Shypd.aiThe largest open index of AI toolsRSN is an open-source research tool for multi-person pose estimation, featuring a Residual Steps Network and Pose Refine Machine. It won the COCO 2019 Human Keypoint Detection Challenge and Best Paper Award.
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RSN, or Residual Steps Network, is an open-source project developed for multi-person pose estimation, recognized as an ECCV 2020 Spotlight paper. This PyTorch-based realization won the COCO 2019 Keypoint Challenge and received the Best Paper Award. The core methodology involves aggregating intra-level features to achieve delicate local representations, crucial for precise keypoint localization. Additionally, it incorporates an efficient attention mechanism called Pose Refine Machine (PRM) to further enhance keypoint accuracy. RSN has demonstrated state-of-the-art results on both COCO and MPII benchmarks without relying on extra training data or pre-trained models. It offers various model configurations, including RSN-18, RSN-50, and RSN-101, with detailed performance metrics available for different datasets and input sizes.
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