mmsegmentation/configs/emanet
谢昕辰 725d5aa002 [Feature] support mim (#549)
* dice loss

* format code, add docstring and calculate denominator without valid_mask

* minor change

* restore

* add metafile

* add manifest.in and add config at setup.py

* add requirements

* modify manifest

* modify manifest

* Update MANIFEST.in

* add metafile

* add metadata

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* Update metafile.yml

* Update metafile.yml

* minor change

* Update metafile.yml

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* add more  metafile

* add config to model_zoo

* fix bug

* Update mminstall.txt

* [fix] Add models

* [Fix] Add collections

* [fix] Modify collection name

* [Fix] Set datasets to unet metafile

* [Fix] Modify collection names

* complement inference time
2021-05-31 15:07:24 -07:00
..
README.md comment tag (#505) 2021-04-24 09:58:59 -07:00
emanet_r50-d8_512x1024_80k_cityscapes.py [Feature] Support EMANet (#34) 2020-09-07 13:06:59 +08:00
emanet_r50-d8_769x769_80k_cityscapes.py [Improvement] Move train_cfg/test_cfg inside model (#341) 2021-01-19 17:06:23 -08:00
emanet_r101-d8_512x1024_80k_cityscapes.py [Feature] Support EMANet (#34) 2020-09-07 13:06:59 +08:00
emanet_r101-d8_769x769_80k_cityscapes.py [Feature] Support EMANet (#34) 2020-09-07 13:06:59 +08:00
metafile.yml [Feature] support mim (#549) 2021-05-31 15:07:24 -07:00

README.md

Expectation-Maximization Attention Networks for Semantic Segmentation

Introduction

@inproceedings{li2019expectation,
  title={Expectation-maximization attention networks for semantic segmentation},
  author={Li, Xia and Zhong, Zhisheng and Wu, Jianlong and Yang, Yibo and Lin, Zhouchen and Liu, Hong},
  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
  pages={9167--9176},
  year={2019}
}

Results and models

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 model | log
EMANet R-101-D8 512x1024 80000 6.2 2.87 79.10 81.21 config model | log
EMANet R-50-D8 769x769 80000 8.9 1.97 79.33 80.49 config model | log
EMANet R-101-D8 769x769 80000 10.1 1.22 79.62 81.00 config model | log