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Models/RTNet

RTNet

Reported on 12 benchmarks across 4 tasks · 1 paper · 4 SOTA

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

Computer Vision10 results

  • Scene ParsingonLaPa
    Mean F1· 2021-02-04
    92.5
    best: 93.88 (FaRL-B)
    SOTA
    RoI Tanh-polar Transformer Network for Face Parsing in the WildarXiv:2102.02717
  • Scene ParsingoniBugMask
    Average F1· 2021-02-04
    86.466666667
    SOTA
    RoI Tanh-polar Transformer Network for Face Parsing in the WildarXiv:2102.02717
  • VideoonDAVIS 2016 val
    F
    84.7
    best: 90.2 (DEVA (DIS))
  • VideoonDAVIS 2016 val
    G
    85.2
    best: 88.9 (GSANet)
  • VideoonDAVIS 2016 val
    J
    85.6
    best: 88.3 (GSANet)
  • VideoonYouTube-Objects
    J
    70.1
    best: 75.1 (FakeFlow)
  • Video Object SegmentationonDAVIS 2016 val
    F
    84.7
    best: 90.2 (DEVA (DIS))
  • Video Object SegmentationonDAVIS 2016 val
    G
    85.2
    best: 88.9 (GSANet)
  • Video Object SegmentationonDAVIS 2016 val
    J
    85.6
    best: 88.3 (GSANet)
  • Video Object SegmentationonYouTube-Objects
    J
    70.1
    best: 75.1 (FakeFlow)

Audio2 results

  • 2D Semantic SegmentationonLaPa
    Mean F1· 2021-02-04
    92.5
    best: 93.88 (FaRL-B)
    SOTA
    RoI Tanh-polar Transformer Network for Face Parsing in the WildarXiv:2102.02717
  • 2D Semantic SegmentationoniBugMask
    Average F1· 2021-02-04
    86.466666667
    SOTA
    RoI Tanh-polar Transformer Network for Face Parsing in the WildarXiv:2102.02717