Please refer to install.md for detailed installation guide.Ĭonda create -n openmmlab python=3.8 pytorch=1.10 cudatoolkit=11.3 torchvision -c pytorch -y
#Poser definition update#
Update Swin models with better performance.Add RLE pre-trained model on COCO dataset.We provide detailed documentation and API reference, as well as unittests. Pose estimation framework by combining different modules.
We decompose MMPose into different components and one can easily construct a customized See data_preparation.md for more information. The toolbox directly supports multiple popular and representative datasets, COCO, AIC, MPII, MPII-TRB, OCHuman etc. We achieve faster training speed and higher accuracy than other popular codebases, such as HRNet. MMPose implements multiple state-of-the-art (SOTA) deep learning models, including both top-down & bottom-up approaches. We support a wide spectrum of mainstream 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, 3d human mesh recovery, fashion landmark detection and animal pose estimation. The master branch works with PyTorch 1.5+. MMPose is an open-source toolbox for pose estimation based on PyTorch.
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