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Models/MergedNet-Max

MergedNet-Max

Reported on 10 benchmarks across 2 tasks

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

Computer Vision10 results

  • Image ClassificationonCUB 200 5-way 5-shot
    Accuracy
    83.42
    best: 98.7 (CAML [Laion-2b])
  • Image ClassificationonCUB 200 5-way 1-shot
    Accuracy
    75.34
    best: 95.8 (PT+MAP+SF+SOT (transductive))
  • Image ClassificationonMini-Imagenet 5-way (5-shot)
    Accuracy
    80.4
    best: 98.72 (SgVA-CLIP)
  • Image ClassificationonMini-Imagenet 5-way (1-shot)
    Accuracy
    68.05
    best: 97.95 (SgVA-CLIP)
  • Image ClassificationonCaltech-256 5-way (1-shot)
    Accuracy
    65.77
    best: 74.7 (UL-Hopfield (ULH))
  • Few-Shot Image ClassificationonCUB 200 5-way 5-shot
    Accuracy
    83.42
    best: 98.7 (CAML [Laion-2b])
  • Few-Shot Image ClassificationonCUB 200 5-way 1-shot
    Accuracy
    75.34
    best: 95.8 (PT+MAP+SF+SOT (transductive))
  • Few-Shot Image ClassificationonMini-Imagenet 5-way (5-shot)
    Accuracy
    80.4
    best: 98.72 (SgVA-CLIP)
  • Few-Shot Image ClassificationonMini-Imagenet 5-way (1-shot)
    Accuracy
    68.05
    best: 97.95 (SgVA-CLIP)
  • Few-Shot Image ClassificationonCaltech-256 5-way (1-shot)
    Accuracy
    65.77
    best: 74.7 (UL-Hopfield (ULH))