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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+.

v0.0.1 was released in 7/11/2022:

• First Commit

Please refer to changelog.md for details and release history.

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
• customizing runtime
• training tricks
• useful tools

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

Results and models are available in the model zoo.

Supported VAEs:

• Non-Local (TIP'2021)
• Swin Transformer (Arxiv'2022)

Supported context model:

• Masked3DCNN (TIP'2021)
• MCM (Arxiv'2022)

Supported learining based image compression methods:

• NLAIC (TIP'2021)
• TinyLIC (Arxiv'2022)

Supported conventional image compression methods:

• JPEG
• HM (BPG)
• VVC

Supported datasets:

• Flicker2W
• Kodak24
• IEEE1857

Please refer to FAQ for frequently asked questions.

We appreciate all contributions to improve MMCompression. Please refer to CONTRIBUTING.md for the contributing guideline.

MMCompression 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 AI compression methods.

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

@misc{mmcomp2022,
    title={{MMCompression}: OpenMMLab Semantic Segmentation Toolbox and Benchmark},
    author={MMSegmentation Contributors},
    howpublished = {\url{https://github.com/open-mmlab/mmcompression}},
    year={2022}
}

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

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