xiexinch / mmsegmentation

OpenMMLab Semantic Segmentation Toolbox and Benchmark.

Home Page:https://mmsegmentation.readthedocs.io/en/latest/

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Documentation: https://mmsegmentation.readthedocs.io/en/1.x/

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

The 1.x branch works with PyTorch 1.6+.

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

v1.0.0rc5 was released on 01/02/2023. Please refer to changelog.md for details and release history.

  • Support ISNet (ICCV'2021) in projects (#2400)
  • Support HSSN (CVPR'2022) in projects (#2444)


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

Get Started

Please see Overview for the general introduction of MMSegmentation.

Please see user guides for the basic usage of MMSegmentation. There are also advanced tutorials for in-depth understanding of mmseg design and implementation .

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

To migrate from MMSegmentation 1.x, please refer to migration.

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 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.


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{https://github.com/open-mmlab/mmsegmentation}},


This project is released under the Apache 2.0 license.

Projects in OpenMMLab

  • MMEngine: OpenMMLab foundational library for training deep learning models
  • MMCV: OpenMMLab foundational library for computer vision.
  • MIM: MIM installs OpenMMLab packages.
  • MMEval: A unified evaluation library for multiple machine learning libraries.
  • 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.
  • MMYOLO: OpenMMLab YOLO series 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.


OpenMMLab Semantic Segmentation Toolbox and Benchmark.


License:Apache License 2.0


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