📘Documentation | 🛠️Installation | 👀Model Zoo | 🤔Reporting Issues
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⭐ MMRazor for Large Models is Available Now! Please refer to MMRazorLarge
MMRazor is a model compression toolkit for model slimming and AutoML, which includes 4 mainstream technologies:
- Neural Architecture Search (NAS)
- Pruning
- Knowledge Distillation (KD)
- Quantization
It is a part of the OpenMMLab project.
Major features:
-
Compatibility
MMRazor can be easily applied to various projects in OpenMMLab, due to the similar architecture design of OpenMMLab as well as the decoupling of slimming algorithms and vision tasks.
-
Flexibility
Different algorithms, e.g., NAS, pruning and KD, can be incorporated in a plug-n-play manner to build a more powerful system.
-
Convenience
With better modular design, developers can implement new model compression algorithms with only a few codes, or even by simply modifying config files.
About MMRazor's design and implementation, please refer to tutorials for more details.
The default branch is now main and the code on the branch has been upgraded to v1.0.0. The old master branch code now exists on the 0.x branch
MMRazor v1.0.0 was released in 2023-4-24, Major updates from 1.0.0rc2 include:
- MMRazor quantization is released.
- Add a new pruning algorithm named GroupFisher.
- Support distilling rtmdet with MMRazor.
To know more about the updates in MMRazor 1.0, please refer to Changelog for more details!
Results and models are available in the model zoo.
Supported algorithms:
Neural Architecture Search
Knowledge Distillation
MMRazor depends on PyTorch, MMCV and MMEngine.
Please refer to installation.md for more detailed instruction.
Please refer to user guides for the basic usage of MMRazor. There are also advanced guides:
We appreciate all contributions to improve MMRazor. Please refer to CONTRUBUTING.md for the contributing guideline.
MMRazor is an open source project that is contributed by researchers and engineers from various colleges and companies. We appreciate all the contributors who implement their methods or add new features, as well as users who give valuable feedbacks. We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to reimplement existing methods and develop their own new model compression methods.
If you find this project useful in your research, please consider cite:
@misc{2021mmrazor,
title={OpenMMLab Model Compression Toolbox and Benchmark},
author={MMRazor Contributors},
howpublished = {\url{https://github.com/open-mmlab/mmrazor}},
year={2021}
}This project is released under the Apache 2.0 license.
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