mmpose 0.10.0

OpenMMLab Pose Estimation Toolbox and Benchmark.

Introduction

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+.

COCO 17-keypoint pose estimation

133-keypoint whole-body pose estimation (full HD version)

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, 133 keypoint whole-body human pose estimation, 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. See 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 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

Supported backbones for human pose estimation:

• AlexNet
• VGG
• HRNet
• MobilenetV2
• ResNet
• ResNetV1D
• ResNeSt
• ResNext
• SCNet
• SEResNet
• ShufflenetV1
• ShufflenetV2

Supported methods for human pose estimation:

• CPM
• SimpleBaseline
• HRNet
• Hourglass
• SCNet
• Associative Embedding
• HigherHRNet
• DarkPose
• UDP
• MSPN
• RSN

Supported datasets:

• COCO
• COCO-WholeBody
• MPII
• MPII-TRB
• AI Challenger
• OCHuman
• CrowdPose
• PoseTrack18
• MHP
• sub-JHMDB
• OneHand10K
• FreiHand
• CMU Panoptic HandDB
• InterHand2.6M
• Human3.6M

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.

Benchmark

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.

Installation

Please refer to install.md for installation.

Data Preparation

Please refer to data_preparation.md for a general knowledge of data preparation.

Get Started

Please see getting_started.md for the basic usage of MMPose. There are also tutorials: finetune model, add new dataset, customize data pipelines, add new modules, export a model to ONNX and customize runtime settings.

FAQ

Please refer to FAQ for frequently asked questions.

License

This project is released under the Apache 2.0 license.

Contributing

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

Acknowledgement

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.