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

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

Themain branch works with PyTorch 1.6+.

We are thrilled to announce the official release of MMSegmentation's latest version! For this new release, themain branch serves as the primary branch, while the development branch isdev-1.x. The stable branch for the previous release remains as the0.x branch. Please note that themaster branch will only be maintained for a limited time before being removed. We encourage you to be mindful of branch selection and updates during use. Thank you for your unwavering support and enthusiasm, and let's work together to make MMSegmentation even more robust and powerful! 💪

MMSegmentation v1.x brings remarkable improvements over the 0.x release, offering a more flexible and feature-packed experience. To utilize the new features in v1.x, we kindly invite you to consult our detailed📚 migration guide, which will help you seamlessly transition your projects. Your support is invaluable, and we eagerly await your feedback!

• 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.

v1.2.0 was released on 10/12/2023, from 1.1.0 to 1.2.0, we have added or updated the following features:

• Support for the open-vocabulary semantic segmentation algorithmSAN

• Support monocular depth estimation task, please refer toVPD andAdabins for more details.

  

• Add new projects: open-vocabulary semantic segmentation algorithmCAT-Seg, real-time semantic segmentation algofithmPP-MobileSeg

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

Please seeOverview for the general introduction of MMSegmentation.

Please seeuser guides for the basic usage of MMSegmentation.There are alsoadvanced tutorials for in-depth understanding of mmseg design and implementation .

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

To migrate from MMSegmentation 0.x, please refer tomigration.

Results and models are available in themodel zoo.

Please refer toFAQ for frequently asked questions.

Here are some implementations of SOTA models and solutions built on MMSegmentation, which are supported and maintained by community users. These projects demonstrate the best practices based on MMSegmentation for research and product development. We welcome and appreciate all the contributions to OpenMMLab ecosystem.

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}}

This project is released under theApache 2.0 license.

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