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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
encnet_r50-d8_512x512_20k_voc12aug.py init commit 2020-07-10 02:39:01 +08:00
encnet_r50-d8_512x512_40k_voc12aug.py init commit 2020-07-10 02:39:01 +08:00
encnet_r50-d8_512x512_80k_ade20k.py init commit 2020-07-10 02:39:01 +08:00
encnet_r50-d8_512x512_160k_ade20k.py init commit 2020-07-10 02:39:01 +08:00
encnet_r50-d8_512x1024_40k_cityscapes.py init commit 2020-07-10 02:39:01 +08:00
encnet_r50-d8_512x1024_80k_cityscapes.py init commit 2020-07-10 02:39:01 +08:00
encnet_r50-d8_769x769_40k_cityscapes.py [Improvement] Move train_cfg/test_cfg inside model (#341) 2021-01-19 17:06:23 -08:00
encnet_r50-d8_769x769_80k_cityscapes.py [Improvement] Move train_cfg/test_cfg inside model (#341) 2021-01-19 17:06:23 -08:00
encnet_r50s-d8_512x512_80k_ade20k.py init commit 2020-07-10 02:39:01 +08:00
encnet_r101-d8_512x512_20k_voc12aug.py init commit 2020-07-10 02:39:01 +08:00
encnet_r101-d8_512x512_40k_voc12aug.py init commit 2020-07-10 02:39:01 +08:00
encnet_r101-d8_512x512_80k_ade20k.py init commit 2020-07-10 02:39:01 +08:00
encnet_r101-d8_512x512_160k_ade20k.py init commit 2020-07-10 02:39:01 +08:00
encnet_r101-d8_512x1024_40k_cityscapes.py init commit 2020-07-10 02:39:01 +08:00
encnet_r101-d8_512x1024_80k_cityscapes.py init commit 2020-07-10 02:39:01 +08:00
encnet_r101-d8_769x769_40k_cityscapes.py init commit 2020-07-10 02:39:01 +08:00
encnet_r101-d8_769x769_80k_cityscapes.py init commit 2020-07-10 02:39:01 +08:00
metafile.yml [Feature] support mim (#549) 2021-05-31 15:07:24 -07:00

README.md

Context Encoding for Semantic Segmentation

Introduction

@InProceedings{Zhang_2018_CVPR,
author = {Zhang, Hang and Dana, Kristin and Shi, Jianping and Zhang, Zhongyue and Wang, Xiaogang and Tyagi, Ambrish and Agrawal, Amit},
title = {Context Encoding for Semantic Segmentation},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2018}
}

Results and models

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 model | log
encnet R-101-D8 512x1024 40000 12.1 2.66 75.81 77.21 config model | log
encnet R-50-D8 769x769 40000 9.8 1.82 76.24 77.85 config model | log
encnet R-101-D8 769x769 40000 13.7 1.26 74.25 76.25 config model | log
encnet R-50-D8 512x1024 80000 - - 77.94 79.13 config model | log
encnet R-101-D8 512x1024 80000 - - 78.55 79.47 config model | log
encnet R-50-D8 769x769 80000 - - 77.44 78.72 config model | log
encnet R-101-D8 769x769 80000 - - 76.10 76.97 config model | log

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 model | log
encnet R-101-D8 512x512 80000 13.6 14.87 42.11 43.61 config model | log
encnet R-50-D8 512x512 160000 - - 40.10 41.71 config model | log
encnet R-101-D8 512x512 160000 - - 42.61 44.01 config model | log