MMPose is an open-source toolbox for pose estimation based on PyTorch. It is a part of the OpenMMLab project.
The master branch works with PyTorch 1.3+.
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Support top-down & bottom-up approaches
MMPose implements multiple state-of-the-art (SOTA) deep learning models for human pose estimation, including both top-down and bottom-up approaches.
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Higher efficiency and Higher Accuracy
We achieve faster training speed and higher accuracy than other popular codebases, such as HRNet. See benchmark.md for more information.
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Support for various datasets
The toolbox directly supports multiple datasets, COCO, AIC, MPII, MPII-TRB, OCHuman etc. See data_preparation.md for more information.
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Well designed, tested and documented
We decompose MMPose into different components and one can easily construct a customized pose estimation framework by combining different modules. We provide detailed documentation and API reference, as well as unittests.
Supported backbones for human pose estimation:
Supported methods for human pose estimation:
Supported datasets:
Results and models are available in the README.md of each method's config directory. A summary can be found in the model zoo page. We will keep up with the latest progress of the community, and support more popular algorithms and frameworks.
If you have any feature requests, please feel free to leave a comment in Issues.
We demonstrate the superiority of our MMPose framework in terms of speed and accuracy on the standard COCO keypoint detection benchmark.
| Model | Input size | MMPose (s/iter) | HRNet (s/iter) | MMPose (mAP) | HRNet (mAP) |
|---|---|---|---|---|---|
| resnet_50 | 256x192 | 0.28 | 0.64 | 0.718 | 0.704 |
| resnet_50 | 384x288 | 0.81 | 1.24 | 0.731 | 0.722 |
| resnet_101 | 256x192 | 0.36 | 0.84 | 0.726 | 0.714 |
| resnet_101 | 384x288 | 0.79 | 1.53 | 0.748 | 0.736 |
| resnet_152 | 256x192 | 0.49 | 1.00 | 0.735 | 0.720 |
| resnet_152 | 384x288 | 0.96 | 1.65 | 0.750 | 0.743 |
| hrnet_w32 | 256x192 | 0.54 | 1.31 | 0.746 | 0.744 |
| hrnet_w32 | 384x288 | 0.76 | 2.00 | 0.760 | 0.758 |
| hrnet_w48 | 256x192 | 0.66 | 1.55 | 0.756 | 0.751 |
| hrnet_w48 | 384x288 | 1.23 | 2.20 | 0.767 | 0.763 |
More details about the benchmark are available on benchmark.md.
Please refer to install.md for installation.
Please refer to data_preparation.md for a general knowledge of data preparation.
Please see getting_started.md for the basic usage of MMPose. There are also tutorials for finetuning model, adding new dataset, adding new modules.
This project is released under the Apache 2.0 license.
We appreciate all contributions to improve MMPose. Please refer to CONTRIBUTING.md for the contributing guideline.
MMPose 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 models.