133-keypoint whole-body pose estimation ([full HD version](https://www.youtube.com/watch?v=pIJpQg8mXUU))
### Major Features
- **Support diverse tasks**
We support a wide spectrum of mainstream human pose analysis tasks in current research community, including 2d multi-person human pose estimation, 2d hand pose estimation, 2d face landmark detection, 133 keypoint whole-body human pose estimation, fashion landmark detection and 3d human mesh recovery.
- **Higher efficiency and higher accuracy**
MMPose implements multiple state-of-the-art (SOTA) deep learning models, including both top-down & bottom-up approaches. We achieve faster training speed and higher accuracy than other popular codebases, such as [HRNet](https://github.com/leoxiaobin/deep-high-resolution-net.pytorch).
See [benchmark.md](docs/benchmark.md) for more information.
- **Support for various datasets**
The toolbox directly supports multiple popular and representative datasets, COCO, AIC, MPII, MPII-TRB, OCHuman etc.
See [data_preparation.md](docs/data_preparation.md) for more information.
- **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.
## [Model Zoo](https://mmpose.readthedocs.io/en/latest/modelzoo.html)
Supported algorithms:
