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Models/PraNet

PraNet

Reported on 56 benchmarks across 8 tasks · 1 paper · 52 SOTA

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

Medical28 results

  • Medical Image SegmentationonKvasir-SEG
    Average MAE· 2020-06-13
    0.03
    best: 0.021 (BDG-Net)
    SOTA
    PraNet: Parallel Reverse Attention Network for Polyp SegmentationarXiv:2006.11392
  • Medical Image SegmentationonKvasir-SEG
    S-Measure· 2020-06-13
    0.915
    best: 0.929 (CaraNet)
    SOTA
    PraNet: Parallel Reverse Attention Network for Polyp SegmentationarXiv:2006.11392
  • Medical Image SegmentationonKvasir-SEG
    mIoU· 2020-06-13
    0.849
    best: 0.9065 (EffiSegNet-B5)
    SOTA
    PraNet: Parallel Reverse Attention Network for Polyp SegmentationarXiv:2006.11392
  • Medical Image SegmentationonKvasir-SEG
    max E-Measure· 2020-06-13
    0.948
    best: 0.972 (BDG-Net)
    SOTA
    PraNet: Parallel Reverse Attention Network for Polyp SegmentationarXiv:2006.11392
  • Medical Image SegmentationonKvasir-SEG
    mean Dice· 2020-06-13
    0.898
    best: 0.9502 (DUCK-Net)
    SOTA
    PraNet: Parallel Reverse Attention Network for Polyp SegmentationarXiv:2006.11392
  • Medical Image SegmentationonETIS-LARIBPOLYPDB
    Average MAE· 2020-06-13
    0.031
    best: 0.012 (UACANet-L)
    SOTA
    PraNet: Parallel Reverse Attention Network for Polyp SegmentationarXiv:2006.11392
  • Medical Image SegmentationonETIS-LARIBPOLYPDB
    S-Measure· 2020-06-13
    0.794
    best: 0.868 (CaraNet)
    SOTA
    PraNet: Parallel Reverse Attention Network for Polyp SegmentationarXiv:2006.11392
  • Medical Image SegmentationonETIS-LARIBPOLYPDB
    max E-Measure· 2020-06-13
    0.841
    best: 0.905 (UACANet-L)
    SOTA
    PraNet: Parallel Reverse Attention Network for Polyp SegmentationarXiv:2006.11392
  • Medical Image SegmentationonCVC-ColonDB
    Average MAE· 2020-06-13
    0.045
    best: 0.026 (DuAT)
    SOTA
    PraNet: Parallel Reverse Attention Network for Polyp SegmentationarXiv:2006.11392
  • Medical Image SegmentationonCVC-ColonDB
    S-Measure· 2020-06-13
    0.819
    best: 0.865 (Polyp-PVT)
    SOTA
    PraNet: Parallel Reverse Attention Network for Polyp SegmentationarXiv:2006.11392
  • Medical Image SegmentationonCVC-ColonDB
    mIoU· 2020-06-13
    0.649
    best: 0.9096 (RAPUNet)
    SOTA
    PraNet: Parallel Reverse Attention Network for Polyp SegmentationarXiv:2006.11392
  • Medical Image SegmentationonCVC-ColonDB
    max E-Measure· 2020-06-13
    0.869
    best: 0.913 (Polyp-PVT)
    SOTA
    PraNet: Parallel Reverse Attention Network for Polyp SegmentationarXiv:2006.11392
  • Medical Image SegmentationonCVC-ColonDB
    mean Dice· 2020-06-13
    0.709
    best: 0.9526 (RAPUNet)
    SOTA
    PraNet: Parallel Reverse Attention Network for Polyp SegmentationarXiv:2006.11392
  • Medical Image SegmentationonSUN-SEG-Easy (Unseen)
    Dice· 2020-06-13
    0.621
    best: 0.9 (YOLO-SAM 2)
    SOTA
    PraNet: Parallel Reverse Attention Network for Polyp SegmentationarXiv:2006.11392
  • Medical Image SegmentationonSUN-SEG-Easy (Unseen)
    S measure· 2020-06-13
    0.733
    best: 0.9 (YOLO-SAM 2)
    SOTA
    PraNet: Parallel Reverse Attention Network for Polyp SegmentationarXiv:2006.11392
  • Medical Image SegmentationonSUN-SEG-Easy (Unseen)
    Sensitivity· 2020-06-13
    0.524
    best: 83.7 (YOLO-SAM 2)
    SOTA
    PraNet: Parallel Reverse Attention Network for Polyp SegmentationarXiv:2006.11392
  • Medical Image SegmentationonSUN-SEG-Easy (Unseen)
    mean E-measure· 2020-06-13
    0.753
    best: 93.8 (YOLO-SAM 2)
    SOTA
    PraNet: Parallel Reverse Attention Network for Polyp SegmentationarXiv:2006.11392
  • Medical Image SegmentationonSUN-SEG-Easy (Unseen)
    mean F-measure· 2020-06-13
    0.632
    best: 93.8 (YOLO-SAM 2)
    SOTA
    PraNet: Parallel Reverse Attention Network for Polyp SegmentationarXiv:2006.11392
  • Medical Image SegmentationonSUN-SEG-Easy (Unseen)
    weighted F-measure· 2020-06-13
    0.572
    best: 0.794 (SALI)
    SOTA
    PraNet: Parallel Reverse Attention Network for Polyp SegmentationarXiv:2006.11392
  • Medical Image SegmentationonSUN-SEG-Hard (Unseen)
    S-Measure· 2020-06-13
    0.717
    best: 0.894 (YOLO-SAM 2)
    SOTA
    PraNet: Parallel Reverse Attention Network for Polyp SegmentationarXiv:2006.11392
  • Medical Image SegmentationonSUN-SEG-Hard (Unseen)
    Sensitivity· 2020-06-13
    0.512
    best: 0.852 (YOLO-SAM 2)
    SOTA
    PraNet: Parallel Reverse Attention Network for Polyp SegmentationarXiv:2006.11392
  • Medical Image SegmentationonSUN-SEG-Hard (Unseen)
    mean E-measure· 2020-06-13
    0.735
    best: 0.941 (YOLO-SAM 2)
    SOTA
    PraNet: Parallel Reverse Attention Network for Polyp SegmentationarXiv:2006.11392
  • Medical Image SegmentationonSUN-SEG-Hard (Unseen)
    mean F-measure· 2020-06-13
    0.607
    best: 0.932 (YOLO-SAM 2)
    SOTA
    PraNet: Parallel Reverse Attention Network for Polyp SegmentationarXiv:2006.11392
  • Medical Image SegmentationonSUN-SEG-Hard (Unseen)
    weighted F-measure· 2020-06-13
    0.544
    best: 0.79 (SALI)
    SOTA
    PraNet: Parallel Reverse Attention Network for Polyp SegmentationarXiv:2006.11392
  • Medical Image SegmentationonETIS-LARIBPOLYPDB
    mIoU· 2020-06-13
    0.567
    best: 0.9179 (RAPUNet)
    PraNet: Parallel Reverse Attention Network for Polyp SegmentationarXiv:2006.11392
  • Medical Image SegmentationonETIS-LARIBPOLYPDB
    mean Dice· 2020-06-13
    0.628
    best: 0.9572 (RAPUNet)
    PraNet: Parallel Reverse Attention Network for Polyp SegmentationarXiv:2006.11392
  • Medical Image SegmentationonCVC-ClinicDB
    mean Dice· 2020-06-13
    0.899
    best: 0.9684 (DUCK-Net)
    PraNet: Parallel Reverse Attention Network for Polyp SegmentationarXiv:2006.11392
  • Medical Image SegmentationonSUN-SEG-Hard (Unseen)
    Dice· 2020-06-13
    0.598
    best: 0.902 (YOLO-SAM 2)
    PraNet: Parallel Reverse Attention Network for Polyp SegmentationarXiv:2006.11392

Methodology16 results

  • 3DonPCOD_1200
    S-Measure· 2020-06-13
    0.904
    best: 0.922 (CMX)
    SOTA
    PraNet: Parallel Reverse Attention Network for Polyp SegmentationarXiv:2006.11392
  • 3DonCAMO
    MAE· uses extra data· 2020-06-13
    0.094
    best: 0.025 (FOCUS)
    SOTA
    PraNet: Parallel Reverse Attention Network for Polyp SegmentationarXiv:2006.11392
  • 3DonCAMO
    S-Measure· uses extra data· 2020-06-13
    0.769
    best: 0.912 (FOCUS)
    SOTA
    PraNet: Parallel Reverse Attention Network for Polyp SegmentationarXiv:2006.11392
  • 3DonCAMO
    Weighted F-Measure· uses extra data· 2020-06-13
    0.663
    best: 0.904 (FOCUS)
    SOTA
    PraNet: Parallel Reverse Attention Network for Polyp SegmentationarXiv:2006.11392
  • 2D ClassificationonPCOD_1200
    S-Measure· 2020-06-13
    0.904
    best: 0.922 (CMX)
    SOTA
    PraNet: Parallel Reverse Attention Network for Polyp SegmentationarXiv:2006.11392
  • 2D ClassificationonCAMO
    MAE· uses extra data· 2020-06-13
    0.094
    best: 0.025 (FOCUS)
    SOTA
    PraNet: Parallel Reverse Attention Network for Polyp SegmentationarXiv:2006.11392
  • 2D ClassificationonCAMO
    S-Measure· uses extra data· 2020-06-13
    0.769
    best: 0.912 (FOCUS)
    SOTA
    PraNet: Parallel Reverse Attention Network for Polyp SegmentationarXiv:2006.11392
  • 2D ClassificationonCAMO
    Weighted F-Measure· uses extra data· 2020-06-13
    0.663
    best: 0.904 (FOCUS)
    SOTA
    PraNet: Parallel Reverse Attention Network for Polyp SegmentationarXiv:2006.11392
  • 2D Object DetectiononPCOD_1200
    S-Measure· 2020-06-13
    0.904
    best: 0.922 (CMX)
    SOTA
    PraNet: Parallel Reverse Attention Network for Polyp SegmentationarXiv:2006.11392
  • 2D Object DetectiononCAMO
    MAE· uses extra data· 2020-06-13
    0.094
    best: 0.025 (FOCUS)
    SOTA
    PraNet: Parallel Reverse Attention Network for Polyp SegmentationarXiv:2006.11392
  • 2D Object DetectiononCAMO
    S-Measure· uses extra data· 2020-06-13
    0.769
    best: 0.912 (FOCUS)
    SOTA
    PraNet: Parallel Reverse Attention Network for Polyp SegmentationarXiv:2006.11392
  • 2D Object DetectiononCAMO
    Weighted F-Measure· uses extra data· 2020-06-13
    0.663
    best: 0.904 (FOCUS)
    SOTA
    PraNet: Parallel Reverse Attention Network for Polyp SegmentationarXiv:2006.11392
  • 16konPCOD_1200
    S-Measure· 2020-06-13
    0.904
    best: 0.922 (CMX)
    SOTA
    PraNet: Parallel Reverse Attention Network for Polyp SegmentationarXiv:2006.11392
  • 16konCAMO
    MAE· uses extra data· 2020-06-13
    0.094
    best: 0.025 (FOCUS)
    SOTA
    PraNet: Parallel Reverse Attention Network for Polyp SegmentationarXiv:2006.11392
  • 16konCAMO
    S-Measure· uses extra data· 2020-06-13
    0.769
    best: 0.912 (FOCUS)
    SOTA
    PraNet: Parallel Reverse Attention Network for Polyp SegmentationarXiv:2006.11392
  • 16konCAMO
    Weighted F-Measure· uses extra data· 2020-06-13
    0.663
    best: 0.904 (FOCUS)
    SOTA
    PraNet: Parallel Reverse Attention Network for Polyp SegmentationarXiv:2006.11392

Computer Vision12 results

  • Object DetectiononPCOD_1200
    S-Measure· 2020-06-13
    0.904
    best: 0.922 (CMX)
    SOTA
    PraNet: Parallel Reverse Attention Network for Polyp SegmentationarXiv:2006.11392
  • Object DetectiononCAMO
    MAE· uses extra data· 2020-06-13
    0.094
    best: 0.025 (FOCUS)
    SOTA
    PraNet: Parallel Reverse Attention Network for Polyp SegmentationarXiv:2006.11392
  • Object DetectiononCAMO
    S-Measure· uses extra data· 2020-06-13
    0.769
    best: 0.912 (FOCUS)
    SOTA
    PraNet: Parallel Reverse Attention Network for Polyp SegmentationarXiv:2006.11392
  • Object DetectiononCAMO
    Weighted F-Measure· uses extra data· 2020-06-13
    0.663
    best: 0.904 (FOCUS)
    SOTA
    PraNet: Parallel Reverse Attention Network for Polyp SegmentationarXiv:2006.11392
  • Camouflaged Object SegmentationonPCOD_1200
    S-Measure· 2020-06-13
    0.904
    best: 0.922 (CMX)
    SOTA
    PraNet: Parallel Reverse Attention Network for Polyp SegmentationarXiv:2006.11392
  • Camouflaged Object SegmentationonCAMO
    MAE· uses extra data· 2020-06-13
    0.094
    best: 0.025 (FOCUS)
    SOTA
    PraNet: Parallel Reverse Attention Network for Polyp SegmentationarXiv:2006.11392
  • Camouflaged Object SegmentationonCAMO
    S-Measure· uses extra data· 2020-06-13
    0.769
    best: 0.912 (FOCUS)
    SOTA
    PraNet: Parallel Reverse Attention Network for Polyp SegmentationarXiv:2006.11392
  • Camouflaged Object SegmentationonCAMO
    Weighted F-Measure· uses extra data· 2020-06-13
    0.663
    best: 0.904 (FOCUS)
    SOTA
    PraNet: Parallel Reverse Attention Network for Polyp SegmentationarXiv:2006.11392
  • Object SegmentationonPCOD_1200
    S-Measure· 2020-06-13
    0.904
    best: 0.922 (CMX)
    SOTA
    PraNet: Parallel Reverse Attention Network for Polyp SegmentationarXiv:2006.11392
  • Object SegmentationonCAMO
    MAE· uses extra data· 2020-06-13
    0.094
    best: 0.025 (FOCUS)
    SOTA
    PraNet: Parallel Reverse Attention Network for Polyp SegmentationarXiv:2006.11392
  • Object SegmentationonCAMO
    S-Measure· uses extra data· 2020-06-13
    0.769
    best: 0.912 (FOCUS)
    SOTA
    PraNet: Parallel Reverse Attention Network for Polyp SegmentationarXiv:2006.11392
  • Object SegmentationonCAMO
    Weighted F-Measure· uses extra data· 2020-06-13
    0.663
    best: 0.904 (FOCUS)
    SOTA
    PraNet: Parallel Reverse Attention Network for Polyp SegmentationarXiv:2006.11392