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Sep 15, 2022
Jul 9, 2020
Jul 9, 2020

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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+.

demo image

Major features
  • 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.

What's New

💎 Stable version

v0.28.0 was released in 9/08/2022:

  • Support Tversky Loss
  • Fix binary segmentation

Please refer to for details and release history.

🌟 Preview of 1.x version

A brand new version of MMSegmentation v1.0.0rc0 was released in 31/8/2022:

  • Unifies interfaces of all components based on MMEngine.
  • Faster training and testing speed with complete support of mixed precision training.
  • Refactored and more flexible architecture.

Find more new features in 1.x branch. Issues and PRs are welcome!


Please refer to for installation and for dataset preparation.

Get Started

Please see and for the basic usage of MMSegmentation. There are also tutorials for:

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

Benchmark and model zoo

Results and models are available in the model zoo.

Supported backbones:

Supported methods:

Supported datasets:


Please refer to FAQ for frequently asked questions.


We appreciate all contributions to improve MMSegmentation. Please refer to 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.


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

    title={{MMSegmentation}: OpenMMLab Semantic Segmentation Toolbox and Benchmark},
    author={MMSegmentation Contributors},
    howpublished = {\url{}},


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

Projects in OpenMMLab

  • MMCV: OpenMMLab foundational library for computer vision.
  • MIM: MIM installs OpenMMLab packages.
  • MMClassification: OpenMMLab image classification toolbox and benchmark.
  • MMDetection: OpenMMLab detection toolbox and benchmark.
  • MMDetection3D: OpenMMLab's next-generation platform for general 3D object detection.
  • MMRotate: OpenMMLab rotated object detection toolbox and benchmark.
  • MMSegmentation: OpenMMLab semantic segmentation toolbox and benchmark.
  • MMOCR: OpenMMLab text detection, recognition, and understanding toolbox.
  • MMPose: OpenMMLab pose estimation toolbox and benchmark.
  • MMHuman3D: OpenMMLab 3D human parametric model toolbox and benchmark.
  • MMSelfSup: OpenMMLab self-supervised learning toolbox and benchmark.
  • MMRazor: OpenMMLab model compression toolbox and benchmark.
  • MMFewShot: OpenMMLab fewshot learning toolbox and benchmark.
  • MMAction2: OpenMMLab's next-generation action understanding toolbox and benchmark.
  • MMTracking: OpenMMLab video perception toolbox and benchmark.
  • MMFlow: OpenMMLab optical flow toolbox and benchmark.
  • MMEditing: OpenMMLab image and video editing toolbox.
  • MMGeneration: OpenMMLab image and video generative models toolbox.
  • MMDeploy: OpenMMLab Model Deployment Framework.