  We present PYSKL: an open-source toolbox for skeleton-based action
recognition based on PyTorch. The toolbox supports a wide variety of skeleton
action recognition algorithms, including approaches based on GCN and CNN. In
contrast to existing open-source skeleton action recognition projects that
include only one or two algorithms, PYSKL implements six different algorithms
under a unified framework with both the latest and original good practices to
ease the comparison of efficacy and efficiency. We also provide an original
GCN-based skeleton action recognition model named ST-GCN++, which achieves
competitive recognition performance without any complicated attention schemes,
serving as a strong baseline. Meanwhile, PYSKL supports the training and
testing of nine skeleton-based action recognition benchmarks and achieves
state-of-the-art recognition performance on eight of them. To facilitate future
research on skeleton action recognition, we also provide a large number of
trained models and detailed benchmark results to give some insights. PYSKL is
released at https://github.com/kennymckormick/pyskl and is actively maintained.
We will update this report when we add new features or benchmarks. The current
version corresponds to PYSKL v0.2.

Computer Vision and Pattern Recognition