castrol68/pkuseg

Benchmarks for Semantic Segmentation.

PKUSeg

##语义分割工具https://www.yanxishe.com/tweet/38841?from=timeline

Introduction

yuy PKUSeg is an open source semantic segmentation toolbox based on PyTorch, which is maintained by EECS of Peking University. Maintainers are all from Key Laboratory of Machine Perception (MOE).

Major features

• Modular design and easy to use and deploy
  We develop this tool for easier experiments and deployment．
• All kinds of models for semantic segmentation
  We implement many state-of-the-art models in research papers. We not only release codes, but also training checkpoints.
• State-of-the-art results on multiple datasets
  We achieve the state-of-the-art results on multiple datasets including Pascal VOC, Cityscapes, Pascal Context and ADE20K．

Implemented Papers

• PSPNet: Pyramid Scene Parsing Network CVPR2017
• DeepLabV3: Rethinking Atrous Convolution for Semantic Image Segmentation CVPR2017
• DeepLabV3+: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation ECCV2018
• DenseASPP: DenseASPP for Semantic Segmentation in Street Scenes CVPR2018
• DANet: Dual Attention Network for Scene Segmentation CVPR2019
• EMANet: Expectation-Maximization Attention Networks for Semantic Segmentation ICCV2019

Performances with PKUSeg

All the performances showed below fully reimplemented the papers' results.

PASCAL VOC

• Single Scale Whole Image Test: Base LR 0.01, Crop Size 513

Model	Backbone	Train	Test	mIOU	BS	Iters	Scripts
PSPNet	3x3-Res101	train	val	78.75	16	3W	PSPNet
DeepLabV3	3x3-Res101	train	val	78.95	16	3W	DeepLabV3
EMANet	3x3-Res101	train	val	79.79	16	3W	EMANet

Cityscapes

• Single Scale Whole Image Test: Base LR 0.01, Crop Size 769

Model	Backbone	Train	Test	mIOU	BS	Iters	Scripts
PSPNet	3x3-Res101	train	val	78.20	8	4W	PSPNet
DeepLabV3	3x3-Res101	train	val	79.13	8	4W	DeepLabV3

ADE20K

• Single Scale Whole Image Test: Base LR 0.02, Crop Size 520

Model	Backbone	Train	Test	mIOU	PixelACC	BS	Iters	Scripts
PSPNet	3x3-Res50	train	val	41.52	80.09	16	15W	PSPNet
DeepLabv3	3x3-Res50	train	val	42.16	80.36	16	15W	DeepLabV3
PSPNet	3x3-Res101	train	val	43.60	81.30	16	15W	PSPNet
DeepLabv3	3x3-Res101	train	val	44.13	81.42	16	15W	DeepLabV3

License

This project is released under the Apache 2.0 license.