PYSKL: Towards Good Practices for Skeleton Action Recognition

• URL: http://arxiv.org/abs/2205.09443v1
• Date: Thu, 19 May 2022 09:58:32 GMT
• Title: PYSKL: Towards Good Practices for Skeleton Action Recognition
• Authors: Haodong Duan, Jiaqi Wang, Kai Chen, Dahua Lin
• Abstract summary: PYSKL is an open-source toolbox for skeleton-based action recognition based on PyTorch. It implements six different algorithms under a unified framework to ease the comparison of efficacy and efficiency. 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.
• Score: 77.87404524458809
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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