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Models/ET-Net

ET-Net

Reported on 16 benchmarks across 4 tasks · 1 paper · 7 SOTA

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

Medical8 results

  • Medical Image SegmentationonDRIVE
    Accuracy· 2019-07-25
    0.956
    best: 0.9712 (U-Net)
    SOTA
    ET-Net: A Generic Edge-aTtention Guidance Network for Medical Image SegmentationarXiv:1907.10936
  • Medical Image SegmentationonDRIVE
    mIoU· 2019-07-25
    0.7744
    best: 0.8406 (FSG-Net)
    SOTA
    ET-Net: A Generic Edge-aTtention Guidance Network for Medical Image SegmentationarXiv:1907.10936
  • Medical Image SegmentationonMontgomery County
    Accuracy· 2019-07-25
    0.9865
    SOTA
    ET-Net: A Generic Edge-aTtention Guidance Network for Medical Image SegmentationarXiv:1907.10936
  • Medical Image SegmentationonMontgomery County
    mIoU· 2019-07-25
    0.942
    SOTA
    ET-Net: A Generic Edge-aTtention Guidance Network for Medical Image SegmentationarXiv:1907.10936
  • Medical Image SegmentationonLUNA
    mIoU· 2019-07-25
    0.9623
    SOTA
    ET-Net: A Generic Edge-aTtention Guidance Network for Medical Image SegmentationarXiv:1907.10936
  • Retinal Vessel SegmentationonDRIVE
    Accuracy· 2019-07-25
    0.956
    best: 0.9712 (U-Net)
    SOTA
    ET-Net: A Generic Edge-aTtention Guidance Network for Medical Image SegmentationarXiv:1907.10936
  • Retinal Vessel SegmentationonDRIVE
    mIoU· 2019-07-25
    0.7744
    best: 0.8406 (FSG-Net)
    SOTA
    ET-Net: A Generic Edge-aTtention Guidance Network for Medical Image SegmentationarXiv:1907.10936
  • Medical Image SegmentationonLUNA
    Accuracy· 2019-07-25
    0.9868
    best: 0.99 (CE-Net)
    ET-Net: A Generic Edge-aTtention Guidance Network for Medical Image SegmentationarXiv:1907.10936

Methodology4 results

  • 3DonMVSEC
    LPIPS
    0.489
    best: 0.476 (HyperE2VID)
  • 3DonMVSEC
    Mean Squared Error
    0.107
    best: 0.076 (HyperE2VID)
  • 3DonEvent-Camera Dataset
    LPIPS
    0.224
    best: 0.212 (HyperE2VID)
  • 3DonEvent-Camera Dataset
    Mean Squared Error
    0.047
    best: 0.033 (HyperE2VID)

Computer Vision4 results

  • Video ReconstructiononMVSEC
    LPIPS
    0.489
    best: 0.476 (HyperE2VID)
  • Video ReconstructiononMVSEC
    Mean Squared Error
    0.107
    best: 0.076 (HyperE2VID)
  • Video ReconstructiononEvent-Camera Dataset
    LPIPS
    0.224
    best: 0.212 (HyperE2VID)
  • Video ReconstructiononEvent-Camera Dataset
    Mean Squared Error
    0.047
    best: 0.033 (HyperE2VID)