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MMSegmentation is an open source semantic segmentation toolbox based on PyTorch.It is a part of theOpenMMLab project.

The master branch works withPyTorch 1.5+.

v0.30.0 was released on 01/11/2023:

• Add 'Projects/' folder, and the first example project
• Support Delving into High-Quality Synthetic Face Occlusion Segmentation Datasets

Please refer tochangelog.md for details and release history.

A brand new version ofMMSegmentation v1.0.0rc3 was released in 12/31/2022:

• Unifies interfaces of all components based onMMEngine.
• Faster training and testing speed with complete support of mixed precision training.
• Refactored and more flexiblearchitecture.

Find more new features in1.x branch. Issues and PRs are welcome!

Please refer toget_started.md for installation anddataset_prepare.md for dataset preparation.

Please seetrain.md andinference.md for the basic usage of MMSegmentation.There are also tutorials for:

• customizing dataset
• designing data pipeline
• customizing modules
• customizing runtime
• training tricks
• useful tools

A Colab tutorial is also provided. You may preview the notebookhere or directlyrun on Colab.

Results and models are available in themodel zoo.

Supported backbones:

• ResNet (CVPR'2016)
• ResNeXt (CVPR'2017)
• HRNet (CVPR'2019)
• ResNeSt (ArXiv'2020)
• MobileNetV2 (CVPR'2018)
• MobileNetV3 (ICCV'2019)
• Vision Transformer (ICLR'2021)
• Swin Transformer (ICCV'2021)
• Twins (NeurIPS'2021)
• BEiT (ICLR'2022)
• ConvNeXt (CVPR'2022)
• MAE (CVPR'2022)
• PoolFormer (CVPR'2022)

Supported methods:

• FCN (CVPR'2015/TPAMI'2017)
• ERFNet (T-ITS'2017)
• UNet (MICCAI'2016/Nat. Methods'2019)
• PSPNet (CVPR'2017)
• DeepLabV3 (ArXiv'2017)
• BiSeNetV1 (ECCV'2018)
• PSANet (ECCV'2018)
• DeepLabV3+ (CVPR'2018)
• UPerNet (ECCV'2018)
• ICNet (ECCV'2018)
• NonLocal Net (CVPR'2018)
• EncNet (CVPR'2018)
• Semantic FPN (CVPR'2019)
• DANet (CVPR'2019)
• APCNet (CVPR'2019)
• EMANet (ICCV'2019)
• CCNet (ICCV'2019)
• DMNet (ICCV'2019)
• ANN (ICCV'2019)
• GCNet (ICCVW'2019/TPAMI'2020)
• FastFCN (ArXiv'2019)
• Fast-SCNN (ArXiv'2019)
• ISANet (ArXiv'2019/IJCV'2021)
• OCRNet (ECCV'2020)
• DNLNet (ECCV'2020)
• PointRend (CVPR'2020)
• CGNet (TIP'2020)
• BiSeNetV2 (IJCV'2021)
• STDC (CVPR'2021)
• SETR (CVPR'2021)
• DPT (ArXiv'2021)
• Segmenter (ICCV'2021)
• SegFormer (NeurIPS'2021)
• K-Net (NeurIPS'2021)

Supported datasets:

• Cityscapes
• PASCAL VOC
• ADE20K
• Pascal Context
• COCO-Stuff 10k
• COCO-Stuff 164k
• CHASE_DB1
• DRIVE
• HRF
• STARE
• Dark Zurich
• Nighttime Driving
• LoveDA
• Potsdam
• Vaihingen
• iSAID
• High quality synthetic face occlusion

Please refer toFAQ for frequently asked questions.

We appreciate all contributions to improve MMSegmentation. Please refer toCONTRIBUTING.md for the contributing guideline.

MMSegmentation is an open source project that welcome any contribution and feedback.We wish that the toolbox and benchmark could serve the growing researchcommunity by providing a flexible as well as standardized toolkit to reimplement existing methodsand develop their own new semantic segmentation methods.

If you find this project useful in your research, please consider cite:

@misc{mmseg2020,title={{MMSegmentation}: OpenMMLab Semantic Segmentation Toolbox and Benchmark},author={MMSegmentation Contributors},howpublished ={\url{https://github.com/open-mmlab/mmsegmentation}},year={2020}}

MMSegmentation is released under the Apache 2.0 license, while some specific features in this library are with other licenses. Please refer toLICENSES.md for the careful check, if you are using our code for commercial matters.

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