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谢昕辰 725d5aa002 [Feature] support mim (#549) 
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 		2021-05-31 15:07:24 -07:00
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README.md comment tag (#505) 2021-04-24 09:58:59 -07:00
dnl_r50-d8_512x512_80k_ade20k.py Add "disentangled non-local (DNL) neural networks" [ECCV2020] (#37) 2020-09-07 16:22:00 +08:00
dnl_r50-d8_512x512_160k_ade20k.py Add "disentangled non-local (DNL) neural networks" [ECCV2020] (#37) 2020-09-07 16:22:00 +08:00
dnl_r50-d8_512x1024_40k_cityscapes.py Add "disentangled non-local (DNL) neural networks" [ECCV2020] (#37) 2020-09-07 16:22:00 +08:00
dnl_r50-d8_512x1024_80k_cityscapes.py Add "disentangled non-local (DNL) neural networks" [ECCV2020] (#37) 2020-09-07 16:22:00 +08:00
dnl_r50-d8_769x769_40k_cityscapes.py [Improvement] Move train_cfg/test_cfg inside model (#341) 2021-01-19 17:06:23 -08:00
dnl_r50-d8_769x769_80k_cityscapes.py [Improvement] Move train_cfg/test_cfg inside model (#341) 2021-01-19 17:06:23 -08:00
dnl_r101-d8_512x512_80k_ade20k.py Add "disentangled non-local (DNL) neural networks" [ECCV2020] (#37) 2020-09-07 16:22:00 +08:00
dnl_r101-d8_512x512_160k_ade20k.py Add "disentangled non-local (DNL) neural networks" [ECCV2020] (#37) 2020-09-07 16:22:00 +08:00
dnl_r101-d8_512x1024_40k_cityscapes.py Add "disentangled non-local (DNL) neural networks" [ECCV2020] (#37) 2020-09-07 16:22:00 +08:00
dnl_r101-d8_512x1024_80k_cityscapes.py Add "disentangled non-local (DNL) neural networks" [ECCV2020] (#37) 2020-09-07 16:22:00 +08:00
dnl_r101-d8_769x769_40k_cityscapes.py Add "disentangled non-local (DNL) neural networks" [ECCV2020] (#37) 2020-09-07 16:22:00 +08:00
dnl_r101-d8_769x769_80k_cityscapes.py Add "disentangled non-local (DNL) neural networks" [ECCV2020] (#37) 2020-09-07 16:22:00 +08:00
metafile.yml [Feature] support mim (#549) 2021-05-31 15:07:24 -07:00

README.md

Disentangled Non-Local Neural Networks

Introduction

This example is to reproduce "Disentangled Non-Local Neural Networks" for semantic segmentation. It is still in progress.

Citation

@misc{yin2020disentangled,
    title={Disentangled Non-Local Neural Networks},
    author={Minghao Yin and Zhuliang Yao and Yue Cao and Xiu Li and Zheng Zhang and Stephen Lin and Han Hu},
    year={2020},
    booktitle={ECCV}
}

Results and models (in progress)

Cityscapes

Method Backbone Crop Size Lr schd Mem (GB) Inf time (fps) mIoU mIoU(ms+flip) config download
dnl R-50-D8 512x1024 40000 7.3 2.56 78.61 - config model | log
dnl R-101-D8 512x1024 40000 10.9 1.96 78.31 - config model | log
dnl R-50-D8 769x769 40000 9.2 1.50 78.44 80.27 config model | log
dnl R-101-D8 769x769 40000 12.6 1.02 76.39 77.77 config model | log
dnl R-50-D8 512x1024 80000 - - 79.33 - config model | log
dnl R-101-D8 512x1024 80000 - - 80.41 - config model | log
dnl R-50-D8 769x769 80000 - - 79.36 80.70 config model | log
dnl R-101-D8 769x769 80000 - - 79.41 80.68 config model | log

ADE20K

Method Backbone Crop Size Lr schd Mem (GB) Inf time (fps) mIoU mIoU(ms+flip) config download
DNL R-50-D8 512x512 80000 8.8 20.66 41.76 42.99 config model | log
DNL R-101-D8 512x512 80000 12.8 12.54 43.76 44.91 config model | log
DNL R-50-D8 512x512 160000 - - 41.87 43.01 config model | log
DNL R-101-D8 512x512 160000 - - 44.25 45.78 config model | log