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MMSegmentation is an open source semantic segmentation toolbox based on PyTorch.

The main branch works with PyTorch 2.0+.

We are thrilled to announce the official release of MMSegmentation's latest version! For this new release, the main branch serves as the primary branch. 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.

The VBTI development team is reviving MMLabs code, making it work with newer pytorch versions and fixing bugs. We are only a small team, so your help is appreciated.

v1.3.0 was released in August 2025, from 1.2.0 to 1.3.0, we have added or updated the following features:

• Disabled tests on RS_inferencer due to difficulties installing gdal
• Disabled tests on VPD Model due to the latent-diffusion needing to be installed in editable mode. This is not playing nice with uv.

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

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 0.x, please refer to migration.

Results and models are available in the model zoo.

Please refer to FAQ 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 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:

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

This project is released under the Apache 2.0 license.

• MMEngine: Foundational library for training deep learning models.
• MMCV: Foundational library for computer vision.
• MMPreTrain: Pre-training toolbox and benchmark.
• MMDetection: Detection toolbox and benchmark.
• MMRotate: Rotated object detection toolbox and benchmark.
• MMSegmentation: Semantic segmentation toolbox and benchmark.
• MMDeploy: Model deployment framework.
• MIM: MIM installs VBTI packages.