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Models/HRNet-w32

HRNet-w32

Reported on 6 benchmarks across 5 tasks · 2 papers

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Computer Vision2 results

  • Pose EstimationonAP-10K
    AP· uses extra data· 2022-12-07
    72.2
    best: 82.4 (ViTPose+-H)
    ViTPose++: Vision Transformer for Generic Body Pose EstimationarXiv:2212.04246
  • Animal Pose EstimationonAP-10K
    AP· uses extra data· 2022-12-07
    72.2
    best: 82.4 (ViTPose+-H)
    ViTPose++: Vision Transformer for Generic Body Pose EstimationarXiv:2212.04246

Knowledge Base2 results

  • 2D Human Pose EstimationonHuman-Art
    AP· 2019-02-25
    0.399
    best: 0.759 (UniPose)
    Deep High-Resolution Representation Learning for Human Pose EstimationarXiv:1902.09212
  • 2D Human Pose EstimationonHuman-Art
    AP (gt bbox)· 2019-02-25
    0.754
    best: 0.8 (ViTPose-h)
    Deep High-Resolution Representation Learning for Human Pose EstimationarXiv:1902.09212

Methodology1 result

  • 3DonAP-10K
    AP· uses extra data· 2022-12-07
    72.2
    best: 82.4 (ViTPose+-H)
    ViTPose++: Vision Transformer for Generic Body Pose EstimationarXiv:2212.04246

Audio1 result

  • 1 Image, 2*2 StitchionAP-10K
    AP· uses extra data· 2022-12-07
    72.2
    best: 82.4 (ViTPose+-H)
    ViTPose++: Vision Transformer for Generic Body Pose EstimationarXiv:2212.04246