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Models/PGNet (ResNet-50)

PGNet (ResNet-50)

Reported on 12 benchmarks across 3 tasks

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

Methodology8 results

  • Few-Shot LearningonPASCAL-5i (1-Shot)
    Mean IoU
    56
    best: 83.2 (SegGPT (ViT))
  • Few-Shot LearningonPASCAL-5i (1-Shot)
    learnable parameters (million)
    17.2
    best: 31.5 (PPNet (ResNet-50))
  • Few-Shot LearningonPASCAL-5i (5-Shot)
    Mean IoU
    58.5
    best: 89.8 (SegGPT (ViT))
  • Few-Shot LearningonPASCAL-5i (5-Shot)
    learnable parameters (million)
    17.2
    best: 43 (FWB (ResNet-101))
  • Meta-LearningonPASCAL-5i (1-Shot)
    Mean IoU
    56
    best: 83.2 (SegGPT (ViT))
  • Meta-LearningonPASCAL-5i (1-Shot)
    learnable parameters (million)
    17.2
    best: 31.5 (PPNet (ResNet-50))
  • Meta-LearningonPASCAL-5i (5-Shot)
    Mean IoU
    58.5
    best: 89.8 (SegGPT (ViT))
  • Meta-LearningonPASCAL-5i (5-Shot)
    learnable parameters (million)
    17.2
    best: 43 (FWB (ResNet-101))

Computer Vision4 results

  • Few-Shot Semantic SegmentationonPASCAL-5i (1-Shot)
    Mean IoU
    56
    best: 83.2 (SegGPT (ViT))
  • Few-Shot Semantic SegmentationonPASCAL-5i (1-Shot)
    learnable parameters (million)
    17.2
    best: 31.5 (PPNet (ResNet-50))
  • Few-Shot Semantic SegmentationonPASCAL-5i (5-Shot)
    Mean IoU
    58.5
    best: 89.8 (SegGPT (ViT))
  • Few-Shot Semantic SegmentationonPASCAL-5i (5-Shot)
    learnable parameters (million)
    17.2
    best: 43 (FWB (ResNet-101))