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Models/PANet

PANet

Reported on 7 benchmarks across 6 tasks · 1 paper · 6 SOTA

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

Methodology4 results

  • 3DoniSAID
    Average Precision· 2018-03-05
    41.66
    best: 47 (PANet++)
    SOTA
    Path Aggregation Network for Instance SegmentationarXiv:1803.01534
  • 2D ClassificationoniSAID
    Average Precision· 2018-03-05
    41.66
    best: 47 (PANet++)
    SOTA
    Path Aggregation Network for Instance SegmentationarXiv:1803.01534
  • 2D Object DetectiononiSAID
    Average Precision· 2018-03-05
    41.66
    best: 47 (PANet++)
    SOTA
    Path Aggregation Network for Instance SegmentationarXiv:1803.01534
  • 16koniSAID
    Average Precision· 2018-03-05
    41.66
    best: 47 (PANet++)
    SOTA
    Path Aggregation Network for Instance SegmentationarXiv:1803.01534

Computer Vision3 results

  • Object DetectiononiSAID
    Average Precision· 2018-03-05
    41.66
    best: 47 (PANet++)
    SOTA
    Path Aggregation Network for Instance SegmentationarXiv:1803.01534
  • Instance SegmentationonCOCO test-dev
    mask AP· 2018-03-05
    42
    best: 57.1 (Co-DETR)
    SOTA
    Path Aggregation Network for Instance SegmentationarXiv:1803.01534
  • Instance SegmentationoniSAID
    Average Precision· 2018-03-05
    34.17
    best: 40 (PANet++)
    Path Aggregation Network for Instance SegmentationarXiv:1803.01534