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Models/PyConvSegNet-152

PyConvSegNet-152

Reported on 8 benchmarks across 2 tasks · 1 paper · 4 SOTA

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

Medical4 results

  • Semantic SegmentationonADE20K val
    Pixel Accuracy· 2020-06-20
    82.49
    best: 83.43 (gSwin-S)
    SOTA
    Pyramidal Convolution: Rethinking Convolutional Neural Networks for Visual RecognitionarXiv:2006.11538
  • Semantic SegmentationonADE20K
    Test Score· 2020-06-20
    56.52
    best: 62.8 (Swin-L (UperNet, ImageNet-22k pretrain))
    SOTA
    Pyramidal Convolution: Rethinking Convolutional Neural Networks for Visual RecognitionarXiv:2006.11538
  • Semantic SegmentationonADE20K val
    mIoU· 2020-06-20
    45.99
    best: 62.8 (BEiT-3)
    Pyramidal Convolution: Rethinking Convolutional Neural Networks for Visual RecognitionarXiv:2006.11538
  • Semantic SegmentationonADE20K
    Validation mIoU· 2020-06-20
    45.99
    best: 63.6 (ViT-P (InternImage-H))
    Pyramidal Convolution: Rethinking Convolutional Neural Networks for Visual RecognitionarXiv:2006.11538

Audio4 results

  • 10-shot image generationonADE20K val
    Pixel Accuracy· 2020-06-20
    82.49
    best: 83.43 (gSwin-S)
    SOTA
    Pyramidal Convolution: Rethinking Convolutional Neural Networks for Visual RecognitionarXiv:2006.11538
  • 10-shot image generationonADE20K
    Test Score· 2020-06-20
    56.52
    best: 62.8 (Swin-L (UperNet, ImageNet-22k pretrain))
    SOTA
    Pyramidal Convolution: Rethinking Convolutional Neural Networks for Visual RecognitionarXiv:2006.11538
  • 10-shot image generationonADE20K val
    mIoU· 2020-06-20
    45.99
    best: 62.8 (BEiT-3)
    Pyramidal Convolution: Rethinking Convolutional Neural Networks for Visual RecognitionarXiv:2006.11538
  • 10-shot image generationonADE20K
    Validation mIoU· 2020-06-20
    45.99
    best: 63.6 (ViT-P (InternImage-H))
    Pyramidal Convolution: Rethinking Convolutional Neural Networks for Visual RecognitionarXiv:2006.11538