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Models/DPAN

DPAN

Reported on 18 benchmarks across 3 tasks

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

Computer Vision12 results

  • Scene ParsingonSVT
    Accuracy
    93.9
    best: 99.1 (CLIP4STR-H (DFN-5B))
  • Scene ParsingonSVTP
    Accuracy
    89
    best: 98.6 (DTrOCR 105M)
  • Scene ParsingonCUTE80
    Accuracy
    91.9
    best: 99.7 (CPPD)
  • Scene ParsingonICDAR2015
    Accuracy
    85.5
    best: 93.5 (DTrOCR 105M)
  • Scene ParsingonIIIT5k
    Accuracy
    96.2
    best: 99.6 (CLIP4STR-L (DataComp-1B))
  • Scene ParsingonICDAR2013
    Accuracy
    97.7
    best: 99.42 (CLIP4STR-L*)
  • Scene Text RecognitiononSVT
    Accuracy
    93.9
    best: 99.1 (CLIP4STR-H (DFN-5B))
  • Scene Text RecognitiononSVTP
    Accuracy
    89
    best: 98.6 (DTrOCR 105M)
  • Scene Text RecognitiononCUTE80
    Accuracy
    91.9
    best: 99.7 (CPPD)
  • Scene Text RecognitiononICDAR2015
    Accuracy
    85.5
    best: 93.5 (DTrOCR 105M)
  • Scene Text RecognitiononIIIT5k
    Accuracy
    96.2
    best: 99.6 (CLIP4STR-L (DataComp-1B))
  • Scene Text RecognitiononICDAR2013
    Accuracy
    97.7
    best: 99.42 (CLIP4STR-L*)

Audio6 results

  • 2D Semantic SegmentationonSVT
    Accuracy
    93.9
    best: 99.1 (CLIP4STR-H (DFN-5B))
  • 2D Semantic SegmentationonSVTP
    Accuracy
    89
    best: 98.6 (DTrOCR 105M)
  • 2D Semantic SegmentationonCUTE80
    Accuracy
    91.9
    best: 99.7 (CPPD)
  • 2D Semantic SegmentationonICDAR2015
    Accuracy
    85.5
    best: 93.5 (DTrOCR 105M)
  • 2D Semantic SegmentationonIIIT5k
    Accuracy
    96.2
    best: 99.6 (CLIP4STR-L (DataComp-1B))
  • 2D Semantic SegmentationonICDAR2013
    Accuracy
    97.7
    best: 99.42 (CLIP4STR-L*)