_r101-d8_832x832_80k_cityscapes.py b/configs/icnet/icnet_r101-d8_4xb2-160k_cityscapes-832x832.py similarity index 50% rename from configs/icnet/icnet_r101-d8_832x832_80k_cityscapes.py rename to configs/icnet/icnet_r101-d8_4xb2-160k_cityscapes-832x832.py index f3338b5944..ef60446bc5 100644 --- a/configs/icnet/icnet_r101-d8_832x832_80k_cityscapes.py +++ b/configs/icnet/icnet_r101-d8_4xb2-160k_cityscapes-832x832.py @@ -1,2 +1,2 @@ -_base_ = './icnet_r50-d8_832x832_80k_cityscapes.py' +_base_ = './icnet_r50-d8_4xb2-160k_cityscapes-832x832.py' model = dict(backbone=dict(backbone_cfg=dict(depth=101))) diff --git a/configs/icnet/icnet_r101-d8_832x832_160k_cityscapes.py b/configs/icnet/icnet_r101-d8_4xb2-80k_cityscapes-832x832.py similarity index 50% rename from configs/icnet/icnet_r101-d8_832x832_160k_cityscapes.py rename to configs/icnet/icnet_r101-d8_4xb2-80k_cityscapes-832x832.py index 24cbf537d4..5173d2d6f8 100644 --- a/configs/icnet/icnet_r101-d8_832x832_160k_cityscapes.py +++ b/configs/icnet/icnet_r101-d8_4xb2-80k_cityscapes-832x832.py @@ -1,2 +1,2 @@ -_base_ = './icnet_r50-d8_832x832_160k_cityscapes.py' +_base_ = './icnet_r50-d8_4xb2-80k_cityscapes-832x832.py' model = dict(backbone=dict(backbone_cfg=dict(depth=101))) diff --git a/configs/icnet/icnet_r18-d8_in1k-pre_832x832_80k_cityscapes.py b/configs/icnet/icnet_r18-d8-in1k-pre_4xb2-160k_cityscapes-832x832.py similarity index 79% rename from configs/icnet/icnet_r18-d8_in1k-pre_832x832_80k_cityscapes.py rename to configs/icnet/icnet_r18-d8-in1k-pre_4xb2-160k_cityscapes-832x832.py index 00b0fe0522..5f72daab65 100644 --- a/configs/icnet/icnet_r18-d8_in1k-pre_832x832_80k_cityscapes.py +++ b/configs/icnet/icnet_r18-d8-in1k-pre_4xb2-160k_cityscapes-832x832.py @@ -1,4 +1,4 @@ -_base_ = './icnet_r50-d8_832x832_80k_cityscapes.py' +_base_ = './icnet_r50-d8_4xb2-160k_cityscapes-832x832.py' model = dict( backbone=dict( layer_channels=(128, 512), diff --git a/configs/icnet/icnet_r18-d8_in1k-pre_832x832_160k_cityscapes.py b/configs/icnet/icnet_r18-d8-in1k-pre_4xb2-80k_cityscapes-832x832.py similarity index 79% rename from configs/icnet/icnet_r18-d8_in1k-pre_832x832_160k_cityscapes.py rename to configs/icnet/icnet_r18-d8-in1k-pre_4xb2-80k_cityscapes-832x832.py index cc47951f3d..2fc79ab197 100644 --- a/configs/icnet/icnet_r18-d8_in1k-pre_832x832_160k_cityscapes.py +++ b/configs/icnet/icnet_r18-d8-in1k-pre_4xb2-80k_cityscapes-832x832.py @@ -1,4 +1,4 @@ -_base_ = './icnet_r50-d8_832x832_160k_cityscapes.py' +_base_ = './icnet_r50-d8_4xb2-80k_cityscapes-832x832.py' model = dict( backbone=dict( layer_channels=(128, 512), diff --git a/configs/icnet/icnet_r18-d8_832x832_160k_cityscapes.py b/configs/icnet/icnet_r18-d8_4xb2-160k_cityscapes-832x832.py similarity index 60% rename from configs/icnet/icnet_r18-d8_832x832_160k_cityscapes.py rename to configs/icnet/icnet_r18-d8_4xb2-160k_cityscapes-832x832.py index 877b775afc..2c70e94810 100644 --- a/configs/icnet/icnet_r18-d8_832x832_160k_cityscapes.py +++ b/configs/icnet/icnet_r18-d8_4xb2-160k_cityscapes-832x832.py @@ -1,3 +1,3 @@ -_base_ = './icnet_r50-d8_832x832_160k_cityscapes.py' +_base_ = './icnet_r50-d8_4xb2-160k_cityscapes-832x832.py' model = dict( backbone=dict(layer_channels=(128, 512), backbone_cfg=dict(depth=18))) diff --git a/configs/icnet/icnet_r18-d8_832x832_80k_cityscapes.py b/configs/icnet/icnet_r18-d8_4xb2-80k_cityscapes-832x832.py similarity index 60% rename from configs/icnet/icnet_r18-d8_832x832_80k_cityscapes.py rename to configs/icnet/icnet_r18-d8_4xb2-80k_cityscapes-832x832.py index 786c7cc92a..23c7ac2990 100644 --- a/configs/icnet/icnet_r18-d8_832x832_80k_cityscapes.py +++ b/configs/icnet/icnet_r18-d8_4xb2-80k_cityscapes-832x832.py @@ -1,3 +1,3 @@ -_base_ = './icnet_r50-d8_832x832_80k_cityscapes.py' +_base_ = './icnet_r50-d8_4xb2-80k_cityscapes-832x832.py' model = dict( backbone=dict(layer_channels=(128, 512), backbone_cfg=dict(depth=18))) diff --git a/configs/icnet/icnet_r50-d8_in1k-pre_832x832_160k_cityscapes.py b/configs/icnet/icnet_r50-d8-in1k-pre_4xb2-160k_cityscapes-832x832.py similarity index 73% rename from configs/icnet/icnet_r50-d8_in1k-pre_832x832_160k_cityscapes.py rename to configs/icnet/icnet_r50-d8-in1k-pre_4xb2-160k_cityscapes-832x832.py index 6f7a0a1a36..f9ab863402 100644 --- a/configs/icnet/icnet_r50-d8_in1k-pre_832x832_160k_cityscapes.py +++ b/configs/icnet/icnet_r50-d8-in1k-pre_4xb2-160k_cityscapes-832x832.py @@ -1,4 +1,4 @@ -_base_ = './icnet_r50-d8_832x832_160k_cityscapes.py' +_base_ = './icnet_r50-d8_4xb2-160k_cityscapes-832x832.py' model = dict( backbone=dict( backbone_cfg=dict( diff --git a/configs/icnet/icnet_r50-d8_in1k-pre_832x832_80k_cityscapes.py b/configs/icnet/icnet_r50-d8-in1k-pre_4xb2-80k_cityscapes-832x832.py similarity index 74% rename from configs/icnet/icnet_r50-d8_in1k-pre_832x832_80k_cityscapes.py rename to configs/icnet/icnet_r50-d8-in1k-pre_4xb2-80k_cityscapes-832x832.py index 57546cd291..9a085d4f61 100644 --- a/configs/icnet/icnet_r50-d8_in1k-pre_832x832_80k_cityscapes.py +++ b/configs/icnet/icnet_r50-d8-in1k-pre_4xb2-80k_cityscapes-832x832.py @@ -1,4 +1,4 @@ -_base_ = './icnet_r50-d8_832x832_80k_cityscapes.py' +_base_ = './icnet_r50-d8_4xb2-80k_cityscapes-832x832.py' model = dict( backbone=dict( backbone_cfg=dict( diff --git a/configs/icnet/icnet_r50-d8_832x832_160k_cityscapes.py b/configs/icnet/icnet_r50-d8_4xb2-160k_cityscapes-832x832.py similarity index 100% rename from configs/icnet/icnet_r50-d8_832x832_160k_cityscapes.py rename to configs/icnet/icnet_r50-d8_4xb2-160k_cityscapes-832x832.py diff --git a/configs/icnet/icnet_r50-d8_832x832_80k_cityscapes.py b/configs/icnet/icnet_r50-d8_4xb2-80k_cityscapes-832x832.py similarity index 100% rename from configs/icnet/icnet_r50-d8_832x832_80k_cityscapes.py rename to configs/icnet/icnet_r50-d8_4xb2-80k_cityscapes-832x832.py diff --git a/configs/isanet/README.md b/configs/isanet/README.md index d1c268dae2..db93dae234 100644 --- a/configs/isanet/README.md +++ b/configs/isanet/README.md @@ -50,31 +50,31 @@ The technical report above is also presented at: ### 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/master/configs/isanet/isanet_r50-d8_512x1024_40k_cityscapes.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/master/configs/isanet/isanet_r50-d8_512x1024_80k_cityscapes.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/master/configs/isanet/isanet_r50-d8_769x769_40k_cityscapes.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/master/configs/isanet/isanet_r50-d8_769x769_80k_cityscapes.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/master/configs/isanet/isanet_r101-d8_512x1024_40k_cityscapes.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/master/configs/isanet/isanet_r101-d8_512x1024_80k_cityscapes.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/master/configs/isanet/isanet_r101-d8_769x769_40k_cityscapes.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/master/configs/isanet/isanet_r101-d8_769x769_80k_cityscapes.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) | +| 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/master/configs/isanet/isanet_r50-d8_512x512_80k_ade20k.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/master/configs/isanet/isanet_r50-d8_512x512_160k_ade20k.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/master/configs/isanet/isanet_r101-d8_512x512_80k_ade20k.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/master/configs/isanet/isanet_r101-d8_512x512_160k_ade20k.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) | +| 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/master/configs/isanet/isanet_r50-d8_512x512_20k_voc12aug.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/master/configs/isanet/isanet_r50-d8_512x512_40k_voc12aug.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/master/configs/isanet/isanet_r101-d8_512x512_20k_voc12aug.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/master/configs/isanet/isanet_r101-d8_512x512_40k_voc12aug.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) | +| 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 index 8c65bcfb05..405b3c1231 100644 --- a/configs/isanet/isanet.yml +++ b/configs/isanet/isanet.yml @@ -15,7 +15,7 @@ Collections: Converted From: Code: https://github.com/openseg-group/openseg.pytorch Models: -- Name: isanet_r50-d8_512x1024_40k_cityscapes +- Name: isanet_r50-d8_4xb2-40k_cityscapes-512x1024 In Collection: ISANet Metadata: backbone: R-50-D8 @@ -35,9 +35,9 @@ Models: Metrics: mIoU: 78.49 mIoU(ms+flip): 79.44 - Config: configs/isanet/isanet_r50-d8_512x1024_40k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: isanet_r50-d8_4xb2-80k_cityscapes-512x1024 In Collection: ISANet Metadata: backbone: R-50-D8 @@ -57,9 +57,9 @@ Models: Metrics: mIoU: 78.68 mIoU(ms+flip): 80.25 - Config: configs/isanet/isanet_r50-d8_512x1024_80k_cityscapes.py + 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_769x769_40k_cityscapes +- Name: isanet_r50-d8_4xb2-40k_cityscapes-769x769 In Collection: ISANet Metadata: backbone: R-50-D8 @@ -79,9 +79,9 @@ Models: Metrics: mIoU: 78.7 mIoU(ms+flip): 80.28 - Config: configs/isanet/isanet_r50-d8_769x769_40k_cityscapes.py + 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_769x769_80k_cityscapes +- Name: isanet_r50-d8_4xb2-80k_cityscapes-769x769 In Collection: ISANet Metadata: backbone: R-50-D8 @@ -101,9 +101,9 @@ Models: Metrics: mIoU: 79.29 mIoU(ms+flip): 80.53 - Config: configs/isanet/isanet_r50-d8_769x769_80k_cityscapes.py + 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_512x1024_40k_cityscapes +- Name: isanet_r101-d8_4xb2-40k_cityscapes-512x1024 In Collection: ISANet Metadata: backbone: R-101-D8 @@ -123,9 +123,9 @@ Models: Metrics: mIoU: 79.58 mIoU(ms+flip): 81.05 - Config: configs/isanet/isanet_r101-d8_512x1024_40k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: isanet_r101-d8_4xb2-80k_cityscapes-512x1024 In Collection: ISANet Metadata: backbone: R-101-D8 @@ -145,9 +145,9 @@ Models: Metrics: mIoU: 80.32 mIoU(ms+flip): 81.58 - Config: configs/isanet/isanet_r101-d8_512x1024_80k_cityscapes.py + 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_769x769_40k_cityscapes +- Name: isanet_r101-d8_4xb2-40k_cityscapes-769x769 In Collection: ISANet Metadata: backbone: R-101-D8 @@ -167,9 +167,9 @@ Models: Metrics: mIoU: 79.68 mIoU(ms+flip): 80.95 - Config: configs/isanet/isanet_r101-d8_769x769_40k_cityscapes.py + 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_769x769_80k_cityscapes +- Name: isanet_r101-d8_4xb2-80k_cityscapes-769x769 In Collection: ISANet Metadata: backbone: R-101-D8 @@ -189,9 +189,9 @@ Models: Metrics: mIoU: 80.61 mIoU(ms+flip): 81.59 - Config: configs/isanet/isanet_r101-d8_769x769_80k_cityscapes.py + 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_512x512_80k_ade20k +- Name: isanet_r50-d8_4xb4-80k_ade20k-512x512 In Collection: ISANet Metadata: backbone: R-50-D8 @@ -211,9 +211,9 @@ Models: Metrics: mIoU: 41.12 mIoU(ms+flip): 42.35 - Config: configs/isanet/isanet_r50-d8_512x512_80k_ade20k.py + 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_512x512_160k_ade20k +- Name: isanet_r50-d8_4xb4-160k_ade20k-512x512 In Collection: ISANet Metadata: backbone: R-50-D8 @@ -233,9 +233,9 @@ Models: Metrics: mIoU: 42.59 mIoU(ms+flip): 43.07 - Config: configs/isanet/isanet_r50-d8_512x512_160k_ade20k.py + 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_512x512_80k_ade20k +- Name: isanet_r101-d8_4xb4-80k_ade20k-512x512 In Collection: ISANet Metadata: backbone: R-101-D8 @@ -255,9 +255,9 @@ Models: Metrics: mIoU: 43.51 mIoU(ms+flip): 44.38 - Config: configs/isanet/isanet_r101-d8_512x512_80k_ade20k.py + 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_512x512_160k_ade20k +- Name: isanet_r101-d8_4xb4-160k_ade20k-512x512 In Collection: ISANet Metadata: backbone: R-101-D8 @@ -277,9 +277,9 @@ Models: Metrics: mIoU: 43.8 mIoU(ms+flip): 45.4 - Config: configs/isanet/isanet_r101-d8_512x512_160k_ade20k.py + 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_512x512_20k_voc12aug +- Name: isanet_r50-d8_4xb4-20k_voc12aug-512x512 In Collection: ISANet Metadata: backbone: R-50-D8 @@ -299,9 +299,9 @@ Models: Metrics: mIoU: 76.78 mIoU(ms+flip): 77.79 - Config: configs/isanet/isanet_r50-d8_512x512_20k_voc12aug.py + 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_512x512_40k_voc12aug +- Name: isanet_r50-d8_4xb4-40k_voc12aug-512x512 In Collection: ISANet Metadata: backbone: R-50-D8 @@ -321,9 +321,9 @@ Models: Metrics: mIoU: 76.2 mIoU(ms+flip): 77.22 - Config: configs/isanet/isanet_r50-d8_512x512_40k_voc12aug.py + 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_512x512_20k_voc12aug +- Name: isanet_r101-d8_4xb4-20k_voc12aug-512x512 In Collection: ISANet Metadata: backbone: R-101-D8 @@ -343,9 +343,9 @@ Models: Metrics: mIoU: 78.46 mIoU(ms+flip): 79.16 - Config: configs/isanet/isanet_r101-d8_512x512_20k_voc12aug.py + 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_512x512_40k_voc12aug +- Name: isanet_r101-d8_4xb4-40k_voc12aug-512x512 In Collection: ISANet Metadata: backbone: R-101-D8 @@ -365,5 +365,5 @@ Models: Metrics: mIoU: 78.12 mIoU(ms+flip): 79.04 - Config: configs/isanet/isanet_r101-d8_512x512_40k_voc12aug.py + 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/isanet_r101-d8_4xb2-40k_cityscapes-512x1024.py b/configs/isanet/isanet_r101-d8_4xb2-40k_cityscapes-512x1024.py new file mode 100644 index 0000000000..6093aeb4f7 --- /dev/null +++ b/configs/isanet/isanet_r101-d8_4xb2-40k_cityscapes-512x1024.py @@ -0,0 +1,2 @@ +_base_ = './isanet_r50-d8_4xb2-40k_cityscapes-512x1024.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/isanet/isanet_r101-d8_4xb2-40k_cityscapes-769x769.py b/configs/isanet/isanet_r101-d8_4xb2-40k_cityscapes-769x769.py new file mode 100644 index 0000000000..dc14c76dfb --- /dev/null +++ b/configs/isanet/isanet_r101-d8_4xb2-40k_cityscapes-769x769.py @@ -0,0 +1,2 @@ +_base_ = './isanet_r50-d8_4xb2-40k_cityscapes-769x769.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/isanet/isanet_r101-d8_4xb2-80k_cityscapes-512x1024.py b/configs/isanet/isanet_r101-d8_4xb2-80k_cityscapes-512x1024.py new file mode 100644 index 0000000000..1735f89d41 --- /dev/null +++ b/configs/isanet/isanet_r101-d8_4xb2-80k_cityscapes-512x1024.py @@ -0,0 +1,2 @@ +_base_ = './isanet_r50-d8_4xb2-80k_cityscapes-512x1024.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/isanet/isanet_r101-d8_4xb2-80k_cityscapes-769x769.py b/configs/isanet/isanet_r101-d8_4xb2-80k_cityscapes-769x769.py new file mode 100644 index 0000000000..b1a6371b76 --- /dev/null +++ b/configs/isanet/isanet_r101-d8_4xb2-80k_cityscapes-769x769.py @@ -0,0 +1,2 @@ +_base_ = './isanet_r50-d8_4xb2-80k_cityscapes-769x769.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/isanet/isanet_r101-d8_4xb4-160k_ade20k-512x512.py b/configs/isanet/isanet_r101-d8_4xb4-160k_ade20k-512x512.py new file mode 100644 index 0000000000..c2fb09e374 --- /dev/null +++ b/configs/isanet/isanet_r101-d8_4xb4-160k_ade20k-512x512.py @@ -0,0 +1,2 @@ +_base_ = './isanet_r50-d8_4xb4-160k_ade20k-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/isanet/isanet_r101-d8_4xb4-20k_voc12aug-512x512.py b/configs/isanet/isanet_r101-d8_4xb4-20k_voc12aug-512x512.py new file mode 100644 index 0000000000..7c225cfe3a --- /dev/null +++ b/configs/isanet/isanet_r101-d8_4xb4-20k_voc12aug-512x512.py @@ -0,0 +1,2 @@ +_base_ = './isanet_r50-d8_4xb4-20k_voc12aug-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/isanet/isanet_r101-d8_4xb4-40k_voc12aug-512x512.py b/configs/isanet/isanet_r101-d8_4xb4-40k_voc12aug-512x512.py new file mode 100644 index 0000000000..5e86ee584f --- /dev/null +++ b/configs/isanet/isanet_r101-d8_4xb4-40k_voc12aug-512x512.py @@ -0,0 +1,2 @@ +_base_ = './isanet_r50-d8_4xb4-40k_voc12aug-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/isanet/isanet_r101-d8_4xb4-80k_ade20k-512x512.py b/configs/isanet/isanet_r101-d8_4xb4-80k_ade20k-512x512.py new file mode 100644 index 0000000000..090e86f243 --- /dev/null +++ b/configs/isanet/isanet_r101-d8_4xb4-80k_ade20k-512x512.py @@ -0,0 +1,2 @@ +_base_ = './isanet_r50-d8_4xb4-80k_ade20k-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/isanet/isanet_r101-d8_512x1024_40k_cityscapes.py b/configs/isanet/isanet_r101-d8_512x1024_40k_cityscapes.py deleted file mode 100644 index f5cd8cbb7c..0000000000 --- a/configs/isanet/isanet_r101-d8_512x1024_40k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './isanet_r50-d8_512x1024_40k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/isanet/isanet_r101-d8_512x1024_80k_cityscapes.py b/configs/isanet/isanet_r101-d8_512x1024_80k_cityscapes.py deleted file mode 100644 index ebc15cbfec..0000000000 --- a/configs/isanet/isanet_r101-d8_512x1024_80k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './isanet_r50-d8_512x1024_80k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/isanet/isanet_r101-d8_512x512_160k_ade20k.py b/configs/isanet/isanet_r101-d8_512x512_160k_ade20k.py deleted file mode 100644 index 33290100d5..0000000000 --- a/configs/isanet/isanet_r101-d8_512x512_160k_ade20k.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './isanet_r50-d8_512x512_160k_ade20k.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/isanet/isanet_r101-d8_512x512_20k_voc12aug.py b/configs/isanet/isanet_r101-d8_512x512_20k_voc12aug.py deleted file mode 100644 index 46fee9155d..0000000000 --- a/configs/isanet/isanet_r101-d8_512x512_20k_voc12aug.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './isanet_r50-d8_512x512_20k_voc12aug.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/isanet/isanet_r101-d8_512x512_40k_voc12aug.py b/configs/isanet/isanet_r101-d8_512x512_40k_voc12aug.py deleted file mode 100644 index 64bd8c1044..0000000000 --- a/configs/isanet/isanet_r101-d8_512x512_40k_voc12aug.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './isanet_r50-d8_512x512_40k_voc12aug.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/isanet/isanet_r101-d8_512x512_80k_ade20k.py b/configs/isanet/isanet_r101-d8_512x512_80k_ade20k.py deleted file mode 100644 index 6e13e20ca5..0000000000 --- a/configs/isanet/isanet_r101-d8_512x512_80k_ade20k.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './isanet_r50-d8_512x512_80k_ade20k.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/isanet/isanet_r101-d8_769x769_40k_cityscapes.py b/configs/isanet/isanet_r101-d8_769x769_40k_cityscapes.py deleted file mode 100644 index cf362aaacb..0000000000 --- a/configs/isanet/isanet_r101-d8_769x769_40k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './isanet_r50-d8_769x769_40k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/isanet/isanet_r101-d8_769x769_80k_cityscapes.py b/configs/isanet/isanet_r101-d8_769x769_80k_cityscapes.py deleted file mode 100644 index 3c2283bdba..0000000000 --- a/configs/isanet/isanet_r101-d8_769x769_80k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './isanet_r50-d8_769x769_80k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/isanet/isanet_r50-d8_512x1024_40k_cityscapes.py b/configs/isanet/isanet_r50-d8_4xb2-40k_cityscapes-512x1024.py similarity index 100% rename from configs/isanet/isanet_r50-d8_512x1024_40k_cityscapes.py rename to configs/isanet/isanet_r50-d8_4xb2-40k_cityscapes-512x1024.py diff --git a/configs/isanet/isanet_r50-d8_769x769_40k_cityscapes.py b/configs/isanet/isanet_r50-d8_4xb2-40k_cityscapes-769x769.py similarity index 100% rename from configs/isanet/isanet_r50-d8_769x769_40k_cityscapes.py rename to configs/isanet/isanet_r50-d8_4xb2-40k_cityscapes-769x769.py diff --git a/configs/isanet/isanet_r50-d8_512x1024_80k_cityscapes.py b/configs/isanet/isanet_r50-d8_4xb2-80k_cityscapes-512x1024.py similarity index 100% rename from configs/isanet/isanet_r50-d8_512x1024_80k_cityscapes.py rename to configs/isanet/isanet_r50-d8_4xb2-80k_cityscapes-512x1024.py diff --git a/configs/isanet/isanet_r50-d8_769x769_80k_cityscapes.py b/configs/isanet/isanet_r50-d8_4xb2-80k_cityscapes-769x769.py similarity index 100% rename from configs/isanet/isanet_r50-d8_769x769_80k_cityscapes.py rename to configs/isanet/isanet_r50-d8_4xb2-80k_cityscapes-769x769.py diff --git a/configs/isanet/isanet_r50-d8_512x512_160k_ade20k.py b/configs/isanet/isanet_r50-d8_4xb4-160k_ade20k-512x512.py similarity index 100% rename from configs/isanet/isanet_r50-d8_512x512_160k_ade20k.py rename to configs/isanet/isanet_r50-d8_4xb4-160k_ade20k-512x512.py diff --git a/configs/isanet/isanet_r50-d8_512x512_20k_voc12aug.py b/configs/isanet/isanet_r50-d8_4xb4-20k_voc12aug-512x512.py similarity index 100% rename from configs/isanet/isanet_r50-d8_512x512_20k_voc12aug.py rename to configs/isanet/isanet_r50-d8_4xb4-20k_voc12aug-512x512.py diff --git a/configs/isanet/isanet_r50-d8_512x512_40k_voc12aug.py b/configs/isanet/isanet_r50-d8_4xb4-40k_voc12aug-512x512.py similarity index 100% rename from configs/isanet/isanet_r50-d8_512x512_40k_voc12aug.py rename to configs/isanet/isanet_r50-d8_4xb4-40k_voc12aug-512x512.py diff --git a/configs/isanet/isanet_r50-d8_512x512_80k_ade20k.py b/configs/isanet/isanet_r50-d8_4xb4-80k_ade20k-512x512.py similarity index 100% rename from configs/isanet/isanet_r50-d8_512x512_80k_ade20k.py rename to configs/isanet/isanet_r50-d8_4xb4-80k_ade20k-512x512.py diff --git a/configs/knet/README.md b/configs/knet/README.md index cad14a6ea7..ed5bc06257 100644 --- a/configs/knet/README.md +++ b/configs/knet/README.md @@ -35,15 +35,15 @@ Semantic, instance, and panoptic segmentations have been addressed using differe ### 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/master/configs/knet/knet_s3_fcn_r50-d8_8x2_512x512_adamw_80k_ade20k.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/master/configs/knet/knet_s3_pspnet_r50-d8_8x2_512x512_adamw_80k_ade20k.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/master/configs/knet/knet_s3_deeplabv3_r50-d8_8x2_512x512_adamw_80k_ade20k.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/master/configs/knet/knet_s3_upernet_r50-d8_8x2_512x512_adamw_80k_ade20k.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/master/configs/knet/knet_s3_upernet_swin-t_8x2_512x512_adamw_80k_ade20k.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/master/configs/knet/knet_s3_upernet_swin-l_8x2_512x512_adamw_80k_ade20k.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/master/configs/knet/knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k.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) | 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) | Note: diff --git a/configs/knet/knet_s3_deeplabv3_r50-d8_8x2_512x512_adamw_80k_ade20k.py b/configs/knet/knet-s3_r50-d8_deeplabv3_8xb2-adamw-80k_ade20k-512x512.py similarity index 100% rename from configs/knet/knet_s3_deeplabv3_r50-d8_8x2_512x512_adamw_80k_ade20k.py rename to configs/knet/knet-s3_r50-d8_deeplabv3_8xb2-adamw-80k_ade20k-512x512.py diff --git a/configs/knet/knet_s3_fcn_r50-d8_8x2_512x512_adamw_80k_ade20k.py b/configs/knet/knet-s3_r50-d8_fcn_8xb2-adamw-80k_ade20k-512x512.py similarity index 100% rename from configs/knet/knet_s3_fcn_r50-d8_8x2_512x512_adamw_80k_ade20k.py rename to configs/knet/knet-s3_r50-d8_fcn_8xb2-adamw-80k_ade20k-512x512.py diff --git a/configs/knet/knet_s3_pspnet_r50-d8_8x2_512x512_adamw_80k_ade20k.py b/configs/knet/knet-s3_r50-d8_pspnet_8xb2-adamw-80k_ade20k-512x512.py similarity index 100% rename from configs/knet/knet_s3_pspnet_r50-d8_8x2_512x512_adamw_80k_ade20k.py rename to configs/knet/knet-s3_r50-d8_pspnet_8xb2-adamw-80k_ade20k-512x512.py diff --git a/configs/knet/knet_s3_upernet_r50-d8_8x2_512x512_adamw_80k_ade20k.py b/configs/knet/knet-s3_r50-d8_upernet_8xb2-adamw-80k_ade20k-512x512.py similarity index 100% rename from configs/knet/knet_s3_upernet_r50-d8_8x2_512x512_adamw_80k_ade20k.py rename to configs/knet/knet-s3_r50-d8_upernet_8xb2-adamw-80k_ade20k-512x512.py diff --git a/configs/knet/knet_s3_upernet_swin-l_8x2_512x512_adamw_80k_ade20k.py b/configs/knet/knet-s3_swin-l_upernet_8xb2-adamw-80k_ade20k-512x512.py similarity index 91% rename from configs/knet/knet_s3_upernet_swin-l_8x2_512x512_adamw_80k_ade20k.py rename to configs/knet/knet-s3_swin-l_upernet_8xb2-adamw-80k_ade20k-512x512.py index c27f56b741..c6f4eb6ae2 100644 --- a/configs/knet/knet_s3_upernet_swin-l_8x2_512x512_adamw_80k_ade20k.py +++ b/configs/knet/knet-s3_swin-l_upernet_8xb2-adamw-80k_ade20k-512x512.py @@ -1,4 +1,4 @@ -_base_ = 'knet_s3_upernet_swin-t_8x2_512x512_adamw_80k_ade20k.py' +_base_ = 'knet-s3_swin-t_upernet_8xb2-adamw-80k_ade20k-512x512.py' checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_large_patch4_window7_224_22k_20220308-d5bdebaf.pth' # noqa # model settings diff --git a/configs/knet/knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k.py b/configs/knet/knet-s3_swin-l_upernet_8xb2-adamw-80k_ade20k-640x640.py similarity index 96% rename from configs/knet/knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k.py rename to configs/knet/knet-s3_swin-l_upernet_8xb2-adamw-80k_ade20k-640x640.py index 1dcb1d4860..84c3d8cc6a 100644 --- a/configs/knet/knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k.py +++ b/configs/knet/knet-s3_swin-l_upernet_8xb2-adamw-80k_ade20k-640x640.py @@ -1,4 +1,4 @@ -_base_ = 'knet_s3_upernet_swin-t_8x2_512x512_adamw_80k_ade20k.py' +_base_ = 'knet-s3_swin-t_upernet_8xb2-adamw-80k_ade20k-512x512.py' checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_large_patch4_window7_224_22k_20220308-d5bdebaf.pth' # noqa # model settings diff --git a/configs/knet/knet_s3_upernet_swin-t_8x2_512x512_adamw_80k_ade20k.py b/configs/knet/knet-s3_swin-t_upernet_8xb2-adamw-80k_ade20k-512x512.py similarity index 96% rename from configs/knet/knet_s3_upernet_swin-t_8x2_512x512_adamw_80k_ade20k.py rename to configs/knet/knet-s3_swin-t_upernet_8xb2-adamw-80k_ade20k-512x512.py index 78642804b0..a7acec4996 100644 --- a/configs/knet/knet_s3_upernet_swin-t_8x2_512x512_adamw_80k_ade20k.py +++ b/configs/knet/knet-s3_swin-t_upernet_8xb2-adamw-80k_ade20k-512x512.py @@ -1,4 +1,4 @@ -_base_ = 'knet_s3_upernet_r50-d8_8x2_512x512_adamw_80k_ade20k.py' +_base_ = 'knet-s3_r50-d8_upernet_8xb2-adamw-80k_ade20k-512x512.py' checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220308-f41b89d3.pth' # noqa diff --git a/configs/knet/knet.yml b/configs/knet/knet.yml index 5e2e529557..1c98e4703c 100644 --- a/configs/knet/knet.yml +++ b/configs/knet/knet.yml @@ -13,7 +13,7 @@ Collections: Converted From: Code: https://github.com/ZwwWayne/K-Net/ Models: -- Name: knet_s3_fcn_r50-d8_8x2_512x512_adamw_80k_ade20k +- Name: knet-s3_r50-d8_fcn_8xb2-adamw-80k_ade20k-512x512 In Collection: KNet Metadata: backbone: R-50-D8 @@ -33,9 +33,9 @@ Models: Metrics: mIoU: 43.6 mIoU(ms+flip): 45.12 - Config: configs/knet/knet_s3_fcn_r50-d8_8x2_512x512_adamw_80k_ade20k.py + 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_pspnet_r50-d8_8x2_512x512_adamw_80k_ade20k +- Name: knet-s3_r50-d8_pspnet_8xb2-adamw-80k_ade20k-512x512 In Collection: KNet Metadata: backbone: R-50-D8 @@ -55,9 +55,9 @@ Models: Metrics: mIoU: 44.18 mIoU(ms+flip): 45.58 - Config: configs/knet/knet_s3_pspnet_r50-d8_8x2_512x512_adamw_80k_ade20k.py + 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_deeplabv3_r50-d8_8x2_512x512_adamw_80k_ade20k +- Name: knet-s3_r50-d8_deeplabv3_8xb2-adamw-80k_ade20k-512x512 In Collection: KNet Metadata: backbone: R-50-D8 @@ -77,9 +77,9 @@ Models: Metrics: mIoU: 45.06 mIoU(ms+flip): 46.11 - Config: configs/knet/knet_s3_deeplabv3_r50-d8_8x2_512x512_adamw_80k_ade20k.py + 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_upernet_r50-d8_8x2_512x512_adamw_80k_ade20k +- Name: knet-s3_r50-d8_upernet_8xb2-adamw-80k_ade20k-512x512 In Collection: KNet Metadata: backbone: R-50-D8 @@ -99,9 +99,9 @@ Models: Metrics: mIoU: 43.45 mIoU(ms+flip): 44.07 - Config: configs/knet/knet_s3_upernet_r50-d8_8x2_512x512_adamw_80k_ade20k.py + 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_upernet_swin-t_8x2_512x512_adamw_80k_ade20k +- Name: knet-s3_swin-t_upernet_8xb2-adamw-80k_ade20k-512x512 In Collection: KNet Metadata: backbone: Swin-T @@ -121,9 +121,9 @@ Models: Metrics: mIoU: 45.84 mIoU(ms+flip): 46.27 - Config: configs/knet/knet_s3_upernet_swin-t_8x2_512x512_adamw_80k_ade20k.py + 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_upernet_swin-l_8x2_512x512_adamw_80k_ade20k +- Name: knet-s3_swin-l_upernet_8xb2-adamw-80k_ade20k-512x512 In Collection: KNet Metadata: backbone: Swin-L @@ -143,9 +143,9 @@ Models: Metrics: mIoU: 52.05 mIoU(ms+flip): 53.24 - Config: configs/knet/knet_s3_upernet_swin-l_8x2_512x512_adamw_80k_ade20k.py + 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_upernet_swin-l_8x2_640x640_adamw_80k_ade20k +- Name: knet-s3_swin-l_upernet_8xb2-adamw-80k_ade20k-640x640 In Collection: KNet Metadata: backbone: Swin-L @@ -165,5 +165,5 @@ Models: Metrics: mIoU: 52.21 mIoU(ms+flip): 53.34 - Config: configs/knet/knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k.py + 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/mae/README.md b/configs/mae/README.md index 562f6f8bf0..330749732e 100644 --- a/configs/mae/README.md +++ b/configs/mae/README.md @@ -77,6 +77,6 @@ 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/master/configs/mae/upernet_mae-base_fp16_8x2_512x512_160k_ade20k.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) | 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) | diff --git a/configs/mae/upernet_mae-base_fp16_512x512_160k_ade20k_ms.py b/configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512-ms.py similarity index 90% rename from configs/mae/upernet_mae-base_fp16_512x512_160k_ade20k_ms.py rename to configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512-ms.py index 81b913f6fd..ec32fea54b 100644 --- a/configs/mae/upernet_mae-base_fp16_512x512_160k_ade20k_ms.py +++ b/configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512-ms.py @@ -1,4 +1,4 @@ -_base_ = './upernet_mae-base_fp16_8x2_512x512_160k_ade20k.py' +_base_ = './mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py' test_pipeline = [ dict(type='LoadImageFromFile'), diff --git a/configs/mae/upernet_mae-base_fp16_8x2_512x512_160k_ade20k.py b/configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py similarity index 100% rename from configs/mae/upernet_mae-base_fp16_8x2_512x512_160k_ade20k.py rename to configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py diff --git a/configs/mae/mae.yml b/configs/mae/mae.yml index d78f99c86c..72b2cc7f12 100644 --- a/configs/mae/mae.yml +++ b/configs/mae/mae.yml @@ -1,5 +1,5 @@ Models: -- Name: upernet_mae-base_fp16_8x2_512x512_160k_ade20k +- Name: mae-base_upernet_8xb2-amp-160k_ade20k-512x512 In Collection: UPerNet Metadata: backbone: ViT-B @@ -10,7 +10,7 @@ Models: hardware: V100 backend: PyTorch batch size: 1 - mode: FP16 + mode: AMP resolution: (512,512) Training Memory (GB): 9.96 Results: @@ -19,5 +19,5 @@ Models: Metrics: mIoU: 48.13 mIoU(ms+flip): 48.7 - Config: configs/mae/upernet_mae-base_fp16_8x2_512x512_160k_ade20k.py + 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/mobilenet_v2/README.md b/configs/mobilenet_v2/README.md index 3ea8a463ae..c1010044a9 100644 --- a/configs/mobilenet_v2/README.md +++ b/configs/mobilenet_v2/README.md @@ -39,18 +39,18 @@ The MobileNetV2 architecture is based on an inverted residual structure where th ### 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 | 61.54 | - | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/mobilenet_v2/fcn_m-v2-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/fcn_m-v2-d8_512x1024_80k_cityscapes/fcn_m-v2-d8_512x1024_80k_cityscapes_20200825_124817-d24c28c1.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/fcn_m-v2-d8_512x1024_80k_cityscapes/fcn_m-v2-d8_512x1024_80k_cityscapes-20200825_124817.log.json) | -| PSPNet | M-V2-D8 | 512x1024 | 80000 | 3.6 | 11.2 | 70.23 | - | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/mobilenet_v2/pspnet_m-v2-d8_512x1024_80k_cityscapes.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/master/configs/mobilenet_v2/deeplabv3_m-v2-d8_512x1024_80k_cityscapes.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/master/configs/mobilenet_v2/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes.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) | +| 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 | 61.54 | - | [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/fcn_m-v2-d8_512x1024_80k_cityscapes/fcn_m-v2-d8_512x1024_80k_cityscapes_20200825_124817-d24c28c1.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/fcn_m-v2-d8_512x1024_80k_cityscapes/fcn_m-v2-d8_512x1024_80k_cityscapes-20200825_124817.log.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/master/configs/mobilenet_v2/fcn_m-v2-d8_512x512_160k_ade20k.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/master/configs/mobilenet_v2/pspnet_m-v2-d8_512x512_160k_ade20k.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/master/configs/mobilenet_v2/deeplabv3_m-v2-d8_512x512_160k_ade20k.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/master/configs/mobilenet_v2/deeplabv3plus_m-v2-d8_512x512_160k_ade20k.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) | +| 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/fcn_m-v2-d8_512x512_160k_ade20k.py b/configs/mobilenet_v2/mobilenet-v2-d8_deeplabv3_4xb2-80k_cityscapes-512x1024.py similarity index 82% rename from configs/mobilenet_v2/fcn_m-v2-d8_512x512_160k_ade20k.py rename to configs/mobilenet_v2/mobilenet-v2-d8_deeplabv3_4xb2-80k_cityscapes-512x1024.py index c5f6ab0d62..436ba4a144 100644 --- a/configs/mobilenet_v2/fcn_m-v2-d8_512x512_160k_ade20k.py +++ b/configs/mobilenet_v2/mobilenet-v2-d8_deeplabv3_4xb2-80k_cityscapes-512x1024.py @@ -1,4 +1,4 @@ -_base_ = '../fcn/fcn_r101-d8_512x512_160k_ade20k.py' +_base_ = '../deeplabv3/deeplabv3_r101-d8_4xb2-80k_cityscapes-512x1024.py' model = dict( pretrained='mmcls://mobilenet_v2', backbone=dict( diff --git a/configs/mobilenet_v2/deeplabv3_m-v2-d8_512x1024_80k_cityscapes.py b/configs/mobilenet_v2/mobilenet-v2-d8_deeplabv3_4xb4-160k_ade20k-512x512.py similarity index 83% rename from configs/mobilenet_v2/deeplabv3_m-v2-d8_512x1024_80k_cityscapes.py rename to configs/mobilenet_v2/mobilenet-v2-d8_deeplabv3_4xb4-160k_ade20k-512x512.py index 267483d88f..30dd882535 100644 --- a/configs/mobilenet_v2/deeplabv3_m-v2-d8_512x1024_80k_cityscapes.py +++ b/configs/mobilenet_v2/mobilenet-v2-d8_deeplabv3_4xb4-160k_ade20k-512x512.py @@ -1,4 +1,4 @@ -_base_ = '../deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes.py' +_base_ = '../deeplabv3/deeplabv3_r101-d8_4xb4-160k_ade20k-512x512.py' model = dict( pretrained='mmcls://mobilenet_v2', backbone=dict( diff --git a/configs/mobilenet_v2/deeplabv3plus_m-v2-d8_512x512_160k_ade20k.py b/configs/mobilenet_v2/mobilenet-v2-d8_deeplabv3plus_4xb2-80k_cityscapes-512x1024.py similarity index 80% rename from configs/mobilenet_v2/deeplabv3plus_m-v2-d8_512x512_160k_ade20k.py rename to configs/mobilenet_v2/mobilenet-v2-d8_deeplabv3plus_4xb2-80k_cityscapes-512x1024.py index 7615a7c19a..f0fd513bd9 100644 --- a/configs/mobilenet_v2/deeplabv3plus_m-v2-d8_512x512_160k_ade20k.py +++ b/configs/mobilenet_v2/mobilenet-v2-d8_deeplabv3plus_4xb2-80k_cityscapes-512x1024.py @@ -1,4 +1,6 @@ -_base_ = '../deeplabv3plus/deeplabv3plus_r101-d8_512x512_160k_ade20k.py' +_base_ = [ + '../deeplabv3plus/deeplabv3plus_r101-d8_4xb2-80k_cityscapes-512x1024.py' +] model = dict( pretrained='mmcls://mobilenet_v2', backbone=dict( diff --git a/configs/mobilenet_v2/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes.py b/configs/mobilenet_v2/mobilenet-v2-d8_deeplabv3plus_4xb4-160k_ade20k-512x512.py similarity index 82% rename from configs/mobilenet_v2/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes.py rename to configs/mobilenet_v2/mobilenet-v2-d8_deeplabv3plus_4xb4-160k_ade20k-512x512.py index d4533d79a2..17fb52a7cd 100644 --- a/configs/mobilenet_v2/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes.py +++ b/configs/mobilenet_v2/mobilenet-v2-d8_deeplabv3plus_4xb4-160k_ade20k-512x512.py @@ -1,4 +1,4 @@ -_base_ = '../deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes.py' +_base_ = '../deeplabv3plus/deeplabv3plus_r101-d8_4xb4-160k_ade20k-512x512.py' model = dict( pretrained='mmcls://mobilenet_v2', backbone=dict( diff --git a/configs/mobilenet_v2/pspnet_m-v2-d8_512x512_160k_ade20k.py b/configs/mobilenet_v2/mobilenet-v2-d8_fcn_4xb2-80k_cityscapes-512x1024.py similarity index 84% rename from configs/mobilenet_v2/pspnet_m-v2-d8_512x512_160k_ade20k.py rename to configs/mobilenet_v2/mobilenet-v2-d8_fcn_4xb2-80k_cityscapes-512x1024.py index 5b72ac830b..1453adb8ec 100644 --- a/configs/mobilenet_v2/pspnet_m-v2-d8_512x512_160k_ade20k.py +++ b/configs/mobilenet_v2/mobilenet-v2-d8_fcn_4xb2-80k_cityscapes-512x1024.py @@ -1,4 +1,4 @@ -_base_ = '../pspnet/pspnet_r101-d8_512x512_160k_ade20k.py' +_base_ = '../fcn/fcn_r101-d8_4xb2-80k_cityscapes-512x1024.py' model = dict( pretrained='mmcls://mobilenet_v2', backbone=dict( diff --git a/configs/mobilenet_v2/fcn_m-v2-d8_512x1024_80k_cityscapes.py b/configs/mobilenet_v2/mobilenet-v2-d8_fcn_4xb4-160k_ade20k-512x512.py similarity index 85% rename from configs/mobilenet_v2/fcn_m-v2-d8_512x1024_80k_cityscapes.py rename to configs/mobilenet_v2/mobilenet-v2-d8_fcn_4xb4-160k_ade20k-512x512.py index a535bd0ed8..64e715ca90 100644 --- a/configs/mobilenet_v2/fcn_m-v2-d8_512x1024_80k_cityscapes.py +++ b/configs/mobilenet_v2/mobilenet-v2-d8_fcn_4xb4-160k_ade20k-512x512.py @@ -1,4 +1,4 @@ -_base_ = '../fcn/fcn_r101-d8_512x1024_80k_cityscapes.py' +_base_ = '../fcn/fcn_r101-d8_4xb4-160k_ade20k-512x512.py' model = dict( pretrained='mmcls://mobilenet_v2', backbone=dict( diff --git a/configs/mobilenet_v2/deeplabv3_m-v2-d8_512x512_160k_ade20k.py b/configs/mobilenet_v2/mobilenet-v2-d8_pspnet_4xb2-80k_cityscapes-512x1024.py similarity index 83% rename from configs/mobilenet_v2/deeplabv3_m-v2-d8_512x512_160k_ade20k.py rename to configs/mobilenet_v2/mobilenet-v2-d8_pspnet_4xb2-80k_cityscapes-512x1024.py index e15b8cc82b..da44ef8efb 100644 --- a/configs/mobilenet_v2/deeplabv3_m-v2-d8_512x512_160k_ade20k.py +++ b/configs/mobilenet_v2/mobilenet-v2-d8_pspnet_4xb2-80k_cityscapes-512x1024.py @@ -1,4 +1,4 @@ -_base_ = '../deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k.py' +_base_ = '../pspnet/pspnet_r101-d8_4xb2-80k_cityscapes-512x1024.py' model = dict( pretrained='mmcls://mobilenet_v2', backbone=dict( diff --git a/configs/mobilenet_v2/pspnet_m-v2-d8_512x1024_80k_cityscapes.py b/configs/mobilenet_v2/mobilenet-v2-d8_pspnet_4xb4-160k_ade20k-512x512.py similarity index 84% rename from configs/mobilenet_v2/pspnet_m-v2-d8_512x1024_80k_cityscapes.py rename to configs/mobilenet_v2/mobilenet-v2-d8_pspnet_4xb4-160k_ade20k-512x512.py index 7403bee864..17a4d211e4 100644 --- a/configs/mobilenet_v2/pspnet_m-v2-d8_512x1024_80k_cityscapes.py +++ b/configs/mobilenet_v2/mobilenet-v2-d8_pspnet_4xb4-160k_ade20k-512x512.py @@ -1,4 +1,4 @@ -_base_ = '../pspnet/pspnet_r101-d8_512x1024_80k_cityscapes.py' +_base_ = '../pspnet/pspnet_r101-d8_4xb4-160k_ade20k-512x512.py' model = dict( pretrained='mmcls://mobilenet_v2', backbone=dict( diff --git a/configs/mobilenet_v2/mobilenet_v2.yml b/configs/mobilenet_v2/mobilenet_v2.yml index 5527ba82ba..69d73d568a 100644 --- a/configs/mobilenet_v2/mobilenet_v2.yml +++ b/configs/mobilenet_v2/mobilenet_v2.yml @@ -1,5 +1,5 @@ Models: -- Name: fcn_m-v2-d8_512x1024_80k_cityscapes +- Name: mobilenet-v2-d8_fcn_4xb2-80k_cityscapes-512x1024 In Collection: FCN Metadata: backbone: M-V2-D8 @@ -18,9 +18,9 @@ Models: Dataset: Cityscapes Metrics: mIoU: 61.54 - Config: configs/mobilenet_v2/fcn_m-v2-d8_512x1024_80k_cityscapes.py + Config: configs/mobilenet_v2/mobilenet-v2-d8_fcn_4xb2-80k_cityscapes-512x1024.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/fcn_m-v2-d8_512x1024_80k_cityscapes/fcn_m-v2-d8_512x1024_80k_cityscapes_20200825_124817-d24c28c1.pth -- Name: pspnet_m-v2-d8_512x1024_80k_cityscapes +- Name: mobilenet-v2-d8_pspnet_4xb2-80k_cityscapes-512x1024 In Collection: PSPNet Metadata: backbone: M-V2-D8 @@ -39,9 +39,9 @@ Models: Dataset: Cityscapes Metrics: mIoU: 70.23 - Config: configs/mobilenet_v2/pspnet_m-v2-d8_512x1024_80k_cityscapes.py + 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: deeplabv3_m-v2-d8_512x1024_80k_cityscapes +- Name: mobilenet-v2-d8_deeplabv3_4xb2-80k_cityscapes-512x1024 In Collection: DeepLabV3 Metadata: backbone: M-V2-D8 @@ -60,9 +60,9 @@ Models: Dataset: Cityscapes Metrics: mIoU: 73.84 - Config: configs/mobilenet_v2/deeplabv3_m-v2-d8_512x1024_80k_cityscapes.py + 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: deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes +- Name: mobilenet-v2-d8_deeplabv3plus_4xb2-80k_cityscapes-512x1024 In Collection: DeepLabV3+ Metadata: backbone: M-V2-D8 @@ -81,9 +81,9 @@ Models: Dataset: Cityscapes Metrics: mIoU: 75.2 - Config: configs/mobilenet_v2/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes.py + 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: fcn_m-v2-d8_512x512_160k_ade20k +- Name: mobilenet-v2-d8_fcn_4xb4-160k_ade20k-512x512 In Collection: FCN Metadata: backbone: M-V2-D8 @@ -102,9 +102,9 @@ Models: Dataset: ADE20K Metrics: mIoU: 19.71 - Config: configs/mobilenet_v2/fcn_m-v2-d8_512x512_160k_ade20k.py + 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: pspnet_m-v2-d8_512x512_160k_ade20k +- Name: mobilenet-v2-d8_pspnet_4xb4-160k_ade20k-512x512 In Collection: PSPNet Metadata: backbone: M-V2-D8 @@ -123,9 +123,9 @@ Models: Dataset: ADE20K Metrics: mIoU: 29.68 - Config: configs/mobilenet_v2/pspnet_m-v2-d8_512x512_160k_ade20k.py + 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: deeplabv3_m-v2-d8_512x512_160k_ade20k +- Name: mobilenet-v2-d8_deeplabv3_4xb4-160k_ade20k-512x512 In Collection: DeepLabV3 Metadata: backbone: M-V2-D8 @@ -144,9 +144,9 @@ Models: Dataset: ADE20K Metrics: mIoU: 34.08 - Config: configs/mobilenet_v2/deeplabv3_m-v2-d8_512x512_160k_ade20k.py + 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: deeplabv3plus_m-v2-d8_512x512_160k_ade20k +- Name: mobilenet-v2-d8_deeplabv3plus_4xb4-160k_ade20k-512x512 In Collection: DeepLabV3+ Metadata: backbone: M-V2-D8 @@ -165,5 +165,5 @@ Models: Dataset: ADE20K Metrics: mIoU: 34.02 - Config: configs/mobilenet_v2/deeplabv3plus_m-v2-d8_512x512_160k_ade20k.py + 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 66f20688b9..c2fed06ccd 100644 --- a/configs/mobilenet_v3/README.md +++ b/configs/mobilenet_v3/README.md @@ -42,9 +42,9 @@ We present the next generation of MobileNets based on a combination of complemen ### 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/master/configs/mobilenet_v3/lraspp_m-v3-d8_512x1024_320k_cityscapes.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/master/configs/mobilenet_v3/lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes.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/master/configs/mobilenet_v3/lraspp_m-v3s-d8_512x1024_320k_cityscapes.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/master/configs/mobilenet_v3/lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes.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) | +| 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/lraspp_m-v3s-d8_512x1024_320k_cityscapes.py b/configs/mobilenet_v3/mobilenet-v3-d8-s_lraspp_4xb4-320k_cityscapes-512x1024.py similarity index 91% rename from configs/mobilenet_v3/lraspp_m-v3s-d8_512x1024_320k_cityscapes.py rename to configs/mobilenet_v3/mobilenet-v3-d8-s_lraspp_4xb4-320k_cityscapes-512x1024.py index d4e368b2a1..bc6322fe40 100644 --- a/configs/mobilenet_v3/lraspp_m-v3s-d8_512x1024_320k_cityscapes.py +++ b/configs/mobilenet_v3/mobilenet-v3-d8-s_lraspp_4xb4-320k_cityscapes-512x1024.py @@ -1,4 +1,4 @@ -_base_ = './lraspp_m-v3-d8_512x1024_320k_cityscapes.py' +_base_ = './mobilenet-v3-d8_lraspp_4xb4-320k_cityscapes-512x1024.py' norm_cfg = dict(type='SyncBN', eps=0.001, requires_grad=True) model = dict( type='EncoderDecoder', diff --git a/configs/mobilenet_v3/lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes.py b/configs/mobilenet_v3/mobilenet-v3-d8-scratch-s_lraspp_4xb4-320k_cityscapes-512x1024.py similarity index 89% rename from configs/mobilenet_v3/lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes.py rename to configs/mobilenet_v3/mobilenet-v3-d8-scratch-s_lraspp_4xb4-320k_cityscapes-512x1024.py index 0c5f707200..7260936e60 100644 --- a/configs/mobilenet_v3/lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes.py +++ b/configs/mobilenet_v3/mobilenet-v3-d8-scratch-s_lraspp_4xb4-320k_cityscapes-512x1024.py @@ -1,4 +1,4 @@ -_base_ = './lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes.py' +_base_ = './mobilenet-v3-d8-scratch_lraspp_4xb4-320k_cityscapes-512x1024.py' norm_cfg = dict(type='SyncBN', eps=0.001, requires_grad=True) model = dict( type='EncoderDecoder', diff --git a/configs/mobilenet_v3/lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes.py b/configs/mobilenet_v3/mobilenet-v3-d8-scratch_lraspp_4xb4-320k_cityscapes-512x1024.py similarity index 100% rename from configs/mobilenet_v3/lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes.py rename to configs/mobilenet_v3/mobilenet-v3-d8-scratch_lraspp_4xb4-320k_cityscapes-512x1024.py diff --git a/configs/mobilenet_v3/lraspp_m-v3-d8_512x1024_320k_cityscapes.py b/configs/mobilenet_v3/mobilenet-v3-d8_lraspp_4xb4-320k_cityscapes-512x1024.py similarity index 100% rename from configs/mobilenet_v3/lraspp_m-v3-d8_512x1024_320k_cityscapes.py rename to configs/mobilenet_v3/mobilenet-v3-d8_lraspp_4xb4-320k_cityscapes-512x1024.py diff --git a/configs/mobilenet_v3/mobilenet_v3.yml b/configs/mobilenet_v3/mobilenet_v3.yml index 003cbe530c..067a150cea 100644 --- a/configs/mobilenet_v3/mobilenet_v3.yml +++ b/configs/mobilenet_v3/mobilenet_v3.yml @@ -13,7 +13,7 @@ Collections: Converted From: Code: https://github.com/tensorflow/models/tree/master/research/deeplab Models: -- Name: lraspp_m-v3-d8_512x1024_320k_cityscapes +- Name: mobilenet-v3-d8_lraspp_4xb4-320k_cityscapes-512x1024 In Collection: LRASPP Metadata: backbone: M-V3-D8 @@ -33,9 +33,9 @@ Models: Metrics: mIoU: 69.54 mIoU(ms+flip): 70.89 - Config: configs/mobilenet_v3/lraspp_m-v3-d8_512x1024_320k_cityscapes.py + 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: lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes +- Name: mobilenet-v3-d8-scratch_lraspp_4xb4-320k_cityscapes-512x1024 In Collection: LRASPP Metadata: backbone: M-V3-D8 (scratch) @@ -55,9 +55,9 @@ Models: Metrics: mIoU: 67.87 mIoU(ms+flip): 69.78 - Config: configs/mobilenet_v3/lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes.py + 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: lraspp_m-v3s-d8_512x1024_320k_cityscapes +- Name: mobilenet-v3-d8-s_lraspp_4xb4-320k_cityscapes-512x1024 In Collection: LRASPP Metadata: backbone: M-V3s-D8 @@ -77,9 +77,9 @@ Models: Metrics: mIoU: 64.11 mIoU(ms+flip): 66.42 - Config: configs/mobilenet_v3/lraspp_m-v3s-d8_512x1024_320k_cityscapes.py + 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: lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes +- Name: mobilenet-v3-d8-scratch-s_lraspp_4xb4-320k_cityscapes-512x1024 In Collection: LRASPP Metadata: backbone: M-V3s-D8 (scratch) @@ -99,5 +99,5 @@ Models: Metrics: mIoU: 62.74 mIoU(ms+flip): 65.01 - Config: configs/mobilenet_v3/lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes.py + 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 1109599332..80d45ab589 100644 --- a/configs/nonlocal_net/README.md +++ b/configs/nonlocal_net/README.md @@ -38,31 +38,31 @@ Both convolutional and recurrent operations are building blocks that process one ### 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/master/configs/nonlocal_net/nonlocal_r50-d8_512x1024_40k_cityscapes.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/master/configs/nonlocal_net/nonlocal_r101-d8_512x1024_40k_cityscapes.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/master/configs/nonlocal_net/nonlocal_r50-d8_769x769_40k_cityscapes.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/master/configs/nonlocal_net/nonlocal_r101-d8_769x769_40k_cityscapes.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/master/configs/nonlocal_net/nonlocal_r50-d8_512x1024_80k_cityscapes.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/master/configs/nonlocal_net/nonlocal_r101-d8_512x1024_80k_cityscapes.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/master/configs/nonlocal_net/nonlocal_r50-d8_769x769_80k_cityscapes.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/master/configs/nonlocal_net/nonlocal_r101-d8_769x769_80k_cityscapes.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) | +| 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/master/configs/nonlocal_net/nonlocal_r50-d8_512x512_80k_ade20k.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/master/configs/nonlocal_net/nonlocal_r101-d8_512x512_80k_ade20k.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/master/configs/nonlocal_net/nonlocal_r50-d8_512x512_160k_ade20k.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/master/configs/nonlocal_net/nonlocal_r101-d8_512x512_160k_ade20k.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) | +| 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/master/configs/nonlocal_net/nonlocal_r50-d8_512x512_20k_voc12aug.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/master/configs/nonlocal_net/nonlocal_r101-d8_512x512_20k_voc12aug.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/master/configs/nonlocal_net/nonlocal_r50-d8_512x512_40k_voc12aug.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/master/configs/nonlocal_net/nonlocal_r101-d8_512x512_40k_voc12aug.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) | +| 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/nonlocal_net.yml b/configs/nonlocal_net/nonlocal_net.yml index bab38ce9c2..22f32c5abb 100644 --- a/configs/nonlocal_net/nonlocal_net.yml +++ b/configs/nonlocal_net/nonlocal_net.yml @@ -15,7 +15,7 @@ Collections: Converted From: Code: https://github.com/facebookresearch/video-nonlocal-net Models: -- Name: nonlocal_r50-d8_512x1024_40k_cityscapes +- Name: nonlocal_r50-d8_4xb2-40k_cityscapes-512x1024 In Collection: NonLocalNet Metadata: backbone: R-50-D8 @@ -34,9 +34,9 @@ Models: Dataset: Cityscapes Metrics: mIoU: 78.24 - Config: configs/nonlocal_net/nonlocal_r50-d8_512x1024_40k_cityscapes.py + 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_512x1024_40k_cityscapes +- Name: nonlocal_r101-d8_4xb2-40k_cityscapes-512x1024 In Collection: NonLocalNet Metadata: backbone: R-101-D8 @@ -55,9 +55,9 @@ Models: Dataset: Cityscapes Metrics: mIoU: 78.66 - Config: configs/nonlocal_net/nonlocal_r101-d8_512x1024_40k_cityscapes.py + 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_769x769_40k_cityscapes +- Name: nonlocal_r50-d8_4xb2-40k_cityscapes-769x769 In Collection: NonLocalNet Metadata: backbone: R-50-D8 @@ -77,9 +77,9 @@ Models: Metrics: mIoU: 78.33 mIoU(ms+flip): 79.92 - Config: configs/nonlocal_net/nonlocal_r50-d8_769x769_40k_cityscapes.py + 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_769x769_40k_cityscapes +- Name: nonlocal_r101-d8_4xb2-40k_cityscapes-769x769 In Collection: NonLocalNet Metadata: backbone: R-101-D8 @@ -99,9 +99,9 @@ Models: Metrics: mIoU: 78.57 mIoU(ms+flip): 80.29 - Config: configs/nonlocal_net/nonlocal_r101-d8_769x769_40k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: nonlocal_r50-d8_4xb2-80k_cityscapes-512x1024 In Collection: NonLocalNet Metadata: backbone: R-50-D8 @@ -112,9 +112,9 @@ Models: Dataset: Cityscapes Metrics: mIoU: 78.01 - Config: configs/nonlocal_net/nonlocal_r50-d8_512x1024_80k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: nonlocal_r101-d8_4xb2-80k_cityscapes-512x1024 In Collection: NonLocalNet Metadata: backbone: R-101-D8 @@ -125,9 +125,9 @@ Models: Dataset: Cityscapes Metrics: mIoU: 78.93 - Config: configs/nonlocal_net/nonlocal_r101-d8_512x1024_80k_cityscapes.py + 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_769x769_80k_cityscapes +- Name: nonlocal_r50-d8_4xb2-80k_cityscapes-769x769 In Collection: NonLocalNet Metadata: backbone: R-50-D8 @@ -139,9 +139,9 @@ Models: Metrics: mIoU: 79.05 mIoU(ms+flip): 80.68 - Config: configs/nonlocal_net/nonlocal_r50-d8_769x769_80k_cityscapes.py + 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_769x769_80k_cityscapes +- Name: nonlocal_r101-d8_4xb2-80k_cityscapes-769x769 In Collection: NonLocalNet Metadata: backbone: R-101-D8 @@ -153,9 +153,9 @@ Models: Metrics: mIoU: 79.4 mIoU(ms+flip): 80.85 - Config: configs/nonlocal_net/nonlocal_r101-d8_769x769_80k_cityscapes.py + 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_512x512_80k_ade20k +- Name: nonlocal_r50-d8_4xb4-80k_ade20k-512x512 In Collection: NonLocalNet Metadata: backbone: R-50-D8 @@ -175,9 +175,9 @@ Models: Metrics: mIoU: 40.75 mIoU(ms+flip): 42.05 - Config: configs/nonlocal_net/nonlocal_r50-d8_512x512_80k_ade20k.py + 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_512x512_80k_ade20k +- Name: nonlocal_r101-d8_4xb4-80k_ade20k-512x512 In Collection: NonLocalNet Metadata: backbone: R-101-D8 @@ -197,9 +197,9 @@ Models: Metrics: mIoU: 42.9 mIoU(ms+flip): 44.27 - Config: configs/nonlocal_net/nonlocal_r101-d8_512x512_80k_ade20k.py + 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_512x512_160k_ade20k +- Name: nonlocal_r50-d8_4xb4-160k_ade20k-512x512 In Collection: NonLocalNet Metadata: backbone: R-50-D8 @@ -211,9 +211,9 @@ Models: Metrics: mIoU: 42.03 mIoU(ms+flip): 43.04 - Config: configs/nonlocal_net/nonlocal_r50-d8_512x512_160k_ade20k.py + 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_512x512_160k_ade20k +- Name: nonlocal_r101-d8_4xb4-160k_ade20k-512x512 In Collection: NonLocalNet Metadata: backbone: R-101-D8 @@ -225,9 +225,9 @@ Models: Metrics: mIoU: 44.63 mIoU(ms+flip): 45.79 - Config: configs/nonlocal_net/nonlocal_r101-d8_512x512_160k_ade20k.py + 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_512x512_20k_voc12aug +- Name: nonlocal_r50-d8_4xb4-20k_voc12aug-512x512 In Collection: NonLocalNet Metadata: backbone: R-50-D8 @@ -247,9 +247,9 @@ Models: Metrics: mIoU: 76.2 mIoU(ms+flip): 77.12 - Config: configs/nonlocal_net/nonlocal_r50-d8_512x512_20k_voc12aug.py + 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_512x512_20k_voc12aug +- Name: nonlocal_r101-d8_4xb4-20k_voc12aug-512x512 In Collection: NonLocalNet Metadata: backbone: R-101-D8 @@ -269,9 +269,9 @@ Models: Metrics: mIoU: 78.15 mIoU(ms+flip): 78.86 - Config: configs/nonlocal_net/nonlocal_r101-d8_512x512_20k_voc12aug.py + 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_512x512_40k_voc12aug +- Name: nonlocal_r50-d8_4xb4-40k_voc12aug-512x512 In Collection: NonLocalNet Metadata: backbone: R-50-D8 @@ -283,9 +283,9 @@ Models: Metrics: mIoU: 76.65 mIoU(ms+flip): 77.47 - Config: configs/nonlocal_net/nonlocal_r50-d8_512x512_40k_voc12aug.py + 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_512x512_40k_voc12aug +- Name: nonlocal_r101-d8_4xb4-40k_voc12aug-512x512 In Collection: NonLocalNet Metadata: backbone: R-101-D8 @@ -297,5 +297,5 @@ Models: Metrics: mIoU: 78.27 mIoU(ms+flip): 79.12 - Config: configs/nonlocal_net/nonlocal_r101-d8_512x512_40k_voc12aug.py + 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/nonlocal_net/nonlocal_r101-d8_4xb2-40k_cityscapes-512x1024.py b/configs/nonlocal_net/nonlocal_r101-d8_4xb2-40k_cityscapes-512x1024.py new file mode 100644 index 0000000000..5fcf7bcb16 --- /dev/null +++ b/configs/nonlocal_net/nonlocal_r101-d8_4xb2-40k_cityscapes-512x1024.py @@ -0,0 +1,2 @@ +_base_ = './nonlocal_r50-d8_4xb2-40k_cityscapes-512x1024.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/nonlocal_net/nonlocal_r101-d8_4xb2-40k_cityscapes-769x769.py b/configs/nonlocal_net/nonlocal_r101-d8_4xb2-40k_cityscapes-769x769.py new file mode 100644 index 0000000000..ee984c2bbd --- /dev/null +++ b/configs/nonlocal_net/nonlocal_r101-d8_4xb2-40k_cityscapes-769x769.py @@ -0,0 +1,2 @@ +_base_ = './nonlocal_r50-d8_4xb2-40k_cityscapes-769x769.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/nonlocal_net/nonlocal_r101-d8_4xb2-80k_cityscapes-512x1024.py b/configs/nonlocal_net/nonlocal_r101-d8_4xb2-80k_cityscapes-512x1024.py new file mode 100644 index 0000000000..aca80d676a --- /dev/null +++ b/configs/nonlocal_net/nonlocal_r101-d8_4xb2-80k_cityscapes-512x1024.py @@ -0,0 +1,2 @@ +_base_ = './nonlocal_r50-d8_4xb2-80k_cityscapes-512x1024.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/nonlocal_net/nonlocal_r101-d8_4xb2-80k_cityscapes-769x769.py b/configs/nonlocal_net/nonlocal_r101-d8_4xb2-80k_cityscapes-769x769.py new file mode 100644 index 0000000000..8a7aeea7f6 --- /dev/null +++ b/configs/nonlocal_net/nonlocal_r101-d8_4xb2-80k_cityscapes-769x769.py @@ -0,0 +1,2 @@ +_base_ = './nonlocal_r50-d8_4xb2-80k_cityscapes-769x769.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/nonlocal_net/nonlocal_r101-d8_4xb4-160k_ade20k-512x512.py b/configs/nonlocal_net/nonlocal_r101-d8_4xb4-160k_ade20k-512x512.py new file mode 100644 index 0000000000..0cdb3caaf3 --- /dev/null +++ b/configs/nonlocal_net/nonlocal_r101-d8_4xb4-160k_ade20k-512x512.py @@ -0,0 +1,2 @@ +_base_ = './nonlocal_r50-d8_4xb4-160k_ade20k-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/nonlocal_net/nonlocal_r101-d8_4xb4-20k_voc12aug-512x512.py b/configs/nonlocal_net/nonlocal_r101-d8_4xb4-20k_voc12aug-512x512.py new file mode 100644 index 0000000000..a7cacea517 --- /dev/null +++ b/configs/nonlocal_net/nonlocal_r101-d8_4xb4-20k_voc12aug-512x512.py @@ -0,0 +1,2 @@ +_base_ = './nonlocal_r50-d8_4xb4-20k_voc12aug-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/nonlocal_net/nonlocal_r101-d8_4xb4-40k_voc12aug-512x512.py b/configs/nonlocal_net/nonlocal_r101-d8_4xb4-40k_voc12aug-512x512.py new file mode 100644 index 0000000000..ec475443e8 --- /dev/null +++ b/configs/nonlocal_net/nonlocal_r101-d8_4xb4-40k_voc12aug-512x512.py @@ -0,0 +1,2 @@ +_base_ = './nonlocal_r50-d8_4xb4-40k_voc12aug-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/nonlocal_net/nonlocal_r101-d8_4xb4-80k_ade20k-512x512.py b/configs/nonlocal_net/nonlocal_r101-d8_4xb4-80k_ade20k-512x512.py new file mode 100644 index 0000000000..ca79f6fdc0 --- /dev/null +++ b/configs/nonlocal_net/nonlocal_r101-d8_4xb4-80k_ade20k-512x512.py @@ -0,0 +1,2 @@ +_base_ = './nonlocal_r50-d8_4xb4-80k_ade20k-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/nonlocal_net/nonlocal_r101-d8_512x1024_40k_cityscapes.py b/configs/nonlocal_net/nonlocal_r101-d8_512x1024_40k_cityscapes.py deleted file mode 100644 index ef7b06dd38..0000000000 --- a/configs/nonlocal_net/nonlocal_r101-d8_512x1024_40k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './nonlocal_r50-d8_512x1024_40k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/nonlocal_net/nonlocal_r101-d8_512x1024_80k_cityscapes.py b/configs/nonlocal_net/nonlocal_r101-d8_512x1024_80k_cityscapes.py deleted file mode 100644 index 7a1e66cf1c..0000000000 --- a/configs/nonlocal_net/nonlocal_r101-d8_512x1024_80k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './nonlocal_r50-d8_512x1024_80k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/nonlocal_net/nonlocal_r101-d8_512x512_160k_ade20k.py b/configs/nonlocal_net/nonlocal_r101-d8_512x512_160k_ade20k.py deleted file mode 100644 index df9c2aca9c..0000000000 --- a/configs/nonlocal_net/nonlocal_r101-d8_512x512_160k_ade20k.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './nonlocal_r50-d8_512x512_160k_ade20k.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/nonlocal_net/nonlocal_r101-d8_512x512_20k_voc12aug.py b/configs/nonlocal_net/nonlocal_r101-d8_512x512_20k_voc12aug.py deleted file mode 100644 index 490f9873a2..0000000000 --- a/configs/nonlocal_net/nonlocal_r101-d8_512x512_20k_voc12aug.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './nonlocal_r50-d8_512x512_20k_voc12aug.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/nonlocal_net/nonlocal_r101-d8_512x512_40k_voc12aug.py b/configs/nonlocal_net/nonlocal_r101-d8_512x512_40k_voc12aug.py deleted file mode 100644 index 40d9190fba..0000000000 --- a/configs/nonlocal_net/nonlocal_r101-d8_512x512_40k_voc12aug.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './nonlocal_r50-d8_512x512_40k_voc12aug.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/nonlocal_net/nonlocal_r101-d8_512x512_80k_ade20k.py b/configs/nonlocal_net/nonlocal_r101-d8_512x512_80k_ade20k.py deleted file mode 100644 index 0c6f60dac7..0000000000 --- a/configs/nonlocal_net/nonlocal_r101-d8_512x512_80k_ade20k.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './nonlocal_r50-d8_512x512_80k_ade20k.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/nonlocal_net/nonlocal_r101-d8_769x769_40k_cityscapes.py b/configs/nonlocal_net/nonlocal_r101-d8_769x769_40k_cityscapes.py deleted file mode 100644 index 23e6da7f23..0000000000 --- a/configs/nonlocal_net/nonlocal_r101-d8_769x769_40k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './nonlocal_r50-d8_769x769_40k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/nonlocal_net/nonlocal_r101-d8_769x769_80k_cityscapes.py b/configs/nonlocal_net/nonlocal_r101-d8_769x769_80k_cityscapes.py deleted file mode 100644 index 0627e2b5a7..0000000000 --- a/configs/nonlocal_net/nonlocal_r101-d8_769x769_80k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './nonlocal_r50-d8_769x769_80k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/nonlocal_net/nonlocal_r50-d8_512x1024_40k_cityscapes.py b/configs/nonlocal_net/nonlocal_r50-d8_4xb2-40k_cityscapes-512x1024.py similarity index 100% rename from configs/nonlocal_net/nonlocal_r50-d8_512x1024_40k_cityscapes.py rename to configs/nonlocal_net/nonlocal_r50-d8_4xb2-40k_cityscapes-512x1024.py diff --git a/configs/nonlocal_net/nonlocal_r50-d8_769x769_40k_cityscapes.py b/configs/nonlocal_net/nonlocal_r50-d8_4xb2-40k_cityscapes-769x769.py similarity index 100% rename from configs/nonlocal_net/nonlocal_r50-d8_769x769_40k_cityscapes.py rename to configs/nonlocal_net/nonlocal_r50-d8_4xb2-40k_cityscapes-769x769.py diff --git a/configs/nonlocal_net/nonlocal_r50-d8_512x1024_80k_cityscapes.py b/configs/nonlocal_net/nonlocal_r50-d8_4xb2-80k_cityscapes-512x1024.py similarity index 100% rename from configs/nonlocal_net/nonlocal_r50-d8_512x1024_80k_cityscapes.py rename to configs/nonlocal_net/nonlocal_r50-d8_4xb2-80k_cityscapes-512x1024.py diff --git a/configs/nonlocal_net/nonlocal_r50-d8_769x769_80k_cityscapes.py b/configs/nonlocal_net/nonlocal_r50-d8_4xb2-80k_cityscapes-769x769.py similarity index 100% rename from configs/nonlocal_net/nonlocal_r50-d8_769x769_80k_cityscapes.py rename to configs/nonlocal_net/nonlocal_r50-d8_4xb2-80k_cityscapes-769x769.py diff --git a/configs/nonlocal_net/nonlocal_r50-d8_512x512_160k_ade20k.py b/configs/nonlocal_net/nonlocal_r50-d8_4xb4-160k_ade20k-512x512.py similarity index 100% rename from configs/nonlocal_net/nonlocal_r50-d8_512x512_160k_ade20k.py rename to configs/nonlocal_net/nonlocal_r50-d8_4xb4-160k_ade20k-512x512.py diff --git a/configs/nonlocal_net/nonlocal_r50-d8_512x512_20k_voc12aug.py b/configs/nonlocal_net/nonlocal_r50-d8_4xb4-20k_voc12aug-512x512.py similarity index 100% rename from configs/nonlocal_net/nonlocal_r50-d8_512x512_20k_voc12aug.py rename to configs/nonlocal_net/nonlocal_r50-d8_4xb4-20k_voc12aug-512x512.py diff --git a/configs/nonlocal_net/nonlocal_r50-d8_512x512_40k_voc12aug.py b/configs/nonlocal_net/nonlocal_r50-d8_4xb4-40k_voc12aug-512x512.py similarity index 100% rename from configs/nonlocal_net/nonlocal_r50-d8_512x512_40k_voc12aug.py rename to configs/nonlocal_net/nonlocal_r50-d8_4xb4-40k_voc12aug-512x512.py diff --git a/configs/nonlocal_net/nonlocal_r50-d8_512x512_80k_ade20k.py b/configs/nonlocal_net/nonlocal_r50-d8_4xb4-80k_ade20k-512x512.py similarity index 100% rename from configs/nonlocal_net/nonlocal_r50-d8_512x512_80k_ade20k.py rename to configs/nonlocal_net/nonlocal_r50-d8_4xb4-80k_ade20k-512x512.py diff --git a/configs/ocrnet/README.md b/configs/ocrnet/README.md index 1c3dba2b62..5cbfbabfce 100644 --- a/configs/ocrnet/README.md +++ b/configs/ocrnet/README.md @@ -46,44 +46,44 @@ In this paper, we address the problem of semantic segmentation and focus on the #### 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 | 74.30 | 75.95 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ocrnet/ocrnet_hr18s_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x1024_40k_cityscapes/ocrnet_hr18s_512x1024_40k_cityscapes_20200601_033304-fa2436c2.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x1024_40k_cityscapes/ocrnet_hr18s_512x1024_40k_cityscapes_20200601_033304.log.json) | -| OCRNet | HRNetV2p-W18 | 512x1024 | 40000 | 4.7 | 7.50 | 77.72 | 79.49 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ocrnet/ocrnet_hr18_512x1024_40k_cityscapes.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/master/configs/ocrnet/ocrnet_hr48_512x1024_40k_cityscapes.py) | [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/master/configs/ocrnet/ocrnet_hr18s_512x1024_80k_cityscapes.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/master/configs/ocrnet/ocrnet_hr18_512x1024_80k_cityscapes.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/master/configs/ocrnet/ocrnet_hr48_512x1024_80k_cityscapes.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/master/configs/ocrnet/ocrnet_hr18s_512x1024_160k_cityscapes.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/master/configs/ocrnet/ocrnet_hr18_512x1024_160k_cityscapes.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/master/configs/ocrnet/ocrnet_hr48_512x1024_160k_cityscapes.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) | +| 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 | 74.30 | 75.95 | [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_512x1024_40k_cityscapes/ocrnet_hr18s_512x1024_40k_cityscapes_20200601_033304-fa2436c2.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x1024_40k_cityscapes/ocrnet_hr18s_512x1024_40k_cityscapes_20200601_033304.log.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/master/configs/ocrnet/ocrnet_r101-d8_512x1024_40k_b8_cityscapes.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/master/configs/ocrnet/ocrnet_r101-d8_512x1024_40k_b16_cityscapes.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/master/configs/ocrnet/ocrnet_r101-d8_512x1024_80k_b16_cityscapes.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) | +| 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/master/configs/ocrnet/ocrnet_hr18s_512x512_80k_ade20k.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/master/configs/ocrnet/ocrnet_hr18_512x512_80k_ade20k.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/master/configs/ocrnet/ocrnet_hr48_512x512_80k_ade20k.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/master/configs/ocrnet/ocrnet_hr18s_512x512_160k_ade20k.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/master/configs/ocrnet/ocrnet_hr18_512x512_160k_ade20k.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/master/configs/ocrnet/ocrnet_hr48_512x512_160k_ade20k.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) | +| 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/master/configs/ocrnet/ocrnet_hr18s_512x512_20k_voc12aug.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/master/configs/ocrnet/ocrnet_hr18_512x512_20k_voc12aug.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/master/configs/ocrnet/ocrnet_hr48_512x512_20k_voc12aug.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/master/configs/ocrnet/ocrnet_hr18s_512x512_40k_voc12aug.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/master/configs/ocrnet/ocrnet_hr18_512x512_40k_voc12aug.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/master/configs/ocrnet/ocrnet_hr48_512x512_40k_voc12aug.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) | +| 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/ocrnet.yml b/configs/ocrnet/ocrnet.yml index d599f0a533..a81aec2c75 100644 --- a/configs/ocrnet/ocrnet.yml +++ b/configs/ocrnet/ocrnet.yml @@ -15,7 +15,7 @@ Collections: Converted From: Code: https://github.com/openseg-group/OCNet.pytorch Models: -- Name: ocrnet_hr18s_512x1024_40k_cityscapes +- Name: ocrnet_hr18s_4xb2-40k_cityscapes-512x1024 In Collection: OCRNet Metadata: backbone: HRNetV2p-W18-Small @@ -35,9 +35,9 @@ Models: Metrics: mIoU: 74.3 mIoU(ms+flip): 75.95 - Config: configs/ocrnet/ocrnet_hr18s_512x1024_40k_cityscapes.py + Config: configs/ocrnet/ocrnet_hr18s_4xb2-40k_cityscapes-512x1024.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x1024_40k_cityscapes/ocrnet_hr18s_512x1024_40k_cityscapes_20200601_033304-fa2436c2.pth -- Name: ocrnet_hr18_512x1024_40k_cityscapes +- Name: ocrnet_hr18_4xb2-40k_cityscapes-512x1024 In Collection: OCRNet Metadata: backbone: HRNetV2p-W18 @@ -57,9 +57,9 @@ Models: Metrics: mIoU: 77.72 mIoU(ms+flip): 79.49 - Config: configs/ocrnet/ocrnet_hr18_512x1024_40k_cityscapes.py + 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_512x1024_40k_cityscapes +- Name: ocrnet_hr48_4xb2-40k_cityscapes-512x1024 In Collection: OCRNet Metadata: backbone: HRNetV2p-W48 @@ -79,9 +79,9 @@ Models: Metrics: mIoU: 80.58 mIoU(ms+flip): 81.79 - Config: configs/ocrnet/ocrnet_hr48_512x1024_40k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: ocrnet_hr18s_4xb2-80k_cityscapes-512x1024 In Collection: OCRNet Metadata: backbone: HRNetV2p-W18-Small @@ -93,9 +93,9 @@ Models: Metrics: mIoU: 77.16 mIoU(ms+flip): 78.66 - Config: configs/ocrnet/ocrnet_hr18s_512x1024_80k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: ocrnet_hr18_4xb2-80k_cityscapes-512x1024 In Collection: OCRNet Metadata: backbone: HRNetV2p-W18 @@ -107,9 +107,9 @@ Models: Metrics: mIoU: 78.57 mIoU(ms+flip): 80.46 - Config: configs/ocrnet/ocrnet_hr18_512x1024_80k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: ocrnet_hr48_4xb2-80k_cityscapes-512x1024 In Collection: OCRNet Metadata: backbone: HRNetV2p-W48 @@ -121,9 +121,9 @@ Models: Metrics: mIoU: 80.7 mIoU(ms+flip): 81.87 - Config: configs/ocrnet/ocrnet_hr48_512x1024_80k_cityscapes.py + 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_512x1024_160k_cityscapes +- Name: ocrnet_hr18s_4xb2-160k_cityscapes-512x1024 In Collection: OCRNet Metadata: backbone: HRNetV2p-W18-Small @@ -135,9 +135,9 @@ Models: Metrics: mIoU: 78.45 mIoU(ms+flip): 79.97 - Config: configs/ocrnet/ocrnet_hr18s_512x1024_160k_cityscapes.py + 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_512x1024_160k_cityscapes +- Name: ocrnet_hr18_4xb2-160k_cityscapes-512x1024 In Collection: OCRNet Metadata: backbone: HRNetV2p-W18 @@ -149,9 +149,9 @@ Models: Metrics: mIoU: 79.47 mIoU(ms+flip): 80.91 - Config: configs/ocrnet/ocrnet_hr18_512x1024_160k_cityscapes.py + 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_512x1024_160k_cityscapes +- Name: ocrnet_hr48_4xb2-160k_cityscapes-512x1024 In Collection: OCRNet Metadata: backbone: HRNetV2p-W48 @@ -163,9 +163,9 @@ Models: Metrics: mIoU: 81.35 mIoU(ms+flip): 82.7 - Config: configs/ocrnet/ocrnet_hr48_512x1024_160k_cityscapes.py + 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_512x1024_40k_b8_cityscapes +- Name: ocrnet_r101-d8_4xb2-40k_cityscapes-512x1024 In Collection: OCRNet Metadata: backbone: R-101-D8 @@ -176,9 +176,9 @@ Models: Dataset: Cityscapes Metrics: mIoU: 80.09 - Config: configs/ocrnet/ocrnet_r101-d8_512x1024_40k_b8_cityscapes.py + 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_512x1024_40k_b16_cityscapes +- Name: ocrnet_r101-d8_8xb2-40k_cityscapes-512x1024 In Collection: OCRNet Metadata: backbone: R-101-D8 @@ -197,9 +197,9 @@ Models: Dataset: Cityscapes Metrics: mIoU: 80.3 - Config: configs/ocrnet/ocrnet_r101-d8_512x1024_40k_b16_cityscapes.py + 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_512x1024_80k_b16_cityscapes +- Name: ocrnet_r101-d8_8xb2-80k_cityscapes-512x1024 In Collection: OCRNet Metadata: backbone: R-101-D8 @@ -218,9 +218,9 @@ Models: Dataset: Cityscapes Metrics: mIoU: 80.81 - Config: configs/ocrnet/ocrnet_r101-d8_512x1024_80k_b16_cityscapes.py + 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_512x512_80k_ade20k +- Name: ocrnet_hr18s_4xb4-80k_ade20k-512x512 In Collection: OCRNet Metadata: backbone: HRNetV2p-W18-Small @@ -240,9 +240,9 @@ Models: Metrics: mIoU: 35.06 mIoU(ms+flip): 35.8 - Config: configs/ocrnet/ocrnet_hr18s_512x512_80k_ade20k.py + 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_512x512_80k_ade20k +- Name: ocrnet_hr18_4xb4-80k_ade20k-512x512 In Collection: OCRNet Metadata: backbone: HRNetV2p-W18 @@ -262,9 +262,9 @@ Models: Metrics: mIoU: 37.79 mIoU(ms+flip): 39.16 - Config: configs/ocrnet/ocrnet_hr18_512x512_80k_ade20k.py + 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_512x512_80k_ade20k +- Name: ocrnet_hr48_4xb4-80k_ade20k-512x512 In Collection: OCRNet Metadata: backbone: HRNetV2p-W48 @@ -284,9 +284,9 @@ Models: Metrics: mIoU: 43.0 mIoU(ms+flip): 44.3 - Config: configs/ocrnet/ocrnet_hr48_512x512_80k_ade20k.py + 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_512x512_160k_ade20k +- Name: ocrnet_hr18s_4xb4-80k_ade20k-512x512 In Collection: OCRNet Metadata: backbone: HRNetV2p-W18-Small @@ -298,9 +298,9 @@ Models: Metrics: mIoU: 37.19 mIoU(ms+flip): 38.4 - Config: configs/ocrnet/ocrnet_hr18s_512x512_160k_ade20k.py + 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_512x512_160k_ade20k +- Name: ocrnet_hr18_4xb4-80k_ade20k-512x512 In Collection: OCRNet Metadata: backbone: HRNetV2p-W18 @@ -312,9 +312,9 @@ Models: Metrics: mIoU: 39.32 mIoU(ms+flip): 40.8 - Config: configs/ocrnet/ocrnet_hr18_512x512_160k_ade20k.py + 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_512x512_160k_ade20k +- Name: ocrnet_hr48_4xb4-160k_ade20k-512x512 In Collection: OCRNet Metadata: backbone: HRNetV2p-W48 @@ -326,9 +326,9 @@ Models: Metrics: mIoU: 43.25 mIoU(ms+flip): 44.88 - Config: configs/ocrnet/ocrnet_hr48_512x512_160k_ade20k.py + 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_512x512_20k_voc12aug +- Name: ocrnet_hr18s_4xb4-20k_voc12aug-512x512 In Collection: OCRNet Metadata: backbone: HRNetV2p-W18-Small @@ -348,9 +348,9 @@ Models: Metrics: mIoU: 71.7 mIoU(ms+flip): 73.84 - Config: configs/ocrnet/ocrnet_hr18s_512x512_20k_voc12aug.py + 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_512x512_20k_voc12aug +- Name: ocrnet_hr18_4xb4-20k_voc12aug-512x512 In Collection: OCRNet Metadata: backbone: HRNetV2p-W18 @@ -370,9 +370,9 @@ Models: Metrics: mIoU: 74.75 mIoU(ms+flip): 77.11 - Config: configs/ocrnet/ocrnet_hr18_512x512_20k_voc12aug.py + 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_512x512_20k_voc12aug +- Name: ocrnet_hr48_4xb4-20k_voc12aug-512x512 In Collection: OCRNet Metadata: backbone: HRNetV2p-W48 @@ -392,9 +392,9 @@ Models: Metrics: mIoU: 77.72 mIoU(ms+flip): 79.87 - Config: configs/ocrnet/ocrnet_hr48_512x512_20k_voc12aug.py + 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_512x512_40k_voc12aug +- Name: ocrnet_hr18s_4xb4-40k_voc12aug-512x512 In Collection: OCRNet Metadata: backbone: HRNetV2p-W18-Small @@ -406,9 +406,9 @@ Models: Metrics: mIoU: 72.76 mIoU(ms+flip): 74.6 - Config: configs/ocrnet/ocrnet_hr18s_512x512_40k_voc12aug.py + 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_512x512_40k_voc12aug +- Name: ocrnet_hr18_4xb4-40k_voc12aug-512x512 In Collection: OCRNet Metadata: backbone: HRNetV2p-W18 @@ -420,9 +420,9 @@ Models: Metrics: mIoU: 74.98 mIoU(ms+flip): 77.4 - Config: configs/ocrnet/ocrnet_hr18_512x512_40k_voc12aug.py + 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_512x512_40k_voc12aug +- Name: ocrnet_hr48_4xb4-40k_voc12aug-512x512 In Collection: OCRNet Metadata: backbone: HRNetV2p-W48 @@ -434,5 +434,5 @@ Models: Metrics: mIoU: 77.14 mIoU(ms+flip): 79.71 - Config: configs/ocrnet/ocrnet_hr48_512x512_40k_voc12aug.py + 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/ocrnet/ocrnet_hr18_512x1024_160k_cityscapes.py b/configs/ocrnet/ocrnet_hr18_4xb2-160k_cityscapes-512x1024.py similarity index 100% rename from configs/ocrnet/ocrnet_hr18_512x1024_160k_cityscapes.py rename to configs/ocrnet/ocrnet_hr18_4xb2-160k_cityscapes-512x1024.py diff --git a/configs/ocrnet/ocrnet_hr18_512x1024_40k_cityscapes.py b/configs/ocrnet/ocrnet_hr18_4xb2-40k_cityscapes-512x1024.py similarity index 100% rename from configs/ocrnet/ocrnet_hr18_512x1024_40k_cityscapes.py rename to configs/ocrnet/ocrnet_hr18_4xb2-40k_cityscapes-512x1024.py diff --git a/configs/ocrnet/ocrnet_hr18_512x1024_80k_cityscapes.py b/configs/ocrnet/ocrnet_hr18_4xb2-80k_cityscapes-512x1024.py similarity index 100% rename from configs/ocrnet/ocrnet_hr18_512x1024_80k_cityscapes.py rename to configs/ocrnet/ocrnet_hr18_4xb2-80k_cityscapes-512x1024.py diff --git a/configs/ocrnet/ocrnet_hr18_512x512_160k_ade20k.py b/configs/ocrnet/ocrnet_hr18_4xb4-160k_ade20k-512x512.py similarity index 100% rename from configs/ocrnet/ocrnet_hr18_512x512_160k_ade20k.py rename to configs/ocrnet/ocrnet_hr18_4xb4-160k_ade20k-512x512.py diff --git a/configs/ocrnet/ocrnet_hr18_512x512_20k_voc12aug.py b/configs/ocrnet/ocrnet_hr18_4xb4-20k_voc12aug-512x512.py similarity index 100% rename from configs/ocrnet/ocrnet_hr18_512x512_20k_voc12aug.py rename to configs/ocrnet/ocrnet_hr18_4xb4-20k_voc12aug-512x512.py diff --git a/configs/ocrnet/ocrnet_hr18_512x512_40k_voc12aug.py b/configs/ocrnet/ocrnet_hr18_4xb4-40k_voc12aug-512x512.py similarity index 100% rename from configs/ocrnet/ocrnet_hr18_512x512_40k_voc12aug.py rename to configs/ocrnet/ocrnet_hr18_4xb4-40k_voc12aug-512x512.py diff --git a/configs/ocrnet/ocrnet_hr18_512x512_80k_ade20k.py b/configs/ocrnet/ocrnet_hr18_4xb4-80k_ade20k-512x512.py similarity index 100% rename from configs/ocrnet/ocrnet_hr18_512x512_80k_ade20k.py rename to configs/ocrnet/ocrnet_hr18_4xb4-80k_ade20k-512x512.py diff --git a/configs/ocrnet/ocrnet_hr18s_4xb2-160k_cityscapes-512x1024.py b/configs/ocrnet/ocrnet_hr18s_4xb2-160k_cityscapes-512x1024.py new file mode 100644 index 0000000000..c5388fb751 --- /dev/null +++ b/configs/ocrnet/ocrnet_hr18s_4xb2-160k_cityscapes-512x1024.py @@ -0,0 +1,9 @@ +_base_ = './ocrnet_hr18_4xb2-160k_cityscapes-512x1024.py' +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w18_small', + backbone=dict( + extra=dict( + stage1=dict(num_blocks=(2, )), + stage2=dict(num_blocks=(2, 2)), + stage3=dict(num_modules=3, num_blocks=(2, 2, 2)), + stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2))))) diff --git a/configs/ocrnet/ocrnet_hr18s_4xb2-40k_cityscapes-512x1024.py b/configs/ocrnet/ocrnet_hr18s_4xb2-40k_cityscapes-512x1024.py new file mode 100644 index 0000000000..2335f3b762 --- /dev/null +++ b/configs/ocrnet/ocrnet_hr18s_4xb2-40k_cityscapes-512x1024.py @@ -0,0 +1,9 @@ +_base_ = './ocrnet_hr18_4xb2-40k_cityscapes-512x1024.py' +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w18_small', + backbone=dict( + extra=dict( + stage1=dict(num_blocks=(2, )), + stage2=dict(num_blocks=(2, 2)), + stage3=dict(num_modules=3, num_blocks=(2, 2, 2)), + stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2))))) diff --git a/configs/ocrnet/ocrnet_hr18s_4xb2-80k_cityscapes-512x1024.py b/configs/ocrnet/ocrnet_hr18s_4xb2-80k_cityscapes-512x1024.py new file mode 100644 index 0000000000..b2d1a8fa84 --- /dev/null +++ b/configs/ocrnet/ocrnet_hr18s_4xb2-80k_cityscapes-512x1024.py @@ -0,0 +1,9 @@ +_base_ = './ocrnet_hr18_4xb2-80k_cityscapes-512x1024.py' +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w18_small', + backbone=dict( + extra=dict( + stage1=dict(num_blocks=(2, )), + stage2=dict(num_blocks=(2, 2)), + stage3=dict(num_modules=3, num_blocks=(2, 2, 2)), + stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2))))) diff --git a/configs/ocrnet/ocrnet_hr18s_4xb4-160k_ade20k-512x512.py b/configs/ocrnet/ocrnet_hr18s_4xb4-160k_ade20k-512x512.py new file mode 100644 index 0000000000..fabf5826cd --- /dev/null +++ b/configs/ocrnet/ocrnet_hr18s_4xb4-160k_ade20k-512x512.py @@ -0,0 +1,9 @@ +_base_ = './ocrnet_hr18_4xb4-160k_ade20k-512x512.py' +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w18_small', + backbone=dict( + extra=dict( + stage1=dict(num_blocks=(2, )), + stage2=dict(num_blocks=(2, 2)), + stage3=dict(num_modules=3, num_blocks=(2, 2, 2)), + stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2))))) diff --git a/configs/ocrnet/ocrnet_hr18s_4xb4-20k_voc12aug-512x512.py b/configs/ocrnet/ocrnet_hr18s_4xb4-20k_voc12aug-512x512.py new file mode 100644 index 0000000000..0eca655cfc --- /dev/null +++ b/configs/ocrnet/ocrnet_hr18s_4xb4-20k_voc12aug-512x512.py @@ -0,0 +1,9 @@ +_base_ = './ocrnet_hr18_4xb4-20k_voc12aug-512x512.py' +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w18_small', + backbone=dict( + extra=dict( + stage1=dict(num_blocks=(2, )), + stage2=dict(num_blocks=(2, 2)), + stage3=dict(num_modules=3, num_blocks=(2, 2, 2)), + stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2))))) diff --git a/configs/ocrnet/ocrnet_hr18s_4xb4-40k_voc12aug-512x512.py b/configs/ocrnet/ocrnet_hr18s_4xb4-40k_voc12aug-512x512.py new file mode 100644 index 0000000000..13b02b9df6 --- /dev/null +++ b/configs/ocrnet/ocrnet_hr18s_4xb4-40k_voc12aug-512x512.py @@ -0,0 +1,9 @@ +_base_ = './ocrnet_hr18_4xb4-40k_voc12aug-512x512.py' +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w18_small', + backbone=dict( + extra=dict( + stage1=dict(num_blocks=(2, )), + stage2=dict(num_blocks=(2, 2)), + stage3=dict(num_modules=3, num_blocks=(2, 2, 2)), + stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2))))) diff --git a/configs/hrnet/fcn_hr18s_480x480_80k_pascal_context.py b/configs/ocrnet/ocrnet_hr18s_4xb4-80k_ade20k-512x512.py similarity index 86% rename from configs/hrnet/fcn_hr18s_480x480_80k_pascal_context.py rename to configs/ocrnet/ocrnet_hr18s_4xb4-80k_ade20k-512x512.py index 584b7135fd..60c79c2dc5 100644 --- a/configs/hrnet/fcn_hr18s_480x480_80k_pascal_context.py +++ b/configs/ocrnet/ocrnet_hr18s_4xb4-80k_ade20k-512x512.py @@ -1,4 +1,4 @@ -_base_ = './fcn_hr18_480x480_80k_pascal_context.py' +_base_ = './ocrnet_hr18_4xb4-80k_ade20k-512x512.py' model = dict( pretrained='open-mmlab://msra/hrnetv2_w18_small', backbone=dict( diff --git a/configs/ocrnet/ocrnet_hr18s_512x1024_160k_cityscapes.py b/configs/ocrnet/ocrnet_hr18s_512x1024_160k_cityscapes.py deleted file mode 100644 index fc7909785f..0000000000 --- a/configs/ocrnet/ocrnet_hr18s_512x1024_160k_cityscapes.py +++ /dev/null @@ -1,9 +0,0 @@ -_base_ = './ocrnet_hr18_512x1024_160k_cityscapes.py' -model = dict( - pretrained='open-mmlab://msra/hrnetv2_w18_small', - backbone=dict( - extra=dict( - stage1=dict(num_blocks=(2, )), - stage2=dict(num_blocks=(2, 2)), - stage3=dict(num_modules=3, num_blocks=(2, 2, 2)), - stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2))))) diff --git a/configs/ocrnet/ocrnet_hr18s_512x1024_40k_cityscapes.py b/configs/ocrnet/ocrnet_hr18s_512x1024_40k_cityscapes.py deleted file mode 100644 index 923731f74f..0000000000 --- a/configs/ocrnet/ocrnet_hr18s_512x1024_40k_cityscapes.py +++ /dev/null @@ -1,9 +0,0 @@ -_base_ = './ocrnet_hr18_512x1024_40k_cityscapes.py' -model = dict( - pretrained='open-mmlab://msra/hrnetv2_w18_small', - backbone=dict( - extra=dict( - stage1=dict(num_blocks=(2, )), - stage2=dict(num_blocks=(2, 2)), - stage3=dict(num_modules=3, num_blocks=(2, 2, 2)), - stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2))))) diff --git a/configs/ocrnet/ocrnet_hr18s_512x1024_80k_cityscapes.py b/configs/ocrnet/ocrnet_hr18s_512x1024_80k_cityscapes.py deleted file mode 100644 index be6bf16a2f..0000000000 --- a/configs/ocrnet/ocrnet_hr18s_512x1024_80k_cityscapes.py +++ /dev/null @@ -1,9 +0,0 @@ -_base_ = './ocrnet_hr18_512x1024_80k_cityscapes.py' -model = dict( - pretrained='open-mmlab://msra/hrnetv2_w18_small', - backbone=dict( - extra=dict( - stage1=dict(num_blocks=(2, )), - stage2=dict(num_blocks=(2, 2)), - stage3=dict(num_modules=3, num_blocks=(2, 2, 2)), - stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2))))) diff --git a/configs/ocrnet/ocrnet_hr18s_512x512_160k_ade20k.py b/configs/ocrnet/ocrnet_hr18s_512x512_160k_ade20k.py deleted file mode 100644 index 81f3d5cb91..0000000000 --- a/configs/ocrnet/ocrnet_hr18s_512x512_160k_ade20k.py +++ /dev/null @@ -1,9 +0,0 @@ -_base_ = './ocrnet_hr18_512x512_160k_ade20k.py' -model = dict( - pretrained='open-mmlab://msra/hrnetv2_w18_small', - backbone=dict( - extra=dict( - stage1=dict(num_blocks=(2, )), - stage2=dict(num_blocks=(2, 2)), - stage3=dict(num_modules=3, num_blocks=(2, 2, 2)), - stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2))))) diff --git a/configs/ocrnet/ocrnet_hr18s_512x512_20k_voc12aug.py b/configs/ocrnet/ocrnet_hr18s_512x512_20k_voc12aug.py deleted file mode 100644 index ceb944815b..0000000000 --- a/configs/ocrnet/ocrnet_hr18s_512x512_20k_voc12aug.py +++ /dev/null @@ -1,9 +0,0 @@ -_base_ = './ocrnet_hr18_512x512_20k_voc12aug.py' -model = dict( - pretrained='open-mmlab://msra/hrnetv2_w18_small', - backbone=dict( - extra=dict( - stage1=dict(num_blocks=(2, )), - stage2=dict(num_blocks=(2, 2)), - stage3=dict(num_modules=3, num_blocks=(2, 2, 2)), - stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2))))) diff --git a/configs/ocrnet/ocrnet_hr18s_512x512_40k_voc12aug.py b/configs/ocrnet/ocrnet_hr18s_512x512_40k_voc12aug.py deleted file mode 100644 index 70babc91c9..0000000000 --- a/configs/ocrnet/ocrnet_hr18s_512x512_40k_voc12aug.py +++ /dev/null @@ -1,9 +0,0 @@ -_base_ = './ocrnet_hr18_512x512_40k_voc12aug.py' -model = dict( - pretrained='open-mmlab://msra/hrnetv2_w18_small', - backbone=dict( - extra=dict( - stage1=dict(num_blocks=(2, )), - stage2=dict(num_blocks=(2, 2)), - stage3=dict(num_modules=3, num_blocks=(2, 2, 2)), - stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2))))) diff --git a/configs/ocrnet/ocrnet_hr18s_512x512_80k_ade20k.py b/configs/ocrnet/ocrnet_hr18s_512x512_80k_ade20k.py deleted file mode 100644 index 36e77219ac..0000000000 --- a/configs/ocrnet/ocrnet_hr18s_512x512_80k_ade20k.py +++ /dev/null @@ -1,9 +0,0 @@ -_base_ = './ocrnet_hr18_512x512_80k_ade20k.py' -model = dict( - pretrained='open-mmlab://msra/hrnetv2_w18_small', - backbone=dict( - extra=dict( - stage1=dict(num_blocks=(2, )), - stage2=dict(num_blocks=(2, 2)), - stage3=dict(num_modules=3, num_blocks=(2, 2, 2)), - stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2))))) diff --git a/configs/ocrnet/ocrnet_hr48_512x1024_160k_cityscapes.py b/configs/ocrnet/ocrnet_hr48_4xb2-160k_cityscapes-512x1024.py similarity index 95% rename from configs/ocrnet/ocrnet_hr48_512x1024_160k_cityscapes.py rename to configs/ocrnet/ocrnet_hr48_4xb2-160k_cityscapes-512x1024.py index c094391b1d..184d38dd2c 100644 --- a/configs/ocrnet/ocrnet_hr48_512x1024_160k_cityscapes.py +++ b/configs/ocrnet/ocrnet_hr48_4xb2-160k_cityscapes-512x1024.py @@ -1,4 +1,4 @@ -_base_ = './ocrnet_hr18_512x1024_160k_cityscapes.py' +_base_ = './ocrnet_hr18_4xb2-160k_cityscapes-512x1024.py' norm_cfg = dict(type='SyncBN', requires_grad=True) model = dict( pretrained='open-mmlab://msra/hrnetv2_w48', diff --git a/configs/ocrnet/ocrnet_hr48_512x1024_40k_cityscapes.py b/configs/ocrnet/ocrnet_hr48_4xb2-40k_cityscapes-512x1024.py similarity index 95% rename from configs/ocrnet/ocrnet_hr48_512x1024_40k_cityscapes.py rename to configs/ocrnet/ocrnet_hr48_4xb2-40k_cityscapes-512x1024.py index 0aada9d8dc..7025ee9e77 100644 --- a/configs/ocrnet/ocrnet_hr48_512x1024_40k_cityscapes.py +++ b/configs/ocrnet/ocrnet_hr48_4xb2-40k_cityscapes-512x1024.py @@ -1,4 +1,4 @@ -_base_ = './ocrnet_hr18_512x1024_40k_cityscapes.py' +_base_ = './ocrnet_hr18_4xb2-40k_cityscapes-512x1024.py' norm_cfg = dict(type='SyncBN', requires_grad=True) model = dict( pretrained='open-mmlab://msra/hrnetv2_w48', diff --git a/configs/ocrnet/ocrnet_hr48_512x1024_80k_cityscapes.py b/configs/ocrnet/ocrnet_hr48_4xb2-80k_cityscapes-512x1024.py similarity index 95% rename from configs/ocrnet/ocrnet_hr48_512x1024_80k_cityscapes.py rename to configs/ocrnet/ocrnet_hr48_4xb2-80k_cityscapes-512x1024.py index 1b2e009439..9c68a15fc5 100644 --- a/configs/ocrnet/ocrnet_hr48_512x1024_80k_cityscapes.py +++ b/configs/ocrnet/ocrnet_hr48_4xb2-80k_cityscapes-512x1024.py @@ -1,4 +1,4 @@ -_base_ = './ocrnet_hr18_512x1024_80k_cityscapes.py' +_base_ = './ocrnet_hr18_4xb2-80k_cityscapes-512x1024.py' norm_cfg = dict(type='SyncBN', requires_grad=True) model = dict( pretrained='open-mmlab://msra/hrnetv2_w48', diff --git a/configs/ocrnet/ocrnet_hr48_512x512_160k_ade20k.py b/configs/ocrnet/ocrnet_hr48_4xb4-160k_ade20k-512x512.py similarity index 96% rename from configs/ocrnet/ocrnet_hr48_512x512_160k_ade20k.py rename to configs/ocrnet/ocrnet_hr48_4xb4-160k_ade20k-512x512.py index 3b3e8af953..e74976c805 100644 --- a/configs/ocrnet/ocrnet_hr48_512x512_160k_ade20k.py +++ b/configs/ocrnet/ocrnet_hr48_4xb4-160k_ade20k-512x512.py @@ -1,4 +1,4 @@ -_base_ = './ocrnet_hr18_512x512_160k_ade20k.py' +_base_ = './ocrnet_hr18_4xb4-160k_ade20k-512x512.py' norm_cfg = dict(type='SyncBN', requires_grad=True) model = dict( pretrained='open-mmlab://msra/hrnetv2_w48', diff --git a/configs/ocrnet/ocrnet_hr48_512x512_20k_voc12aug.py b/configs/ocrnet/ocrnet_hr48_4xb4-20k_voc12aug-512x512.py similarity index 96% rename from configs/ocrnet/ocrnet_hr48_512x512_20k_voc12aug.py rename to configs/ocrnet/ocrnet_hr48_4xb4-20k_voc12aug-512x512.py index c2dd6d1158..f015b920e1 100644 --- a/configs/ocrnet/ocrnet_hr48_512x512_20k_voc12aug.py +++ b/configs/ocrnet/ocrnet_hr48_4xb4-20k_voc12aug-512x512.py @@ -1,4 +1,4 @@ -_base_ = './ocrnet_hr18_512x512_20k_voc12aug.py' +_base_ = './ocrnet_hr18_4xb4-20k_voc12aug-512x512.py' norm_cfg = dict(type='SyncBN', requires_grad=True) model = dict( pretrained='open-mmlab://msra/hrnetv2_w48', diff --git a/configs/ocrnet/ocrnet_hr48_512x512_40k_voc12aug.py b/configs/ocrnet/ocrnet_hr48_4xb4-40k_voc12aug-512x512.py similarity index 96% rename from configs/ocrnet/ocrnet_hr48_512x512_40k_voc12aug.py rename to configs/ocrnet/ocrnet_hr48_4xb4-40k_voc12aug-512x512.py index 89e6309f55..baafa380d4 100644 --- a/configs/ocrnet/ocrnet_hr48_512x512_40k_voc12aug.py +++ b/configs/ocrnet/ocrnet_hr48_4xb4-40k_voc12aug-512x512.py @@ -1,4 +1,4 @@ -_base_ = './ocrnet_hr18_512x512_40k_voc12aug.py' +_base_ = './ocrnet_hr18_4xb4-40k_voc12aug-512x512.py' norm_cfg = dict(type='SyncBN', requires_grad=True) model = dict( pretrained='open-mmlab://msra/hrnetv2_w48', diff --git a/configs/ocrnet/ocrnet_hr48_512x512_80k_ade20k.py b/configs/ocrnet/ocrnet_hr48_4xb4-80k_ade20k-512x512.py similarity index 96% rename from configs/ocrnet/ocrnet_hr48_512x512_80k_ade20k.py rename to configs/ocrnet/ocrnet_hr48_4xb4-80k_ade20k-512x512.py index 04971226eb..85514b9d7e 100644 --- a/configs/ocrnet/ocrnet_hr48_512x512_80k_ade20k.py +++ b/configs/ocrnet/ocrnet_hr48_4xb4-80k_ade20k-512x512.py @@ -1,4 +1,4 @@ -_base_ = './ocrnet_hr18_512x512_80k_ade20k.py' +_base_ = './ocrnet_hr18_4xb4-80k_ade20k-512x512.py' norm_cfg = dict(type='SyncBN', requires_grad=True) model = dict( pretrained='open-mmlab://msra/hrnetv2_w48', diff --git a/configs/ocrnet/ocrnet_r101-d8_512x1024_40k_b8_cityscapes.py b/configs/ocrnet/ocrnet_r101-d8_4xb2-40k_cityscapes-512x1024.py similarity index 100% rename from configs/ocrnet/ocrnet_r101-d8_512x1024_40k_b8_cityscapes.py rename to configs/ocrnet/ocrnet_r101-d8_4xb2-40k_cityscapes-512x1024.py diff --git a/configs/ocrnet/ocrnet_r101-d8_512x1024_40k_b16_cityscapes.py b/configs/ocrnet/ocrnet_r101-d8_8xb2-40k_cityscapes-512x1024.py similarity index 100% rename from configs/ocrnet/ocrnet_r101-d8_512x1024_40k_b16_cityscapes.py rename to configs/ocrnet/ocrnet_r101-d8_8xb2-40k_cityscapes-512x1024.py diff --git a/configs/ocrnet/ocrnet_r101-d8_512x1024_80k_b16_cityscapes.py b/configs/ocrnet/ocrnet_r101-d8_8xb2-80k_cityscapes-512x1024.py similarity index 100% rename from configs/ocrnet/ocrnet_r101-d8_512x1024_80k_b16_cityscapes.py rename to configs/ocrnet/ocrnet_r101-d8_8xb2-80k_cityscapes-512x1024.py diff --git a/configs/point_rend/README.md b/configs/point_rend/README.md index 2644f46c6b..2690e7b9e6 100644 --- a/configs/point_rend/README.md +++ b/configs/point_rend/README.md @@ -38,14 +38,14 @@ We present a new method for efficient high-quality image segmentation of objects ### 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/master/configs/point_rend/pointrend_r50_512x1024_80k_cityscapes.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/master/configs/point_rend/pointrend_r101_512x1024_80k_cityscapes.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) | +| 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/master/configs/point_rend/pointrend_r50_512x512_160k_ade20k.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/master/configs/point_rend/pointrend_r101_512x512_160k_ade20k.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) | +| 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/point_rend.yml b/configs/point_rend/point_rend.yml index 3abe81d7d6..a4539081f3 100644 --- a/configs/point_rend/point_rend.yml +++ b/configs/point_rend/point_rend.yml @@ -14,7 +14,7 @@ Collections: Converted From: Code: https://github.com/facebookresearch/detectron2/tree/master/projects/PointRend Models: -- Name: pointrend_r50_512x1024_80k_cityscapes +- Name: pointrend_r50_4xb2-80k_cityscapes-512x1024 In Collection: PointRend Metadata: backbone: R-50 @@ -34,9 +34,9 @@ Models: Metrics: mIoU: 76.47 mIoU(ms+flip): 78.13 - Config: configs/point_rend/pointrend_r50_512x1024_80k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: pointrend_r101_4xb2-80k_cityscapes-512x1024 In Collection: PointRend Metadata: backbone: R-101 @@ -56,9 +56,9 @@ Models: Metrics: mIoU: 78.3 mIoU(ms+flip): 79.97 - Config: configs/point_rend/pointrend_r101_512x1024_80k_cityscapes.py + 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_512x512_160k_ade20k +- Name: pointrend_r50_4xb4-160k_ade20k-512x512 In Collection: PointRend Metadata: backbone: R-50 @@ -78,9 +78,9 @@ Models: Metrics: mIoU: 37.64 mIoU(ms+flip): 39.17 - Config: configs/point_rend/pointrend_r50_512x512_160k_ade20k.py + 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_512x512_160k_ade20k +- Name: pointrend_r101_4xb4-160k_ade20k-512x512 In Collection: PointRend Metadata: backbone: R-101 @@ -100,5 +100,5 @@ Models: Metrics: mIoU: 40.02 mIoU(ms+flip): 41.6 - Config: configs/point_rend/pointrend_r101_512x512_160k_ade20k.py + 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/point_rend/pointrend_r101_4xb2-80k_cityscapes-512x1024.py b/configs/point_rend/pointrend_r101_4xb2-80k_cityscapes-512x1024.py new file mode 100644 index 0000000000..ca2a19a196 --- /dev/null +++ b/configs/point_rend/pointrend_r101_4xb2-80k_cityscapes-512x1024.py @@ -0,0 +1,2 @@ +_base_ = './pointrend_r50_4xb2-80k_cityscapes-512x1024.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/point_rend/pointrend_r101_4xb4-160k_ade20k-512x512.py b/configs/point_rend/pointrend_r101_4xb4-160k_ade20k-512x512.py new file mode 100644 index 0000000000..6729d3b672 --- /dev/null +++ b/configs/point_rend/pointrend_r101_4xb4-160k_ade20k-512x512.py @@ -0,0 +1,2 @@ +_base_ = './pointrend_r50_4xb4-160k_ade20k-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/point_rend/pointrend_r101_512x1024_80k_cityscapes.py b/configs/point_rend/pointrend_r101_512x1024_80k_cityscapes.py deleted file mode 100644 index a8c14c8cf9..0000000000 --- a/configs/point_rend/pointrend_r101_512x1024_80k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './pointrend_r50_512x1024_80k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/point_rend/pointrend_r101_512x512_160k_ade20k.py b/configs/point_rend/pointrend_r101_512x512_160k_ade20k.py deleted file mode 100644 index 4d1f8c8154..0000000000 --- a/configs/point_rend/pointrend_r101_512x512_160k_ade20k.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './pointrend_r50_512x512_160k_ade20k.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/point_rend/pointrend_r50_512x1024_80k_cityscapes.py b/configs/point_rend/pointrend_r50_4xb2-80k_cityscapes-512x1024.py similarity index 100% rename from configs/point_rend/pointrend_r50_512x1024_80k_cityscapes.py rename to configs/point_rend/pointrend_r50_4xb2-80k_cityscapes-512x1024.py diff --git a/configs/point_rend/pointrend_r50_512x512_160k_ade20k.py b/configs/point_rend/pointrend_r50_4xb4-160k_ade20k-512x512.py similarity index 100% rename from configs/point_rend/pointrend_r50_512x512_160k_ade20k.py rename to configs/point_rend/pointrend_r50_4xb4-160k_ade20k-512x512.py diff --git a/configs/psanet/README.md b/configs/psanet/README.md index 9f307b2d29..7182e500a1 100644 --- a/configs/psanet/README.md +++ b/configs/psanet/README.md @@ -38,31 +38,31 @@ We notice information flow in convolutional neural networksis restricted insid ### 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/master/configs/psanet/psanet_r50-d8_512x1024_40k_cityscapes.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/master/configs/psanet/psanet_r101-d8_512x1024_40k_cityscapes.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/master/configs/psanet/psanet_r50-d8_769x769_40k_cityscapes.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/master/configs/psanet/psanet_r101-d8_769x769_40k_cityscapes.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/master/configs/psanet/psanet_r50-d8_512x1024_80k_cityscapes.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/master/configs/psanet/psanet_r101-d8_512x1024_80k_cityscapes.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/master/configs/psanet/psanet_r50-d8_769x769_80k_cityscapes.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/master/configs/psanet/psanet_r101-d8_769x769_80k_cityscapes.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) | +| 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/master/configs/psanet/psanet_r50-d8_512x512_80k_ade20k.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/master/configs/psanet/psanet_r101-d8_512x512_80k_ade20k.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/master/configs/psanet/psanet_r50-d8_512x512_160k_ade20k.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/master/configs/psanet/psanet_r101-d8_512x512_160k_ade20k.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) | +| 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/master/configs/psanet/psanet_r50-d8_512x512_20k_voc12aug.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/master/configs/psanet/psanet_r101-d8_512x512_20k_voc12aug.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/master/configs/psanet/psanet_r50-d8_512x512_40k_voc12aug.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/master/configs/psanet/psanet_r101-d8_512x512_40k_voc12aug.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) | +| 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/psanet.yml b/configs/psanet/psanet.yml index 353c890c8f..fca1ac1b40 100644 --- a/configs/psanet/psanet.yml +++ b/configs/psanet/psanet.yml @@ -15,7 +15,7 @@ Collections: Converted From: Code: https://github.com/hszhao/PSANet Models: -- Name: psanet_r50-d8_512x1024_40k_cityscapes +- Name: psanet_r50-d8_4xb2-40k_cityscapes-512x1024 In Collection: PSANet Metadata: backbone: R-50-D8 @@ -35,9 +35,9 @@ Models: Metrics: mIoU: 77.63 mIoU(ms+flip): 79.04 - Config: configs/psanet/psanet_r50-d8_512x1024_40k_cityscapes.py + 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_512x1024_40k_cityscapes +- Name: psanet_r101-d8_4xb2-40k_cityscapes-512x1024 In Collection: PSANet Metadata: backbone: R-101-D8 @@ -57,9 +57,9 @@ Models: Metrics: mIoU: 79.14 mIoU(ms+flip): 80.19 - Config: configs/psanet/psanet_r101-d8_512x1024_40k_cityscapes.py + 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_769x769_40k_cityscapes +- Name: psanet_r50-d8_4xb2-40k_cityscapes-769x769 In Collection: PSANet Metadata: backbone: R-50-D8 @@ -79,9 +79,9 @@ Models: Metrics: mIoU: 77.99 mIoU(ms+flip): 79.64 - Config: configs/psanet/psanet_r50-d8_769x769_40k_cityscapes.py + 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_769x769_40k_cityscapes +- Name: psanet_r101-d8_4xb2-40k_cityscapes-769x769 In Collection: PSANet Metadata: backbone: R-101-D8 @@ -101,9 +101,9 @@ Models: Metrics: mIoU: 78.43 mIoU(ms+flip): 80.26 - Config: configs/psanet/psanet_r101-d8_769x769_40k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: psanet_r50-d8_4xb2-80k_cityscapes-512x1024 In Collection: PSANet Metadata: backbone: R-50-D8 @@ -115,9 +115,9 @@ Models: Metrics: mIoU: 77.24 mIoU(ms+flip): 78.69 - Config: configs/psanet/psanet_r50-d8_512x1024_80k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: psanet_r101-d8_4xb2-80k_cityscapes-512x1024 In Collection: PSANet Metadata: backbone: R-101-D8 @@ -129,9 +129,9 @@ Models: Metrics: mIoU: 79.31 mIoU(ms+flip): 80.53 - Config: configs/psanet/psanet_r101-d8_512x1024_80k_cityscapes.py + 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_769x769_80k_cityscapes +- Name: psanet_r50-d8_4xb2-80k_cityscapes-769x769 In Collection: PSANet Metadata: backbone: R-50-D8 @@ -143,9 +143,9 @@ Models: Metrics: mIoU: 79.31 mIoU(ms+flip): 80.91 - Config: configs/psanet/psanet_r50-d8_769x769_80k_cityscapes.py + 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_769x769_80k_cityscapes +- Name: psanet_r101-d8_4xb2-80k_cityscapes-769x769 In Collection: PSANet Metadata: backbone: R-101-D8 @@ -157,9 +157,9 @@ Models: Metrics: mIoU: 79.69 mIoU(ms+flip): 80.89 - Config: configs/psanet/psanet_r101-d8_769x769_80k_cityscapes.py + 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_512x512_80k_ade20k +- Name: psanet_r50-d8_4xb4-80k_ade20k-512x512 In Collection: PSANet Metadata: backbone: R-50-D8 @@ -179,9 +179,9 @@ Models: Metrics: mIoU: 41.14 mIoU(ms+flip): 41.91 - Config: configs/psanet/psanet_r50-d8_512x512_80k_ade20k.py + 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_512x512_80k_ade20k +- Name: psanet_r101-d8_4xb4-80k_ade20k-512x512 In Collection: PSANet Metadata: backbone: R-101-D8 @@ -201,9 +201,9 @@ Models: Metrics: mIoU: 43.8 mIoU(ms+flip): 44.75 - Config: configs/psanet/psanet_r101-d8_512x512_80k_ade20k.py + 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_512x512_160k_ade20k +- Name: psanet_r50-d8_4xb4-160k_ade20k-512x512 In Collection: PSANet Metadata: backbone: R-50-D8 @@ -215,9 +215,9 @@ Models: Metrics: mIoU: 41.67 mIoU(ms+flip): 42.95 - Config: configs/psanet/psanet_r50-d8_512x512_160k_ade20k.py + 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_512x512_160k_ade20k +- Name: psanet_r101-d8_4xb4-160k_ade20k-512x512 In Collection: PSANet Metadata: backbone: R-101-D8 @@ -229,9 +229,9 @@ Models: Metrics: mIoU: 43.74 mIoU(ms+flip): 45.38 - Config: configs/psanet/psanet_r101-d8_512x512_160k_ade20k.py + 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_512x512_20k_voc12aug +- Name: psanet_r50-d8_4xb4-20k_voc12aug-512x512 In Collection: PSANet Metadata: backbone: R-50-D8 @@ -251,9 +251,9 @@ Models: Metrics: mIoU: 76.39 mIoU(ms+flip): 77.34 - Config: configs/psanet/psanet_r50-d8_512x512_20k_voc12aug.py + 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_512x512_20k_voc12aug +- Name: psanet_r101-d8_4xb4-20k_voc12aug-512x512 In Collection: PSANet Metadata: backbone: R-101-D8 @@ -273,9 +273,9 @@ Models: Metrics: mIoU: 77.91 mIoU(ms+flip): 79.3 - Config: configs/psanet/psanet_r101-d8_512x512_20k_voc12aug.py + 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_512x512_40k_voc12aug +- Name: psanet_r50-d8_4xb4-40k_voc12aug-512x512 In Collection: PSANet Metadata: backbone: R-50-D8 @@ -287,9 +287,9 @@ Models: Metrics: mIoU: 76.3 mIoU(ms+flip): 77.35 - Config: configs/psanet/psanet_r50-d8_512x512_40k_voc12aug.py + 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_512x512_40k_voc12aug +- Name: psanet_r101-d8_4xb4-40k_voc12aug-512x512 In Collection: PSANet Metadata: backbone: R-101-D8 @@ -301,5 +301,5 @@ Models: Metrics: mIoU: 77.73 mIoU(ms+flip): 79.05 - Config: configs/psanet/psanet_r101-d8_512x512_40k_voc12aug.py + 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/psanet/psanet_r101-d8_4xb2-40k_cityscapes-512x1024.py b/configs/psanet/psanet_r101-d8_4xb2-40k_cityscapes-512x1024.py new file mode 100644 index 0000000000..e69cf42703 --- /dev/null +++ b/configs/psanet/psanet_r101-d8_4xb2-40k_cityscapes-512x1024.py @@ -0,0 +1,2 @@ +_base_ = './psanet_r50-d8_4xb2-40k_cityscapes-512x1024.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/psanet/psanet_r101-d8_4xb2-40k_cityscapes-769x769.py b/configs/psanet/psanet_r101-d8_4xb2-40k_cityscapes-769x769.py new file mode 100644 index 0000000000..e543099842 --- /dev/null +++ b/configs/psanet/psanet_r101-d8_4xb2-40k_cityscapes-769x769.py @@ -0,0 +1,2 @@ +_base_ = './psanet_r50-d8_4xb2-40k_cityscapes-769x769.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/psanet/psanet_r101-d8_4xb2-80k_cityscapes-512x1024.py b/configs/psanet/psanet_r101-d8_4xb2-80k_cityscapes-512x1024.py new file mode 100644 index 0000000000..b8636384d0 --- /dev/null +++ b/configs/psanet/psanet_r101-d8_4xb2-80k_cityscapes-512x1024.py @@ -0,0 +1,2 @@ +_base_ = './psanet_r50-d8_4xb2-80k_cityscapes-512x1024.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/psanet/psanet_r101-d8_4xb2-80k_cityscapes-769x769.py b/configs/psanet/psanet_r101-d8_4xb2-80k_cityscapes-769x769.py new file mode 100644 index 0000000000..097b1c58ce --- /dev/null +++ b/configs/psanet/psanet_r101-d8_4xb2-80k_cityscapes-769x769.py @@ -0,0 +1,2 @@ +_base_ = './psanet_r50-d8_4xb2-80k_cityscapes-769x769.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/psanet/psanet_r101-d8_4xb4-160k_ade20k-512x512.py b/configs/psanet/psanet_r101-d8_4xb4-160k_ade20k-512x512.py new file mode 100644 index 0000000000..ac86306cb6 --- /dev/null +++ b/configs/psanet/psanet_r101-d8_4xb4-160k_ade20k-512x512.py @@ -0,0 +1,2 @@ +_base_ = './psanet_r50-d8_4xb4-160k_ade20k-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/psanet/psanet_r101-d8_4xb4-20k_voc12aug-512x512.py b/configs/psanet/psanet_r101-d8_4xb4-20k_voc12aug-512x512.py new file mode 100644 index 0000000000..abd8e56512 --- /dev/null +++ b/configs/psanet/psanet_r101-d8_4xb4-20k_voc12aug-512x512.py @@ -0,0 +1,2 @@ +_base_ = './psanet_r50-d8_4xb4-20k_voc12aug-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/psanet/psanet_r101-d8_4xb4-40k_voc12aug-512x512.py b/configs/psanet/psanet_r101-d8_4xb4-40k_voc12aug-512x512.py new file mode 100644 index 0000000000..d3154a8f14 --- /dev/null +++ b/configs/psanet/psanet_r101-d8_4xb4-40k_voc12aug-512x512.py @@ -0,0 +1,2 @@ +_base_ = './psanet_r50-d8_4xb4-40k_voc12aug-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/psanet/psanet_r101-d8_4xb4-80k_ade20k-512x512.py b/configs/psanet/psanet_r101-d8_4xb4-80k_ade20k-512x512.py new file mode 100644 index 0000000000..b34d4248e8 --- /dev/null +++ b/configs/psanet/psanet_r101-d8_4xb4-80k_ade20k-512x512.py @@ -0,0 +1,2 @@ +_base_ = './psanet_r50-d8_4xb4-80k_ade20k-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/psanet/psanet_r101-d8_512x1024_40k_cityscapes.py b/configs/psanet/psanet_r101-d8_512x1024_40k_cityscapes.py deleted file mode 100644 index 69d212f158..0000000000 --- a/configs/psanet/psanet_r101-d8_512x1024_40k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './psanet_r50-d8_512x1024_40k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/psanet/psanet_r101-d8_512x1024_80k_cityscapes.py b/configs/psanet/psanet_r101-d8_512x1024_80k_cityscapes.py deleted file mode 100644 index bc25d6aaf6..0000000000 --- a/configs/psanet/psanet_r101-d8_512x1024_80k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './psanet_r50-d8_512x1024_80k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/psanet/psanet_r101-d8_512x512_160k_ade20k.py b/configs/psanet/psanet_r101-d8_512x512_160k_ade20k.py deleted file mode 100644 index 7f6795e5ef..0000000000 --- a/configs/psanet/psanet_r101-d8_512x512_160k_ade20k.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './psanet_r50-d8_512x512_160k_ade20k.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/psanet/psanet_r101-d8_512x512_20k_voc12aug.py b/configs/psanet/psanet_r101-d8_512x512_20k_voc12aug.py deleted file mode 100644 index 1a3c43495b..0000000000 --- a/configs/psanet/psanet_r101-d8_512x512_20k_voc12aug.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './psanet_r50-d8_512x512_20k_voc12aug.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/psanet/psanet_r101-d8_512x512_40k_voc12aug.py b/configs/psanet/psanet_r101-d8_512x512_40k_voc12aug.py deleted file mode 100644 index f62eef9773..0000000000 --- a/configs/psanet/psanet_r101-d8_512x512_40k_voc12aug.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './psanet_r50-d8_512x512_40k_voc12aug.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/psanet/psanet_r101-d8_512x512_80k_ade20k.py b/configs/psanet/psanet_r101-d8_512x512_80k_ade20k.py deleted file mode 100644 index f8865a7c4d..0000000000 --- a/configs/psanet/psanet_r101-d8_512x512_80k_ade20k.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './psanet_r50-d8_512x512_80k_ade20k.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/psanet/psanet_r101-d8_769x769_40k_cityscapes.py b/configs/psanet/psanet_r101-d8_769x769_40k_cityscapes.py deleted file mode 100644 index ffc99f0109..0000000000 --- a/configs/psanet/psanet_r101-d8_769x769_40k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './psanet_r50-d8_769x769_40k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/psanet/psanet_r101-d8_769x769_80k_cityscapes.py b/configs/psanet/psanet_r101-d8_769x769_80k_cityscapes.py deleted file mode 100644 index 6a9efc55ad..0000000000 --- a/configs/psanet/psanet_r101-d8_769x769_80k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './psanet_r50-d8_769x769_80k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/psanet/psanet_r50-d8_512x1024_40k_cityscapes.py b/configs/psanet/psanet_r50-d8_4xb2-40k_cityscapes-512x1024.py similarity index 100% rename from configs/psanet/psanet_r50-d8_512x1024_40k_cityscapes.py rename to configs/psanet/psanet_r50-d8_4xb2-40k_cityscapes-512x1024.py diff --git a/configs/psanet/psanet_r50-d8_769x769_40k_cityscapes.py b/configs/psanet/psanet_r50-d8_4xb2-40k_cityscapes-769x769.py similarity index 100% rename from configs/psanet/psanet_r50-d8_769x769_40k_cityscapes.py rename to configs/psanet/psanet_r50-d8_4xb2-40k_cityscapes-769x769.py diff --git a/configs/psanet/psanet_r50-d8_512x1024_80k_cityscapes.py b/configs/psanet/psanet_r50-d8_4xb2-80k_cityscapes-512x1024.py similarity index 100% rename from configs/psanet/psanet_r50-d8_512x1024_80k_cityscapes.py rename to configs/psanet/psanet_r50-d8_4xb2-80k_cityscapes-512x1024.py diff --git a/configs/psanet/psanet_r50-d8_769x769_80k_cityscapes.py b/configs/psanet/psanet_r50-d8_4xb2-80k_cityscapes-769x769.py similarity index 100% rename from configs/psanet/psanet_r50-d8_769x769_80k_cityscapes.py rename to configs/psanet/psanet_r50-d8_4xb2-80k_cityscapes-769x769.py diff --git a/configs/psanet/psanet_r50-d8_512x512_160k_ade20k.py b/configs/psanet/psanet_r50-d8_4xb4-160k_ade20k-512x512.py similarity index 100% rename from configs/psanet/psanet_r50-d8_512x512_160k_ade20k.py rename to configs/psanet/psanet_r50-d8_4xb4-160k_ade20k-512x512.py diff --git a/configs/psanet/psanet_r50-d8_512x512_20k_voc12aug.py b/configs/psanet/psanet_r50-d8_4xb4-20k_voc12aug-512x512.py similarity index 100% rename from configs/psanet/psanet_r50-d8_512x512_20k_voc12aug.py rename to configs/psanet/psanet_r50-d8_4xb4-20k_voc12aug-512x512.py diff --git a/configs/psanet/psanet_r50-d8_512x512_40k_voc12aug.py b/configs/psanet/psanet_r50-d8_4xb4-40k_voc12aug-512x512.py similarity index 100% rename from configs/psanet/psanet_r50-d8_512x512_40k_voc12aug.py rename to configs/psanet/psanet_r50-d8_4xb4-40k_voc12aug-512x512.py diff --git a/configs/psanet/psanet_r50-d8_512x512_80k_ade20k.py b/configs/psanet/psanet_r50-d8_4xb4-80k_ade20k-512x512.py similarity index 100% rename from configs/psanet/psanet_r50-d8_512x512_80k_ade20k.py rename to configs/psanet/psanet_r50-d8_4xb4-80k_ade20k-512x512.py diff --git a/configs/pspnet/README.md b/configs/pspnet/README.md index 83da76bc34..2194a28e53 100644 --- a/configs/pspnet/README.md +++ b/configs/pspnet/README.md @@ -46,128 +46,128 @@ Scene parsing is challenging for unrestricted open vocabulary and diverse scenes ### 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/master/configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.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/master/configs/pspnet/pspnet_r101-d8_512x1024_40k_cityscapes.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/master/configs/pspnet/pspnet_r50-d8_769x769_40k_cityscapes.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/master/configs/pspnet/pspnet_r101-d8_769x769_40k_cityscapes.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/master/configs/pspnet/pspnet_r18-d8_512x1024_80k_cityscapes.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/master/configs/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes.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/master/configs/pspnet/pspnet_r50-d8_rsb-pretrain_512x1024_adamw_80k_cityscapes.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/master/configs/pspnet/pspnet_r101-d8_512x1024_80k_cityscapes.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/master/configs/pspnet/pspnet_r101-d8_fp16_512x1024_80k_cityscapes.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/master/configs/pspnet/pspnet_r18-d8_769x769_80k_cityscapes.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/master/configs/pspnet/pspnet_r50-d8_769x769_80k_cityscapes.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/master/configs/pspnet/pspnet_r101-d8_769x769_80k_cityscapes.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/master/configs/pspnet/pspnet_r18b-d8_512x1024_80k_cityscapes.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/master/configs/pspnet/pspnet_r50b-d8_512x1024_80k_cityscapes.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/master/configs/pspnet/pspnet_r101b-d8_512x1024_80k_cityscapes.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/master/configs/pspnet/pspnet_r18b-d8_769x769_80k_cityscapes.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/master/configs/pspnet/pspnet_r50b-d8_769x769_80k_cityscapes.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/master/configs/pspnet/pspnet_r101b-d8_769x769_80k_cityscapes.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/master/configs/pspnet/pspnet_r50-d32_512x1024_80k_cityscapes.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/master/configs/pspnet/pspnet_r50-d32_rsb-pretrain_512x1024_adamw_80k_cityscapes.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/master/configs/pspnet/pspnet_r50b-d32_512x1024_80k_cityscapes.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) | 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) | ### 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/master/configs/pspnet/pspnet_r50-d8_512x512_80k_ade20k.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/master/configs/pspnet/pspnet_r101-d8_512x512_80k_ade20k.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/master/configs/pspnet/pspnet_r50-d8_512x512_160k_ade20k.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/master/configs/pspnet/pspnet_r101-d8_512x512_160k_ade20k.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) | 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) | ### 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/master/configs/pspnet/pspnet_r50-d8_512x512_20k_voc12aug.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/master/configs/pspnet/pspnet_r101-d8_512x512_20k_voc12aug.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/master/configs/pspnet/pspnet_r50-d8_512x512_40k_voc12aug.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/master/configs/pspnet/pspnet_r101-d8_512x512_40k_voc12aug.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) | 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) | ### 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/master/configs/pspnet/pspnet_r101-d8_480x480_40k_pascal_context.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/master/configs/pspnet/pspnet_r101-d8_480x480_80k_pascal_context.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) | 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) | ### 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/master/configs/pspnet/pspnet_r101-d8_480x480_40k_pascal_context_59.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/master/configs/pspnet/pspnet_r101-d8_480x480_80k_pascal_context_59.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) | 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) | ### Dark Zurich and Nighttime Driving We support evaluation results on these two datasets using models above trained on Cityscapes training set. -| Method | Backbone | Training Dataset | Test Dataset | mIoU | config | evaluation checkpoint | -| ------ | --------- | ----------------------- | ------------------------- | ----- | ------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | -| PSPNet | R-50-D8 | Cityscapes Training set | Dark Zurich | 10.91 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r50-d8_512x1024_40k_dark.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-50-D8 | Cityscapes Training set | Nighttime Driving | 23.02 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r50-d8_512x1024_40k_night_driving.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-50-D8 | Cityscapes Training set | Cityscapes Validation set | 77.85 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.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 | Cityscapes Training set | Dark Zurich | 10.16 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r101-d8_512x1024_40k_dark.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-101-D8 | Cityscapes Training set | Nighttime Driving | 20.25 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r101-d8_512x1024_40k_night_driving.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-101-D8 | Cityscapes Training set | Cityscapes Validation set | 78.34 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r101-d8_512x1024_40k_cityscapes.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-101b-D8 | Cityscapes Training set | Dark Zurich | 15.54 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r101b-d8_512x1024_80k_dark.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-101b-D8 | Cityscapes Training set | Nighttime Driving | 22.25 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r101b-d8_512x1024_80k_night_driving.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-101b-D8 | Cityscapes Training set | Cityscapes Validation set | 79.69 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r101b-d8_512x1024_80k_cityscapes.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) | +| Method | Backbone | Training Dataset | Test Dataset | mIoU | config | evaluation checkpoint | +| ------ | --------- | ----------------------- | ------------------------- | ----- | ---------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| PSPNet | R-50-D8 | Cityscapes Training set | Dark Zurich | 10.91 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r50-d8_4xb2-40k_cityscapes-512x1024_dark-zurich-1920x1080.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-50-D8 | Cityscapes Training set | Nighttime Driving | 23.02 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r50-d8_4xb2-40k_cityscapes-512x1024_night-driving-1920x1080.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-50-D8 | Cityscapes Training set | Cityscapes Validation set | 77.85 | [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 | Cityscapes Training set | Dark Zurich | 10.16 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r101-d8_4xb2-40k_cityscapes-512x1024_dark-zurich-1920x1080.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-101-D8 | Cityscapes Training set | Nighttime Driving | 20.25 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r101-d8_4xb2-40k_cityscapes-512x1024_night-driving-1920x1080.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-101-D8 | Cityscapes Training set | Cityscapes Validation set | 78.34 | [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-101b-D8 | Cityscapes Training set | Dark Zurich | 15.54 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r101b-d8_4xb2-80k_cityscapes-512x1024_dark-zurich-1920x1080.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-101b-D8 | Cityscapes Training set | Nighttime Driving | 22.25 | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/pspnet/pspnet_r101b-d8_4xb2-80k_cityscapes-512x1024_night-driving-1920x1080.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-101b-D8 | Cityscapes Training set | Cityscapes Validation set | 79.69 | [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) | ### 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/master/configs/pspnet/pspnet_r50-d8_512x512_4x4_20k_coco-stuff10k.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/master/configs/pspnet/pspnet_r101-d8_512x512_4x4_20k_coco-stuff10k.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/master/configs/pspnet/pspnet_r50-d8_512x512_4x4_40k_coco-stuff10k.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/master/configs/pspnet/pspnet_r101-d8_512x512_4x4_40k_coco-stuff10k.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) | 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) | ### 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/master/configs/pspnet/pspnet_r50-d8_512x512_4x4_80k_coco-stuff164k.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/master/configs/pspnet/pspnet_r101-d8_512x512_4x4_80k_coco-stuff164k.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/master/configs/pspnet/pspnet_r50-d8_512x512_4x4_160k_coco-stuff164k.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/master/configs/pspnet/pspnet_r101-d8_512x512_4x4_160k_coco-stuff164k.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/master/configs/pspnet/pspnet_r50-d8_512x512_4x4_320k_coco-stuff164k.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/master/configs/pspnet/pspnet_r101-d8_512x512_4x4_320k_coco-stuff164k.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) | 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) | ### 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/master/configs/pspnet/pspnet_r18-d8_512x512_80k_loveda.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/master/configs/pspnet/pspnet_r50-d8_512x512_80k_loveda.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/master/configs/pspnet/pspnet_r101-d8_512x512_80k_loveda.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) | 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) | ### 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/master/configs/pspnet/pspnet_r18-d8_4x4_512x512_80k_potsdam.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/master/configs/pspnet/pspnet_r50-d8_4x4_512x512_80k_potsdam.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/master/configs/pspnet/pspnet_r101-d8_4x4_512x512_80k_potsdam.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) | 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) | ### 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/master/configs/pspnet/pspnet_r18-d8_4x4_512x512_80k_vaihingen.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/master/configs/pspnet/pspnet_r50-d8_4x4_512x512_80k_vaihingen.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/master/configs/pspnet/pspnet_r101-d8_4x4_512x512_80k_vaihingen.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) | 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) | ### 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/master/configs/pspnet/pspnet_r18-d8_4x4_896x896_80k_isaid.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/master/configs/pspnet/pspnet_r50-d8_4x4_896x896_80k_isaid.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) | 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) | Note: diff --git a/configs/pspnet/pspnet.yml b/configs/pspnet/pspnet.yml index 2a1fa8882b..7f811efd0f 100644 --- a/configs/pspnet/pspnet.yml +++ b/configs/pspnet/pspnet.yml @@ -24,7 +24,7 @@ Collections: Converted From: Code: https://github.com/hszhao/PSPNet Models: -- Name: pspnet_r50-d8_512x1024_40k_cityscapes +- Name: pspnet_r50-d8_4xb2-40k_cityscapes-512x1024 In Collection: PSPNet Metadata: backbone: R-50-D8 @@ -44,9 +44,9 @@ Models: Metrics: mIoU: 77.85 mIoU(ms+flip): 79.18 - Config: configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py + 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_512x1024_40k_cityscapes +- Name: pspnet_r101-d8_4xb2-40k_cityscapes-512x1024 In Collection: PSPNet Metadata: backbone: R-101-D8 @@ -66,9 +66,9 @@ Models: Metrics: mIoU: 78.34 mIoU(ms+flip): 79.74 - Config: configs/pspnet/pspnet_r101-d8_512x1024_40k_cityscapes.py + 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_769x769_40k_cityscapes +- Name: pspnet_r50-d8_4xb2-40k_cityscapes-769x769 In Collection: PSPNet Metadata: backbone: R-50-D8 @@ -88,9 +88,9 @@ Models: Metrics: mIoU: 78.26 mIoU(ms+flip): 79.88 - Config: configs/pspnet/pspnet_r50-d8_769x769_40k_cityscapes.py + 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_769x769_40k_cityscapes +- Name: pspnet_r101-d8_4xb2-40k_cityscapes-769x769 In Collection: PSPNet Metadata: backbone: R-101-D8 @@ -110,9 +110,9 @@ Models: Metrics: mIoU: 79.08 mIoU(ms+flip): 80.28 - Config: configs/pspnet/pspnet_r101-d8_769x769_40k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: pspnet_r18-d8_4xb2-80k_cityscapes-512x1024 In Collection: PSPNet Metadata: backbone: R-18-D8 @@ -132,9 +132,9 @@ Models: Metrics: mIoU: 74.87 mIoU(ms+flip): 76.04 - Config: configs/pspnet/pspnet_r18-d8_512x1024_80k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: pspnet_r50-d8_4xb2-80k_cityscapes-512x1024 In Collection: PSPNet Metadata: backbone: R-50-D8 @@ -146,9 +146,9 @@ Models: Metrics: mIoU: 78.55 mIoU(ms+flip): 79.79 - Config: configs/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes.py + 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-pretrain_512x1024_adamw_80k_cityscapes +- Name: pspnet_r50-d8-rsb_4xb2-adamw-80k_cityscapes-512x1024 In Collection: PSPNet Metadata: backbone: R-50b-D8 rsb @@ -168,9 +168,9 @@ Models: Metrics: mIoU: 78.47 mIoU(ms+flip): 79.45 - Config: configs/pspnet/pspnet_r50-d8_rsb-pretrain_512x1024_adamw_80k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: pspnet_r101-d8_4xb2-80k_cityscapes-512x1024 In Collection: PSPNet Metadata: backbone: R-101-D8 @@ -182,9 +182,9 @@ Models: Metrics: mIoU: 79.76 mIoU(ms+flip): 81.01 - Config: configs/pspnet/pspnet_r101-d8_512x1024_80k_cityscapes.py + 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_fp16_512x1024_80k_cityscapes +- Name: pspnet_r101-d8_4xb2-amp-80k_cityscapes-512x1024 In Collection: PSPNet Metadata: backbone: R-101-D8 @@ -195,7 +195,7 @@ Models: hardware: V100 backend: PyTorch batch size: 1 - mode: FP16 + mode: AMP resolution: (512,1024) Training Memory (GB): 5.34 Results: @@ -203,9 +203,9 @@ Models: Dataset: Cityscapes Metrics: mIoU: 79.46 - Config: configs/pspnet/pspnet_r101-d8_fp16_512x1024_80k_cityscapes.py + 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_769x769_80k_cityscapes +- Name: pspnet_r18-d8_4xb2-80k_cityscapes-769x769 In Collection: PSPNet Metadata: backbone: R-18-D8 @@ -225,9 +225,9 @@ Models: Metrics: mIoU: 75.9 mIoU(ms+flip): 77.86 - Config: configs/pspnet/pspnet_r18-d8_769x769_80k_cityscapes.py + 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_769x769_80k_cityscapes +- Name: pspnet_r50-d8_4xb2-80k_cityscapes-769x769 In Collection: PSPNet Metadata: backbone: R-50-D8 @@ -239,9 +239,9 @@ Models: Metrics: mIoU: 79.59 mIoU(ms+flip): 80.69 - Config: configs/pspnet/pspnet_r50-d8_769x769_80k_cityscapes.py + 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_769x769_80k_cityscapes +- Name: pspnet_r101-d8_4xb2-80k_cityscapes-769x769 In Collection: PSPNet Metadata: backbone: R-101-D8 @@ -253,9 +253,9 @@ Models: Metrics: mIoU: 79.77 mIoU(ms+flip): 81.06 - Config: configs/pspnet/pspnet_r101-d8_769x769_80k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: pspnet_r18b-d8_4xb2-80k_cityscapes-512x1024 In Collection: PSPNet Metadata: backbone: R-18b-D8 @@ -275,9 +275,9 @@ Models: Metrics: mIoU: 74.23 mIoU(ms+flip): 75.79 - Config: configs/pspnet/pspnet_r18b-d8_512x1024_80k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: pspnet_r50b-d8_4xb2-80k_cityscapes-512x1024 In Collection: PSPNet Metadata: backbone: R-50b-D8 @@ -297,9 +297,9 @@ Models: Metrics: mIoU: 78.22 mIoU(ms+flip): 79.46 - Config: configs/pspnet/pspnet_r50b-d8_512x1024_80k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: pspnet_r101b-d8_4xb2-80k_cityscapes-512x1024 In Collection: PSPNet Metadata: backbone: R-101b-D8 @@ -319,9 +319,9 @@ Models: Metrics: mIoU: 79.69 mIoU(ms+flip): 80.79 - Config: configs/pspnet/pspnet_r101b-d8_512x1024_80k_cityscapes.py + 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_769x769_80k_cityscapes +- Name: pspnet_r18b-d8_4xb2-80k_cityscapes-769x769 In Collection: PSPNet Metadata: backbone: R-18b-D8 @@ -341,9 +341,9 @@ Models: Metrics: mIoU: 74.92 mIoU(ms+flip): 76.9 - Config: configs/pspnet/pspnet_r18b-d8_769x769_80k_cityscapes.py + 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_769x769_80k_cityscapes +- Name: pspnet_r50b-d8_4xb2-80k_cityscapes-769x769 In Collection: PSPNet Metadata: backbone: R-50b-D8 @@ -363,9 +363,9 @@ Models: Metrics: mIoU: 78.5 mIoU(ms+flip): 79.96 - Config: configs/pspnet/pspnet_r50b-d8_769x769_80k_cityscapes.py + 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_769x769_80k_cityscapes +- Name: pspnet_r101b-d8_4xb2-80k_cityscapes-769x769 In Collection: PSPNet Metadata: backbone: R-101b-D8 @@ -385,9 +385,9 @@ Models: Metrics: mIoU: 78.87 mIoU(ms+flip): 80.04 - Config: configs/pspnet/pspnet_r101b-d8_769x769_80k_cityscapes.py + 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_r50-d32_512x1024_80k_cityscapes +- Name: pspnet_r50b-d32_4xb2-80k_cityscapes-512x1024 In Collection: PSPNet Metadata: backbone: R-50-D32 @@ -407,9 +407,9 @@ Models: Metrics: mIoU: 73.88 mIoU(ms+flip): 76.85 - Config: configs/pspnet/pspnet_r50-d32_512x1024_80k_cityscapes.py + 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-pretrain_512x1024_adamw_80k_cityscapes +- Name: pspnet_r50-d32_rsb_4xb2-adamw-80k_cityscapes-512x1024 In Collection: PSPNet Metadata: backbone: R-50b-D32 rsb @@ -429,9 +429,9 @@ Models: Metrics: mIoU: 74.09 mIoU(ms+flip): 77.18 - Config: configs/pspnet/pspnet_r50-d32_rsb-pretrain_512x1024_adamw_80k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: pspnet_r50b-d32_4xb2-80k_cityscapes-512x1024 In Collection: PSPNet Metadata: backbone: R-50b-D32 @@ -451,9 +451,9 @@ Models: Metrics: mIoU: 72.61 mIoU(ms+flip): 75.51 - Config: configs/pspnet/pspnet_r50b-d32_512x1024_80k_cityscapes.py + 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_512x512_80k_ade20k +- Name: pspnet_r50-d8_4xb4-80k_ade20k-512x512 In Collection: PSPNet Metadata: backbone: R-50-D8 @@ -473,9 +473,9 @@ Models: Metrics: mIoU: 41.13 mIoU(ms+flip): 41.94 - Config: configs/pspnet/pspnet_r50-d8_512x512_80k_ade20k.py + 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_512x512_80k_ade20k +- Name: pspnet_r101-d8_4xb4-80k_ade20k-512x512 In Collection: PSPNet Metadata: backbone: R-101-D8 @@ -495,9 +495,9 @@ Models: Metrics: mIoU: 43.57 mIoU(ms+flip): 44.35 - Config: configs/pspnet/pspnet_r101-d8_512x512_80k_ade20k.py + 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_512x512_160k_ade20k +- Name: pspnet_r50-d8_4xb4-160k_ade20k-512x512 In Collection: PSPNet Metadata: backbone: R-50-D8 @@ -509,9 +509,9 @@ Models: Metrics: mIoU: 42.48 mIoU(ms+flip): 43.44 - Config: configs/pspnet/pspnet_r50-d8_512x512_160k_ade20k.py + 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_512x512_160k_ade20k +- Name: pspnet_r101-d8_4xb4-160k_ade20k-512x512 In Collection: PSPNet Metadata: backbone: R-101-D8 @@ -523,9 +523,9 @@ Models: Metrics: mIoU: 44.39 mIoU(ms+flip): 45.35 - Config: configs/pspnet/pspnet_r101-d8_512x512_160k_ade20k.py + 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_512x512_20k_voc12aug +- Name: pspnet_r50-d8_4xb4-20k_voc12aug-512x512 In Collection: PSPNet Metadata: backbone: R-50-D8 @@ -545,9 +545,9 @@ Models: Metrics: mIoU: 76.78 mIoU(ms+flip): 77.61 - Config: configs/pspnet/pspnet_r50-d8_512x512_20k_voc12aug.py + 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_512x512_20k_voc12aug +- Name: pspnet_r101-d8_4xb4-20k_voc12aug-512x512 In Collection: PSPNet Metadata: backbone: R-101-D8 @@ -567,9 +567,9 @@ Models: Metrics: mIoU: 78.47 mIoU(ms+flip): 79.25 - Config: configs/pspnet/pspnet_r101-d8_512x512_20k_voc12aug.py + 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_512x512_40k_voc12aug +- Name: pspnet_r50-d8_4xb4-40k_voc12aug-512x512 In Collection: PSPNet Metadata: backbone: R-50-D8 @@ -581,9 +581,9 @@ Models: Metrics: mIoU: 77.29 mIoU(ms+flip): 78.48 - Config: configs/pspnet/pspnet_r50-d8_512x512_40k_voc12aug.py + 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_512x512_40k_voc12aug +- Name: pspnet_r101-d8_4xb4-40k_voc12aug-512x512 In Collection: PSPNet Metadata: backbone: R-101-D8 @@ -595,9 +595,9 @@ Models: Metrics: mIoU: 78.52 mIoU(ms+flip): 79.57 - Config: configs/pspnet/pspnet_r101-d8_512x512_40k_voc12aug.py + 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_480x480_40k_pascal_context +- Name: pspnet_r101-d8_4xb4-40k_pascal-context-480x480 In Collection: PSPNet Metadata: backbone: R-101-D8 @@ -617,9 +617,9 @@ Models: Metrics: mIoU: 46.6 mIoU(ms+flip): 47.78 - Config: configs/pspnet/pspnet_r101-d8_480x480_40k_pascal_context.py + 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_480x480_80k_pascal_context +- Name: pspnet_r101-d8_4xb4-80k_pascal-context-480x480 In Collection: PSPNet Metadata: backbone: R-101-D8 @@ -631,9 +631,9 @@ Models: Metrics: mIoU: 46.03 mIoU(ms+flip): 47.15 - Config: configs/pspnet/pspnet_r101-d8_480x480_80k_pascal_context.py + 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_480x480_40k_pascal_context_59 +- Name: pspnet_r101-d8_4xb4-40k_pascal-context-59-480x480 In Collection: PSPNet Metadata: backbone: R-101-D8 @@ -645,9 +645,9 @@ Models: Metrics: mIoU: 52.02 mIoU(ms+flip): 53.54 - Config: configs/pspnet/pspnet_r101-d8_480x480_40k_pascal_context_59.py + 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_480x480_80k_pascal_context_59 +- Name: pspnet_r101-d8_4xb4-80k_pascal-context-59-480x480 In Collection: PSPNet Metadata: backbone: R-101-D8 @@ -659,9 +659,9 @@ Models: Metrics: mIoU: 52.47 mIoU(ms+flip): 53.99 - Config: configs/pspnet/pspnet_r101-d8_480x480_80k_pascal_context_59.py + 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_512x512_4x4_20k_coco-stuff10k +- Name: pspnet_r50-d8_4xb4-20k_coco-stuff10k-512x512 In Collection: PSPNet Metadata: backbone: R-50-D8 @@ -681,9 +681,9 @@ Models: Metrics: mIoU: 35.69 mIoU(ms+flip): 36.62 - Config: configs/pspnet/pspnet_r50-d8_512x512_4x4_20k_coco-stuff10k.py + 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_512x512_4x4_20k_coco-stuff10k +- Name: pspnet_r101-d8_4xb4-20k_coco-stuff10k-512x512 In Collection: PSPNet Metadata: backbone: R-101-D8 @@ -703,9 +703,9 @@ Models: Metrics: mIoU: 37.26 mIoU(ms+flip): 38.52 - Config: configs/pspnet/pspnet_r101-d8_512x512_4x4_20k_coco-stuff10k.py + 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_512x512_4x4_40k_coco-stuff10k +- Name: pspnet_r50-d8_4xb4-40k_coco-stuff10k-512x512 In Collection: PSPNet Metadata: backbone: R-50-D8 @@ -717,9 +717,9 @@ Models: Metrics: mIoU: 36.33 mIoU(ms+flip): 37.24 - Config: configs/pspnet/pspnet_r50-d8_512x512_4x4_40k_coco-stuff10k.py + 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_512x512_4x4_40k_coco-stuff10k +- Name: pspnet_r101-d8_4xb4-40k_coco-stuff10k-512x512 In Collection: PSPNet Metadata: backbone: R-101-D8 @@ -731,9 +731,9 @@ Models: Metrics: mIoU: 37.76 mIoU(ms+flip): 38.86 - Config: configs/pspnet/pspnet_r101-d8_512x512_4x4_40k_coco-stuff10k.py + 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_512x512_4x4_80k_coco-stuff164k +- Name: pspnet_r50-d8_4xb4-80k_coco-stuff164k-512x512 In Collection: PSPNet Metadata: backbone: R-50-D8 @@ -753,9 +753,9 @@ Models: Metrics: mIoU: 38.8 mIoU(ms+flip): 39.19 - Config: configs/pspnet/pspnet_r50-d8_512x512_4x4_80k_coco-stuff164k.py + 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_512x512_4x4_80k_coco-stuff164k +- Name: pspnet_r101-d8_4xb4-80k_coco-stuff164k-512x512 In Collection: PSPNet Metadata: backbone: R-101-D8 @@ -775,9 +775,9 @@ Models: Metrics: mIoU: 40.34 mIoU(ms+flip): 40.79 - Config: configs/pspnet/pspnet_r101-d8_512x512_4x4_80k_coco-stuff164k.py + 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_512x512_4x4_160k_coco-stuff164k +- Name: pspnet_r50-d8_4xb4-160k_coco-stuff164k-512x512 In Collection: PSPNet Metadata: backbone: R-50-D8 @@ -789,9 +789,9 @@ Models: Metrics: mIoU: 39.64 mIoU(ms+flip): 39.97 - Config: configs/pspnet/pspnet_r50-d8_512x512_4x4_160k_coco-stuff164k.py + 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_512x512_4x4_160k_coco-stuff164k +- Name: pspnet_r101-d8_4xb4-160k_coco-stuff164k-512x512 In Collection: PSPNet Metadata: backbone: R-101-D8 @@ -803,9 +803,9 @@ Models: Metrics: mIoU: 41.28 mIoU(ms+flip): 41.66 - Config: configs/pspnet/pspnet_r101-d8_512x512_4x4_160k_coco-stuff164k.py + 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_512x512_4x4_320k_coco-stuff164k +- Name: pspnet_r50-d8_4xb4-320k_coco-stuff164k-512x512 In Collection: PSPNet Metadata: backbone: R-50-D8 @@ -817,9 +817,9 @@ Models: Metrics: mIoU: 40.53 mIoU(ms+flip): 40.75 - Config: configs/pspnet/pspnet_r50-d8_512x512_4x4_320k_coco-stuff164k.py + 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_512x512_4x4_320k_coco-stuff164k +- Name: pspnet_r101-d8_4xb4-320k_coco-stuff164k-512x512 In Collection: PSPNet Metadata: backbone: R-101-D8 @@ -831,9 +831,9 @@ Models: Metrics: mIoU: 41.95 mIoU(ms+flip): 42.42 - Config: configs/pspnet/pspnet_r101-d8_512x512_4x4_320k_coco-stuff164k.py + 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_512x512_80k_loveda +- Name: pspnet_r18-d8_4xb4-80k_loveda-512x512 In Collection: PSPNet Metadata: backbone: R-18-D8 @@ -853,9 +853,9 @@ Models: Metrics: mIoU: 48.62 mIoU(ms+flip): 47.57 - Config: configs/pspnet/pspnet_r18-d8_512x512_80k_loveda.py + 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_512x512_80k_loveda +- Name: pspnet_r50-d8_4xb4-80k_loveda-512x512 In Collection: PSPNet Metadata: backbone: R-50-D8 @@ -875,9 +875,9 @@ Models: Metrics: mIoU: 50.46 mIoU(ms+flip): 50.19 - Config: configs/pspnet/pspnet_r50-d8_512x512_80k_loveda.py + 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_512x512_80k_loveda +- Name: pspnet_r101-d8_4xb4-80k_loveda-512x512 In Collection: PSPNet Metadata: backbone: R-101-D8 @@ -897,9 +897,9 @@ Models: Metrics: mIoU: 51.86 mIoU(ms+flip): 51.34 - Config: configs/pspnet/pspnet_r101-d8_512x512_80k_loveda.py + 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_4x4_512x512_80k_potsdam +- Name: pspnet_r18-d8_4xb4-80k_potsdam-512x512 In Collection: PSPNet Metadata: backbone: R-18-D8 @@ -919,9 +919,9 @@ Models: Metrics: mIoU: 77.09 mIoU(ms+flip): 78.3 - Config: configs/pspnet/pspnet_r18-d8_4x4_512x512_80k_potsdam.py + 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_4x4_512x512_80k_potsdam +- Name: pspnet_r50-d8_4xb4-80k_potsdam-512x512 In Collection: PSPNet Metadata: backbone: R-50-D8 @@ -941,9 +941,9 @@ Models: Metrics: mIoU: 78.12 mIoU(ms+flip): 78.98 - Config: configs/pspnet/pspnet_r50-d8_4x4_512x512_80k_potsdam.py + 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_4x4_512x512_80k_potsdam +- Name: pspnet_r101-d8_4xb4-80k_potsdam-512x512 In Collection: PSPNet Metadata: backbone: R-101-D8 @@ -963,9 +963,9 @@ Models: Metrics: mIoU: 78.62 mIoU(ms+flip): 79.47 - Config: configs/pspnet/pspnet_r101-d8_4x4_512x512_80k_potsdam.py + 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_4x4_512x512_80k_vaihingen +- Name: pspnet_r18-d8_4xb4-80k_vaihingen-512x512 In Collection: PSPNet Metadata: backbone: R-18-D8 @@ -985,9 +985,9 @@ Models: Metrics: mIoU: 71.46 mIoU(ms+flip): 73.36 - Config: configs/pspnet/pspnet_r18-d8_4x4_512x512_80k_vaihingen.py + 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_4x4_512x512_80k_vaihingen +- Name: pspnet_r50-d8_4xb4-80k_vaihingen-512x512 In Collection: PSPNet Metadata: backbone: R-50-D8 @@ -1007,9 +1007,9 @@ Models: Metrics: mIoU: 72.36 mIoU(ms+flip): 73.75 - Config: configs/pspnet/pspnet_r50-d8_4x4_512x512_80k_vaihingen.py + 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_4x4_512x512_80k_vaihingen +- Name: pspnet_r101-d8_4xb4-80k_vaihingen-512x512 In Collection: PSPNet Metadata: backbone: R-101-D8 @@ -1029,9 +1029,9 @@ Models: Metrics: mIoU: 72.61 mIoU(ms+flip): 74.18 - Config: configs/pspnet/pspnet_r101-d8_4x4_512x512_80k_vaihingen.py + 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_4x4_896x896_80k_isaid +- Name: pspnet_r18-d8_4xb4-80k_isaid-896x896 In Collection: PSPNet Metadata: backbone: R-18-D8 @@ -1051,9 +1051,9 @@ Models: Metrics: mIoU: 60.22 mIoU(ms+flip): 61.25 - Config: configs/pspnet/pspnet_r18-d8_4x4_896x896_80k_isaid.py + 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_4x4_896x896_80k_isaid +- Name: pspnet_r50-d8_4xb4-80k_isaid-896x896 In Collection: PSPNet Metadata: backbone: R-50-D8 @@ -1073,5 +1073,5 @@ Models: Metrics: mIoU: 65.36 mIoU(ms+flip): 66.48 - Config: configs/pspnet/pspnet_r50-d8_4x4_896x896_80k_isaid.py + 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/pspnet/pspnet_r101-d8_480x480_40k_pascal_context.py b/configs/pspnet/pspnet_r101-d8_480x480_40k_pascal_context.py deleted file mode 100644 index 0b5a990604..0000000000 --- a/configs/pspnet/pspnet_r101-d8_480x480_40k_pascal_context.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './pspnet_r50-d8_480x480_40k_pascal_context.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/pspnet/pspnet_r101-d8_480x480_40k_pascal_context_59.py b/configs/pspnet/pspnet_r101-d8_480x480_40k_pascal_context_59.py deleted file mode 100644 index 081cb3732a..0000000000 --- a/configs/pspnet/pspnet_r101-d8_480x480_40k_pascal_context_59.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './pspnet_r50-d8_480x480_40k_pascal_context_59.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/pspnet/pspnet_r101-d8_480x480_80k_pascal_context.py b/configs/pspnet/pspnet_r101-d8_480x480_80k_pascal_context.py deleted file mode 100644 index fda9110603..0000000000 --- a/configs/pspnet/pspnet_r101-d8_480x480_80k_pascal_context.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './pspnet_r50-d8_480x480_80k_pascal_context.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/pspnet/pspnet_r101-d8_480x480_80k_pascal_context_59.py b/configs/pspnet/pspnet_r101-d8_480x480_80k_pascal_context_59.py deleted file mode 100644 index 795c51f8cf..0000000000 --- a/configs/pspnet/pspnet_r101-d8_480x480_80k_pascal_context_59.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './pspnet_r50-d8_480x480_80k_pascal_context_59.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/pspnet/pspnet_r101-d8_4x4_512x512_80k_potsdam.py b/configs/pspnet/pspnet_r101-d8_4x4_512x512_80k_potsdam.py deleted file mode 100644 index 98343dd76c..0000000000 --- a/configs/pspnet/pspnet_r101-d8_4x4_512x512_80k_potsdam.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './pspnet_r50-d8_4x4_512x512_80k_potsdam.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/pspnet/pspnet_r101-d8_4x4_512x512_80k_vaihingen.py b/configs/pspnet/pspnet_r101-d8_4x4_512x512_80k_vaihingen.py deleted file mode 100644 index fd79492e76..0000000000 --- a/configs/pspnet/pspnet_r101-d8_4x4_512x512_80k_vaihingen.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './pspnet_r50-d8_4x4_512x512_80k_vaihingen.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/pspnet/pspnet_r101-d8_4xb2-40k_cityscapes-512x1024.py b/configs/pspnet/pspnet_r101-d8_4xb2-40k_cityscapes-512x1024.py new file mode 100644 index 0000000000..f33d653b76 --- /dev/null +++ b/configs/pspnet/pspnet_r101-d8_4xb2-40k_cityscapes-512x1024.py @@ -0,0 +1,2 @@ +_base_ = './pspnet_r50-d8_4xb2-40k_cityscapes-512x1024.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/pspnet/pspnet_r101-d8_4xb2-40k_cityscapes-512x1024_dark-zurich-1920x1080.py b/configs/pspnet/pspnet_r101-d8_4xb2-40k_cityscapes-512x1024_dark-zurich-1920x1080.py new file mode 100644 index 0000000000..5babaa8851 --- /dev/null +++ b/configs/pspnet/pspnet_r101-d8_4xb2-40k_cityscapes-512x1024_dark-zurich-1920x1080.py @@ -0,0 +1,2 @@ +_base_ = './pspnet_r50-d8_4xb2-40k_cityscapes-512x1024_dark-zurich-1920x1080.py' # noqa +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/pspnet/pspnet_r101-d8_4xb2-40k_cityscapes-512x1024_night-driving-1920x1080.py b/configs/pspnet/pspnet_r101-d8_4xb2-40k_cityscapes-512x1024_night-driving-1920x1080.py new file mode 100644 index 0000000000..a9480c52f8 --- /dev/null +++ b/configs/pspnet/pspnet_r101-d8_4xb2-40k_cityscapes-512x1024_night-driving-1920x1080.py @@ -0,0 +1,2 @@ +_base_ = './pspnet_r50-d8_4xb2-40k_cityscapes-512x1024_night-driving-1920x1080.py' # noqa +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/pspnet/pspnet_r101-d8_4xb2-40k_cityscapes-769x769.py b/configs/pspnet/pspnet_r101-d8_4xb2-40k_cityscapes-769x769.py new file mode 100644 index 0000000000..e05cff6d8e --- /dev/null +++ b/configs/pspnet/pspnet_r101-d8_4xb2-40k_cityscapes-769x769.py @@ -0,0 +1,2 @@ +_base_ = './pspnet_r50-d8_4xb2-40k_cityscapes-769x769.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/pspnet/pspnet_r101-d8_4xb2-80k_cityscapes-512x1024.py b/configs/pspnet/pspnet_r101-d8_4xb2-80k_cityscapes-512x1024.py new file mode 100644 index 0000000000..6704cdd5d2 --- /dev/null +++ b/configs/pspnet/pspnet_r101-d8_4xb2-80k_cityscapes-512x1024.py @@ -0,0 +1,2 @@ +_base_ = './pspnet_r50-d8_4xb2-80k_cityscapes-512x1024.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/pspnet/pspnet_r101-d8_4xb2-80k_cityscapes-769x769.py b/configs/pspnet/pspnet_r101-d8_4xb2-80k_cityscapes-769x769.py new file mode 100644 index 0000000000..3733e69198 --- /dev/null +++ b/configs/pspnet/pspnet_r101-d8_4xb2-80k_cityscapes-769x769.py @@ -0,0 +1,2 @@ +_base_ = './pspnet_r50-d8_4xb2-80k_cityscapes-769x769.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/fcn/fcn_r101-d8_fp16_512x1024_80k_cityscapes.py b/configs/pspnet/pspnet_r101-d8_4xb2-amp-80k_cityscapes-512x1024.py similarity index 73% rename from configs/fcn/fcn_r101-d8_fp16_512x1024_80k_cityscapes.py rename to configs/pspnet/pspnet_r101-d8_4xb2-amp-80k_cityscapes-512x1024.py index da27a90268..52f86b5e75 100644 --- a/configs/fcn/fcn_r101-d8_fp16_512x1024_80k_cityscapes.py +++ b/configs/pspnet/pspnet_r101-d8_4xb2-amp-80k_cityscapes-512x1024.py @@ -1,4 +1,4 @@ -_base_ = './fcn_r101-d8_512x1024_80k_cityscapes.py' +_base_ = './pspnet_r101-d8_4xb2-80k_cityscapes-512x1024.py' optim_wrapper = dict( _delete_=True, type='AmpOptimWrapper', diff --git a/configs/pspnet/pspnet_r101-d8_4xb4-160k_ade20k-512x512.py b/configs/pspnet/pspnet_r101-d8_4xb4-160k_ade20k-512x512.py new file mode 100644 index 0000000000..2231049b8a --- /dev/null +++ b/configs/pspnet/pspnet_r101-d8_4xb4-160k_ade20k-512x512.py @@ -0,0 +1,2 @@ +_base_ = './pspnet_r50-d8_4xb4-160k_ade20k-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/pspnet/pspnet_r101-d8_4xb4-160k_coco-stuff164k-512x512.py b/configs/pspnet/pspnet_r101-d8_4xb4-160k_coco-stuff164k-512x512.py new file mode 100644 index 0000000000..f5390f8c76 --- /dev/null +++ b/configs/pspnet/pspnet_r101-d8_4xb4-160k_coco-stuff164k-512x512.py @@ -0,0 +1,2 @@ +_base_ = './pspnet_r50-d8_4xb4-160k_coco-stuff164k-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/pspnet/pspnet_r101-d8_4xb4-20k_coco-stuff10k-512x512.py b/configs/pspnet/pspnet_r101-d8_4xb4-20k_coco-stuff10k-512x512.py new file mode 100644 index 0000000000..84a986cd9d --- /dev/null +++ b/configs/pspnet/pspnet_r101-d8_4xb4-20k_coco-stuff10k-512x512.py @@ -0,0 +1,2 @@ +_base_ = './pspnet_r50-d8_4xb4-20k_coco-stuff10k-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/pspnet/pspnet_r101-d8_4xb4-20k_voc12aug-512x512.py b/configs/pspnet/pspnet_r101-d8_4xb4-20k_voc12aug-512x512.py new file mode 100644 index 0000000000..71897ddc2d --- /dev/null +++ b/configs/pspnet/pspnet_r101-d8_4xb4-20k_voc12aug-512x512.py @@ -0,0 +1,2 @@ +_base_ = './pspnet_r50-d8_4xb4-20k_voc12aug-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/pspnet/pspnet_r101-d8_4xb4-320k_coco-stuff164k-512x512.py b/configs/pspnet/pspnet_r101-d8_4xb4-320k_coco-stuff164k-512x512.py new file mode 100644 index 0000000000..ebaea36da8 --- /dev/null +++ b/configs/pspnet/pspnet_r101-d8_4xb4-320k_coco-stuff164k-512x512.py @@ -0,0 +1,2 @@ +_base_ = './pspnet_r50-d8_4xb4-320k_coco-stuff164k-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/pspnet/pspnet_r101-d8_4xb4-40k_coco-stuff10k-512x512.py b/configs/pspnet/pspnet_r101-d8_4xb4-40k_coco-stuff10k-512x512.py new file mode 100644 index 0000000000..2a55f53ee9 --- /dev/null +++ b/configs/pspnet/pspnet_r101-d8_4xb4-40k_coco-stuff10k-512x512.py @@ -0,0 +1,2 @@ +_base_ = './pspnet_r50-d8_4xb4-40k_coco-stuff10k-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/pspnet/pspnet_r101-d8_4xb4-40k_pascal-context-480x480.py b/configs/pspnet/pspnet_r101-d8_4xb4-40k_pascal-context-480x480.py new file mode 100644 index 0000000000..205d00bac9 --- /dev/null +++ b/configs/pspnet/pspnet_r101-d8_4xb4-40k_pascal-context-480x480.py @@ -0,0 +1,2 @@ +_base_ = './pspnet_r50-d8_4xb4-40k_pascal-context-480x480.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/pspnet/pspnet_r101-d8_4xb4-40k_pascal-context-59-480x480.py b/configs/pspnet/pspnet_r101-d8_4xb4-40k_pascal-context-59-480x480.py new file mode 100644 index 0000000000..0d7c176073 --- /dev/null +++ b/configs/pspnet/pspnet_r101-d8_4xb4-40k_pascal-context-59-480x480.py @@ -0,0 +1,2 @@ +_base_ = './pspnet_r50-d8_4xb4-40k_pascal-context-59-480x480.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/pspnet/pspnet_r101-d8_4xb4-40k_voc12aug-512x512.py b/configs/pspnet/pspnet_r101-d8_4xb4-40k_voc12aug-512x512.py new file mode 100644 index 0000000000..0599f31f96 --- /dev/null +++ b/configs/pspnet/pspnet_r101-d8_4xb4-40k_voc12aug-512x512.py @@ -0,0 +1,2 @@ +_base_ = './pspnet_r50-d8_4xb4-40k_voc12aug-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/pspnet/pspnet_r101-d8_4xb4-80k_ade20k-512x512.py b/configs/pspnet/pspnet_r101-d8_4xb4-80k_ade20k-512x512.py new file mode 100644 index 0000000000..f95560347a --- /dev/null +++ b/configs/pspnet/pspnet_r101-d8_4xb4-80k_ade20k-512x512.py @@ -0,0 +1,2 @@ +_base_ = './pspnet_r50-d8_4xb4-80k_ade20k-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/pspnet/pspnet_r101-d8_4xb4-80k_coco-stuff164k-512x512.py b/configs/pspnet/pspnet_r101-d8_4xb4-80k_coco-stuff164k-512x512.py new file mode 100644 index 0000000000..4a34f97485 --- /dev/null +++ b/configs/pspnet/pspnet_r101-d8_4xb4-80k_coco-stuff164k-512x512.py @@ -0,0 +1,2 @@ +_base_ = './pspnet_r50-d8_4xb4-80k_coco-stuff164k-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/pspnet/pspnet_r101-d8_512x512_80k_loveda.py b/configs/pspnet/pspnet_r101-d8_4xb4-80k_loveda-512x512.py similarity index 73% rename from configs/pspnet/pspnet_r101-d8_512x512_80k_loveda.py rename to configs/pspnet/pspnet_r101-d8_4xb4-80k_loveda-512x512.py index 03c0251f6c..ee6c97f07a 100644 --- a/configs/pspnet/pspnet_r101-d8_512x512_80k_loveda.py +++ b/configs/pspnet/pspnet_r101-d8_4xb4-80k_loveda-512x512.py @@ -1,4 +1,4 @@ -_base_ = './pspnet_r50-d8_512x512_80k_loveda.py' +_base_ = './pspnet_r50-d8_4xb4-80k_loveda-512x512.py' model = dict( backbone=dict( depth=101, diff --git a/configs/pspnet/pspnet_r101-d8_4xb4-80k_pascal-context-480x480.py b/configs/pspnet/pspnet_r101-d8_4xb4-80k_pascal-context-480x480.py new file mode 100644 index 0000000000..0ac40dc861 --- /dev/null +++ b/configs/pspnet/pspnet_r101-d8_4xb4-80k_pascal-context-480x480.py @@ -0,0 +1,2 @@ +_base_ = './pspnet_r50-d8_4xb4-80k_pascal-context-480x480.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/pspnet/pspnet_r101-d8_4xb4-80k_pascal-context-59-480x480.py b/configs/pspnet/pspnet_r101-d8_4xb4-80k_pascal-context-59-480x480.py new file mode 100644 index 0000000000..307188c783 --- /dev/null +++ b/configs/pspnet/pspnet_r101-d8_4xb4-80k_pascal-context-59-480x480.py @@ -0,0 +1,2 @@ +_base_ = './pspnet_r50-d8_4xb4-80k_pascal-context-59-480x480.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/pspnet/pspnet_r101-d8_4xb4-80k_potsdam-512x512.py b/configs/pspnet/pspnet_r101-d8_4xb4-80k_potsdam-512x512.py new file mode 100644 index 0000000000..31ed2f2938 --- /dev/null +++ b/configs/pspnet/pspnet_r101-d8_4xb4-80k_potsdam-512x512.py @@ -0,0 +1,2 @@ +_base_ = './pspnet_r50-d8_4xb4-80k_potsdam-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/pspnet/pspnet_r101-d8_4xb4-80k_vaihingen-512x512.py b/configs/pspnet/pspnet_r101-d8_4xb4-80k_vaihingen-512x512.py new file mode 100644 index 0000000000..ac33ed7cda --- /dev/null +++ b/configs/pspnet/pspnet_r101-d8_4xb4-80k_vaihingen-512x512.py @@ -0,0 +1,2 @@ +_base_ = './pspnet_r50-d8_4xb4-80k_vaihingen-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/pspnet/pspnet_r101-d8_512x1024_40k_cityscapes.py b/configs/pspnet/pspnet_r101-d8_512x1024_40k_cityscapes.py deleted file mode 100644 index 38fee11bc2..0000000000 --- a/configs/pspnet/pspnet_r101-d8_512x1024_40k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './pspnet_r50-d8_512x1024_40k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/pspnet/pspnet_r101-d8_512x1024_40k_dark.py b/configs/pspnet/pspnet_r101-d8_512x1024_40k_dark.py deleted file mode 100644 index 1057639148..0000000000 --- a/configs/pspnet/pspnet_r101-d8_512x1024_40k_dark.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './pspnet_r50-d8_512x1024_40k_dark.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/pspnet/pspnet_r101-d8_512x1024_40k_night_driving.py b/configs/pspnet/pspnet_r101-d8_512x1024_40k_night_driving.py deleted file mode 100644 index 0ecb9303ab..0000000000 --- a/configs/pspnet/pspnet_r101-d8_512x1024_40k_night_driving.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './pspnet_r50-d8_512x1024_40k_night_driving.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/pspnet/pspnet_r101-d8_512x1024_80k_cityscapes.py b/configs/pspnet/pspnet_r101-d8_512x1024_80k_cityscapes.py deleted file mode 100644 index 9931a07bc2..0000000000 --- a/configs/pspnet/pspnet_r101-d8_512x1024_80k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './pspnet_r50-d8_512x1024_80k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/pspnet/pspnet_r101-d8_512x512_160k_ade20k.py b/configs/pspnet/pspnet_r101-d8_512x512_160k_ade20k.py deleted file mode 100644 index 6107b41544..0000000000 --- a/configs/pspnet/pspnet_r101-d8_512x512_160k_ade20k.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './pspnet_r50-d8_512x512_160k_ade20k.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/pspnet/pspnet_r101-d8_512x512_20k_voc12aug.py b/configs/pspnet/pspnet_r101-d8_512x512_20k_voc12aug.py deleted file mode 100644 index 2221b202d6..0000000000 --- a/configs/pspnet/pspnet_r101-d8_512x512_20k_voc12aug.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './pspnet_r50-d8_512x512_20k_voc12aug.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/pspnet/pspnet_r101-d8_512x512_40k_voc12aug.py b/configs/pspnet/pspnet_r101-d8_512x512_40k_voc12aug.py deleted file mode 100644 index 15f578b600..0000000000 --- a/configs/pspnet/pspnet_r101-d8_512x512_40k_voc12aug.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './pspnet_r50-d8_512x512_40k_voc12aug.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/pspnet/pspnet_r101-d8_512x512_4x4_160k_coco-stuff164k.py b/configs/pspnet/pspnet_r101-d8_512x512_4x4_160k_coco-stuff164k.py deleted file mode 100644 index 7ae2061c51..0000000000 --- a/configs/pspnet/pspnet_r101-d8_512x512_4x4_160k_coco-stuff164k.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './pspnet_r50-d8_512x512_4x4_160k_coco-stuff164k.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/pspnet/pspnet_r101-d8_512x512_4x4_20k_coco-stuff10k.py b/configs/pspnet/pspnet_r101-d8_512x512_4x4_20k_coco-stuff10k.py deleted file mode 100644 index a448496b13..0000000000 --- a/configs/pspnet/pspnet_r101-d8_512x512_4x4_20k_coco-stuff10k.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './pspnet_r50-d8_512x512_4x4_20k_coco-stuff10k.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/pspnet/pspnet_r101-d8_512x512_4x4_320k_coco-stuff164k.py b/configs/pspnet/pspnet_r101-d8_512x512_4x4_320k_coco-stuff164k.py deleted file mode 100644 index 90512b8754..0000000000 --- a/configs/pspnet/pspnet_r101-d8_512x512_4x4_320k_coco-stuff164k.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './pspnet_r50-d8_512x512_4x4_320k_coco-stuff164k.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/pspnet/pspnet_r101-d8_512x512_4x4_40k_coco-stuff10k.py b/configs/pspnet/pspnet_r101-d8_512x512_4x4_40k_coco-stuff10k.py deleted file mode 100644 index 36aa44385f..0000000000 --- a/configs/pspnet/pspnet_r101-d8_512x512_4x4_40k_coco-stuff10k.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './pspnet_r50-d8_512x512_4x4_40k_coco-stuff10k.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/pspnet/pspnet_r101-d8_512x512_4x4_80k_coco-stuff164k.py b/configs/pspnet/pspnet_r101-d8_512x512_4x4_80k_coco-stuff164k.py deleted file mode 100644 index fdddec4658..0000000000 --- a/configs/pspnet/pspnet_r101-d8_512x512_4x4_80k_coco-stuff164k.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './pspnet_r50-d8_512x512_4x4_80k_coco-stuff164k.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/pspnet/pspnet_r101-d8_512x512_80k_ade20k.py b/configs/pspnet/pspnet_r101-d8_512x512_80k_ade20k.py deleted file mode 100644 index fb7c3d55d5..0000000000 --- a/configs/pspnet/pspnet_r101-d8_512x512_80k_ade20k.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './pspnet_r50-d8_512x512_80k_ade20k.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/pspnet/pspnet_r101-d8_769x769_40k_cityscapes.py b/configs/pspnet/pspnet_r101-d8_769x769_40k_cityscapes.py deleted file mode 100644 index c6e7e58508..0000000000 --- a/configs/pspnet/pspnet_r101-d8_769x769_40k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './pspnet_r50-d8_769x769_40k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/pspnet/pspnet_r101-d8_769x769_80k_cityscapes.py b/configs/pspnet/pspnet_r101-d8_769x769_80k_cityscapes.py deleted file mode 100644 index 59b8c6dd5e..0000000000 --- a/configs/pspnet/pspnet_r101-d8_769x769_80k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './pspnet_r50-d8_769x769_80k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/deeplabv3/deeplabv3_r101b-d8_512x1024_80k_cityscapes.py b/configs/pspnet/pspnet_r101b-d8_4xb2-80k_cityscapes-512x1024.py similarity index 63% rename from configs/deeplabv3/deeplabv3_r101b-d8_512x1024_80k_cityscapes.py rename to configs/pspnet/pspnet_r101b-d8_4xb2-80k_cityscapes-512x1024.py index 5186bf614b..d2c0f69638 100644 --- a/configs/deeplabv3/deeplabv3_r101b-d8_512x1024_80k_cityscapes.py +++ b/configs/pspnet/pspnet_r101b-d8_4xb2-80k_cityscapes-512x1024.py @@ -1,4 +1,4 @@ -_base_ = './deeplabv3_r50-d8_512x1024_80k_cityscapes.py' +_base_ = './pspnet_r50-d8_4xb2-80k_cityscapes-512x1024.py' model = dict( pretrained='torchvision://resnet101', backbone=dict(type='ResNet', depth=101)) diff --git a/configs/pspnet/pspnet_r101b-d8_4xb2-80k_cityscapes-512x1024_dark-zurich-1920x1080.py b/configs/pspnet/pspnet_r101b-d8_4xb2-80k_cityscapes-512x1024_dark-zurich-1920x1080.py new file mode 100644 index 0000000000..b181744149 --- /dev/null +++ b/configs/pspnet/pspnet_r101b-d8_4xb2-80k_cityscapes-512x1024_dark-zurich-1920x1080.py @@ -0,0 +1,4 @@ +_base_ = './pspnet_r50-d8_4xb2-80k_cityscapes-512x1024_dark-zurich-1920x1080.py' # noqa +model = dict( + pretrained='torchvision://resnet101', + backbone=dict(type='ResNet', depth=101)) diff --git a/configs/pspnet/pspnet_r101b-d8_4xb2-80k_cityscapes-512x1024_night-driving-1920x1080.py b/configs/pspnet/pspnet_r101b-d8_4xb2-80k_cityscapes-512x1024_night-driving-1920x1080.py new file mode 100644 index 0000000000..6a8994b4c8 --- /dev/null +++ b/configs/pspnet/pspnet_r101b-d8_4xb2-80k_cityscapes-512x1024_night-driving-1920x1080.py @@ -0,0 +1,4 @@ +_base_ = './pspnet_r50-d8_4xb2-80k_cityscapes-512x1024_night-driving-1920x1080.py' # noqa +model = dict( + pretrained='torchvision://resnet101', + backbone=dict(type='ResNet', depth=101)) diff --git a/configs/fcn/fcn_d6_r101b-d16_512x1024_80k_cityscapes.py b/configs/pspnet/pspnet_r101b-d8_4xb2-80k_cityscapes-769x769.py similarity index 63% rename from configs/fcn/fcn_d6_r101b-d16_512x1024_80k_cityscapes.py rename to configs/pspnet/pspnet_r101b-d8_4xb2-80k_cityscapes-769x769.py index af3f765b76..891bfd51ed 100644 --- a/configs/fcn/fcn_d6_r101b-d16_512x1024_80k_cityscapes.py +++ b/configs/pspnet/pspnet_r101b-d8_4xb2-80k_cityscapes-769x769.py @@ -1,4 +1,4 @@ -_base_ = './fcn_d6_r50b-d16_512x1024_80k_cityscapes.py' +_base_ = './pspnet_r50-d8_4xb2-80k_cityscapes-769x769.py' model = dict( pretrained='torchvision://resnet101', backbone=dict(type='ResNet', depth=101)) diff --git a/configs/pspnet/pspnet_r101b-d8_512x1024_80k_cityscapes.py b/configs/pspnet/pspnet_r101b-d8_512x1024_80k_cityscapes.py deleted file mode 100644 index ab8a3d3e3f..0000000000 --- a/configs/pspnet/pspnet_r101b-d8_512x1024_80k_cityscapes.py +++ /dev/null @@ -1,4 +0,0 @@ -_base_ = './pspnet_r50-d8_512x1024_80k_cityscapes.py' -model = dict( - pretrained='torchvision://resnet101', - backbone=dict(type='ResNet', depth=101)) diff --git a/configs/pspnet/pspnet_r101b-d8_512x1024_80k_dark.py b/configs/pspnet/pspnet_r101b-d8_512x1024_80k_dark.py deleted file mode 100644 index 49231d81bc..0000000000 --- a/configs/pspnet/pspnet_r101b-d8_512x1024_80k_dark.py +++ /dev/null @@ -1,4 +0,0 @@ -_base_ = './pspnet_r50-d8_512x1024_80k_dark.py' -model = dict( - pretrained='torchvision://resnet101', - backbone=dict(type='ResNet', depth=101)) diff --git a/configs/pspnet/pspnet_r101b-d8_512x1024_80k_night_driving.py b/configs/pspnet/pspnet_r101b-d8_512x1024_80k_night_driving.py deleted file mode 100644 index c3ed2f147b..0000000000 --- a/configs/pspnet/pspnet_r101b-d8_512x1024_80k_night_driving.py +++ /dev/null @@ -1,4 +0,0 @@ -_base_ = './pspnet_r50-d8_512x1024_80k_night_driving.py' -model = dict( - pretrained='torchvision://resnet101', - backbone=dict(type='ResNet', depth=101)) diff --git a/configs/pspnet/pspnet_r101b-d8_769x769_80k_cityscapes.py b/configs/pspnet/pspnet_r101b-d8_769x769_80k_cityscapes.py deleted file mode 100644 index 1a7cb708e5..0000000000 --- a/configs/pspnet/pspnet_r101b-d8_769x769_80k_cityscapes.py +++ /dev/null @@ -1,4 +0,0 @@ -_base_ = './pspnet_r50-d8_769x769_80k_cityscapes.py' -model = dict( - pretrained='torchvision://resnet101', - backbone=dict(type='ResNet', depth=101)) diff --git a/configs/pspnet/pspnet_r18-d8_4x4_512x512_80k_vaihingen.py b/configs/pspnet/pspnet_r18-d8_4x4_512x512_80k_vaihingen.py deleted file mode 100644 index 2cb69228f8..0000000000 --- a/configs/pspnet/pspnet_r18-d8_4x4_512x512_80k_vaihingen.py +++ /dev/null @@ -1,9 +0,0 @@ -_base_ = './pspnet_r50-d8_4x4_512x512_80k_vaihingen.py' -model = dict( - pretrained='open-mmlab://resnet18_v1c', - backbone=dict(depth=18), - decode_head=dict( - in_channels=512, - channels=128, - ), - auxiliary_head=dict(in_channels=256, channels=64)) diff --git a/configs/pspnet/pspnet_r18-d8_4x4_896x896_80k_isaid.py b/configs/pspnet/pspnet_r18-d8_4x4_896x896_80k_isaid.py deleted file mode 100644 index 4f6f9ab253..0000000000 --- a/configs/pspnet/pspnet_r18-d8_4x4_896x896_80k_isaid.py +++ /dev/null @@ -1,9 +0,0 @@ -_base_ = './pspnet_r50-d8_4x4_896x896_80k_isaid.py' -model = dict( - pretrained='open-mmlab://resnet18_v1c', - backbone=dict(depth=18), - decode_head=dict( - in_channels=512, - channels=128, - ), - auxiliary_head=dict(in_channels=256, channels=64)) diff --git a/configs/pspnet/pspnet_r18-d8_4xb2-80k_cityscapes-512x1024.py b/configs/pspnet/pspnet_r18-d8_4xb2-80k_cityscapes-512x1024.py new file mode 100644 index 0000000000..a4b342ef23 --- /dev/null +++ b/configs/pspnet/pspnet_r18-d8_4xb2-80k_cityscapes-512x1024.py @@ -0,0 +1,9 @@ +_base_ = './pspnet_r50-d8_4xb2-80k_cityscapes-512x1024.py' +model = dict( + pretrained='open-mmlab://resnet18_v1c', + backbone=dict(depth=18), + decode_head=dict( + in_channels=512, + channels=128, + ), + auxiliary_head=dict(in_channels=256, channels=64)) diff --git a/configs/pspnet/pspnet_r18-d8_4xb2-80k_cityscapes-769x769.py b/configs/pspnet/pspnet_r18-d8_4xb2-80k_cityscapes-769x769.py new file mode 100644 index 0000000000..0e7f3e90ac --- /dev/null +++ b/configs/pspnet/pspnet_r18-d8_4xb2-80k_cityscapes-769x769.py @@ -0,0 +1,9 @@ +_base_ = './pspnet_r50-d8_4xb2-80k_cityscapes-769x769.py' +model = dict( + pretrained='open-mmlab://resnet18_v1c', + backbone=dict(depth=18), + decode_head=dict( + in_channels=512, + channels=128, + ), + auxiliary_head=dict(in_channels=256, channels=64)) diff --git a/configs/fcn/fcn_r18-d8_769x769_80k_cityscapes.py b/configs/pspnet/pspnet_r18-d8_4xb4-80k_isaid-896x896.py similarity index 80% rename from configs/fcn/fcn_r18-d8_769x769_80k_cityscapes.py rename to configs/pspnet/pspnet_r18-d8_4xb4-80k_isaid-896x896.py index 6644a58dea..efce7a0e7d 100644 --- a/configs/fcn/fcn_r18-d8_769x769_80k_cityscapes.py +++ b/configs/pspnet/pspnet_r18-d8_4xb4-80k_isaid-896x896.py @@ -1,4 +1,4 @@ -_base_ = './fcn_r50-d8_769x769_80k_cityscapes.py' +_base_ = './pspnet_r50-d8_4xb4-80k_isaid-896x896.py' model = dict( pretrained='open-mmlab://resnet18_v1c', backbone=dict(depth=18), diff --git a/configs/pspnet/pspnet_r18-d8_512x512_80k_loveda.py b/configs/pspnet/pspnet_r18-d8_4xb4-80k_loveda-512x512.py similarity index 83% rename from configs/pspnet/pspnet_r18-d8_512x512_80k_loveda.py rename to configs/pspnet/pspnet_r18-d8_4xb4-80k_loveda-512x512.py index dbb832b244..f95b0301c5 100644 --- a/configs/pspnet/pspnet_r18-d8_512x512_80k_loveda.py +++ b/configs/pspnet/pspnet_r18-d8_4xb4-80k_loveda-512x512.py @@ -1,4 +1,4 @@ -_base_ = './pspnet_r50-d8_512x512_80k_loveda.py' +_base_ = './pspnet_r50-d8_4xb4-80k_loveda-512x512.py' model = dict( backbone=dict( depth=18, diff --git a/configs/pspnet/pspnet_r18-d8_4x4_512x512_80k_potsdam.py b/configs/pspnet/pspnet_r18-d8_4xb4-80k_potsdam-512x512.py similarity index 79% rename from configs/pspnet/pspnet_r18-d8_4x4_512x512_80k_potsdam.py rename to configs/pspnet/pspnet_r18-d8_4xb4-80k_potsdam-512x512.py index be9dc7254b..1ef0585e79 100644 --- a/configs/pspnet/pspnet_r18-d8_4x4_512x512_80k_potsdam.py +++ b/configs/pspnet/pspnet_r18-d8_4xb4-80k_potsdam-512x512.py @@ -1,4 +1,4 @@ -_base_ = './pspnet_r50-d8_4x4_512x512_80k_potsdam.py' +_base_ = './pspnet_r50-d8_4xb4-80k_potsdam-512x512.py' model = dict( pretrained='open-mmlab://resnet18_v1c', backbone=dict(depth=18), diff --git a/configs/deeplabv3/deeplabv3_r18-d8_512x1024_80k_cityscapes.py b/configs/pspnet/pspnet_r18-d8_4xb4-80k_vaihingen-512x512.py similarity index 79% rename from configs/deeplabv3/deeplabv3_r18-d8_512x1024_80k_cityscapes.py rename to configs/pspnet/pspnet_r18-d8_4xb4-80k_vaihingen-512x512.py index e084e95c70..51e66d2e51 100644 --- a/configs/deeplabv3/deeplabv3_r18-d8_512x1024_80k_cityscapes.py +++ b/configs/pspnet/pspnet_r18-d8_4xb4-80k_vaihingen-512x512.py @@ -1,4 +1,4 @@ -_base_ = './deeplabv3_r50-d8_512x1024_80k_cityscapes.py' +_base_ = './pspnet_r50-d8_4xb4-80k_vaihingen-512x512.py' model = dict( pretrained='open-mmlab://resnet18_v1c', backbone=dict(depth=18), diff --git a/configs/pspnet/pspnet_r18-d8_512x1024_80k_cityscapes.py b/configs/pspnet/pspnet_r18-d8_512x1024_80k_cityscapes.py deleted file mode 100644 index d914f93c02..0000000000 --- a/configs/pspnet/pspnet_r18-d8_512x1024_80k_cityscapes.py +++ /dev/null @@ -1,9 +0,0 @@ -_base_ = './pspnet_r50-d8_512x1024_80k_cityscapes.py' -model = dict( - pretrained='open-mmlab://resnet18_v1c', - backbone=dict(depth=18), - decode_head=dict( - in_channels=512, - channels=128, - ), - auxiliary_head=dict(in_channels=256, channels=64)) diff --git a/configs/pspnet/pspnet_r18-d8_769x769_80k_cityscapes.py b/configs/pspnet/pspnet_r18-d8_769x769_80k_cityscapes.py deleted file mode 100644 index 5893e66a41..0000000000 --- a/configs/pspnet/pspnet_r18-d8_769x769_80k_cityscapes.py +++ /dev/null @@ -1,9 +0,0 @@ -_base_ = './pspnet_r50-d8_769x769_80k_cityscapes.py' -model = dict( - pretrained='open-mmlab://resnet18_v1c', - backbone=dict(depth=18), - decode_head=dict( - in_channels=512, - channels=128, - ), - auxiliary_head=dict(in_channels=256, channels=64)) diff --git a/configs/fcn/fcn_r18b-d8_512x1024_80k_cityscapes.py b/configs/pspnet/pspnet_r18b-d8_4xb2-80k_cityscapes-512x1024.py similarity index 79% rename from configs/fcn/fcn_r18b-d8_512x1024_80k_cityscapes.py rename to configs/pspnet/pspnet_r18b-d8_4xb2-80k_cityscapes-512x1024.py index 92accfc703..2e356c5c5f 100644 --- a/configs/fcn/fcn_r18b-d8_512x1024_80k_cityscapes.py +++ b/configs/pspnet/pspnet_r18b-d8_4xb2-80k_cityscapes-512x1024.py @@ -1,4 +1,4 @@ -_base_ = './fcn_r50-d8_512x1024_80k_cityscapes.py' +_base_ = './pspnet_r50-d8_4xb2-80k_cityscapes-512x1024.py' model = dict( pretrained='torchvision://resnet18', backbone=dict(type='ResNet', depth=18), diff --git a/configs/deeplabv3/deeplabv3_r18b-d8_512x1024_80k_cityscapes.py b/configs/pspnet/pspnet_r18b-d8_4xb2-80k_cityscapes-769x769.py similarity index 79% rename from configs/deeplabv3/deeplabv3_r18b-d8_512x1024_80k_cityscapes.py rename to configs/pspnet/pspnet_r18b-d8_4xb2-80k_cityscapes-769x769.py index b25e725ed9..831354d4ce 100644 --- a/configs/deeplabv3/deeplabv3_r18b-d8_512x1024_80k_cityscapes.py +++ b/configs/pspnet/pspnet_r18b-d8_4xb2-80k_cityscapes-769x769.py @@ -1,4 +1,4 @@ -_base_ = './deeplabv3_r50-d8_512x1024_80k_cityscapes.py' +_base_ = './pspnet_r50-d8_4xb2-80k_cityscapes-769x769.py' model = dict( pretrained='torchvision://resnet18', backbone=dict(type='ResNet', depth=18), diff --git a/configs/pspnet/pspnet_r18b-d8_769x769_80k_cityscapes.py b/configs/pspnet/pspnet_r18b-d8_769x769_80k_cityscapes.py deleted file mode 100644 index 284be6d09a..0000000000 --- a/configs/pspnet/pspnet_r18b-d8_769x769_80k_cityscapes.py +++ /dev/null @@ -1,9 +0,0 @@ -_base_ = './pspnet_r50-d8_769x769_80k_cityscapes.py' -model = dict( - pretrained='torchvision://resnet18', - backbone=dict(type='ResNet', depth=18), - decode_head=dict( - in_channels=512, - channels=128, - ), - auxiliary_head=dict(in_channels=256, channels=64)) diff --git a/configs/pspnet/pspnet_r50-d32_512x1024_80k_cityscapes.py b/configs/pspnet/pspnet_r50-d32_4xb2-80k_cityscapes-512x1024.py similarity index 100% rename from configs/pspnet/pspnet_r50-d32_512x1024_80k_cityscapes.py rename to configs/pspnet/pspnet_r50-d32_4xb2-80k_cityscapes-512x1024.py diff --git a/configs/pspnet/pspnet_r50-d32_rsb-pretrain_512x1024_adamw_80k_cityscapes.py b/configs/pspnet/pspnet_r50-d32_rsb_4xb2-adamw-80k_cityscapes-512x1024.py similarity index 100% rename from configs/pspnet/pspnet_r50-d32_rsb-pretrain_512x1024_adamw_80k_cityscapes.py rename to configs/pspnet/pspnet_r50-d32_rsb_4xb2-adamw-80k_cityscapes-512x1024.py diff --git a/configs/pspnet/pspnet_r50-d8_rsb-pretrain_512x1024_adamw_80k_cityscapes.py b/configs/pspnet/pspnet_r50-d8-rsb_4xb2-adamw-80k_cityscapes-512x1024.py similarity index 100% rename from configs/pspnet/pspnet_r50-d8_rsb-pretrain_512x1024_adamw_80k_cityscapes.py rename to configs/pspnet/pspnet_r50-d8-rsb_4xb2-adamw-80k_cityscapes-512x1024.py diff --git a/configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py b/configs/pspnet/pspnet_r50-d8_4xb2-40k_cityscapes-512x1024.py similarity index 100% rename from configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py rename to configs/pspnet/pspnet_r50-d8_4xb2-40k_cityscapes-512x1024.py diff --git a/configs/pspnet/pspnet_r50-d8_512x1024_40k_dark.py b/configs/pspnet/pspnet_r50-d8_4xb2-40k_cityscapes-512x1024_dark-zurich-1920x1080.py similarity index 100% rename from configs/pspnet/pspnet_r50-d8_512x1024_40k_dark.py rename to configs/pspnet/pspnet_r50-d8_4xb2-40k_cityscapes-512x1024_dark-zurich-1920x1080.py diff --git a/configs/pspnet/pspnet_r50-d8_512x1024_40k_night_driving.py b/configs/pspnet/pspnet_r50-d8_4xb2-40k_cityscapes-512x1024_night-driving-1920x1080.py similarity index 100% rename from configs/pspnet/pspnet_r50-d8_512x1024_40k_night_driving.py rename to configs/pspnet/pspnet_r50-d8_4xb2-40k_cityscapes-512x1024_night-driving-1920x1080.py diff --git a/configs/pspnet/pspnet_r50-d8_769x769_40k_cityscapes.py b/configs/pspnet/pspnet_r50-d8_4xb2-40k_cityscapes-769x769.py similarity index 100% rename from configs/pspnet/pspnet_r50-d8_769x769_40k_cityscapes.py rename to configs/pspnet/pspnet_r50-d8_4xb2-40k_cityscapes-769x769.py diff --git a/configs/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes.py b/configs/pspnet/pspnet_r50-d8_4xb2-80k_cityscapes-512x1024.py similarity index 100% rename from configs/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes.py rename to configs/pspnet/pspnet_r50-d8_4xb2-80k_cityscapes-512x1024.py diff --git a/configs/pspnet/pspnet_r50-d8_512x1024_80k_dark.py b/configs/pspnet/pspnet_r50-d8_4xb2-80k_cityscapes-512x1024_dark-zurich-1920x1080.py similarity index 100% rename from configs/pspnet/pspnet_r50-d8_512x1024_80k_dark.py rename to configs/pspnet/pspnet_r50-d8_4xb2-80k_cityscapes-512x1024_dark-zurich-1920x1080.py diff --git a/configs/pspnet/pspnet_r50-d8_512x1024_80k_night_driving.py b/configs/pspnet/pspnet_r50-d8_4xb2-80k_cityscapes-512x1024_night-driving-1920x1080.py similarity index 100% rename from configs/pspnet/pspnet_r50-d8_512x1024_80k_night_driving.py rename to configs/pspnet/pspnet_r50-d8_4xb2-80k_cityscapes-512x1024_night-driving-1920x1080.py diff --git a/configs/pspnet/pspnet_r50-d8_769x769_80k_cityscapes.py b/configs/pspnet/pspnet_r50-d8_4xb2-80k_cityscapes-769x769.py similarity index 100% rename from configs/pspnet/pspnet_r50-d8_769x769_80k_cityscapes.py rename to configs/pspnet/pspnet_r50-d8_4xb2-80k_cityscapes-769x769.py diff --git a/configs/pspnet/pspnet_r50-d8_512x512_160k_ade20k.py b/configs/pspnet/pspnet_r50-d8_4xb4-160k_ade20k-512x512.py similarity index 100% rename from configs/pspnet/pspnet_r50-d8_512x512_160k_ade20k.py rename to configs/pspnet/pspnet_r50-d8_4xb4-160k_ade20k-512x512.py diff --git a/configs/pspnet/pspnet_r50-d8_512x512_4x4_160k_coco-stuff164k.py b/configs/pspnet/pspnet_r50-d8_4xb4-160k_coco-stuff164k-512x512.py similarity index 100% rename from configs/pspnet/pspnet_r50-d8_512x512_4x4_160k_coco-stuff164k.py rename to configs/pspnet/pspnet_r50-d8_4xb4-160k_coco-stuff164k-512x512.py diff --git a/configs/pspnet/pspnet_r50-d8_512x512_4x4_20k_coco-stuff10k.py b/configs/pspnet/pspnet_r50-d8_4xb4-20k_coco-stuff10k-512x512.py similarity index 100% rename from configs/pspnet/pspnet_r50-d8_512x512_4x4_20k_coco-stuff10k.py rename to configs/pspnet/pspnet_r50-d8_4xb4-20k_coco-stuff10k-512x512.py diff --git a/configs/pspnet/pspnet_r50-d8_512x512_20k_voc12aug.py b/configs/pspnet/pspnet_r50-d8_4xb4-20k_voc12aug-512x512.py similarity index 100% rename from configs/pspnet/pspnet_r50-d8_512x512_20k_voc12aug.py rename to configs/pspnet/pspnet_r50-d8_4xb4-20k_voc12aug-512x512.py diff --git a/configs/pspnet/pspnet_r50-d8_512x512_4x4_320k_coco-stuff164k.py b/configs/pspnet/pspnet_r50-d8_4xb4-320k_coco-stuff164k-512x512.py similarity index 100% rename from configs/pspnet/pspnet_r50-d8_512x512_4x4_320k_coco-stuff164k.py rename to configs/pspnet/pspnet_r50-d8_4xb4-320k_coco-stuff164k-512x512.py diff --git a/configs/pspnet/pspnet_r50-d8_512x512_4x4_40k_coco-stuff10k.py b/configs/pspnet/pspnet_r50-d8_4xb4-40k_coco-stuff10k-512x512.py similarity index 100% rename from configs/pspnet/pspnet_r50-d8_512x512_4x4_40k_coco-stuff10k.py rename to configs/pspnet/pspnet_r50-d8_4xb4-40k_coco-stuff10k-512x512.py diff --git a/configs/pspnet/pspnet_r50-d8_480x480_40k_pascal_context.py b/configs/pspnet/pspnet_r50-d8_4xb4-40k_pascal-context-480x480.py similarity index 100% rename from configs/pspnet/pspnet_r50-d8_480x480_40k_pascal_context.py rename to configs/pspnet/pspnet_r50-d8_4xb4-40k_pascal-context-480x480.py diff --git a/configs/pspnet/pspnet_r50-d8_480x480_40k_pascal_context_59.py b/configs/pspnet/pspnet_r50-d8_4xb4-40k_pascal-context-59-480x480.py similarity index 100% rename from configs/pspnet/pspnet_r50-d8_480x480_40k_pascal_context_59.py rename to configs/pspnet/pspnet_r50-d8_4xb4-40k_pascal-context-59-480x480.py diff --git a/configs/pspnet/pspnet_r50-d8_512x512_40k_voc12aug.py b/configs/pspnet/pspnet_r50-d8_4xb4-40k_voc12aug-512x512.py similarity index 100% rename from configs/pspnet/pspnet_r50-d8_512x512_40k_voc12aug.py rename to configs/pspnet/pspnet_r50-d8_4xb4-40k_voc12aug-512x512.py diff --git a/configs/pspnet/pspnet_r50-d8_512x512_80k_ade20k.py b/configs/pspnet/pspnet_r50-d8_4xb4-80k_ade20k-512x512.py similarity index 100% rename from configs/pspnet/pspnet_r50-d8_512x512_80k_ade20k.py rename to configs/pspnet/pspnet_r50-d8_4xb4-80k_ade20k-512x512.py diff --git a/configs/pspnet/pspnet_r50-d8_512x512_4x4_80k_coco-stuff164k.py b/configs/pspnet/pspnet_r50-d8_4xb4-80k_coco-stuff164k-512x512.py similarity index 100% rename from configs/pspnet/pspnet_r50-d8_512x512_4x4_80k_coco-stuff164k.py rename to configs/pspnet/pspnet_r50-d8_4xb4-80k_coco-stuff164k-512x512.py diff --git a/configs/pspnet/pspnet_r50-d8_4x4_896x896_80k_isaid.py b/configs/pspnet/pspnet_r50-d8_4xb4-80k_isaid-896x896.py similarity index 100% rename from configs/pspnet/pspnet_r50-d8_4x4_896x896_80k_isaid.py rename to configs/pspnet/pspnet_r50-d8_4xb4-80k_isaid-896x896.py diff --git a/configs/pspnet/pspnet_r50-d8_512x512_80k_loveda.py b/configs/pspnet/pspnet_r50-d8_4xb4-80k_loveda-512x512.py similarity index 100% rename from configs/pspnet/pspnet_r50-d8_512x512_80k_loveda.py rename to configs/pspnet/pspnet_r50-d8_4xb4-80k_loveda-512x512.py diff --git a/configs/pspnet/pspnet_r50-d8_480x480_80k_pascal_context.py b/configs/pspnet/pspnet_r50-d8_4xb4-80k_pascal-context-480x480.py similarity index 100% rename from configs/pspnet/pspnet_r50-d8_480x480_80k_pascal_context.py rename to configs/pspnet/pspnet_r50-d8_4xb4-80k_pascal-context-480x480.py diff --git a/configs/pspnet/pspnet_r50-d8_480x480_80k_pascal_context_59.py b/configs/pspnet/pspnet_r50-d8_4xb4-80k_pascal-context-59-480x480.py similarity index 100% rename from configs/pspnet/pspnet_r50-d8_480x480_80k_pascal_context_59.py rename to configs/pspnet/pspnet_r50-d8_4xb4-80k_pascal-context-59-480x480.py diff --git a/configs/pspnet/pspnet_r50-d8_4x4_512x512_80k_potsdam.py b/configs/pspnet/pspnet_r50-d8_4xb4-80k_potsdam-512x512.py similarity index 100% rename from configs/pspnet/pspnet_r50-d8_4x4_512x512_80k_potsdam.py rename to configs/pspnet/pspnet_r50-d8_4xb4-80k_potsdam-512x512.py diff --git a/configs/pspnet/pspnet_r50-d8_4x4_512x512_80k_vaihingen.py b/configs/pspnet/pspnet_r50-d8_4xb4-80k_vaihingen-512x512.py similarity index 100% rename from configs/pspnet/pspnet_r50-d8_4x4_512x512_80k_vaihingen.py rename to configs/pspnet/pspnet_r50-d8_4xb4-80k_vaihingen-512x512.py diff --git a/configs/pspnet/pspnet_r50b-d32_512x1024_80k_cityscapes.py b/configs/pspnet/pspnet_r50b-d32_4xb2-80k_cityscapes-512x1024.py similarity index 100% rename from configs/pspnet/pspnet_r50b-d32_512x1024_80k_cityscapes.py rename to configs/pspnet/pspnet_r50b-d32_4xb2-80k_cityscapes-512x1024.py diff --git a/configs/fcn/fcn_d6_r50b-d16_769x769_80k_cityscapes.py b/configs/pspnet/pspnet_r50b-d8_4xb2-80k_cityscapes-512x1024.py similarity index 57% rename from configs/fcn/fcn_d6_r50b-d16_769x769_80k_cityscapes.py rename to configs/pspnet/pspnet_r50b-d8_4xb2-80k_cityscapes-512x1024.py index fba8948a03..7dd64b332f 100644 --- a/configs/fcn/fcn_d6_r50b-d16_769x769_80k_cityscapes.py +++ b/configs/pspnet/pspnet_r50b-d8_4xb2-80k_cityscapes-512x1024.py @@ -1,2 +1,2 @@ -_base_ = './fcn_d6_r50-d16_769x769_80k_cityscapes.py' +_base_ = './pspnet_r50-d8_4xb2-80k_cityscapes-512x1024.py' model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet')) diff --git a/configs/deeplabv3/deeplabv3_r50b-d8_512x1024_80k_cityscapes.py b/configs/pspnet/pspnet_r50b-d8_4xb2-80k_cityscapes-769x769.py similarity index 57% rename from configs/deeplabv3/deeplabv3_r50b-d8_512x1024_80k_cityscapes.py rename to configs/pspnet/pspnet_r50b-d8_4xb2-80k_cityscapes-769x769.py index e742d9a5ec..3875c092fe 100644 --- a/configs/deeplabv3/deeplabv3_r50b-d8_512x1024_80k_cityscapes.py +++ b/configs/pspnet/pspnet_r50b-d8_4xb2-80k_cityscapes-769x769.py @@ -1,2 +1,2 @@ -_base_ = './deeplabv3_r50-d8_512x1024_80k_cityscapes.py' +_base_ = './pspnet_r50-d8_4xb2-80k_cityscapes-769x769.py' model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet')) diff --git a/configs/pspnet/pspnet_r50b-d8_512x1024_80k_cityscapes.py b/configs/pspnet/pspnet_r50b-d8_512x1024_80k_cityscapes.py deleted file mode 100644 index 946bf4fc84..0000000000 --- a/configs/pspnet/pspnet_r50b-d8_512x1024_80k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './pspnet_r50-d8_512x1024_80k_cityscapes.py' -model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet')) diff --git a/configs/pspnet/pspnet_r50b-d8_769x769_80k_cityscapes.py b/configs/pspnet/pspnet_r50b-d8_769x769_80k_cityscapes.py deleted file mode 100644 index b6087dcf9f..0000000000 --- a/configs/pspnet/pspnet_r50b-d8_769x769_80k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './pspnet_r50-d8_769x769_80k_cityscapes.py' -model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet')) diff --git a/configs/resnest/README.md b/configs/resnest/README.md index 1b116dc5ea..7f07d147b7 100644 --- a/configs/resnest/README.md +++ b/configs/resnest/README.md @@ -37,18 +37,18 @@ year={2020} ### 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/master/configs/resnest/fcn_s101-d8_512x1024_80k_cityscapes.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/master/configs/resnest/pspnet_s101-d8_512x1024_80k_cityscapes.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/master/configs/resnest/deeplabv3_s101-d8_512x1024_80k_cityscapes.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/master/configs/resnest/deeplabv3plus_s101-d8_512x1024_80k_cityscapes.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) | +| 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/master/configs/resnest/fcn_s101-d8_512x512_160k_ade20k.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/master/configs/resnest/pspnet_s101-d8_512x512_160k_ade20k.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/master/configs/resnest/deeplabv3_s101-d8_512x512_160k_ade20k.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/master/configs/resnest/deeplabv3plus_s101-d8_512x512_160k_ade20k.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) | +| 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/fcn_s101-d8_512x512_160k_ade20k.py b/configs/resnest/fcn_s101-d8_512x512_160k_ade20k.py deleted file mode 100644 index dcee8c280e..0000000000 --- a/configs/resnest/fcn_s101-d8_512x512_160k_ade20k.py +++ /dev/null @@ -1,9 +0,0 @@ -_base_ = '../fcn/fcn_r101-d8_512x512_160k_ade20k.py' -model = dict( - pretrained='open-mmlab://resnest101', - backbone=dict( - type='ResNeSt', - stem_channels=128, - radix=2, - reduction_factor=4, - avg_down_stride=True)) diff --git a/configs/resnest/resnest.yml b/configs/resnest/resnest.yml index b2ca2590b8..ab897e3bd5 100644 --- a/configs/resnest/resnest.yml +++ b/configs/resnest/resnest.yml @@ -1,5 +1,5 @@ Models: -- Name: fcn_s101-d8_512x1024_80k_cityscapes +- Name: resnest_s101-d8_fcn_4xb2-80k_cityscapes-512x1024 In Collection: FCN Metadata: backbone: S-101-D8 @@ -19,9 +19,9 @@ Models: Metrics: mIoU: 77.56 mIoU(ms+flip): 78.98 - Config: configs/resnest/fcn_s101-d8_512x1024_80k_cityscapes.py + 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: pspnet_s101-d8_512x1024_80k_cityscapes +- Name: resnest_s101-d8_pspnet_4xb2-80k_cityscapes512x1024 In Collection: PSPNet Metadata: backbone: S-101-D8 @@ -41,9 +41,9 @@ Models: Metrics: mIoU: 78.57 mIoU(ms+flip): 79.19 - Config: configs/resnest/pspnet_s101-d8_512x1024_80k_cityscapes.py + 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: deeplabv3_s101-d8_512x1024_80k_cityscapes +- Name: resnest_s101-d8_deeplabv3_4xb2-80k_cityscapes-512x1024 In Collection: DeepLabV3 Metadata: backbone: S-101-D8 @@ -63,9 +63,9 @@ Models: Metrics: mIoU: 79.67 mIoU(ms+flip): 80.51 - Config: configs/resnest/deeplabv3_s101-d8_512x1024_80k_cityscapes.py + 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: deeplabv3plus_s101-d8_512x1024_80k_cityscapes +- Name: resnest_s101-d8_deeplabv3plus_4xb2-80k_cityscapes-512x1024 In Collection: DeepLabV3+ Metadata: backbone: S-101-D8 @@ -85,9 +85,9 @@ Models: Metrics: mIoU: 79.62 mIoU(ms+flip): 80.27 - Config: configs/resnest/deeplabv3plus_s101-d8_512x1024_80k_cityscapes.py + 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: fcn_s101-d8_512x512_160k_ade20k +- Name: resnest_s101-d8_fcn_4xb4-160k_ade20k-512x512 In Collection: FCN Metadata: backbone: S-101-D8 @@ -107,9 +107,9 @@ Models: Metrics: mIoU: 45.62 mIoU(ms+flip): 46.16 - Config: configs/resnest/fcn_s101-d8_512x512_160k_ade20k.py + 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: pspnet_s101-d8_512x512_160k_ade20k +- Name: resnest_s101-d8_pspnet_4xb4-160k_ade20k-512x512 In Collection: PSPNet Metadata: backbone: S-101-D8 @@ -129,9 +129,9 @@ Models: Metrics: mIoU: 45.44 mIoU(ms+flip): 46.28 - Config: configs/resnest/pspnet_s101-d8_512x512_160k_ade20k.py + 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: deeplabv3_s101-d8_512x512_160k_ade20k +- Name: resnest_s101-d8_deeplabv3_4xb4-160k_ade20k-512x512 In Collection: DeepLabV3 Metadata: backbone: S-101-D8 @@ -151,9 +151,9 @@ Models: Metrics: mIoU: 45.71 mIoU(ms+flip): 46.59 - Config: configs/resnest/deeplabv3_s101-d8_512x512_160k_ade20k.py + 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: deeplabv3plus_s101-d8_512x512_160k_ade20k +- Name: resnest_s101-d8_deeplabv3plus_4xb4-160k_ade20k-512x512 In Collection: DeepLabV3+ Metadata: backbone: S-101-D8 @@ -173,5 +173,5 @@ Models: Metrics: mIoU: 46.47 mIoU(ms+flip): 47.27 - Config: configs/resnest/deeplabv3plus_s101-d8_512x512_160k_ade20k.py + 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/resnest/deeplabv3plus_s101-d8_512x512_160k_ade20k.py b/configs/resnest/resnest_s101-d8_deeplabv3_4xb2-80k_cityscapes-512x1024.py similarity index 73% rename from configs/resnest/deeplabv3plus_s101-d8_512x512_160k_ade20k.py rename to configs/resnest/resnest_s101-d8_deeplabv3_4xb2-80k_cityscapes-512x1024.py index d51bccb965..7ece894b56 100644 --- a/configs/resnest/deeplabv3plus_s101-d8_512x512_160k_ade20k.py +++ b/configs/resnest/resnest_s101-d8_deeplabv3_4xb2-80k_cityscapes-512x1024.py @@ -1,4 +1,4 @@ -_base_ = '../deeplabv3plus/deeplabv3plus_r101-d8_512x512_160k_ade20k.py' +_base_ = '../deeplabv3/deeplabv3_r101-d8_4xb2-80k_cityscapes-512x1024.py' model = dict( pretrained='open-mmlab://resnest101', backbone=dict( diff --git a/configs/resnest/deeplabv3_s101-d8_512x1024_80k_cityscapes.py b/configs/resnest/resnest_s101-d8_deeplabv3_4xb4-160k_ade20k-512x512.py similarity index 74% rename from configs/resnest/deeplabv3_s101-d8_512x1024_80k_cityscapes.py rename to configs/resnest/resnest_s101-d8_deeplabv3_4xb4-160k_ade20k-512x512.py index f98398690e..c2852301fc 100644 --- a/configs/resnest/deeplabv3_s101-d8_512x1024_80k_cityscapes.py +++ b/configs/resnest/resnest_s101-d8_deeplabv3_4xb4-160k_ade20k-512x512.py @@ -1,4 +1,4 @@ -_base_ = '../deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes.py' +_base_ = '../deeplabv3/deeplabv3_r101-d8_4xb4-160k_ade20k-512x512.py' model = dict( pretrained='open-mmlab://resnest101', backbone=dict( diff --git a/configs/resnest/resnest_s101-d8_deeplabv3plus_4xb2-80k_cityscapes-512x1024.py b/configs/resnest/resnest_s101-d8_deeplabv3plus_4xb2-80k_cityscapes-512x1024.py new file mode 100644 index 0000000000..5c43a9547d --- /dev/null +++ b/configs/resnest/resnest_s101-d8_deeplabv3plus_4xb2-80k_cityscapes-512x1024.py @@ -0,0 +1,9 @@ +_base_ = '../deeplabv3plus/deeplabv3plus_r101-d8_4xb2-80k_cityscapes-512x1024.py' # noqa +model = dict( + pretrained='open-mmlab://resnest101', + backbone=dict( + type='ResNeSt', + stem_channels=128, + radix=2, + reduction_factor=4, + avg_down_stride=True)) diff --git a/configs/resnest/deeplabv3plus_s101-d8_512x1024_80k_cityscapes.py b/configs/resnest/resnest_s101-d8_deeplabv3plus_4xb4-160k_ade20k-512x512.py similarity index 72% rename from configs/resnest/deeplabv3plus_s101-d8_512x1024_80k_cityscapes.py rename to configs/resnest/resnest_s101-d8_deeplabv3plus_4xb4-160k_ade20k-512x512.py index 69bef72383..ce39d3709f 100644 --- a/configs/resnest/deeplabv3plus_s101-d8_512x1024_80k_cityscapes.py +++ b/configs/resnest/resnest_s101-d8_deeplabv3plus_4xb4-160k_ade20k-512x512.py @@ -1,4 +1,4 @@ -_base_ = '../deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes.py' +_base_ = '../deeplabv3plus/deeplabv3plus_r101-d8_4xb4-160k_ade20k-512x512.py' model = dict( pretrained='open-mmlab://resnest101', backbone=dict( diff --git a/configs/resnest/pspnet_s101-d8_512x512_160k_ade20k.py b/configs/resnest/resnest_s101-d8_fcn_4xb2-80k_cityscapes-512x1024.py similarity index 76% rename from configs/resnest/pspnet_s101-d8_512x512_160k_ade20k.py rename to configs/resnest/resnest_s101-d8_fcn_4xb2-80k_cityscapes-512x1024.py index 6a622eae96..fc333e4ff0 100644 --- a/configs/resnest/pspnet_s101-d8_512x512_160k_ade20k.py +++ b/configs/resnest/resnest_s101-d8_fcn_4xb2-80k_cityscapes-512x1024.py @@ -1,4 +1,4 @@ -_base_ = '../pspnet/pspnet_r101-d8_512x512_160k_ade20k.py' +_base_ = '../fcn/fcn_r101-d8_4xb2-80k_cityscapes-512x1024.py' model = dict( pretrained='open-mmlab://resnest101', backbone=dict( diff --git a/configs/resnest/fcn_s101-d8_512x1024_80k_cityscapes.py b/configs/resnest/resnest_s101-d8_fcn_4xb4-160k_ade20k-512x512.py similarity index 77% rename from configs/resnest/fcn_s101-d8_512x1024_80k_cityscapes.py rename to configs/resnest/resnest_s101-d8_fcn_4xb4-160k_ade20k-512x512.py index 33fa0252d8..af12733444 100644 --- a/configs/resnest/fcn_s101-d8_512x1024_80k_cityscapes.py +++ b/configs/resnest/resnest_s101-d8_fcn_4xb4-160k_ade20k-512x512.py @@ -1,4 +1,4 @@ -_base_ = '../fcn/fcn_r101-d8_512x1024_80k_cityscapes.py' +_base_ = '../fcn/fcn_r101-d8_4xb4-160k_ade20k-512x512.py' model = dict( pretrained='open-mmlab://resnest101', backbone=dict( diff --git a/configs/resnest/deeplabv3_s101-d8_512x512_160k_ade20k.py b/configs/resnest/resnest_s101-d8_pspnet_4xb2-80k_cityscapes512x1024.py similarity index 74% rename from configs/resnest/deeplabv3_s101-d8_512x512_160k_ade20k.py rename to configs/resnest/resnest_s101-d8_pspnet_4xb2-80k_cityscapes512x1024.py index e3924ad679..3aab524449 100644 --- a/configs/resnest/deeplabv3_s101-d8_512x512_160k_ade20k.py +++ b/configs/resnest/resnest_s101-d8_pspnet_4xb2-80k_cityscapes512x1024.py @@ -1,4 +1,4 @@ -_base_ = '../deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k.py' +_base_ = '../pspnet/pspnet_r101-d8_4xb2-80k_cityscapes-512x1024.py' model = dict( pretrained='open-mmlab://resnest101', backbone=dict( diff --git a/configs/resnest/pspnet_s101-d8_512x1024_80k_cityscapes.py b/configs/resnest/resnest_s101-d8_pspnet_4xb4-160k_ade20k-512x512.py similarity index 75% rename from configs/resnest/pspnet_s101-d8_512x1024_80k_cityscapes.py rename to configs/resnest/resnest_s101-d8_pspnet_4xb4-160k_ade20k-512x512.py index 9737849cbd..66e6639c18 100644 --- a/configs/resnest/pspnet_s101-d8_512x1024_80k_cityscapes.py +++ b/configs/resnest/resnest_s101-d8_pspnet_4xb4-160k_ade20k-512x512.py @@ -1,4 +1,4 @@ -_base_ = '../pspnet/pspnet_r101-d8_512x1024_80k_cityscapes.py' +_base_ = '../pspnet/pspnet_r101-d8_4xb4-160k_ade20k-512x512.py' model = dict( pretrained='open-mmlab://resnest101', backbone=dict( diff --git a/configs/segformer/README.md b/configs/segformer/README.md index 5ac6f36968..655c2e92a3 100644 --- a/configs/segformer/README.md +++ b/configs/segformer/README.md @@ -49,15 +49,15 @@ 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/master/configs/segformer/segformer_mit-b0_512x512_160k_ade20k.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/master/configs/segformer/segformer_mit-b1_512x512_160k_ade20k.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/master/configs/segformer/segformer_mit-b2_512x512_160k_ade20k.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/master/configs/segformer/segformer_mit-b3_512x512_160k_ade20k.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/master/configs/segformer/segformer_mit-b4_512x512_160k_ade20k.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/master/configs/segformer/segformer_mit-b5_512x512_160k_ade20k.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/master/configs/segformer/segformer_mit-b5_640x640_160k_ade20k.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) | 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) | Evaluation with AlignedResize: @@ -98,11 +98,11 @@ 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/master/configs/segformer/segformer_mit-b0_8x1_1024x1024_160k_cityscapes.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/master/configs/segformer/segformer_mit-b1_8x1_1024x1024_160k_cityscapes.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/master/configs/segformer/segformer_mit-b2_8x1_1024x1024_160k_cityscapes.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/master/configs/segformer/segformer_mit-b3_8x1_1024x1024_160k_cityscapes.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/master/configs/segformer/segformer_mit-b4_8x1_1024x1024_160k_cityscapes.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/master/configs/segformer/segformer_mit-b5_8x1_1024x1024_160k_cityscapes.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) | 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) | diff --git a/configs/segformer/segformer.yml b/configs/segformer/segformer.yml index d28cb16265..4a3818e16e 100644 --- a/configs/segformer/segformer.yml +++ b/configs/segformer/segformer.yml @@ -15,7 +15,7 @@ Collections: Converted From: Code: https://github.com/NVlabs/SegFormer Models: -- Name: segformer_mit-b0_512x512_160k_ade20k +- Name: segformer_mit-b0_8xb2-160k_ade20k-512x512 In Collection: Segformer Metadata: backbone: MIT-B0 @@ -35,9 +35,9 @@ Models: Metrics: mIoU: 37.41 mIoU(ms+flip): 38.34 - Config: configs/segformer/segformer_mit-b0_512x512_160k_ade20k.py + 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_512x512_160k_ade20k +- Name: segformer_mit-b1_8xb2-160k_ade20k-512x512 In Collection: Segformer Metadata: backbone: MIT-B1 @@ -57,9 +57,9 @@ Models: Metrics: mIoU: 40.97 mIoU(ms+flip): 42.54 - Config: configs/segformer/segformer_mit-b1_512x512_160k_ade20k.py + 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_512x512_160k_ade20k +- Name: segformer_mit-b2_8xb2-160k_ade20k-512x512 In Collection: Segformer Metadata: backbone: MIT-B2 @@ -79,9 +79,9 @@ Models: Metrics: mIoU: 45.58 mIoU(ms+flip): 47.03 - Config: configs/segformer/segformer_mit-b2_512x512_160k_ade20k.py + 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_512x512_160k_ade20k +- Name: segformer_mit-b3_8xb2-160k_ade20k-512x512 In Collection: Segformer Metadata: backbone: MIT-B3 @@ -101,9 +101,9 @@ Models: Metrics: mIoU: 47.82 mIoU(ms+flip): 48.81 - Config: configs/segformer/segformer_mit-b3_512x512_160k_ade20k.py + 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_512x512_160k_ade20k +- Name: segformer_mit-b4_8xb2-160k_ade20k-512x512 In Collection: Segformer Metadata: backbone: MIT-B4 @@ -123,9 +123,9 @@ Models: Metrics: mIoU: 48.46 mIoU(ms+flip): 49.76 - Config: configs/segformer/segformer_mit-b4_512x512_160k_ade20k.py + 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_512x512_160k_ade20k +- Name: segformer_mit-b5_8xb2-160k_ade20k-512x512 In Collection: Segformer Metadata: backbone: MIT-B5 @@ -145,9 +145,9 @@ Models: Metrics: mIoU: 49.13 mIoU(ms+flip): 50.22 - Config: configs/segformer/segformer_mit-b5_512x512_160k_ade20k.py + 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_640x640_160k_ade20k +- Name: segformer_mit-b5_8xb2-160k_ade20k-640x640 In Collection: Segformer Metadata: backbone: MIT-B5 @@ -167,9 +167,9 @@ Models: Metrics: mIoU: 49.62 mIoU(ms+flip): 50.36 - Config: configs/segformer/segformer_mit-b5_640x640_160k_ade20k.py + 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_8x1_1024x1024_160k_cityscapes +- Name: segformer_mit-b0_8xb1-160k_cityscapes-1024x1024 In Collection: Segformer Metadata: backbone: MIT-B0 @@ -189,9 +189,9 @@ Models: Metrics: mIoU: 76.54 mIoU(ms+flip): 78.22 - Config: configs/segformer/segformer_mit-b0_8x1_1024x1024_160k_cityscapes.py + 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_8x1_1024x1024_160k_cityscapes +- Name: segformer_mit-b1_8xb1-160k_cityscapes-1024x1024 In Collection: Segformer Metadata: backbone: MIT-B1 @@ -211,9 +211,9 @@ Models: Metrics: mIoU: 78.56 mIoU(ms+flip): 79.73 - Config: configs/segformer/segformer_mit-b1_8x1_1024x1024_160k_cityscapes.py + 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_8x1_1024x1024_160k_cityscapes +- Name: segformer_mit-b2_8xb1-160k_cityscapes-1024x1024 In Collection: Segformer Metadata: backbone: MIT-B2 @@ -233,9 +233,9 @@ Models: Metrics: mIoU: 81.08 mIoU(ms+flip): 82.18 - Config: configs/segformer/segformer_mit-b2_8x1_1024x1024_160k_cityscapes.py + 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_8x1_1024x1024_160k_cityscapes +- Name: segformer_mit-b3_8xb1-160k_cityscapes-1024x1024 In Collection: Segformer Metadata: backbone: MIT-B3 @@ -255,9 +255,9 @@ Models: Metrics: mIoU: 81.94 mIoU(ms+flip): 83.14 - Config: configs/segformer/segformer_mit-b3_8x1_1024x1024_160k_cityscapes.py + 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_8x1_1024x1024_160k_cityscapes +- Name: segformer_mit-b4_8xb1-160k_cityscapes-1024x1024 In Collection: Segformer Metadata: backbone: MIT-B4 @@ -277,9 +277,9 @@ Models: Metrics: mIoU: 81.89 mIoU(ms+flip): 83.38 - Config: configs/segformer/segformer_mit-b4_8x1_1024x1024_160k_cityscapes.py + 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_8x1_1024x1024_160k_cityscapes +- Name: segformer_mit-b5_8xb1-160k_cityscapes-1024x1024 In Collection: Segformer Metadata: backbone: MIT-B5 @@ -299,5 +299,5 @@ Models: Metrics: mIoU: 82.25 mIoU(ms+flip): 83.48 - Config: configs/segformer/segformer_mit-b5_8x1_1024x1024_160k_cityscapes.py + 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/segformer/segformer_mit-b0_8x1_1024x1024_160k_cityscapes.py b/configs/segformer/segformer_mit-b0_8xb1-160k_cityscapes-1024x1024.py similarity index 100% rename from configs/segformer/segformer_mit-b0_8x1_1024x1024_160k_cityscapes.py rename to configs/segformer/segformer_mit-b0_8xb1-160k_cityscapes-1024x1024.py diff --git a/configs/segformer/segformer_mit-b0_512x512_160k_ade20k.py b/configs/segformer/segformer_mit-b0_8xb2-160k_ade20k-512x512.py similarity index 100% rename from configs/segformer/segformer_mit-b0_512x512_160k_ade20k.py rename to configs/segformer/segformer_mit-b0_8xb2-160k_ade20k-512x512.py diff --git a/configs/segformer/segformer_mit-b1_8x1_1024x1024_160k_cityscapes.py b/configs/segformer/segformer_mit-b1_8xb1-160k_cityscapes-1024x1024.py similarity index 74% rename from configs/segformer/segformer_mit-b1_8x1_1024x1024_160k_cityscapes.py rename to configs/segformer/segformer_mit-b1_8xb1-160k_cityscapes-1024x1024.py index a93e33bd88..d6977d4678 100644 --- a/configs/segformer/segformer_mit-b1_8x1_1024x1024_160k_cityscapes.py +++ b/configs/segformer/segformer_mit-b1_8xb1-160k_cityscapes-1024x1024.py @@ -1,4 +1,4 @@ -_base_ = ['./segformer_mit-b0_8x1_1024x1024_160k_cityscapes.py'] +_base_ = ['./segformer_mit-b0_8xb1-160k_cityscapes-1024x1024.py'] model = dict( backbone=dict( diff --git a/configs/segformer/segformer_mit-b1_512x512_160k_ade20k.py b/configs/segformer/segformer_mit-b1_8xb2-160k_ade20k-512x512.py similarity index 78% rename from configs/segformer/segformer_mit-b1_512x512_160k_ade20k.py rename to configs/segformer/segformer_mit-b1_8xb2-160k_ade20k-512x512.py index 5fce602144..3bedca9891 100644 --- a/configs/segformer/segformer_mit-b1_512x512_160k_ade20k.py +++ b/configs/segformer/segformer_mit-b1_8xb2-160k_ade20k-512x512.py @@ -1,4 +1,4 @@ -_base_ = ['./segformer_mit-b0_512x512_160k_ade20k.py'] +_base_ = ['./segformer_mit-b0_8xb2-160k_ade20k-512x512.py'] # model settings model = dict( diff --git a/configs/segformer/segformer_mit-b2_8x1_1024x1024_160k_cityscapes.py b/configs/segformer/segformer_mit-b2_8xb1-160k_cityscapes-1024x1024.py similarity index 77% rename from configs/segformer/segformer_mit-b2_8x1_1024x1024_160k_cityscapes.py rename to configs/segformer/segformer_mit-b2_8xb1-160k_cityscapes-1024x1024.py index fab6be2945..94f5ad33a5 100644 --- a/configs/segformer/segformer_mit-b2_8x1_1024x1024_160k_cityscapes.py +++ b/configs/segformer/segformer_mit-b2_8xb1-160k_cityscapes-1024x1024.py @@ -1,4 +1,4 @@ -_base_ = ['./segformer_mit-b0_8x1_1024x1024_160k_cityscapes.py'] +_base_ = ['./segformer_mit-b0_8xb1-160k_cityscapes-1024x1024.py'] model = dict( backbone=dict( diff --git a/configs/segformer/segformer_mit-b2_512x512_160k_ade20k.py b/configs/segformer/segformer_mit-b2_8xb2-160k_ade20k-512x512.py similarity index 78% rename from configs/segformer/segformer_mit-b2_512x512_160k_ade20k.py rename to configs/segformer/segformer_mit-b2_8xb2-160k_ade20k-512x512.py index afb24b0170..2c3bb101d3 100644 --- a/configs/segformer/segformer_mit-b2_512x512_160k_ade20k.py +++ b/configs/segformer/segformer_mit-b2_8xb2-160k_ade20k-512x512.py @@ -1,4 +1,4 @@ -_base_ = ['./segformer_mit-b0_512x512_160k_ade20k.py'] +_base_ = ['./segformer_mit-b0_8xb2-160k_ade20k-512x512.py'] # model settings model = dict( diff --git a/configs/segformer/segformer_mit-b3_8x1_1024x1024_160k_cityscapes.py b/configs/segformer/segformer_mit-b3_8xb1-160k_cityscapes-1024x1024.py similarity index 77% rename from configs/segformer/segformer_mit-b3_8x1_1024x1024_160k_cityscapes.py rename to configs/segformer/segformer_mit-b3_8xb1-160k_cityscapes-1024x1024.py index 479ce04ea1..87ec0a599d 100644 --- a/configs/segformer/segformer_mit-b3_8x1_1024x1024_160k_cityscapes.py +++ b/configs/segformer/segformer_mit-b3_8xb1-160k_cityscapes-1024x1024.py @@ -1,4 +1,4 @@ -_base_ = ['./segformer_mit-b0_8x1_1024x1024_160k_cityscapes.py'] +_base_ = ['./segformer_mit-b0_8xb1-160k_cityscapes-1024x1024.py'] model = dict( backbone=dict( diff --git a/configs/segformer/segformer_mit-b3_512x512_160k_ade20k.py b/configs/segformer/segformer_mit-b3_8xb2-160k_ade20k-512x512.py similarity index 78% rename from configs/segformer/segformer_mit-b3_512x512_160k_ade20k.py rename to configs/segformer/segformer_mit-b3_8xb2-160k_ade20k-512x512.py index 52348f6fcc..31f5fc1c12 100644 --- a/configs/segformer/segformer_mit-b3_512x512_160k_ade20k.py +++ b/configs/segformer/segformer_mit-b3_8xb2-160k_ade20k-512x512.py @@ -1,4 +1,4 @@ -_base_ = ['./segformer_mit-b0_512x512_160k_ade20k.py'] +_base_ = ['./segformer_mit-b0_8xb2-160k_ade20k-512x512.py'] # model settings model = dict( diff --git a/configs/segformer/segformer_mit-b4_8x1_1024x1024_160k_cityscapes.py b/configs/segformer/segformer_mit-b4_8xb1-160k_cityscapes-1024x1024.py similarity index 77% rename from configs/segformer/segformer_mit-b4_8x1_1024x1024_160k_cityscapes.py rename to configs/segformer/segformer_mit-b4_8xb1-160k_cityscapes-1024x1024.py index 808a1eb41b..e4f436b264 100644 --- a/configs/segformer/segformer_mit-b4_8x1_1024x1024_160k_cityscapes.py +++ b/configs/segformer/segformer_mit-b4_8xb1-160k_cityscapes-1024x1024.py @@ -1,4 +1,4 @@ -_base_ = ['./segformer_mit-b0_8x1_1024x1024_160k_cityscapes.py'] +_base_ = ['./segformer_mit-b0_8xb1-160k_cityscapes-1024x1024.py'] model = dict( backbone=dict( diff --git a/configs/segformer/segformer_mit-b4_512x512_160k_ade20k.py b/configs/segformer/segformer_mit-b4_8xb2-160k_ade20k-512x512.py similarity index 78% rename from configs/segformer/segformer_mit-b4_512x512_160k_ade20k.py rename to configs/segformer/segformer_mit-b4_8xb2-160k_ade20k-512x512.py index 7b50b75608..0015e1623a 100644 --- a/configs/segformer/segformer_mit-b4_512x512_160k_ade20k.py +++ b/configs/segformer/segformer_mit-b4_8xb2-160k_ade20k-512x512.py @@ -1,4 +1,4 @@ -_base_ = ['./segformer_mit-b0_512x512_160k_ade20k.py'] +_base_ = ['./segformer_mit-b0_8xb2-160k_ade20k-512x512.py'] # model settings model = dict( diff --git a/configs/segformer/segformer_mit-b5_8x1_1024x1024_160k_cityscapes.py b/configs/segformer/segformer_mit-b5_8xb1-160k_cityscapes-1024x1024.py similarity index 77% rename from configs/segformer/segformer_mit-b5_8x1_1024x1024_160k_cityscapes.py rename to configs/segformer/segformer_mit-b5_8xb1-160k_cityscapes-1024x1024.py index 1c9422d37c..7fb2ea5b4e 100644 --- a/configs/segformer/segformer_mit-b5_8x1_1024x1024_160k_cityscapes.py +++ b/configs/segformer/segformer_mit-b5_8xb1-160k_cityscapes-1024x1024.py @@ -1,4 +1,4 @@ -_base_ = ['./segformer_mit-b0_8x1_1024x1024_160k_cityscapes.py'] +_base_ = ['./segformer_mit-b0_8xb1-160k_cityscapes-1024x1024.py'] model = dict( backbone=dict( diff --git a/configs/segformer/segformer_mit-b5_512x512_160k_ade20k.py b/configs/segformer/segformer_mit-b5_8xb2-160k_ade20k-512x512.py similarity index 78% rename from configs/segformer/segformer_mit-b5_512x512_160k_ade20k.py rename to configs/segformer/segformer_mit-b5_8xb2-160k_ade20k-512x512.py index 5212fb1f6a..09bb260223 100644 --- a/configs/segformer/segformer_mit-b5_512x512_160k_ade20k.py +++ b/configs/segformer/segformer_mit-b5_8xb2-160k_ade20k-512x512.py @@ -1,4 +1,4 @@ -_base_ = ['./segformer_mit-b0_512x512_160k_ade20k.py'] +_base_ = ['./segformer_mit-b0_8xb2-160k_ade20k-512x512.py'] # model settings model = dict( diff --git a/configs/segformer/segformer_mit-b5_640x640_160k_ade20k.py b/configs/segformer/segformer_mit-b5_8xb2-160k_ade20k-640x640.py similarity index 95% rename from configs/segformer/segformer_mit-b5_640x640_160k_ade20k.py rename to configs/segformer/segformer_mit-b5_8xb2-160k_ade20k-640x640.py index 0d13707ec2..3bba3716ef 100644 --- a/configs/segformer/segformer_mit-b5_640x640_160k_ade20k.py +++ b/configs/segformer/segformer_mit-b5_8xb2-160k_ade20k-640x640.py @@ -1,4 +1,4 @@ -_base_ = ['./segformer_mit-b0_512x512_160k_ade20k.py'] +_base_ = ['./segformer_mit-b0_8xb2-160k_ade20k-512x512.py'] # dataset settings crop_size = (640, 640) diff --git a/configs/segmenter/README.md b/configs/segmenter/README.md index caefe996e2..984ef9f510 100644 --- a/configs/segmenter/README.md +++ b/configs/segmenter/README.md @@ -67,8 +67,8 @@ In our default setting, pretrained models and their corresponding [ViT-AugReg](h | 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/master/configs/segmenter/segmenter_vit-t_mask_8x1_512x512_160k_ade20k.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/master/configs/segmenter/segmenter_vit-s_linear_8x1_512x512_160k_ade20k.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/master/configs/segmenter/segmenter_vit-s_mask_8x1_512x512_160k_ade20k.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/master/configs/segmenter/segmenter_vit-b_mask_8x1_512x512_160k_ade20k.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/master/configs/segmenter/segmenter_vit-l_mask_8x1_512x512_160k_ade20k.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) | +| 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) | diff --git a/configs/segmenter/segmenter.yml b/configs/segmenter/segmenter.yml index dc6e68d3dd..1069f003b7 100644 --- a/configs/segmenter/segmenter.yml +++ b/configs/segmenter/segmenter.yml @@ -13,7 +13,7 @@ Collections: Converted From: Code: https://github.com/rstrudel/segmenter Models: -- Name: segmenter_vit-t_mask_8x1_512x512_160k_ade20k +- Name: segmenter_vit-t_mask_8xb1-160k_ade20k-512x512 In Collection: Segmenter Metadata: backbone: ViT-T_16 @@ -33,9 +33,9 @@ Models: Metrics: mIoU: 39.99 mIoU(ms+flip): 40.83 - Config: configs/segmenter/segmenter_vit-t_mask_8x1_512x512_160k_ade20k.py + 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_linear_8x1_512x512_160k_ade20k +- Name: segmenter_vit-s_fcn_8xb1-160k_ade20k-512x512 In Collection: Segmenter Metadata: backbone: ViT-S_16 @@ -55,9 +55,9 @@ Models: Metrics: mIoU: 45.75 mIoU(ms+flip): 46.82 - Config: configs/segmenter/segmenter_vit-s_linear_8x1_512x512_160k_ade20k.py + 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_8x1_512x512_160k_ade20k +- Name: segmenter_vit-s_mask_8xb1-160k_ade20k-512x512 In Collection: Segmenter Metadata: backbone: ViT-S_16 @@ -77,9 +77,9 @@ Models: Metrics: mIoU: 46.19 mIoU(ms+flip): 47.85 - Config: configs/segmenter/segmenter_vit-s_mask_8x1_512x512_160k_ade20k.py + 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_8x1_512x512_160k_ade20k +- Name: segmenter_vit-b_mask_8xb1-160k_ade20k-512x512 In Collection: Segmenter Metadata: backbone: ViT-B_16 @@ -99,9 +99,9 @@ Models: Metrics: mIoU: 49.6 mIoU(ms+flip): 51.07 - Config: configs/segmenter/segmenter_vit-b_mask_8x1_512x512_160k_ade20k.py + 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_8x1_512x512_160k_ade20k +- Name: segmenter_vit-l_mask_8xb1-160k_ade20k-512x512 In Collection: Segmenter Metadata: backbone: ViT-L_16 @@ -121,5 +121,5 @@ Models: Metrics: mIoU: 52.16 mIoU(ms+flip): 53.65 - Config: configs/segmenter/segmenter_vit-l_mask_8x1_512x512_160k_ade20k.py + 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/segmenter/segmenter_vit-b_mask_8x1_512x512_160k_ade20k.py b/configs/segmenter/segmenter_vit-b_mask_8xb1-160k_ade20k-512x512.py similarity index 100% rename from configs/segmenter/segmenter_vit-b_mask_8x1_512x512_160k_ade20k.py rename to configs/segmenter/segmenter_vit-b_mask_8xb1-160k_ade20k-512x512.py diff --git a/configs/segmenter/segmenter_vit-l_mask_8x1_512x512_160k_ade20k.py b/configs/segmenter/segmenter_vit-l_mask_8xb1-160k_ade20k-512x512.py similarity index 100% rename from configs/segmenter/segmenter_vit-l_mask_8x1_512x512_160k_ade20k.py rename to configs/segmenter/segmenter_vit-l_mask_8xb1-160k_ade20k-512x512.py diff --git a/configs/segmenter/segmenter_vit-s_linear_8x1_512x512_160k_ade20k.py b/configs/segmenter/segmenter_vit-s_fcn_8xb1-160k_ade20k-512x512.py similarity index 84% rename from configs/segmenter/segmenter_vit-s_linear_8x1_512x512_160k_ade20k.py rename to configs/segmenter/segmenter_vit-s_fcn_8xb1-160k_ade20k-512x512.py index adc8c1b283..a31592557a 100644 --- a/configs/segmenter/segmenter_vit-s_linear_8x1_512x512_160k_ade20k.py +++ b/configs/segmenter/segmenter_vit-s_fcn_8xb1-160k_ade20k-512x512.py @@ -1,4 +1,4 @@ -_base_ = './segmenter_vit-s_mask_8x1_512x512_160k_ade20k.py' +_base_ = './segmenter_vit-t_mask_8xb1-160k_ade20k-512x512.py' model = dict( decode_head=dict( diff --git a/configs/segmenter/segmenter_vit-s_mask_8x1_512x512_160k_ade20k.py b/configs/segmenter/segmenter_vit-s_mask_8xb1-160k_ade20k-512x512.py similarity index 100% rename from configs/segmenter/segmenter_vit-s_mask_8x1_512x512_160k_ade20k.py rename to configs/segmenter/segmenter_vit-s_mask_8xb1-160k_ade20k-512x512.py diff --git a/configs/segmenter/segmenter_vit-t_mask_8x1_512x512_160k_ade20k.py b/configs/segmenter/segmenter_vit-t_mask_8xb1-160k_ade20k-512x512.py similarity index 100% rename from configs/segmenter/segmenter_vit-t_mask_8x1_512x512_160k_ade20k.py rename to configs/segmenter/segmenter_vit-t_mask_8xb1-160k_ade20k-512x512.py diff --git a/configs/sem_fpn/README.md b/configs/sem_fpn/README.md index 054d5db4ac..fcef72d2a5 100644 --- a/configs/sem_fpn/README.md +++ b/configs/sem_fpn/README.md @@ -38,14 +38,14 @@ The recently introduced panoptic segmentation task has renewed our community's i ### 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/master/configs/sem_fpn/fpn_r50_512x1024_80k_cityscapes.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/master/configs/sem_fpn/fpn_r101_512x1024_80k_cityscapes.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) | +| 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/master/configs/sem_fpn/fpn_r50_512x512_160k_ade20k.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/master/configs/sem_fpn/fpn_r101_512x512_160k_ade20k.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) | +| 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/fpn_r101_4xb2-80k_cityscapes-512x1024.py b/configs/sem_fpn/fpn_r101_4xb2-80k_cityscapes-512x1024.py new file mode 100644 index 0000000000..1e9bcfbb59 --- /dev/null +++ b/configs/sem_fpn/fpn_r101_4xb2-80k_cityscapes-512x1024.py @@ -0,0 +1,2 @@ +_base_ = './fpn_r50_4xb2-80k_cityscapes-512x1024.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/sem_fpn/fpn_r101_512x512_160k_ade20k.py b/configs/sem_fpn/fpn_r101_4xb4-160k_ade20k-512x512.py similarity index 79% rename from configs/sem_fpn/fpn_r101_512x512_160k_ade20k.py rename to configs/sem_fpn/fpn_r101_4xb4-160k_ade20k-512x512.py index a8b51eb108..adad1a4f38 100644 --- a/configs/sem_fpn/fpn_r101_512x512_160k_ade20k.py +++ b/configs/sem_fpn/fpn_r101_4xb4-160k_ade20k-512x512.py @@ -1,4 +1,4 @@ -_base_ = './fpn_r50_512x512_160k_ade20k.py' +_base_ = './fpn_r50_4xb4-160k_ade20k-512x512.py' model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) crop_size = (512, 512) data_preprocessor = dict(size=crop_size) diff --git a/configs/sem_fpn/fpn_r101_512x1024_80k_cityscapes.py b/configs/sem_fpn/fpn_r101_512x1024_80k_cityscapes.py deleted file mode 100644 index 7f8710d4be..0000000000 --- a/configs/sem_fpn/fpn_r101_512x1024_80k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './fpn_r50_512x1024_80k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/sem_fpn/fpn_r50_512x1024_80k_cityscapes.py b/configs/sem_fpn/fpn_r50_4xb2-80k_cityscapes-512x1024.py similarity index 100% rename from configs/sem_fpn/fpn_r50_512x1024_80k_cityscapes.py rename to configs/sem_fpn/fpn_r50_4xb2-80k_cityscapes-512x1024.py diff --git a/configs/sem_fpn/fpn_r50_512x512_160k_ade20k.py b/configs/sem_fpn/fpn_r50_4xb4-160k_ade20k-512x512.py similarity index 100% rename from configs/sem_fpn/fpn_r50_512x512_160k_ade20k.py rename to configs/sem_fpn/fpn_r50_4xb4-160k_ade20k-512x512.py diff --git a/configs/sem_fpn/sem_fpn.yml b/configs/sem_fpn/sem_fpn.yml index d7ebdfe6fe..79ed0b81c4 100644 --- a/configs/sem_fpn/sem_fpn.yml +++ b/configs/sem_fpn/sem_fpn.yml @@ -14,7 +14,7 @@ Collections: Converted From: Code: https://github.com/facebookresearch/detectron2 Models: -- Name: fpn_r50_512x1024_80k_cityscapes +- Name: fpn_r50_4xb2-80k_cityscapes-512x1024 In Collection: FPN Metadata: backbone: R-50 @@ -34,9 +34,9 @@ Models: Metrics: mIoU: 74.52 mIoU(ms+flip): 76.08 - Config: configs/sem_fpn/fpn_r50_512x1024_80k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: fpn_r101_4xb2-80k_cityscapes-512x1024 In Collection: FPN Metadata: backbone: R-101 @@ -56,9 +56,9 @@ Models: Metrics: mIoU: 75.8 mIoU(ms+flip): 77.4 - Config: configs/sem_fpn/fpn_r101_512x1024_80k_cityscapes.py + 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_512x512_160k_ade20k +- Name: fpn_r50_4xb4-160k_ade20k-512x512 In Collection: FPN Metadata: backbone: R-50 @@ -78,9 +78,9 @@ Models: Metrics: mIoU: 37.49 mIoU(ms+flip): 39.09 - Config: configs/sem_fpn/fpn_r50_512x512_160k_ade20k.py + 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_512x512_160k_ade20k +- Name: fpn_r101_4xb4-160k_ade20k-512x512 In Collection: FPN Metadata: backbone: R-101 @@ -100,5 +100,5 @@ Models: Metrics: mIoU: 39.35 mIoU(ms+flip): 40.72 - Config: configs/sem_fpn/fpn_r101_512x512_160k_ade20k.py + 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 5afd2740a0..3bae5d9e7e 100644 --- a/configs/setr/README.md +++ b/configs/setr/README.md @@ -58,17 +58,17 @@ 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/master/configs/setr/setr_naive_512x512_160k_b16_ade20k.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/master/configs/setr/setr_pup_512x512_160k_b16_ade20k.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/master/configs/setr/setr_mla_512x512_160k_b8_ade20k.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 | 47.54 | 49.37 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/setr/setr_mla_512x512_160k_b16_ade20k.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) | 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 | 47.54 | 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/master/configs/setr/setr_vit-large_naive_8x1_768x768_80k_cityscapes.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/master/configs/setr/setr_vit-large_pup_8x1_768x768_80k_cityscapes.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/master/configs/setr/setr_vit-large_mla_8x1_768x768_80k_cityscapes.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) | 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) | diff --git a/configs/setr/setr.yml b/configs/setr/setr.yml index 27f58e48b0..1e87179ab5 100644 --- a/configs/setr/setr.yml +++ b/configs/setr/setr.yml @@ -15,7 +15,7 @@ Collections: Converted From: Code: https://github.com/fudan-zvg/SETR Models: -- Name: setr_naive_512x512_160k_b16_ade20k +- Name: setr_vit-l_naive_8xb2-160k_ade20k-512x512 In Collection: SETR Metadata: backbone: ViT-L @@ -35,9 +35,9 @@ Models: Metrics: mIoU: 48.28 mIoU(ms+flip): 49.56 - Config: configs/setr/setr_naive_512x512_160k_b16_ade20k.py + 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_pup_512x512_160k_b16_ade20k +- Name: setr_vit-l_pup_8xb2-160k_ade20k-512x512 In Collection: SETR Metadata: backbone: ViT-L @@ -57,9 +57,9 @@ Models: Metrics: mIoU: 48.24 mIoU(ms+flip): 49.99 - Config: configs/setr/setr_pup_512x512_160k_b16_ade20k.py + 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_mla_512x512_160k_b8_ade20k +- Name: setr_vit-l-mla_8xb1-160k_ade20k-512x512 In Collection: SETR Metadata: backbone: ViT-L @@ -72,9 +72,9 @@ Models: Metrics: mIoU: 47.34 mIoU(ms+flip): 49.05 - Config: configs/setr/setr_mla_512x512_160k_b8_ade20k.py + 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_mla_512x512_160k_b16_ade20k +- Name: setr_vit-l_mla_8xb2-160k_ade20k-512x512 In Collection: SETR Metadata: backbone: ViT-L @@ -94,9 +94,9 @@ Models: Metrics: mIoU: 47.54 mIoU(ms+flip): 49.37 - Config: configs/setr/setr_mla_512x512_160k_b16_ade20k.py + 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-large_naive_8x1_768x768_80k_cityscapes +- Name: setr_vit-l_naive_8xb1-80k_cityscapes-768x768 In Collection: SETR Metadata: backbone: ViT-L @@ -116,9 +116,9 @@ Models: Metrics: mIoU: 78.1 mIoU(ms+flip): 80.22 - Config: configs/setr/setr_vit-large_naive_8x1_768x768_80k_cityscapes.py + 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-large_pup_8x1_768x768_80k_cityscapes +- Name: setr_vit-l_pup_8xb1-80k_cityscapes-768x768 In Collection: SETR Metadata: backbone: ViT-L @@ -138,9 +138,9 @@ Models: Metrics: mIoU: 79.21 mIoU(ms+flip): 81.02 - Config: configs/setr/setr_vit-large_pup_8x1_768x768_80k_cityscapes.py + 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-large_mla_8x1_768x768_80k_cityscapes +- Name: setr_vit-l_mla_8xb1-80k_cityscapes-768x768 In Collection: SETR Metadata: backbone: ViT-L @@ -160,5 +160,5 @@ Models: Metrics: mIoU: 77.0 mIoU(ms+flip): 79.59 - Config: configs/setr/setr_vit-large_mla_8x1_768x768_80k_cityscapes.py + 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/setr/setr_mla_512x512_160k_b8_ade20k.py b/configs/setr/setr_vit-l-mla_8xb1-160k_ade20k-512x512.py similarity index 100% rename from configs/setr/setr_mla_512x512_160k_b8_ade20k.py rename to configs/setr/setr_vit-l-mla_8xb1-160k_ade20k-512x512.py diff --git a/configs/setr/setr_vit-large_mla_8x1_768x768_80k_cityscapes.py b/configs/setr/setr_vit-l_mla_8xb1-80k_cityscapes-768x768.py similarity index 100% rename from configs/setr/setr_vit-large_mla_8x1_768x768_80k_cityscapes.py rename to configs/setr/setr_vit-l_mla_8xb1-80k_cityscapes-768x768.py diff --git a/configs/setr/setr_mla_512x512_160k_b16_ade20k.py b/configs/setr/setr_vit-l_mla_8xb2-160k_ade20k-512x512.py similarity index 70% rename from configs/setr/setr_mla_512x512_160k_b16_ade20k.py rename to configs/setr/setr_vit-l_mla_8xb2-160k_ade20k-512x512.py index 710e1ec364..4d3fb7d4e1 100644 --- a/configs/setr/setr_mla_512x512_160k_b16_ade20k.py +++ b/configs/setr/setr_vit-l_mla_8xb2-160k_ade20k-512x512.py @@ -1,4 +1,4 @@ -_base_ = ['./setr_mla_512x512_160k_b8_ade20k.py'] +_base_ = ['./setr_vit-l-mla_8xb1-160k_ade20k-512x512.py'] # num_gpus: 8 -> batch_size: 16 train_dataloader = dict(batch_size=2) diff --git a/configs/setr/setr_vit-large_naive_8x1_768x768_80k_cityscapes.py b/configs/setr/setr_vit-l_naive_8xb1-80k_cityscapes-768x768.py similarity index 100% rename from configs/setr/setr_vit-large_naive_8x1_768x768_80k_cityscapes.py rename to configs/setr/setr_vit-l_naive_8xb1-80k_cityscapes-768x768.py diff --git a/configs/setr/setr_naive_512x512_160k_b16_ade20k.py b/configs/setr/setr_vit-l_naive_8xb2-160k_ade20k-512x512.py similarity index 100% rename from configs/setr/setr_naive_512x512_160k_b16_ade20k.py rename to configs/setr/setr_vit-l_naive_8xb2-160k_ade20k-512x512.py diff --git a/configs/setr/setr_vit-large_pup_8x1_768x768_80k_cityscapes.py b/configs/setr/setr_vit-l_pup_8xb1-80k_cityscapes-768x768.py similarity index 100% rename from configs/setr/setr_vit-large_pup_8x1_768x768_80k_cityscapes.py rename to configs/setr/setr_vit-l_pup_8xb1-80k_cityscapes-768x768.py diff --git a/configs/setr/setr_pup_512x512_160k_b16_ade20k.py b/configs/setr/setr_vit-l_pup_8xb2-160k_ade20k-512x512.py similarity index 100% rename from configs/setr/setr_pup_512x512_160k_b16_ade20k.py rename to configs/setr/setr_vit-l_pup_8xb2-160k_ade20k-512x512.py diff --git a/configs/stdc/README.md b/configs/stdc/README.md index 1c6d70a252..639e6b6986 100644 --- a/configs/stdc/README.md +++ b/configs/stdc/README.md @@ -58,12 +58,12 @@ 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/master/configs/stdc/stdc1_512x1024_80k_cityscapes.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/master/configs/stdc/stdc1_in1k-pre_512x1024_80k_cityscapes.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/master/configs/stdc/stdc2_512x1024_80k_cityscapes.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/master/configs/stdc/stdc2_in1k-pre_512x1024_80k_cityscapes.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) | 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) | Note: diff --git a/configs/stdc/stdc.yml b/configs/stdc/stdc.yml index f584b74bca..22fb37eeba 100644 --- a/configs/stdc/stdc.yml +++ b/configs/stdc/stdc.yml @@ -13,7 +13,7 @@ Collections: Converted From: Code: https://github.com/MichaelFan01/STDC-Seg Models: -- Name: stdc1_512x1024_80k_cityscapes +- Name: stdc1_4xb12-80k_cityscapes-512x1024 In Collection: STDC Metadata: backbone: STDC1 @@ -33,9 +33,9 @@ Models: Metrics: mIoU: 71.82 mIoU(ms+flip): 73.89 - Config: configs/stdc/stdc1_512x1024_80k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: stdc1_in1k-pre_4xb12-80k_cityscapes-512x1024 In Collection: STDC Metadata: backbone: STDC1 @@ -47,9 +47,9 @@ Models: Metrics: mIoU: 74.94 mIoU(ms+flip): 76.97 - Config: configs/stdc/stdc1_in1k-pre_512x1024_80k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: stdc2_4xb12-80k_cityscapes-512x1024 In Collection: STDC Metadata: backbone: STDC2 @@ -69,9 +69,9 @@ Models: Metrics: mIoU: 73.15 mIoU(ms+flip): 76.13 - Config: configs/stdc/stdc2_512x1024_80k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: stdc2_in1k-pre_4xb12-80k_cityscapes-512x1024 In Collection: STDC Metadata: backbone: STDC2 @@ -83,5 +83,5 @@ Models: Metrics: mIoU: 76.67 mIoU(ms+flip): 78.67 - Config: configs/stdc/stdc2_in1k-pre_512x1024_80k_cityscapes.py + 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/stdc/stdc1_512x1024_80k_cityscapes.py b/configs/stdc/stdc1_4xb12-80k_cityscapes-512x1024.py similarity index 100% rename from configs/stdc/stdc1_512x1024_80k_cityscapes.py rename to configs/stdc/stdc1_4xb12-80k_cityscapes-512x1024.py diff --git a/configs/stdc/stdc1_in1k-pre_512x1024_80k_cityscapes.py b/configs/stdc/stdc1_in1k-pre_4xb12-80k_cityscapes-512x1024.py similarity index 82% rename from configs/stdc/stdc1_in1k-pre_512x1024_80k_cityscapes.py rename to configs/stdc/stdc1_in1k-pre_4xb12-80k_cityscapes-512x1024.py index f295bf494e..15e807f9ed 100644 --- a/configs/stdc/stdc1_in1k-pre_512x1024_80k_cityscapes.py +++ b/configs/stdc/stdc1_in1k-pre_4xb12-80k_cityscapes-512x1024.py @@ -1,5 +1,5 @@ checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/stdc/stdc1_20220308-5368626c.pth' # noqa -_base_ = './stdc1_512x1024_80k_cityscapes.py' +_base_ = './stdc1_4xb12-80k_cityscapes-512x1024.py' model = dict( backbone=dict( backbone_cfg=dict( diff --git a/configs/stdc/stdc2_512x1024_80k_cityscapes.py b/configs/stdc/stdc2_4xb12-80k_cityscapes-512x1024.py similarity index 57% rename from configs/stdc/stdc2_512x1024_80k_cityscapes.py rename to configs/stdc/stdc2_4xb12-80k_cityscapes-512x1024.py index f7afb506a0..5657351698 100644 --- a/configs/stdc/stdc2_512x1024_80k_cityscapes.py +++ b/configs/stdc/stdc2_4xb12-80k_cityscapes-512x1024.py @@ -1,2 +1,2 @@ -_base_ = './stdc1_512x1024_80k_cityscapes.py' +_base_ = './stdc1_4xb12-80k_cityscapes-512x1024.py' model = dict(backbone=dict(backbone_cfg=dict(stdc_type='STDCNet2'))) diff --git a/configs/stdc/stdc2_in1k-pre_512x1024_80k_cityscapes.py b/configs/stdc/stdc2_in1k-pre_4xb12-80k_cityscapes-512x1024.py similarity index 82% rename from configs/stdc/stdc2_in1k-pre_512x1024_80k_cityscapes.py rename to configs/stdc/stdc2_in1k-pre_4xb12-80k_cityscapes-512x1024.py index 4148ac4fd0..05a202b74c 100644 --- a/configs/stdc/stdc2_in1k-pre_512x1024_80k_cityscapes.py +++ b/configs/stdc/stdc2_in1k-pre_4xb12-80k_cityscapes-512x1024.py @@ -1,5 +1,5 @@ checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/stdc/stdc2_20220308-7dbd9127.pth' # noqa -_base_ = './stdc2_512x1024_80k_cityscapes.py' +_base_ = './stdc2_4xb12-80k_cityscapes-512x1024.py' model = dict( backbone=dict( backbone_cfg=dict( diff --git a/configs/swin/README.md b/configs/swin/README.md index 6b21b6d1bc..55d119d17d 100644 --- a/configs/swin/README.md +++ b/configs/swin/README.md @@ -66,11 +66,11 @@ 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/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K.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/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K.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/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K.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 | - | - | 50.31 | 51.9 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_22K.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/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_1K.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/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K.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) | 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 | - | - | 50.31 | 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) | diff --git a/configs/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_1K.py b/configs/swin/swin-base-patch4-window12-in1k-384x384-pre_upernet_8xb2-160k_ade20k-512x512.py similarity index 85% rename from configs/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_1K.py rename to configs/swin/swin-base-patch4-window12-in1k-384x384-pre_upernet_8xb2-160k_ade20k-512x512.py index 027bd6f8bc..11cea36703 100644 --- a/configs/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_1K.py +++ b/configs/swin/swin-base-patch4-window12-in1k-384x384-pre_upernet_8xb2-160k_ade20k-512x512.py @@ -1,6 +1,5 @@ _base_ = [ - 'upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_' - 'pretrain_224x224_1K.py' + 'swin-tiny-patch4-window7-in1k-pre_upernet_8xb2-160k_ade20k-512x512.py' ] checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_base_patch4_window12_384_20220317-55b0104a.pth' # noqa model = dict( diff --git a/configs/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K.py b/configs/swin/swin-base-patch4-window12-in22k-384x384-pre_upernet_8xb2-160k_ade20k-512x512.py similarity index 73% rename from configs/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K.py rename to configs/swin/swin-base-patch4-window12-in22k-384x384-pre_upernet_8xb2-160k_ade20k-512x512.py index e662d4f03a..5c1171646e 100644 --- a/configs/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K.py +++ b/configs/swin/swin-base-patch4-window12-in22k-384x384-pre_upernet_8xb2-160k_ade20k-512x512.py @@ -1,6 +1,5 @@ _base_ = [ - './upernet_swin_base_patch4_window12_512x512_160k_ade20k_' - 'pretrain_384x384_1K.py' + './swin-base-patch4-window12-in1k-384x384-pre_upernet_8xb2-160k_ade20k-512x512.py' # noqa ] checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_base_patch4_window12_384_22k_20220317-e5c09f74.pth' # noqa model = dict( diff --git a/configs/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K.py b/configs/swin/swin-base-patch4-window7-in1k-pre_upernet_8xb2-160k_ade20k-512x512.py similarity index 84% rename from configs/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K.py rename to configs/swin/swin-base-patch4-window7-in1k-pre_upernet_8xb2-160k_ade20k-512x512.py index 6e05677d89..73bf6166ef 100644 --- a/configs/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K.py +++ b/configs/swin/swin-base-patch4-window7-in1k-pre_upernet_8xb2-160k_ade20k-512x512.py @@ -1,6 +1,5 @@ _base_ = [ - './upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_' - 'pretrain_224x224_1K.py' + './swin-tiny-patch4-window7-in1k-pre_upernet_8xb2-160k_ade20k-512x512.py' ] checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_base_patch4_window7_224_20220317-e9b98025.pth' # noqa model = dict( diff --git a/configs/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_22K.py b/configs/swin/swin-base-patch4-window7-in22k-pre_upernet_8xb2-160k_ade20k-512x512.py similarity index 74% rename from configs/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_22K.py rename to configs/swin/swin-base-patch4-window7-in22k-pre_upernet_8xb2-160k_ade20k-512x512.py index 7a9c50624f..96148cd71d 100644 --- a/configs/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_22K.py +++ b/configs/swin/swin-base-patch4-window7-in22k-pre_upernet_8xb2-160k_ade20k-512x512.py @@ -1,6 +1,5 @@ _base_ = [ - './upernet_swin_base_patch4_window7_512x512_160k_ade20k_' - 'pretrain_224x224_1K.py' + './swin-base-patch4-window7-in1k-pre_upernet_8xb2-160k_ade20k-512x512.py' ] checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_base_patch4_window7_224_22k_20220317-4f79f7c0.pth' # noqa model = dict( diff --git a/configs/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K.py b/configs/swin/swin-small-patch4-window7-in1k-pre_upernet_8xb2-160k_ade20k-512x512.py similarity index 82% rename from configs/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K.py rename to configs/swin/swin-small-patch4-window7-in1k-pre_upernet_8xb2-160k_ade20k-512x512.py index 1958e0e750..19863dfc82 100644 --- a/configs/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K.py +++ b/configs/swin/swin-small-patch4-window7-in1k-pre_upernet_8xb2-160k_ade20k-512x512.py @@ -1,6 +1,5 @@ _base_ = [ - './upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_' - 'pretrain_224x224_1K.py' + './swin-tiny-patch4-window7-in1k-pre_upernet_8xb2-160k_ade20k-512x512.py' ] checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_small_patch4_window7_224_20220317-7ba6d6dd.pth' # noqa model = dict( diff --git a/configs/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K.py b/configs/swin/swin-tiny-patch4-window7-in1k-pre_upernet_8xb2-160k_ade20k-512x512.py similarity index 100% rename from configs/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K.py rename to configs/swin/swin-tiny-patch4-window7-in1k-pre_upernet_8xb2-160k_ade20k-512x512.py diff --git a/configs/swin/swin.yml b/configs/swin/swin.yml index ef21d2165e..aaf00ec05d 100644 --- a/configs/swin/swin.yml +++ b/configs/swin/swin.yml @@ -1,5 +1,5 @@ Models: -- Name: upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K +- Name: swin-tiny-patch4-window7-in1k-pre_upernet_8xb2-160k_ade20k-512x512 In Collection: UPerNet Metadata: backbone: Swin-T @@ -19,9 +19,9 @@ Models: Metrics: mIoU: 44.41 mIoU(ms+flip): 45.79 - Config: configs/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K.py + 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: upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K +- Name: swin-small-patch4-window7-in1k-pre_upernet_8xb2-160k_ade20k-512x512 In Collection: UPerNet Metadata: backbone: Swin-S @@ -41,9 +41,9 @@ Models: Metrics: mIoU: 47.72 mIoU(ms+flip): 49.24 - Config: configs/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K.py + 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: upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K +- Name: swin-base-patch4-window7-in1k-pre_upernet_8xb2-160k_ade20k-512x512 In Collection: UPerNet Metadata: backbone: Swin-B @@ -63,9 +63,9 @@ Models: Metrics: mIoU: 47.99 mIoU(ms+flip): 49.57 - Config: configs/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K.py + 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: upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_22K +- Name: swin-base-patch4-window7-in22k-pre_upernet_8xb2-160k_ade20k-512x512 In Collection: UPerNet Metadata: backbone: Swin-B @@ -77,9 +77,9 @@ Models: Metrics: mIoU: 50.31 mIoU(ms+flip): 51.9 - Config: configs/swin/upernet_swin_base_patch4_window7_512x512_160k_ade20k_pretrain_224x224_22K.py + 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: upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_1K +- Name: swin-base-patch4-window12-in1k-384x384-pre_upernet_8xb2-160k_ade20k-512x512 In Collection: UPerNet Metadata: backbone: Swin-B @@ -99,9 +99,9 @@ Models: Metrics: mIoU: 48.35 mIoU(ms+flip): 49.65 - Config: configs/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_1K.py + 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: upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K +- Name: swin-base-patch4-window12-in22k-384x384-pre_upernet_8xb2-160k_ade20k-512x512 In Collection: UPerNet Metadata: backbone: Swin-B @@ -113,5 +113,5 @@ Models: Metrics: mIoU: 50.76 mIoU(ms+flip): 52.4 - Config: configs/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K.py + 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 639d074d32..3e741802e6 100644 --- a/configs/twins/README.md +++ b/configs/twins/README.md @@ -55,20 +55,20 @@ 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/master/configs/twins/twins_pcpvt-s_fpn_fpnhead_8x4_512x512_80k_ade20k.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/master/configs/twins/twins_pcpvt-s_uperhead_8x4_512x512_160k_ade20k.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/master/configs/twins/twins_pcpvt-b_fpn_fpnhead_8x4_512x512_80k_ade20k.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/master/configs/twins/twins_pcpvt-b_uperhead_8x2_512x512_160k_ade20k.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/master/configs/twins/twins_pcpvt-l_fpn_fpnhead_8x4_512x512_80k_ade20k.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/master/configs/twins/twins_pcpvt-l_uperhead_8x2_512x512_160k_ade20k.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/master/configs/twins/twins_svt-s_fpn_fpnhead_8x4_512x512_80k_ade20k.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/master/configs/twins/twins_svt-s_uperhead_8x2_512x512_160k_ade20k.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/master/configs/twins/twins_svt-b_fpn_fpnhead_8x4_512x512_80k_ade20k.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/master/configs/twins/twins_svt-b_uperhead_8x2_512x512_160k_ade20k.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/master/configs/twins/twins_svt-l_fpn_fpnhead_8x4_512x512_80k_ade20k.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/master/configs/twins/twins_svt-l_uperhead_8x2_512x512_160k_ade20k.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) | 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) | Note: diff --git a/configs/twins/twins.yml b/configs/twins/twins.yml index 6b5f5c181b..48d25c682f 100644 --- a/configs/twins/twins.yml +++ b/configs/twins/twins.yml @@ -1,5 +1,5 @@ Models: -- Name: twins_pcpvt-s_fpn_fpnhead_8x4_512x512_80k_ade20k +- Name: twins_pcpvt-s_fpn_fpnhead_8xb4-80k_ade20k-512x512 In Collection: FPN Metadata: backbone: PCPVT-S @@ -19,9 +19,9 @@ Models: Metrics: mIoU: 43.26 mIoU(ms+flip): 44.11 - Config: configs/twins/twins_pcpvt-s_fpn_fpnhead_8x4_512x512_80k_ade20k.py + 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_8x4_512x512_160k_ade20k +- Name: twins_pcpvt-s_uperhead_8xb4-160k_ade20k-512x512 In Collection: UPerNet Metadata: backbone: PCPVT-S @@ -41,9 +41,9 @@ Models: Metrics: mIoU: 46.04 mIoU(ms+flip): 46.92 - Config: configs/twins/twins_pcpvt-s_uperhead_8x4_512x512_160k_ade20k.py + 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_8x4_512x512_80k_ade20k +- Name: twins_pcpvt-b_fpn_fpnhead_8xb4-80k_ade20k-512x512 In Collection: FPN Metadata: backbone: PCPVT-B @@ -63,9 +63,9 @@ Models: Metrics: mIoU: 45.66 mIoU(ms+flip): 46.48 - Config: configs/twins/twins_pcpvt-b_fpn_fpnhead_8x4_512x512_80k_ade20k.py + 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_8x2_512x512_160k_ade20k +- Name: twins_pcpvt-b_uperhead_8xb2-160k_ade20k-512x512 In Collection: UPerNet Metadata: backbone: PCPVT-B @@ -85,9 +85,9 @@ Models: Metrics: mIoU: 47.91 mIoU(ms+flip): 48.64 - Config: configs/twins/twins_pcpvt-b_uperhead_8x2_512x512_160k_ade20k.py + 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_8x4_512x512_80k_ade20k +- Name: twins_pcpvt-l_fpn_fpnhead_8xb4-80k_ade20k-512x512 In Collection: FPN Metadata: backbone: PCPVT-L @@ -107,9 +107,9 @@ Models: Metrics: mIoU: 45.94 mIoU(ms+flip): 46.7 - Config: configs/twins/twins_pcpvt-l_fpn_fpnhead_8x4_512x512_80k_ade20k.py + 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_8x2_512x512_160k_ade20k +- Name: twins_pcpvt-l_uperhead_8xb2-160k_ade20k-512x512 In Collection: UPerNet Metadata: backbone: PCPVT-L @@ -129,9 +129,9 @@ Models: Metrics: mIoU: 49.35 mIoU(ms+flip): 50.08 - Config: configs/twins/twins_pcpvt-l_uperhead_8x2_512x512_160k_ade20k.py + 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_8x4_512x512_80k_ade20k +- Name: twins_svt-s_fpn_fpnhead_8xb4-80k_ade20k-512x512 In Collection: FPN Metadata: backbone: SVT-S @@ -151,9 +151,9 @@ Models: Metrics: mIoU: 44.47 mIoU(ms+flip): 45.42 - Config: configs/twins/twins_svt-s_fpn_fpnhead_8x4_512x512_80k_ade20k.py + 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_8x2_512x512_160k_ade20k +- Name: twins_svt-s_uperhead_8xb2-160k_ade20k-512x512 In Collection: UPerNet Metadata: backbone: SVT-S @@ -173,9 +173,9 @@ Models: Metrics: mIoU: 46.08 mIoU(ms+flip): 46.96 - Config: configs/twins/twins_svt-s_uperhead_8x2_512x512_160k_ade20k.py + 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_8x4_512x512_80k_ade20k +- Name: twins_svt-b_fpn_fpnhead_8xb4-80k_ade20k-512x512 In Collection: FPN Metadata: backbone: SVT-B @@ -195,9 +195,9 @@ Models: Metrics: mIoU: 46.77 mIoU(ms+flip): 47.47 - Config: configs/twins/twins_svt-b_fpn_fpnhead_8x4_512x512_80k_ade20k.py + 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_8x2_512x512_160k_ade20k +- Name: twins_svt-b_uperhead_8xb2-160k_ade20k-512x512 In Collection: UPerNet Metadata: backbone: SVT-B @@ -217,9 +217,9 @@ Models: Metrics: mIoU: 48.04 mIoU(ms+flip): 48.87 - Config: configs/twins/twins_svt-b_uperhead_8x2_512x512_160k_ade20k.py + 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_8x4_512x512_80k_ade20k +- Name: twins_svt-l_fpn_fpnhead_8xb4-80k_ade20k-512x512 In Collection: FPN Metadata: backbone: SVT-L @@ -239,9 +239,9 @@ Models: Metrics: mIoU: 46.55 mIoU(ms+flip): 47.74 - Config: configs/twins/twins_svt-l_fpn_fpnhead_8x4_512x512_80k_ade20k.py + 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_svt-l_uperhead_8x2_512x512_160k_ade20k +- Name: twins_pcpvt-l_uperhead_8xb2-160k_ade20k-512x512 In Collection: UPerNet Metadata: backbone: SVT-L @@ -261,5 +261,5 @@ Models: Metrics: mIoU: 49.65 mIoU(ms+flip): 50.63 - Config: configs/twins/twins_svt-l_uperhead_8x2_512x512_160k_ade20k.py + 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/twins/twins_pcpvt-b_fpn_fpnhead_8x4_512x512_80k_ade20k.py b/configs/twins/twins_pcpvt-b_fpn_fpnhead_8xb4-80k_ade20k-512x512.py similarity index 78% rename from configs/twins/twins_pcpvt-b_fpn_fpnhead_8x4_512x512_80k_ade20k.py rename to configs/twins/twins_pcpvt-b_fpn_fpnhead_8xb4-80k_ade20k-512x512.py index b79fefd4a5..4739ad4b0a 100644 --- a/configs/twins/twins_pcpvt-b_fpn_fpnhead_8x4_512x512_80k_ade20k.py +++ b/configs/twins/twins_pcpvt-b_fpn_fpnhead_8xb4-80k_ade20k-512x512.py @@ -1,4 +1,4 @@ -_base_ = ['./twins_pcpvt-s_fpn_fpnhead_8x4_512x512_80k_ade20k.py'] +_base_ = ['./twins_pcpvt-s_fpn_fpnhead_8xb4-80k_ade20k-512x512.py'] checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/twins/pcpvt_base_20220308-0621964c.pth' # noqa diff --git a/configs/twins/twins_pcpvt-b_uperhead_8x2_512x512_160k_ade20k.py b/configs/twins/twins_pcpvt-b_uperhead_8xb2-160k_ade20k-512x512.py similarity index 86% rename from configs/twins/twins_pcpvt-b_uperhead_8x2_512x512_160k_ade20k.py rename to configs/twins/twins_pcpvt-b_uperhead_8xb2-160k_ade20k-512x512.py index b9a3d0681c..ba9748547d 100644 --- a/configs/twins/twins_pcpvt-b_uperhead_8x2_512x512_160k_ade20k.py +++ b/configs/twins/twins_pcpvt-b_uperhead_8xb2-160k_ade20k-512x512.py @@ -1,4 +1,4 @@ -_base_ = ['./twins_pcpvt-s_uperhead_8x4_512x512_160k_ade20k.py'] +_base_ = ['./twins_pcpvt-s_uperhead_8xb4-160k_ade20k-512x512.py'] checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/twins/pcpvt_base_20220308-0621964c.pth' # noqa diff --git a/configs/twins/twins_pcpvt-l_fpn_fpnhead_8x4_512x512_80k_ade20k.py b/configs/twins/twins_pcpvt-l_fpn_fpnhead_8xb4-80k_ade20k-512x512.py similarity index 78% rename from configs/twins/twins_pcpvt-l_fpn_fpnhead_8x4_512x512_80k_ade20k.py rename to configs/twins/twins_pcpvt-l_fpn_fpnhead_8xb4-80k_ade20k-512x512.py index abb652e8e0..bff7c41946 100644 --- a/configs/twins/twins_pcpvt-l_fpn_fpnhead_8x4_512x512_80k_ade20k.py +++ b/configs/twins/twins_pcpvt-l_fpn_fpnhead_8xb4-80k_ade20k-512x512.py @@ -1,4 +1,4 @@ -_base_ = ['./twins_pcpvt-s_fpn_fpnhead_8x4_512x512_80k_ade20k.py'] +_base_ = ['./twins_pcpvt-s_fpn_fpnhead_8xb4-80k_ade20k-512x512.py'] checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/twins/pcpvt_large_20220308-37579dc6.pth' # noqa diff --git a/configs/twins/twins_pcpvt-l_uperhead_8x2_512x512_160k_ade20k.py b/configs/twins/twins_pcpvt-l_uperhead_8xb2-160k_ade20k-512x512.py similarity index 86% rename from configs/twins/twins_pcpvt-l_uperhead_8x2_512x512_160k_ade20k.py rename to configs/twins/twins_pcpvt-l_uperhead_8xb2-160k_ade20k-512x512.py index a3e37ef2ae..666ff5b69c 100644 --- a/configs/twins/twins_pcpvt-l_uperhead_8x2_512x512_160k_ade20k.py +++ b/configs/twins/twins_pcpvt-l_uperhead_8xb2-160k_ade20k-512x512.py @@ -1,4 +1,4 @@ -_base_ = ['./twins_pcpvt-s_uperhead_8x4_512x512_160k_ade20k.py'] +_base_ = ['./twins_pcpvt-s_uperhead_8xb4-160k_ade20k-512x512.py'] checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/twins/pcpvt_large_20220308-37579dc6.pth' # noqa diff --git a/configs/twins/twins_pcpvt-s_fpn_fpnhead_8x4_512x512_80k_ade20k.py b/configs/twins/twins_pcpvt-s_fpn_fpnhead_8xb4-80k_ade20k-512x512.py similarity index 100% rename from configs/twins/twins_pcpvt-s_fpn_fpnhead_8x4_512x512_80k_ade20k.py rename to configs/twins/twins_pcpvt-s_fpn_fpnhead_8xb4-80k_ade20k-512x512.py diff --git a/configs/twins/twins_pcpvt-s_uperhead_8x4_512x512_160k_ade20k.py b/configs/twins/twins_pcpvt-s_uperhead_8xb4-160k_ade20k-512x512.py similarity index 100% rename from configs/twins/twins_pcpvt-s_uperhead_8x4_512x512_160k_ade20k.py rename to configs/twins/twins_pcpvt-s_uperhead_8xb4-160k_ade20k-512x512.py diff --git a/configs/twins/twins_svt-b_fpn_fpnhead_8x4_512x512_80k_ade20k.py b/configs/twins/twins_svt-b_fpn_fpnhead_8xb4-80k_ade20k-512x512.py similarity index 85% rename from configs/twins/twins_svt-b_fpn_fpnhead_8x4_512x512_80k_ade20k.py rename to configs/twins/twins_svt-b_fpn_fpnhead_8xb4-80k_ade20k-512x512.py index 00d89572c6..5e9fa00f88 100644 --- a/configs/twins/twins_svt-b_fpn_fpnhead_8x4_512x512_80k_ade20k.py +++ b/configs/twins/twins_svt-b_fpn_fpnhead_8xb4-80k_ade20k-512x512.py @@ -1,4 +1,4 @@ -_base_ = ['./twins_svt-s_fpn_fpnhead_8x4_512x512_80k_ade20k.py'] +_base_ = ['./twins_svt-s_fpn_fpnhead_8xb4-80k_ade20k-512x512.py'] checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/twins/alt_gvt_base_20220308-1b7eb711.pth' # noqa diff --git a/configs/twins/twins_svt-b_uperhead_8x2_512x512_160k_ade20k.py b/configs/twins/twins_svt-b_uperhead_8xb2-160k_ade20k-512x512.py similarity index 86% rename from configs/twins/twins_svt-b_uperhead_8x2_512x512_160k_ade20k.py rename to configs/twins/twins_svt-b_uperhead_8xb2-160k_ade20k-512x512.py index a969fedfed..6ce2361f5f 100644 --- a/configs/twins/twins_svt-b_uperhead_8x2_512x512_160k_ade20k.py +++ b/configs/twins/twins_svt-b_uperhead_8xb2-160k_ade20k-512x512.py @@ -1,4 +1,4 @@ -_base_ = ['./twins_svt-s_uperhead_8x2_512x512_160k_ade20k.py'] +_base_ = ['./twins_svt-s_uperhead_8xb2-160k_ade20k-512x512.py'] checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/twins/alt_gvt_base_20220308-1b7eb711.pth' # noqa diff --git a/configs/twins/twins_svt-l_fpn_fpnhead_8x4_512x512_80k_ade20k.py b/configs/twins/twins_svt-l_fpn_fpnhead_8xb4-80k_ade20k-512x512.py similarity index 86% rename from configs/twins/twins_svt-l_fpn_fpnhead_8x4_512x512_80k_ade20k.py rename to configs/twins/twins_svt-l_fpn_fpnhead_8xb4-80k_ade20k-512x512.py index c68bfd4a17..b7e5f9cdb8 100644 --- a/configs/twins/twins_svt-l_fpn_fpnhead_8x4_512x512_80k_ade20k.py +++ b/configs/twins/twins_svt-l_fpn_fpnhead_8xb4-80k_ade20k-512x512.py @@ -1,4 +1,4 @@ -_base_ = ['./twins_svt-s_fpn_fpnhead_8x4_512x512_80k_ade20k.py'] +_base_ = ['./twins_svt-s_fpn_fpnhead_8xb4-80k_ade20k-512x512.py'] checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/twins/alt_gvt_large_20220308-fb5936f3.pth' # noqa diff --git a/configs/twins/twins_svt-l_uperhead_8x2_512x512_160k_ade20k.py b/configs/twins/twins_svt-l_uperhead_8xb2-160k_ade20k-512x512.py similarity index 87% rename from configs/twins/twins_svt-l_uperhead_8x2_512x512_160k_ade20k.py rename to configs/twins/twins_svt-l_uperhead_8xb2-160k_ade20k-512x512.py index f98c070b2d..69c69df3b5 100644 --- a/configs/twins/twins_svt-l_uperhead_8x2_512x512_160k_ade20k.py +++ b/configs/twins/twins_svt-l_uperhead_8xb2-160k_ade20k-512x512.py @@ -1,4 +1,4 @@ -_base_ = ['./twins_svt-s_uperhead_8x2_512x512_160k_ade20k.py'] +_base_ = ['./twins_svt-s_uperhead_8xb2-160k_ade20k-512x512.py'] checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/twins/alt_gvt_large_20220308-fb5936f3.pth' # noqa diff --git a/configs/twins/twins_svt-s_fpn_fpnhead_8x4_512x512_80k_ade20k.py b/configs/twins/twins_svt-s_fpn_fpnhead_8xb4-80k_ade20k-512x512.py similarity index 100% rename from configs/twins/twins_svt-s_fpn_fpnhead_8x4_512x512_80k_ade20k.py rename to configs/twins/twins_svt-s_fpn_fpnhead_8xb4-80k_ade20k-512x512.py diff --git a/configs/twins/twins_svt-s_uperhead_8x2_512x512_160k_ade20k.py b/configs/twins/twins_svt-s_uperhead_8xb2-160k_ade20k-512x512.py similarity index 100% rename from configs/twins/twins_svt-s_uperhead_8x2_512x512_160k_ade20k.py rename to configs/twins/twins_svt-s_uperhead_8xb2-160k_ade20k-512x512.py diff --git a/configs/unet/README.md b/configs/unet/README.md index f17e1747d7..f3dc261c22 100644 --- a/configs/unet/README.md +++ b/configs/unet/README.md @@ -39,53 +39,53 @@ There is large consent that successful training of deep networks requires many t ### 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/master/configs/unet/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes.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) | 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) | ### 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/master/configs/unet/fcn_unet_s5-d16_64x64_40k_drive.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/master/configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive.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/master/configs/unet/pspnet_unet_s5-d16_64x64_40k_drive.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/master/configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive.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/master/configs/unet/deeplabv3_unet_s5-d16_64x64_40k_drive.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/master/configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive.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) | 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) | ### 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/master/configs/unet/fcn_unet_s5-d16_128x128_40k_stare.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/master/configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare.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/master/configs/unet/pspnet_unet_s5-d16_128x128_40k_stare.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/master/configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare.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/master/configs/unet/deeplabv3_unet_s5-d16_128x128_40k_stare.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/master/configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare.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) | 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) | ### 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/master/configs/unet/fcn_unet_s5-d16_128x128_40k_chase_db1.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/master/configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1.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/master/configs/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1.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/master/configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1.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/master/configs/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1.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/master/configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1.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) | 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) | ### 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/master/configs/unet/fcn_unet_s5-d16_256x256_40k_hrf.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/master/configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf.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/master/configs/unet/pspnet_unet_s5-d16_256x256_40k_hrf.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/master/configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf.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/master/configs/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf.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/master/configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf.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) | 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) | Note: diff --git a/configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive.py b/configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive.py deleted file mode 100644 index 1b8f860bff..0000000000 --- a/configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive.py +++ /dev/null @@ -1,6 +0,0 @@ -_base_ = './fcn_unet_s5-d16_64x64_40k_drive.py' -model = dict( - decode_head=dict(loss_decode=[ - dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0), - dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0) - ])) diff --git a/configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1.py b/configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1.py deleted file mode 100644 index a63dc11d57..0000000000 --- a/configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1.py +++ /dev/null @@ -1,6 +0,0 @@ -_base_ = './pspnet_unet_s5-d16_128x128_40k_chase_db1.py' -model = dict( - decode_head=dict(loss_decode=[ - dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0), - dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0) - ])) diff --git a/configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare.py b/configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare.py deleted file mode 100644 index 1a3b665821..0000000000 --- a/configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare.py +++ /dev/null @@ -1,6 +0,0 @@ -_base_ = './pspnet_unet_s5-d16_128x128_40k_stare.py' -model = dict( - decode_head=dict(loss_decode=[ - dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0), - dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0) - ])) diff --git a/configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf.py b/configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf.py deleted file mode 100644 index e19d6cf427..0000000000 --- a/configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf.py +++ /dev/null @@ -1,6 +0,0 @@ -_base_ = './pspnet_unet_s5-d16_256x256_40k_hrf.py' -model = dict( - decode_head=dict(loss_decode=[ - dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0), - dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0) - ])) diff --git a/configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive.py b/configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive.py deleted file mode 100644 index 7934923755..0000000000 --- a/configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive.py +++ /dev/null @@ -1,6 +0,0 @@ -_base_ = './pspnet_unet_s5-d16_64x64_40k_drive.py' -model = dict( - decode_head=dict(loss_decode=[ - dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0), - dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0) - ])) diff --git a/configs/unet/deeplabv3_unet_s5-d16_64x64_40k_drive.py b/configs/unet/unet-s5-d16_deeplabv3_4xb4-40k_drive-64x64.py similarity index 100% rename from configs/unet/deeplabv3_unet_s5-d16_64x64_40k_drive.py rename to configs/unet/unet-s5-d16_deeplabv3_4xb4-40k_drive-64x64.py diff --git a/configs/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf.py b/configs/unet/unet-s5-d16_deeplabv3_4xb4-40k_hrf-256x256.py similarity index 100% rename from configs/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf.py rename to configs/unet/unet-s5-d16_deeplabv3_4xb4-40k_hrf-256x256.py diff --git a/configs/unet/deeplabv3_unet_s5-d16_128x128_40k_stare.py b/configs/unet/unet-s5-d16_deeplabv3_4xb4-40k_stare-128x128.py similarity index 100% rename from configs/unet/deeplabv3_unet_s5-d16_128x128_40k_stare.py rename to configs/unet/unet-s5-d16_deeplabv3_4xb4-40k_stare-128x128.py diff --git a/configs/unet/unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_chase-db1-128x128.py b/configs/unet/unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_chase-db1-128x128.py new file mode 100644 index 0000000000..4f30bba9a7 --- /dev/null +++ b/configs/unet/unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_chase-db1-128x128.py @@ -0,0 +1,6 @@ +_base_ = './unet_s5-d16_deeplabv3_4xb4-40k_chase-db1-128x128.py' +model = dict( + decode_head=dict(loss_decode=[ + dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0), + dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0) + ])) diff --git a/configs/unet/unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_drive-64x64.py b/configs/unet/unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_drive-64x64.py new file mode 100644 index 0000000000..823fc6dc51 --- /dev/null +++ b/configs/unet/unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_drive-64x64.py @@ -0,0 +1,6 @@ +_base_ = './unet-s5-d16_deeplabv3_4xb4-40k_drive-64x64.py' +model = dict( + decode_head=dict(loss_decode=[ + dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0), + dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0) + ])) diff --git a/configs/unet/unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_hrf-256x256.py b/configs/unet/unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_hrf-256x256.py new file mode 100644 index 0000000000..174eaf8d93 --- /dev/null +++ b/configs/unet/unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_hrf-256x256.py @@ -0,0 +1,6 @@ +_base_ = './unet-s5-d16_deeplabv3_4xb4-40k_hrf-256x256.py' +model = dict( + decode_head=dict(loss_decode=[ + dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0), + dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0) + ])) diff --git a/configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1.py b/configs/unet/unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_stare-128x128.py similarity index 76% rename from configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1.py rename to configs/unet/unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_stare-128x128.py index 1c48cbc22c..35972bea93 100644 --- a/configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1.py +++ b/configs/unet/unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_stare-128x128.py @@ -1,4 +1,4 @@ -_base_ = './deeplabv3_unet_s5-d16_128x128_40k_chase_db1.py' +_base_ = './unet-s5-d16_deeplabv3_4xb4-40k_stare-128x128.py' model = dict( decode_head=dict(loss_decode=[ dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0), diff --git a/configs/unet/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes.py b/configs/unet/unet-s5-d16_fcn_4xb4-160k_cityscapes-512x1024.py similarity index 100% rename from configs/unet/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes.py rename to configs/unet/unet-s5-d16_fcn_4xb4-160k_cityscapes-512x1024.py diff --git a/configs/unet/fcn_unet_s5-d16_128x128_40k_chase_db1.py b/configs/unet/unet-s5-d16_fcn_4xb4-40k_chase-db1-128x128.py similarity index 100% rename from configs/unet/fcn_unet_s5-d16_128x128_40k_chase_db1.py rename to configs/unet/unet-s5-d16_fcn_4xb4-40k_chase-db1-128x128.py diff --git a/configs/unet/fcn_unet_s5-d16_64x64_40k_drive.py b/configs/unet/unet-s5-d16_fcn_4xb4-40k_drive-64x64.py similarity index 100% rename from configs/unet/fcn_unet_s5-d16_64x64_40k_drive.py rename to configs/unet/unet-s5-d16_fcn_4xb4-40k_drive-64x64.py diff --git a/configs/unet/fcn_unet_s5-d16_256x256_40k_hrf.py b/configs/unet/unet-s5-d16_fcn_4xb4-40k_hrf-256x256.py similarity index 100% rename from configs/unet/fcn_unet_s5-d16_256x256_40k_hrf.py rename to configs/unet/unet-s5-d16_fcn_4xb4-40k_hrf-256x256.py diff --git a/configs/unet/fcn_unet_s5-d16_128x128_40k_stare.py b/configs/unet/unet-s5-d16_fcn_4xb4-40k_stare-128x128.py similarity index 100% rename from configs/unet/fcn_unet_s5-d16_128x128_40k_stare.py rename to configs/unet/unet-s5-d16_fcn_4xb4-40k_stare-128x128.py diff --git a/configs/unet/unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_chase-db1-128x128.py b/configs/unet/unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_chase-db1-128x128.py new file mode 100644 index 0000000000..5a26ccbf96 --- /dev/null +++ b/configs/unet/unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_chase-db1-128x128.py @@ -0,0 +1,6 @@ +_base_ = './unet-s5-d16_fcn_4xb4-40k_chase-db1-128x128.py' +model = dict( + decode_head=dict(loss_decode=[ + dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0), + dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0) + ])) diff --git a/configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf.py b/configs/unet/unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_drive-64x64.py similarity index 79% rename from configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf.py rename to configs/unet/unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_drive-64x64.py index a154d7e689..c3b1488ad5 100644 --- a/configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf.py +++ b/configs/unet/unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_drive-64x64.py @@ -1,4 +1,4 @@ -_base_ = './fcn_unet_s5-d16_256x256_40k_hrf.py' +_base_ = './unet-s5-d16_fcn_4xb4-40k_drive-64x64.py' model = dict( decode_head=dict(loss_decode=[ dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0), diff --git a/configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare.py b/configs/unet/unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_hrf-256x256.py similarity index 79% rename from configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare.py rename to configs/unet/unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_hrf-256x256.py index cf5fa1f0de..dd3a6afc02 100644 --- a/configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare.py +++ b/configs/unet/unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_hrf-256x256.py @@ -1,4 +1,4 @@ -_base_ = './fcn_unet_s5-d16_128x128_40k_stare.py' +_base_ = './unet-s5-d16_fcn_4xb4-40k_hrf-256x256.py' model = dict( decode_head=dict(loss_decode=[ dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0), diff --git a/configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1.py b/configs/unet/unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_stare-128x128.py similarity index 78% rename from configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1.py rename to configs/unet/unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_stare-128x128.py index 5264866291..c8fecf34e9 100644 --- a/configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1.py +++ b/configs/unet/unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_stare-128x128.py @@ -1,4 +1,4 @@ -_base_ = './fcn_unet_s5-d16_128x128_40k_chase_db1.py' +_base_ = './unet-s5-d16_fcn_4xb4-40k_stare-128x128.py' model = dict( decode_head=dict(loss_decode=[ dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0), diff --git a/configs/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1.py b/configs/unet/unet-s5-d16_pspnet_4xb4-40k_chase-db1-128x128.py similarity index 100% rename from configs/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1.py rename to configs/unet/unet-s5-d16_pspnet_4xb4-40k_chase-db1-128x128.py diff --git a/configs/unet/pspnet_unet_s5-d16_64x64_40k_drive.py b/configs/unet/unet-s5-d16_pspnet_4xb4-40k_drive-64x64.py similarity index 100% rename from configs/unet/pspnet_unet_s5-d16_64x64_40k_drive.py rename to configs/unet/unet-s5-d16_pspnet_4xb4-40k_drive-64x64.py diff --git a/configs/unet/pspnet_unet_s5-d16_256x256_40k_hrf.py b/configs/unet/unet-s5-d16_pspnet_4xb4-40k_hrf-256x256.py similarity index 100% rename from configs/unet/pspnet_unet_s5-d16_256x256_40k_hrf.py rename to configs/unet/unet-s5-d16_pspnet_4xb4-40k_hrf-256x256.py diff --git a/configs/unet/pspnet_unet_s5-d16_128x128_40k_stare.py b/configs/unet/unet-s5-d16_pspnet_4xb4-40k_stare-128x128.py similarity index 100% rename from configs/unet/pspnet_unet_s5-d16_128x128_40k_stare.py rename to configs/unet/unet-s5-d16_pspnet_4xb4-40k_stare-128x128.py diff --git a/configs/unet/unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_chase-db1-128x128.py b/configs/unet/unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_chase-db1-128x128.py new file mode 100644 index 0000000000..69a4bbaf82 --- /dev/null +++ b/configs/unet/unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_chase-db1-128x128.py @@ -0,0 +1,6 @@ +_base_ = './unet-s5-d16_pspnet_4xb4-40k_chase-db1-128x128.py' +model = dict( + decode_head=dict(loss_decode=[ + dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0), + dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0) + ])) diff --git a/configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare.py b/configs/unet/unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_drive-64x64.py similarity index 78% rename from configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare.py rename to configs/unet/unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_drive-64x64.py index 1022edee36..1abbd53d8c 100644 --- a/configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare.py +++ b/configs/unet/unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_drive-64x64.py @@ -1,4 +1,4 @@ -_base_ = './deeplabv3_unet_s5-d16_128x128_40k_stare.py' +_base_ = './unet-s5-d16_pspnet_4xb4-40k_drive-64x64.py' model = dict( decode_head=dict(loss_decode=[ dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0), diff --git a/configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf.py b/configs/unet/unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_hrf-256x256.py similarity index 78% rename from configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf.py rename to configs/unet/unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_hrf-256x256.py index fc17da71ed..b3256d759b 100644 --- a/configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf.py +++ b/configs/unet/unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_hrf-256x256.py @@ -1,4 +1,4 @@ -_base_ = './deeplabv3_unet_s5-d16_256x256_40k_hrf.py' +_base_ = './unet-s5-d16_pspnet_4xb4-40k_hrf-256x256.py' model = dict( decode_head=dict(loss_decode=[ dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0), diff --git a/configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive.py b/configs/unet/unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_stare-128x128.py similarity index 77% rename from configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive.py rename to configs/unet/unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_stare-128x128.py index 3f1f12e61e..82aa3da616 100644 --- a/configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive.py +++ b/configs/unet/unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_stare-128x128.py @@ -1,4 +1,4 @@ -_base_ = './deeplabv3_unet_s5-d16_64x64_40k_drive.py' +_base_ = './unet-s5-d16_pspnet_4xb4-40k_stare-128x128.py' model = dict( decode_head=dict(loss_decode=[ dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0), diff --git a/configs/unet/unet.yml b/configs/unet/unet.yml index 5bb5014f81..4a01ce33e2 100644 --- a/configs/unet/unet.yml +++ b/configs/unet/unet.yml @@ -17,7 +17,7 @@ Collections: Converted From: Code: http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net Models: -- Name: fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes +- Name: unet-s5-d16_fcn_4xb4-160k_cityscapes-512x1024 In Collection: UNet Metadata: backbone: UNet-S5-D16 @@ -37,9 +37,9 @@ Models: Metrics: mIoU: 69.1 mIoU(ms+flip): 71.05 - Config: configs/unet/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes.py + 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: fcn_unet_s5-d16_64x64_40k_drive +- Name: unet-s5-d16_fcn_4xb4-40k_drive-64x64 In Collection: UNet Metadata: backbone: UNet-S5-D16 @@ -51,9 +51,9 @@ Models: Dataset: DRIVE Metrics: Dice: 78.67 - Config: configs/unet/fcn_unet_s5-d16_64x64_40k_drive.py + 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: fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive +- Name: unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_drive-64x64 In Collection: UNet Metadata: backbone: UNet-S5-D16 @@ -65,9 +65,9 @@ Models: Dataset: DRIVE Metrics: Dice: 79.32 - Config: configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive.py + 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: pspnet_unet_s5-d16_64x64_40k_drive +- Name: unet-s5-d16_pspnet_4xb4-40k_drive-64x64 In Collection: UNet Metadata: backbone: UNet-S5-D16 @@ -79,9 +79,9 @@ Models: Dataset: DRIVE Metrics: Dice: 78.62 - Config: configs/unet/pspnet_unet_s5-d16_64x64_40k_drive.py + 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: pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive +- Name: unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_drive-64x64 In Collection: UNet Metadata: backbone: UNet-S5-D16 @@ -93,9 +93,9 @@ Models: Dataset: DRIVE Metrics: Dice: 79.42 - Config: configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive.py + 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: deeplabv3_unet_s5-d16_64x64_40k_drive +- Name: unet-s5-d16_deeplabv3_4xb4-40k_drive-64x64 In Collection: UNet Metadata: backbone: UNet-S5-D16 @@ -107,9 +107,9 @@ Models: Dataset: DRIVE Metrics: Dice: 78.69 - Config: configs/unet/deeplabv3_unet_s5-d16_64x64_40k_drive.py + 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: deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive +- Name: unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_drive-64x64 In Collection: UNet Metadata: backbone: UNet-S5-D16 @@ -121,9 +121,9 @@ Models: Dataset: DRIVE Metrics: Dice: 79.56 - Config: configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive.py + 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: fcn_unet_s5-d16_128x128_40k_stare +- Name: unet-s5-d16_fcn_4xb4-40k_stare-128x128 In Collection: UNet Metadata: backbone: UNet-S5-D16 @@ -135,9 +135,9 @@ Models: Dataset: STARE Metrics: Dice: 81.02 - Config: configs/unet/fcn_unet_s5-d16_128x128_40k_stare.py + 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: fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare +- Name: unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_stare-128x128 In Collection: UNet Metadata: backbone: UNet-S5-D16 @@ -149,9 +149,9 @@ Models: Dataset: STARE Metrics: Dice: 82.7 - Config: configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare.py + 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: pspnet_unet_s5-d16_128x128_40k_stare +- Name: unet-s5-d16_pspnet_4xb4-40k_stare-128x128 In Collection: UNet Metadata: backbone: UNet-S5-D16 @@ -163,9 +163,9 @@ Models: Dataset: STARE Metrics: Dice: 81.22 - Config: configs/unet/pspnet_unet_s5-d16_128x128_40k_stare.py + 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: pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare +- Name: unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_stare-128x128 In Collection: UNet Metadata: backbone: UNet-S5-D16 @@ -177,9 +177,9 @@ Models: Dataset: STARE Metrics: Dice: 82.84 - Config: configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare.py + 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: deeplabv3_unet_s5-d16_128x128_40k_stare +- Name: unet-s5-d16_deeplabv3_4xb4-40k_stare-128x128 In Collection: UNet Metadata: backbone: UNet-S5-D16 @@ -191,9 +191,9 @@ Models: Dataset: STARE Metrics: Dice: 80.93 - Config: configs/unet/deeplabv3_unet_s5-d16_128x128_40k_stare.py + 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: deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare +- Name: unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_stare-128x128 In Collection: UNet Metadata: backbone: UNet-S5-D16 @@ -205,9 +205,9 @@ Models: Dataset: STARE Metrics: Dice: 82.71 - Config: configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare.py + 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: fcn_unet_s5-d16_128x128_40k_chase_db1 +- Name: unet-s5-d16_fcn_4xb4-40k_chase-db1-128x128 In Collection: UNet Metadata: backbone: UNet-S5-D16 @@ -219,9 +219,9 @@ Models: Dataset: CHASE_DB1 Metrics: Dice: 80.24 - Config: configs/unet/fcn_unet_s5-d16_128x128_40k_chase_db1.py + 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: fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1 +- Name: unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_chase-db1-128x128 In Collection: UNet Metadata: backbone: UNet-S5-D16 @@ -233,9 +233,9 @@ Models: Dataset: CHASE_DB1 Metrics: Dice: 80.4 - Config: configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1.py + 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: pspnet_unet_s5-d16_128x128_40k_chase_db1 +- Name: unet-s5-d16_pspnet_4xb4-40k_chase-db1-128x128 In Collection: UNet Metadata: backbone: UNet-S5-D16 @@ -247,9 +247,9 @@ Models: Dataset: CHASE_DB1 Metrics: Dice: 80.36 - Config: configs/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1.py + 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: pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1 +- Name: unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_chase-db1-128x128 In Collection: UNet Metadata: backbone: UNet-S5-D16 @@ -261,9 +261,9 @@ Models: Dataset: CHASE_DB1 Metrics: Dice: 80.28 - Config: configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1.py + 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: deeplabv3_unet_s5-d16_128x128_40k_chase_db1 +- Name: unet_s5-d16_deeplabv3_4xb4-40k_chase-db1-128x128 In Collection: UNet Metadata: backbone: UNet-S5-D16 @@ -275,9 +275,9 @@ Models: Dataset: CHASE_DB1 Metrics: Dice: 80.47 - Config: configs/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1.py + 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: deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1 +- Name: unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_chase-db1-128x128 In Collection: UNet Metadata: backbone: UNet-S5-D16 @@ -289,9 +289,9 @@ Models: Dataset: CHASE_DB1 Metrics: Dice: 80.37 - Config: configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1.py + 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: fcn_unet_s5-d16_256x256_40k_hrf +- Name: unet-s5-d16_fcn_4xb4-40k_hrf-256x256 In Collection: UNet Metadata: backbone: UNet-S5-D16 @@ -303,9 +303,9 @@ Models: Dataset: HRF Metrics: Dice: 79.45 - Config: configs/unet/fcn_unet_s5-d16_256x256_40k_hrf.py + 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: fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf +- Name: unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_hrf-256x256 In Collection: UNet Metadata: backbone: UNet-S5-D16 @@ -317,9 +317,9 @@ Models: Dataset: HRF Metrics: Dice: 80.87 - Config: configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf.py + 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: pspnet_unet_s5-d16_256x256_40k_hrf +- Name: unet-s5-d16_pspnet_4xb4-40k_hrf-256x256 In Collection: UNet Metadata: backbone: UNet-S5-D16 @@ -331,9 +331,9 @@ Models: Dataset: HRF Metrics: Dice: 80.07 - Config: configs/unet/pspnet_unet_s5-d16_256x256_40k_hrf.py + 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: pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf +- Name: unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_hrf-256x256 In Collection: UNet Metadata: backbone: UNet-S5-D16 @@ -345,9 +345,9 @@ Models: Dataset: HRF Metrics: Dice: 80.96 - Config: configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf.py + 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: deeplabv3_unet_s5-d16_256x256_40k_hrf +- Name: unet-s5-d16_deeplabv3_4xb4-40k_hrf-256x256 In Collection: UNet Metadata: backbone: UNet-S5-D16 @@ -359,9 +359,9 @@ Models: Dataset: HRF Metrics: Dice: 80.21 - Config: configs/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf.py + 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: deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf +- Name: unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_hrf-256x256 In Collection: UNet Metadata: backbone: UNet-S5-D16 @@ -373,5 +373,5 @@ Models: Dataset: HRF Metrics: Dice: 80.71 - Config: configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf.py + 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/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1.py b/configs/unet/unet_s5-d16_deeplabv3_4xb4-40k_chase-db1-128x128.py similarity index 100% rename from configs/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1.py rename to configs/unet/unet_s5-d16_deeplabv3_4xb4-40k_chase-db1-128x128.py diff --git a/configs/upernet/README.md b/configs/upernet/README.md index dc8eadc6c6..e4a5ee4381 100644 --- a/configs/upernet/README.md +++ b/configs/upernet/README.md @@ -38,31 +38,31 @@ Humans recognize the visual world at multiple levels: we effortlessly categorize ### 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/master/configs/upernet/upernet_r50_512x1024_40k_cityscapes.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/master/configs/upernet/upernet_r101_512x1024_40k_cityscapes.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/master/configs/upernet/upernet_r50_769x769_40k_cityscapes.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/master/configs/upernet/upernet_r101_769x769_40k_cityscapes.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/master/configs/upernet/upernet_r50_512x1024_80k_cityscapes.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/master/configs/upernet/upernet_r101_512x1024_80k_cityscapes.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/master/configs/upernet/upernet_r50_769x769_80k_cityscapes.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/master/configs/upernet/upernet_r101_769x769_80k_cityscapes.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) | +| 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/master/configs/upernet/upernet_r50_512x512_80k_ade20k.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/master/configs/upernet/upernet_r101_512x512_80k_ade20k.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/master/configs/upernet/upernet_r50_512x512_160k_ade20k.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/master/configs/upernet/upernet_r101_512x512_160k_ade20k.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) | +| 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/master/configs/upernet/upernet_r50_512x512_20k_voc12aug.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/master/configs/upernet/upernet_r101_512x512_20k_voc12aug.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/master/configs/upernet/upernet_r50_512x512_40k_voc12aug.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/master/configs/upernet/upernet_r101_512x512_40k_voc12aug.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) | +| 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/upernet.yml b/configs/upernet/upernet.yml index 7c3872a8dd..6892fcf06a 100644 --- a/configs/upernet/upernet.yml +++ b/configs/upernet/upernet.yml @@ -15,7 +15,7 @@ Collections: Converted From: Code: https://github.com/CSAILVision/unifiedparsing Models: -- Name: upernet_r50_512x1024_40k_cityscapes +- Name: upernet_r50_4xb2-40k_cityscapes-512x1024 In Collection: UPerNet Metadata: backbone: R-50 @@ -35,9 +35,9 @@ Models: Metrics: mIoU: 77.1 mIoU(ms+flip): 78.37 - Config: configs/upernet/upernet_r50_512x1024_40k_cityscapes.py + 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_512x1024_40k_cityscapes +- Name: upernet_r101_4xb2-40k_cityscapes-512x1024 In Collection: UPerNet Metadata: backbone: R-101 @@ -57,9 +57,9 @@ Models: Metrics: mIoU: 78.69 mIoU(ms+flip): 80.11 - Config: configs/upernet/upernet_r101_512x1024_40k_cityscapes.py + 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_769x769_40k_cityscapes +- Name: upernet_r50_4xb2-40k_cityscapes-769x769 In Collection: UPerNet Metadata: backbone: R-50 @@ -79,9 +79,9 @@ Models: Metrics: mIoU: 77.98 mIoU(ms+flip): 79.7 - Config: configs/upernet/upernet_r50_769x769_40k_cityscapes.py + 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_769x769_40k_cityscapes +- Name: upernet_r101_4xb2-40k_cityscapes-769x769 In Collection: UPerNet Metadata: backbone: R-101 @@ -101,9 +101,9 @@ Models: Metrics: mIoU: 79.03 mIoU(ms+flip): 80.77 - Config: configs/upernet/upernet_r101_769x769_40k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: upernet_r50_4xb2-80k_cityscapes-512x1024 In Collection: UPerNet Metadata: backbone: R-50 @@ -115,9 +115,9 @@ Models: Metrics: mIoU: 78.19 mIoU(ms+flip): 79.19 - Config: configs/upernet/upernet_r50_512x1024_80k_cityscapes.py + 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_512x1024_80k_cityscapes +- Name: upernet_r101_4xb2-80k_cityscapes-512x1024 In Collection: UPerNet Metadata: backbone: R-101 @@ -129,9 +129,9 @@ Models: Metrics: mIoU: 79.4 mIoU(ms+flip): 80.46 - Config: configs/upernet/upernet_r101_512x1024_80k_cityscapes.py + 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_769x769_80k_cityscapes +- Name: upernet_r50_4xb2-80k_cityscapes-769x769 In Collection: UPerNet Metadata: backbone: R-50 @@ -143,9 +143,9 @@ Models: Metrics: mIoU: 79.39 mIoU(ms+flip): 80.92 - Config: configs/upernet/upernet_r50_769x769_80k_cityscapes.py + 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_769x769_80k_cityscapes +- Name: upernet_r101_4xb2-80k_cityscapes-769x769 In Collection: UPerNet Metadata: backbone: R-101 @@ -157,9 +157,9 @@ Models: Metrics: mIoU: 80.1 mIoU(ms+flip): 81.49 - Config: configs/upernet/upernet_r101_769x769_80k_cityscapes.py + 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_512x512_80k_ade20k +- Name: upernet_r50_4xb4-80k_ade20k-512x512 In Collection: UPerNet Metadata: backbone: R-50 @@ -179,9 +179,9 @@ Models: Metrics: mIoU: 40.7 mIoU(ms+flip): 41.81 - Config: configs/upernet/upernet_r50_512x512_80k_ade20k.py + 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_512x512_80k_ade20k +- Name: upernet_r101_4xb4-80k_ade20k-512x512 In Collection: UPerNet Metadata: backbone: R-101 @@ -201,9 +201,9 @@ Models: Metrics: mIoU: 42.91 mIoU(ms+flip): 43.96 - Config: configs/upernet/upernet_r101_512x512_80k_ade20k.py + 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_512x512_160k_ade20k +- Name: upernet_r50_4xb4-160k_ade20k-512x512 In Collection: UPerNet Metadata: backbone: R-50 @@ -215,9 +215,9 @@ Models: Metrics: mIoU: 42.05 mIoU(ms+flip): 42.78 - Config: configs/upernet/upernet_r50_512x512_160k_ade20k.py + 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_512x512_160k_ade20k +- Name: upernet_r101_4xb4-160k_ade20k-512x512 In Collection: UPerNet Metadata: backbone: R-101 @@ -229,9 +229,9 @@ Models: Metrics: mIoU: 43.82 mIoU(ms+flip): 44.85 - Config: configs/upernet/upernet_r101_512x512_160k_ade20k.py + 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_512x512_20k_voc12aug +- Name: upernet_r50_4xb4-20k_voc12aug-512x512 In Collection: UPerNet Metadata: backbone: R-50 @@ -251,9 +251,9 @@ Models: Metrics: mIoU: 74.82 mIoU(ms+flip): 76.35 - Config: configs/upernet/upernet_r50_512x512_20k_voc12aug.py + 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_512x512_20k_voc12aug +- Name: upernet_r101_4xb4-20k_voc12aug-512x512 In Collection: UPerNet Metadata: backbone: R-101 @@ -273,9 +273,9 @@ Models: Metrics: mIoU: 77.1 mIoU(ms+flip): 78.29 - Config: configs/upernet/upernet_r101_512x512_20k_voc12aug.py + 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_512x512_40k_voc12aug +- Name: upernet_r50_4xb4-40k_voc12aug-512x512 In Collection: UPerNet Metadata: backbone: R-50 @@ -287,9 +287,9 @@ Models: Metrics: mIoU: 75.92 mIoU(ms+flip): 77.44 - Config: configs/upernet/upernet_r50_512x512_40k_voc12aug.py + 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_512x512_40k_voc12aug +- Name: upernet_r101_4xb4-40k_voc12aug-512x512 In Collection: UPerNet Metadata: backbone: R-101 @@ -301,5 +301,5 @@ Models: Metrics: mIoU: 77.43 mIoU(ms+flip): 78.56 - Config: configs/upernet/upernet_r101_512x512_40k_voc12aug.py + 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/upernet/upernet_r101_4xb2-40k_cityscapes-512x1024.py b/configs/upernet/upernet_r101_4xb2-40k_cityscapes-512x1024.py new file mode 100644 index 0000000000..8f5f6aecfe --- /dev/null +++ b/configs/upernet/upernet_r101_4xb2-40k_cityscapes-512x1024.py @@ -0,0 +1,2 @@ +_base_ = './upernet_r50_4xb2-40k_cityscapes-512x1024.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/upernet/upernet_r101_4xb2-40k_cityscapes-769x769.py b/configs/upernet/upernet_r101_4xb2-40k_cityscapes-769x769.py new file mode 100644 index 0000000000..28b5d3e968 --- /dev/null +++ b/configs/upernet/upernet_r101_4xb2-40k_cityscapes-769x769.py @@ -0,0 +1,2 @@ +_base_ = './upernet_r50_4xb2-40k_cityscapes-769x769.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/upernet/upernet_r101_4xb2-80k_cityscapes-512x1024.py b/configs/upernet/upernet_r101_4xb2-80k_cityscapes-512x1024.py new file mode 100644 index 0000000000..cafd8a2091 --- /dev/null +++ b/configs/upernet/upernet_r101_4xb2-80k_cityscapes-512x1024.py @@ -0,0 +1,2 @@ +_base_ = './upernet_r50_4xb2-80k_cityscapes-512x1024.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/upernet/upernet_r101_4xb2-80k_cityscapes-769x769.py b/configs/upernet/upernet_r101_4xb2-80k_cityscapes-769x769.py new file mode 100644 index 0000000000..e17572054f --- /dev/null +++ b/configs/upernet/upernet_r101_4xb2-80k_cityscapes-769x769.py @@ -0,0 +1,2 @@ +_base_ = './upernet_r50_4xb2-80k_cityscapes-769x769.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/upernet/upernet_r101_4xb4-160k_ade20k-512x512.py b/configs/upernet/upernet_r101_4xb4-160k_ade20k-512x512.py new file mode 100644 index 0000000000..7a6152774c --- /dev/null +++ b/configs/upernet/upernet_r101_4xb4-160k_ade20k-512x512.py @@ -0,0 +1,2 @@ +_base_ = './upernet_r50_4xb4-160k_ade20k-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/upernet/upernet_r101_4xb4-20k_voc12aug-512x512.py b/configs/upernet/upernet_r101_4xb4-20k_voc12aug-512x512.py new file mode 100644 index 0000000000..be8f0848df --- /dev/null +++ b/configs/upernet/upernet_r101_4xb4-20k_voc12aug-512x512.py @@ -0,0 +1,2 @@ +_base_ = './upernet_r50_4xb4-20k_voc12aug-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/upernet/upernet_r101_4xb4-40k_voc12aug-512x512.py b/configs/upernet/upernet_r101_4xb4-40k_voc12aug-512x512.py new file mode 100644 index 0000000000..db1d976498 --- /dev/null +++ b/configs/upernet/upernet_r101_4xb4-40k_voc12aug-512x512.py @@ -0,0 +1,2 @@ +_base_ = './upernet_r50_4xb4-40k_voc12aug-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/upernet/upernet_r101_4xb4-80k_ade20k-512x512.py b/configs/upernet/upernet_r101_4xb4-80k_ade20k-512x512.py new file mode 100644 index 0000000000..84549a421d --- /dev/null +++ b/configs/upernet/upernet_r101_4xb4-80k_ade20k-512x512.py @@ -0,0 +1,2 @@ +_base_ = './upernet_r50_4xb4-80k_ade20k-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/upernet/upernet_r101_512x1024_40k_cityscapes.py b/configs/upernet/upernet_r101_512x1024_40k_cityscapes.py deleted file mode 100644 index b90b597d83..0000000000 --- a/configs/upernet/upernet_r101_512x1024_40k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './upernet_r50_512x1024_40k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/upernet/upernet_r101_512x1024_80k_cityscapes.py b/configs/upernet/upernet_r101_512x1024_80k_cityscapes.py deleted file mode 100644 index 420ca2e428..0000000000 --- a/configs/upernet/upernet_r101_512x1024_80k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './upernet_r50_512x1024_80k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/upernet/upernet_r101_512x512_160k_ade20k.py b/configs/upernet/upernet_r101_512x512_160k_ade20k.py deleted file mode 100644 index 146f13eb79..0000000000 --- a/configs/upernet/upernet_r101_512x512_160k_ade20k.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './upernet_r50_512x512_160k_ade20k.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/upernet/upernet_r101_512x512_20k_voc12aug.py b/configs/upernet/upernet_r101_512x512_20k_voc12aug.py deleted file mode 100644 index 56345d1806..0000000000 --- a/configs/upernet/upernet_r101_512x512_20k_voc12aug.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './upernet_r50_512x512_20k_voc12aug.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/upernet/upernet_r101_512x512_40k_voc12aug.py b/configs/upernet/upernet_r101_512x512_40k_voc12aug.py deleted file mode 100644 index 0669b741b9..0000000000 --- a/configs/upernet/upernet_r101_512x512_40k_voc12aug.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './upernet_r50_512x512_40k_voc12aug.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/upernet/upernet_r101_512x512_80k_ade20k.py b/configs/upernet/upernet_r101_512x512_80k_ade20k.py deleted file mode 100644 index abfb9c5d9f..0000000000 --- a/configs/upernet/upernet_r101_512x512_80k_ade20k.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './upernet_r50_512x512_80k_ade20k.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/upernet/upernet_r101_769x769_40k_cityscapes.py b/configs/upernet/upernet_r101_769x769_40k_cityscapes.py deleted file mode 100644 index e5f3a3fae1..0000000000 --- a/configs/upernet/upernet_r101_769x769_40k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './upernet_r50_769x769_40k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/upernet/upernet_r101_769x769_80k_cityscapes.py b/configs/upernet/upernet_r101_769x769_80k_cityscapes.py deleted file mode 100644 index a709165657..0000000000 --- a/configs/upernet/upernet_r101_769x769_80k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './upernet_r50_769x769_80k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/upernet/upernet_r50_512x1024_40k_cityscapes.py b/configs/upernet/upernet_r50_4xb2-40k_cityscapes-512x1024.py similarity index 100% rename from configs/upernet/upernet_r50_512x1024_40k_cityscapes.py rename to configs/upernet/upernet_r50_4xb2-40k_cityscapes-512x1024.py diff --git a/configs/upernet/upernet_r50_769x769_40k_cityscapes.py b/configs/upernet/upernet_r50_4xb2-40k_cityscapes-769x769.py similarity index 100% rename from configs/upernet/upernet_r50_769x769_40k_cityscapes.py rename to configs/upernet/upernet_r50_4xb2-40k_cityscapes-769x769.py diff --git a/configs/upernet/upernet_r50_512x1024_80k_cityscapes.py b/configs/upernet/upernet_r50_4xb2-80k_cityscapes-512x1024.py similarity index 100% rename from configs/upernet/upernet_r50_512x1024_80k_cityscapes.py rename to configs/upernet/upernet_r50_4xb2-80k_cityscapes-512x1024.py diff --git a/configs/upernet/upernet_r50_769x769_80k_cityscapes.py b/configs/upernet/upernet_r50_4xb2-80k_cityscapes-769x769.py similarity index 100% rename from configs/upernet/upernet_r50_769x769_80k_cityscapes.py rename to configs/upernet/upernet_r50_4xb2-80k_cityscapes-769x769.py diff --git a/configs/upernet/upernet_r50_512x512_160k_ade20k.py b/configs/upernet/upernet_r50_4xb4-160k_ade20k-512x512.py similarity index 100% rename from configs/upernet/upernet_r50_512x512_160k_ade20k.py rename to configs/upernet/upernet_r50_4xb4-160k_ade20k-512x512.py diff --git a/configs/upernet/upernet_r50_512x512_20k_voc12aug.py b/configs/upernet/upernet_r50_4xb4-20k_voc12aug-512x512.py similarity index 100% rename from configs/upernet/upernet_r50_512x512_20k_voc12aug.py rename to configs/upernet/upernet_r50_4xb4-20k_voc12aug-512x512.py diff --git a/configs/upernet/upernet_r50_512x512_40k_voc12aug.py b/configs/upernet/upernet_r50_4xb4-40k_voc12aug-512x512.py similarity index 100% rename from configs/upernet/upernet_r50_512x512_40k_voc12aug.py rename to configs/upernet/upernet_r50_4xb4-40k_voc12aug-512x512.py diff --git a/configs/upernet/upernet_r50_512x512_80k_ade20k.py b/configs/upernet/upernet_r50_4xb4-80k_ade20k-512x512.py similarity index 100% rename from configs/upernet/upernet_r50_512x512_80k_ade20k.py rename to configs/upernet/upernet_r50_4xb4-80k_ade20k-512x512.py diff --git a/configs/vit/README.md b/configs/vit/README.md index bfa20f4225..b7f242549d 100644 --- a/configs/vit/README.md +++ b/configs/vit/README.md @@ -55,16 +55,16 @@ 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/master/configs/vit/upernet_vit-b16_mln_512x512_80k_ade20k.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/master/configs/vit/upernet_vit-b16_mln_512x512_160k_ade20k.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/master/configs/vit/upernet_vit-b16_ln_mln_512x512_160k_ade20k.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/master/configs/vit/upernet_deit-s16_512x512_80k_ade20k.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/master/configs/vit/upernet_deit-s16_512x512_160k_ade20k.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/master/configs/vit/upernet_deit-s16_mln_512x512_160k_ade20k.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/master/configs/vit/upernet_deit-s16_ln_mln_512x512_160k_ade20k.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/master/configs/vit/upernet_deit-b16_512x512_80k_ade20k.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/master/configs/vit/upernet_deit-b16_512x512_160k_ade20k.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/master/configs/vit/upernet_deit-b16_mln_512x512_160k_ade20k.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/master/configs/vit/upernet_deit-b16_ln_mln_512x512_160k_ade20k.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) | 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) | diff --git a/configs/vit/vit.yml b/configs/vit/vit.yml index 35e4952e03..613d866ac4 100644 --- a/configs/vit/vit.yml +++ b/configs/vit/vit.yml @@ -1,5 +1,5 @@ Models: -- Name: upernet_vit-b16_mln_512x512_80k_ade20k +- Name: vit_vit-b16_mln_upernet_8xb2-80k_ade20k-512x512 In Collection: UPerNet Metadata: backbone: ViT-B + MLN @@ -19,9 +19,9 @@ Models: Metrics: mIoU: 47.71 mIoU(ms+flip): 49.51 - Config: configs/vit/upernet_vit-b16_mln_512x512_80k_ade20k.py + 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: upernet_vit-b16_mln_512x512_160k_ade20k +- Name: vit_vit-b16_mln_upernet_8xb2-160k_ade20k-512x512 In Collection: UPerNet Metadata: backbone: ViT-B + MLN @@ -41,9 +41,9 @@ Models: Metrics: mIoU: 46.75 mIoU(ms+flip): 48.46 - Config: configs/vit/upernet_vit-b16_mln_512x512_160k_ade20k.py + 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: upernet_vit-b16_ln_mln_512x512_160k_ade20k +- Name: vit_vit-b16-ln_mln_upernet_8xb2-160k_ade20k-512x512 In Collection: UPerNet Metadata: backbone: ViT-B + LN + MLN @@ -63,9 +63,9 @@ Models: Metrics: mIoU: 47.73 mIoU(ms+flip): 49.95 - Config: configs/vit/upernet_vit-b16_ln_mln_512x512_160k_ade20k.py + 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: upernet_deit-s16_512x512_80k_ade20k +- Name: vit_deit-s16_upernet_8xb2-80k_ade20k-512x512 In Collection: UPerNet Metadata: backbone: DeiT-S @@ -85,9 +85,9 @@ Models: Metrics: mIoU: 42.96 mIoU(ms+flip): 43.79 - Config: configs/vit/upernet_deit-s16_512x512_80k_ade20k.py + 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: upernet_deit-s16_512x512_160k_ade20k +- Name: vit_deit-s16_upernet_8xb2-160k_ade20k-512x512 In Collection: UPerNet Metadata: backbone: DeiT-S @@ -107,9 +107,9 @@ Models: Metrics: mIoU: 42.87 mIoU(ms+flip): 43.79 - Config: configs/vit/upernet_deit-s16_512x512_160k_ade20k.py + 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: upernet_deit-s16_mln_512x512_160k_ade20k +- Name: vit_deit-s16_mln_upernet_8xb2-160k_ade20k-512x512 In Collection: UPerNet Metadata: backbone: DeiT-S + MLN @@ -129,9 +129,9 @@ Models: Metrics: mIoU: 43.82 mIoU(ms+flip): 45.07 - Config: configs/vit/upernet_deit-s16_mln_512x512_160k_ade20k.py + 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: upernet_deit-s16_ln_mln_512x512_160k_ade20k +- Name: vit_deit-s16-ln_mln_upernet_512x512_160k_ade20k-512x512 In Collection: UPerNet Metadata: backbone: DeiT-S + LN + MLN @@ -151,9 +151,9 @@ Models: Metrics: mIoU: 43.52 mIoU(ms+flip): 45.01 - Config: configs/vit/upernet_deit-s16_ln_mln_512x512_160k_ade20k.py + 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: upernet_deit-b16_512x512_80k_ade20k +- Name: vit_deit-b16_upernet_8xb2-80k_ade20k-512x512 In Collection: UPerNet Metadata: backbone: DeiT-B @@ -173,9 +173,9 @@ Models: Metrics: mIoU: 45.24 mIoU(ms+flip): 46.73 - Config: configs/vit/upernet_deit-b16_512x512_80k_ade20k.py + 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: upernet_deit-b16_512x512_160k_ade20k +- Name: vit_deit-b16_upernet_8xb2-160k_ade20k-512x512 In Collection: UPerNet Metadata: backbone: DeiT-B @@ -195,9 +195,9 @@ Models: Metrics: mIoU: 45.36 mIoU(ms+flip): 47.16 - Config: configs/vit/upernet_deit-b16_512x512_160k_ade20k.py + 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: upernet_deit-b16_mln_512x512_160k_ade20k +- Name: vit_deit-b16_mln_upernet_8xb2-160k_ade20k-512x512 In Collection: UPerNet Metadata: backbone: DeiT-B + MLN @@ -217,9 +217,9 @@ Models: Metrics: mIoU: 45.46 mIoU(ms+flip): 47.16 - Config: configs/vit/upernet_deit-b16_mln_512x512_160k_ade20k.py + 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: upernet_deit-b16_ln_mln_512x512_160k_ade20k +- Name: vit_deit-b16-ln_mln_upernet_8xb2-160k_ade20k-512x512 In Collection: UPerNet Metadata: backbone: DeiT-B + LN + MLN @@ -239,5 +239,5 @@ Models: Metrics: mIoU: 45.37 mIoU(ms+flip): 47.23 - Config: configs/vit/upernet_deit-b16_ln_mln_512x512_160k_ade20k.py + 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/upernet_deit-b16_ln_mln_512x512_160k_ade20k.py b/configs/vit/vit_deit-b16-ln_mln_upernet_8xb2-160k_ade20k-512x512.py similarity index 67% rename from configs/vit/upernet_deit-b16_ln_mln_512x512_160k_ade20k.py rename to configs/vit/vit_deit-b16-ln_mln_upernet_8xb2-160k_ade20k-512x512.py index 32909ffa13..39d1c54faf 100644 --- a/configs/vit/upernet_deit-b16_ln_mln_512x512_160k_ade20k.py +++ b/configs/vit/vit_deit-b16-ln_mln_upernet_8xb2-160k_ade20k-512x512.py @@ -1,4 +1,4 @@ -_base_ = './upernet_vit-b16_mln_512x512_160k_ade20k.py' +_base_ = './vit_vit-b16_mln_upernet_8xb2-160k_ade20k-512x512.py' model = dict( pretrained='pretrain/deit_base_patch16_224-b5f2ef4d.pth', diff --git a/configs/vit/upernet_deit-b16_mln_512x512_160k_ade20k.py b/configs/vit/vit_deit-b16_mln_upernet_8xb2-160k_ade20k-512x512.py similarity index 64% rename from configs/vit/upernet_deit-b16_mln_512x512_160k_ade20k.py rename to configs/vit/vit_deit-b16_mln_upernet_8xb2-160k_ade20k-512x512.py index 4abefe8dc1..706673f6b1 100644 --- a/configs/vit/upernet_deit-b16_mln_512x512_160k_ade20k.py +++ b/configs/vit/vit_deit-b16_mln_upernet_8xb2-160k_ade20k-512x512.py @@ -1,4 +1,4 @@ -_base_ = './upernet_vit-b16_mln_512x512_160k_ade20k.py' +_base_ = './vit_vit-b16_mln_upernet_8xb2-160k_ade20k-512x512.py' model = dict( pretrained='pretrain/deit_base_patch16_224-b5f2ef4d.pth', diff --git a/configs/vit/upernet_deit-b16_512x512_80k_ade20k.py b/configs/vit/vit_deit-b16_upernet_8xb2-160k_ade20k-512x512.py similarity index 66% rename from configs/vit/upernet_deit-b16_512x512_80k_ade20k.py rename to configs/vit/vit_deit-b16_upernet_8xb2-160k_ade20k-512x512.py index 720482616d..23a23582d7 100644 --- a/configs/vit/upernet_deit-b16_512x512_80k_ade20k.py +++ b/configs/vit/vit_deit-b16_upernet_8xb2-160k_ade20k-512x512.py @@ -1,4 +1,4 @@ -_base_ = './upernet_vit-b16_mln_512x512_80k_ade20k.py' +_base_ = './vit_vit-b16_mln_upernet_8xb2-160k_ade20k-512x512.py' model = dict( pretrained='pretrain/deit_base_patch16_224-b5f2ef4d.pth', diff --git a/configs/vit/upernet_deit-b16_512x512_160k_ade20k.py b/configs/vit/vit_deit-b16_upernet_8xb2-80k_ade20k-512x512.py similarity index 67% rename from configs/vit/upernet_deit-b16_512x512_160k_ade20k.py rename to configs/vit/vit_deit-b16_upernet_8xb2-80k_ade20k-512x512.py index 68f4bd42ba..4c8bc939ee 100644 --- a/configs/vit/upernet_deit-b16_512x512_160k_ade20k.py +++ b/configs/vit/vit_deit-b16_upernet_8xb2-80k_ade20k-512x512.py @@ -1,4 +1,4 @@ -_base_ = './upernet_vit-b16_mln_512x512_160k_ade20k.py' +_base_ = './vit_vit-b16_mln_upernet_8xb2-80k_ade20k-512x512.py' model = dict( pretrained='pretrain/deit_base_patch16_224-b5f2ef4d.pth', diff --git a/configs/vit/upernet_deit-s16_ln_mln_512x512_160k_ade20k.py b/configs/vit/vit_deit-s16-ln_mln_upernet_512x512_160k_ade20k-512x512.py similarity index 85% rename from configs/vit/upernet_deit-s16_ln_mln_512x512_160k_ade20k.py rename to configs/vit/vit_deit-s16-ln_mln_upernet_512x512_160k_ade20k-512x512.py index ef743a20e0..8e626fe0de 100644 --- a/configs/vit/upernet_deit-s16_ln_mln_512x512_160k_ade20k.py +++ b/configs/vit/vit_deit-s16-ln_mln_upernet_512x512_160k_ade20k-512x512.py @@ -1,4 +1,4 @@ -_base_ = './upernet_vit-b16_mln_512x512_160k_ade20k.py' +_base_ = './vit_vit-b16_mln_upernet_8xb2-160k_ade20k-512x512.py' model = dict( pretrained='pretrain/deit_small_patch16_224-cd65a155.pth', diff --git a/configs/vit/upernet_deit-s16_mln_512x512_160k_ade20k.py b/configs/vit/vit_deit-s16_mln_upernet_8xb2-160k_ade20k-512x512.py similarity index 84% rename from configs/vit/upernet_deit-s16_mln_512x512_160k_ade20k.py rename to configs/vit/vit_deit-s16_mln_upernet_8xb2-160k_ade20k-512x512.py index 069cab74f6..9a69a892b3 100644 --- a/configs/vit/upernet_deit-s16_mln_512x512_160k_ade20k.py +++ b/configs/vit/vit_deit-s16_mln_upernet_8xb2-160k_ade20k-512x512.py @@ -1,4 +1,4 @@ -_base_ = './upernet_vit-b16_mln_512x512_160k_ade20k.py' +_base_ = './vit_vit-b16_mln_upernet_8xb2-160k_ade20k-512x512.py' model = dict( pretrained='pretrain/deit_small_patch16_224-cd65a155.pth', diff --git a/configs/vit/upernet_deit-s16_512x512_160k_ade20k.py b/configs/vit/vit_deit-s16_upernet_8xb2-160k_ade20k-512x512.py similarity index 82% rename from configs/vit/upernet_deit-s16_512x512_160k_ade20k.py rename to configs/vit/vit_deit-s16_upernet_8xb2-160k_ade20k-512x512.py index 290ff19ed3..9ef699d5d5 100644 --- a/configs/vit/upernet_deit-s16_512x512_160k_ade20k.py +++ b/configs/vit/vit_deit-s16_upernet_8xb2-160k_ade20k-512x512.py @@ -1,4 +1,4 @@ -_base_ = './upernet_vit-b16_mln_512x512_160k_ade20k.py' +_base_ = './vit_vit-b16_mln_upernet_8xb2-80k_ade20k-512x512.py' model = dict( pretrained='pretrain/deit_small_patch16_224-cd65a155.pth', diff --git a/configs/vit/upernet_deit-s16_512x512_80k_ade20k.py b/configs/vit/vit_deit-s16_upernet_8xb2-80k_ade20k-512x512.py similarity index 82% rename from configs/vit/upernet_deit-s16_512x512_80k_ade20k.py rename to configs/vit/vit_deit-s16_upernet_8xb2-80k_ade20k-512x512.py index 605d264a74..9ef699d5d5 100644 --- a/configs/vit/upernet_deit-s16_512x512_80k_ade20k.py +++ b/configs/vit/vit_deit-s16_upernet_8xb2-80k_ade20k-512x512.py @@ -1,4 +1,4 @@ -_base_ = './upernet_vit-b16_mln_512x512_80k_ade20k.py' +_base_ = './vit_vit-b16_mln_upernet_8xb2-80k_ade20k-512x512.py' model = dict( pretrained='pretrain/deit_small_patch16_224-cd65a155.pth', diff --git a/configs/vit/upernet_vit-b16_ln_mln_512x512_160k_ade20k.py b/configs/vit/vit_vit-b16-ln_mln_upernet_8xb2-160k_ade20k-512x512.py similarity index 100% rename from configs/vit/upernet_vit-b16_ln_mln_512x512_160k_ade20k.py rename to configs/vit/vit_vit-b16-ln_mln_upernet_8xb2-160k_ade20k-512x512.py diff --git a/configs/vit/upernet_vit-b16_mln_512x512_160k_ade20k.py b/configs/vit/vit_vit-b16_mln_upernet_8xb2-160k_ade20k-512x512.py similarity index 100% rename from configs/vit/upernet_vit-b16_mln_512x512_160k_ade20k.py rename to configs/vit/vit_vit-b16_mln_upernet_8xb2-160k_ade20k-512x512.py diff --git a/configs/vit/upernet_vit-b16_mln_512x512_80k_ade20k.py b/configs/vit/vit_vit-b16_mln_upernet_8xb2-80k_ade20k-512x512.py similarity index 100% rename from configs/vit/upernet_vit-b16_mln_512x512_80k_ade20k.py rename to configs/vit/vit_vit-b16_mln_upernet_8xb2-80k_ade20k-512x512.py