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Documentation: https://mmsegmentation.readthedocs.io/

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

The master branch works with PyTorch 1.5+.

• Unified Benchmark

  We provide a unified benchmark toolbox for various semantic segmentation methods.

• Modular Design

  We decompose the semantic segmentation framework into different components and one can easily construct a customized semantic segmentation framework by combining different modules.

• Support of multiple methods out of box

  The toolbox directly supports popular and contemporary semantic segmentation frameworks, e.g. PSPNet, DeepLabV3, PSANet, DeepLabV3+, etc.

• High efficiency

  The training speed is faster than or comparable to other codebases.

This project is released under the Apache 2.0 license.

v0.20.0 was released in 12/10/2021. Please refer to changelog.md for details and release history.

Results and models are available in the model 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)

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)
• SegFormer (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

Please refer to get_started.md for installation and dataset_prepare.md for dataset preparation.

Please see train.md and inference.md for the basic usage of MMSegmentation. There are also tutorials for customizing dataset, designing data pipeline, customizing modules, and customizing runtime. We also provide many training tricks for better training and useful tools for deployment.

A Colab tutorial is also provided. You may preview the notebook here or directly run on Colab.

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}
}

We appreciate all contributions to improve MMSegmentation. Please refer to CONTRIBUTING.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 research community by providing a flexible as well as standardized toolkit to reimplement existing methods and develop their own new semantic segmentation methods.

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