• OpenPifPaf model support (Python Training & C++ Inference), 3.1x SPEED UP 🚀🚀🚀
• Docker container updated to 2.2.0: https://hub.docker.com/repository/docker/tensorlayer/hyperpose
  • support CUDA 10 series (low driver requirement) and advanced CUDA 10.2 - CuDNN 8 - TensorRT 8 series.
• Pip installation! https://pypi.org/project/hyperpose/
• Add a bunch of new pre-trained models, please check: https://drive.google.com/drive/folders/1w9EjMkrjxOmMw3Rf6fXXkiv_ge7M99jR
  • OpenPifPaf, OpenPose-VGGv2, OpenPose-MobileNet
• Heavily refactored documentation for a better reading experience! https://hyperpose.readthedocs.io/en/latest/index.html

• OpenPifPaf model support (Python Training + C++ Inference);
• Docker container updated to 2.2.0: https://hub.docker.com/repository/docker/tensorlayer/hyperpose
• Add a bunch of new pre-trained models, please check: https://drive.google.com/drive/folders/1w9EjMkrjxOmMw3Rf6fXXkiv_ge7M99jR
• Refactored documents.

• Docker support: https://hub.docker.com/repository/docker/tensorlayer/hyperpose
• HyperPose command-line tool: hyperpose-cli
• Add functionality to keep the original aspect ratio(this is good for precision).
• New README.
• Visualization:
  • Line size varies with person size.
  • Translucent connection line in hyperpose-cli.

Key Features:

• Python Training Module
  • User-defined model arch
  • MS COCO + MPII dataset support
  • User-defined dataset filter and training pipeline
• C++ Accelerated Prediction
  • Operator API
  • Stream API
• More model supports
  • OpenPose
  • Lightweight-OpenPose
  • PoseProposal Network
• Documentation
• More pretrained models(Continuous updating)

The 1st version of OpenPose-Plus provides the functionality to do low-level inference, local training, and distributed training. The inference API of the 1st version is quite straightforward and low-level(without much high-level abstraction).

As a new version of OpenPose-Plus is upcoming, we made the old version a release.