mmsegmentation/configs/emanet
Xia Li 李夏 dbca8b44a9
[Feature] Support EMANet (#34)
* add emanet

* fixed bug and typos

* add emanet config

* fixed padding

* fixed identity

* rename

* rename

* add concat_input

* fallback to update last

* Fixed concat

* update EMANet

* Add tests

* remove self-implement norm

Co-authored-by: Jiarui XU <xvjiarui0826@gmail.com>
2020-09-07 13:06:59 +08:00
..
README.md [Feature] Support EMANet (#34) 2020-09-07 13:06:59 +08:00
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 [Feature] Support EMANet (#34) 2020-09-07 13:06:59 +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

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) download
EMANet R-50-D8 512x1024 80000 5.4 4.58 77.59 79.44 model | log
EMANet R-101-D8 512x1024 80000 6.2 2.87 79.10 81.21 model | log
EMANet R-50-D8 769x769 80000 8.9 1.97 79.33 80.49 model | log
EMANet R-101-D8 769x769 80000 10.1 1.22 79.62 81.00 model | log