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Support ResNeSt backbone (open-mmlab#47)
* Support ResNeSt backbone * fixed avg_down * add docstring and test * update table * update docs and tests * fixed test * rename * refactor splits
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‎README.md‎

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Supported backbones:
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- [x] ResNet
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- [x] ResNeXt
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- [x] HRNet
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- [x] [HRNet](configs/hrnet/README.md)
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- [x] [ResNeSt](configs/resnest/README.md)
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Supported methods:
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- [x] [FCN](configs/fcn)

‎configs/resnest/README.md‎

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# ResNeSt: Split-Attention Networks
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## Introduction
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```
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@article{zhang2020resnest,
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title={ResNeSt: Split-Attention Networks},
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author={Zhang, Hang and Wu, Chongruo and Zhang, Zhongyue and Zhu, Yi and Zhang, Zhi and Lin, Haibin and Sun, Yue and He, Tong and Muller, Jonas and Manmatha, R. and Li, Mu and Smola, Alexander},
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journal={arXiv preprint arXiv:2004.08955},
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year={2020}
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}
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```
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## Results and models
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### Cityscapes
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| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download |
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|------------|----------|-----------|--------:|---------:|----------------|------:|---------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| FCN | S-101-D8 | 512x1024 | 80000 | 11.4 | 2.39 | 77.56 | 78.98 | [model](https://openmmlab.oss-accelerate.aliyuncs.com/mmsegmentation/v0.5/resnest/fcn_s101-d8_512x1024_80k_cityscapes/fcn_s101-d8_512x1024_80k_cityscapes_20200807_140631-f8d155b3.pth) | [log](https://openmmlab.oss-accelerate.aliyuncs.com/mmsegmentation/v0.5/resnest/fcn_s101-d8_512x1024_80k_cityscapes/fcn_s101-d8_512x1024_80k_cityscapes-20200807_140631.log.json) |
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| PSPNet | S-101-D8 | 512x1024 | 80000 | 11.8 | 2.52 | 78.57 | 79.19 | [model](https://openmmlab.oss-accelerate.aliyuncs.com/mmsegmentation/v0.5/resnest/pspnet_s101-d8_512x1024_80k_cityscapes/pspnet_s101-d8_512x1024_80k_cityscapes_20200807_140631-c75f3b99.pth) | [log](https://openmmlab.oss-accelerate.aliyuncs.com/mmsegmentation/v0.5/resnest/pspnet_s101-d8_512x1024_80k_cityscapes/pspnet_s101-d8_512x1024_80k_cityscapes-20200807_140631.log.json) |
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| DeepLabV3 | S-101-D8 | 512x1024 | 80000 | 11.9 | 1.88 | 79.67 | 80.51 | [model](https://openmmlab.oss-accelerate.aliyuncs.com/mmsegmentation/v0.5/resnest/deeplabv3_s101-d8_512x1024_80k_cityscapes/deeplabv3_s101-d8_512x1024_80k_cityscapes_20200807_144429-b73c4270.pth) | [log](https://openmmlab.oss-accelerate.aliyuncs.com/mmsegmentation/v0.5/resnest/deeplabv3_s101-d8_512x1024_80k_cityscapes/deeplabv3_s101-d8_512x1024_80k_cityscapes-20200807_144429.log.json) |
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| DeepLabV3+ | S-101-D8 | 512x1024 | 80000 | 13.2 | 2.36 | 79.62 | 80.27 | [model](https://openmmlab.oss-accelerate.aliyuncs.com/mmsegmentation/v0.5/resnest/deeplabv3plus_s101-d8_512x1024_80k_cityscapes/deeplabv3plus_s101-d8_512x1024_80k_cityscapes_20200807_144429-1239eb43.pth) | [log](https://openmmlab.oss-accelerate.aliyuncs.com/mmsegmentation/v0.5/resnest/deeplabv3plus_s101-d8_512x1024_80k_cityscapes/deeplabv3plus_s101-d8_512x1024_80k_cityscapes-20200807_144429.log.json) |
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### ADE20k
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| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download |
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|------------|----------|-----------|--------:|---------:|----------------|------:|---------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| FCN | S-101-D8 | 512x512 | 160000 | 14.2 | 12.86 | 45.62 | 46.16 | [model](https://openmmlab.oss-accelerate.aliyuncs.com/mmsegmentation/v0.5/resnest/fcn_s101-d8_512x512_160k_ade20k/fcn_s101-d8_512x512_160k_ade20k_20200807_145416-d3160329.pth) | [log](https://openmmlab.oss-accelerate.aliyuncs.com/mmsegmentation/v0.5/resnest/fcn_s101-d8_512x512_160k_ade20k/fcn_s101-d8_512x512_160k_ade20k-20200807_145416.log.json) |
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| PSPNet | S-101-D8 | 512x512 | 160000 | 14.2 | 13.02 | 45.44 | 46.28 | [model](https://openmmlab.oss-accelerate.aliyuncs.com/mmsegmentation/v0.5/resnest/pspnet_s101-d8_512x512_160k_ade20k/pspnet_s101-d8_512x512_160k_ade20k_20200807_145416-a6daa92a.pth) | [log](https://openmmlab.oss-accelerate.aliyuncs.com/mmsegmentation/v0.5/resnest/pspnet_s101-d8_512x512_160k_ade20k/pspnet_s101-d8_512x512_160k_ade20k-20200807_145416.log.json) |
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| DeepLabV3 | S-101-D8 | 512x512 | 160000 | 14.6 | 9.28 | 45.71 | 46.59 | [model](https://openmmlab.oss-accelerate.aliyuncs.com/mmsegmentation/v0.5/resnest/deeplabv3_s101-d8_512x512_160k_ade20k/deeplabv3_s101-d8_512x512_160k_ade20k_20200807_144503-17ecabe5.pth) | [log](https://openmmlab.oss-accelerate.aliyuncs.com/mmsegmentation/v0.5/resnest/deeplabv3_s101-d8_512x512_160k_ade20k/deeplabv3_s101-d8_512x512_160k_ade20k-20200807_144503.log.json) |
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| DeepLabV3+ | S-101-D8 | 512x512 | 160000 | 16.2 | 11.96 | 46.47 | 47.27 | [model](https://openmmlab.oss-accelerate.aliyuncs.com/mmsegmentation/v0.5/resnest/deeplabv3plus_s101-d8_512x512_160k_ade20k/deeplabv3plus_s101-d8_512x512_160k_ade20k_20200807_144503-27b26226.pth) | [log](https://openmmlab.oss-accelerate.aliyuncs.com/mmsegmentation/v0.5/resnest/deeplabv3plus_s101-d8_512x512_160k_ade20k/deeplabv3plus_s101-d8_512x512_160k_ade20k-20200807_144503.log.json) |

‎configs/resnest/deeplabv3_s101-d8_512x1024_80k_cityscapes.py‎

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_base_ = '../deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes.py'
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model = dict(
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pretrained='open-mmlab://resnest101',
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backbone=dict(
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type='ResNeSt',
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stem_channels=128,
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radix=2,
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reduction_factor=4,
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avg_down_stride=True))

‎configs/resnest/deeplabv3_s101-d8_512x512_160k_ade20k.py‎

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_base_ = '../deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k.py'
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model = dict(
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pretrained='open-mmlab://resnest101',
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backbone=dict(
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type='ResNeSt',
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stem_channels=128,
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radix=2,
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reduction_factor=4,
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avg_down_stride=True))

‎configs/resnest/deeplabv3plus_s101-d8_512x1024_80k_cityscapes.py‎

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_base_ = '../deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes.py'
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model = dict(
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pretrained='open-mmlab://resnest101',
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backbone=dict(
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type='ResNeSt',
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stem_channels=128,
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radix=2,
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reduction_factor=4,
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avg_down_stride=True))

‎configs/resnest/deeplabv3plus_s101-d8_512x512_160k_ade20k.py‎

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_base_ = '../deeplabv3plus/deeplabv3plus_r101-d8_512x512_160k_ade20k.py'
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model = dict(
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pretrained='open-mmlab://resnest101',
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backbone=dict(
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type='ResNeSt',
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stem_channels=128,
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radix=2,
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reduction_factor=4,
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avg_down_stride=True))

‎configs/resnest/fcn_s101-d8_512x1024_80k_cityscapes.py‎

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_base_ = '../fcn/fcn_r101-d8_512x1024_80k_cityscapes.py'
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model = dict(
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pretrained='open-mmlab://resnest101',
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backbone=dict(
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type='ResNeSt',
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stem_channels=128,
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radix=2,
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reduction_factor=4,
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avg_down_stride=True))

‎configs/resnest/fcn_s101-d8_512x512_160k_ade20k.py‎

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_base_ = '../fcn/fcn_r101-d8_512x512_160k_ade20k.py'
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model = dict(
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pretrained='open-mmlab://resnest101',
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backbone=dict(
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type='ResNeSt',
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stem_channels=128,
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radix=2,
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reduction_factor=4,
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avg_down_stride=True))

‎configs/resnest/pspnet_s101-d8_512x1024_80k_cityscapes.py‎

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_base_ = '../pspnet/pspnet_r101-d8_512x1024_80k_cityscapes.py'
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model = dict(
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pretrained='open-mmlab://resnest101',
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backbone=dict(
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type='ResNeSt',
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stem_channels=128,
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radix=2,
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reduction_factor=4,
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avg_down_stride=True))

‎configs/resnest/pspnet_s101-d8_512x512_160k_ade20k.py‎

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_base_ = '../pspnet/pspnet_r101-d8_512x512_160k_ade20k.py'
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model = dict(
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pretrained='open-mmlab://resnest101',
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backbone=dict(
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type='ResNeSt',
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stem_channels=128,
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radix=2,
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reduction_factor=4,
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avg_down_stride=True))

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