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Jerry Jiarui XU c8b250df4a Support ResNeSt backbone (#47) 
* Support ResNeSt backbone

* fixed avg_down

* add docstring and test

* update table

* update docs and tests

* fixed test

* rename

* refactor splits
 		2020-08-17 00:54:01 +08:00
..
README.md Support ResNeSt backbone (#47) 2020-08-17 00:54:01 +08:00
deeplabv3_s101-d8_512x512_160k_ade20k.py Support ResNeSt backbone (#47) 2020-08-17 00:54:01 +08:00
deeplabv3_s101-d8_512x1024_80k_cityscapes.py Support ResNeSt backbone (#47) 2020-08-17 00:54:01 +08:00
deeplabv3plus_s101-d8_512x512_160k_ade20k.py Support ResNeSt backbone (#47) 2020-08-17 00:54:01 +08:00
deeplabv3plus_s101-d8_512x1024_80k_cityscapes.py Support ResNeSt backbone (#47) 2020-08-17 00:54:01 +08:00
fcn_s101-d8_512x512_160k_ade20k.py Support ResNeSt backbone (#47) 2020-08-17 00:54:01 +08:00
fcn_s101-d8_512x1024_80k_cityscapes.py Support ResNeSt backbone (#47) 2020-08-17 00:54:01 +08:00
pspnet_s101-d8_512x512_160k_ade20k.py Support ResNeSt backbone (#47) 2020-08-17 00:54:01 +08:00
pspnet_s101-d8_512x1024_80k_cityscapes.py Support ResNeSt backbone (#47) 2020-08-17 00:54:01 +08:00

README.md

ResNeSt: Split-Attention Networks

Introduction

@article{zhang2020resnest,
title={ResNeSt: Split-Attention Networks},
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},
journal={arXiv preprint arXiv:2004.08955},
year={2020}
}

Results and models

Cityscapes

Method Backbone Crop Size Lr schd Mem (GB) Inf time (fps) mIoU mIoU(ms+flip) download
FCN S-101-D8 512x1024 80000 11.4 2.39 77.56 78.98 model | log
PSPNet S-101-D8 512x1024 80000 11.8 2.52 78.57 79.19 model | log
DeepLabV3 S-101-D8 512x1024 80000 11.9 1.88 79.67 80.51 model | log
DeepLabV3+ S-101-D8 512x1024 80000 13.2 2.36 79.62 80.27 model | log

ADE20k

Method Backbone Crop Size Lr schd Mem (GB) Inf time (fps) mIoU mIoU(ms+flip) download
FCN S-101-D8 512x512 160000 14.2 12.86 45.62 46.16 model | log
PSPNet S-101-D8 512x512 160000 14.2 13.02 45.44 46.28 model | log
DeepLabV3 S-101-D8 512x512 160000 14.6 9.28 45.71 46.59 model | log
DeepLabV3+ S-101-D8 512x512 160000 16.2 11.96 46.47 47.27 model | log