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	# Expectation-Maximization Attention Networks for Semantic Segmentation

	## Introduction

	<!-- [ALGORITHM] -->

	```latex
	@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](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/emanet/emanet_r50-d8_512x1024_80k_cityscapes.py) \| [model](https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r50-d8_512x1024_80k_cityscapes/emanet_r50-d8_512x1024_80k_cityscapes_20200901_100301-c43fcef1.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r50-d8_512x1024_80k_cityscapes/emanet_r50-d8_512x1024_80k_cityscapes-20200901_100301.log.json) \|
	\| EMANet \| R-101-D8 \| 512x1024 \| 80000 \| 6.2 \| 2.87 \| 79.10 \| 81.21 \| [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/emanet/emanet_r101-d8_512x1024_80k_cityscapes.py) \| [model](https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r101-d8_512x1024_80k_cityscapes/emanet_r101-d8_512x1024_80k_cityscapes_20200901_100301-2d970745.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r101-d8_512x1024_80k_cityscapes/emanet_r101-d8_512x1024_80k_cityscapes-20200901_100301.log.json) \|
	\| EMANet \| R-50-D8 \| 769x769 \| 80000 \| 8.9 \| 1.97 \| 79.33 \| 80.49 \| [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/emanet/emanet_r50-d8_769x769_80k_cityscapes.py) \| [model](https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r50-d8_769x769_80k_cityscapes/emanet_r50-d8_769x769_80k_cityscapes_20200901_100301-16f8de52.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r50-d8_769x769_80k_cityscapes/emanet_r50-d8_769x769_80k_cityscapes-20200901_100301.log.json) \|
	\| EMANet \| R-101-D8 \| 769x769 \| 80000 \| 10.1 \| 1.22 \| 79.62 \| 81.00 \| [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/emanet/emanet_r101-d8_769x769_80k_cityscapes.py) \| [model](https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r101-d8_769x769_80k_cityscapes/emanet_r101-d8_769x769_80k_cityscapes_20200901_100301-47a324ce.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r101-d8_769x769_80k_cityscapes/emanet_r101-d8_769x769_80k_cityscapes-20200901_100301.log.json) \|