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Models/MAWS (ViT-2B)

MAWS (ViT-2B)

Reported on 16 benchmarks across 3 tasks · 1 paper · 7 SOTA

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

Computer Vision16 results

  • Image ClassificationoniNaturalist 2018 - 1-shot
    Top 1 Accuracy· uses extra data· 2023-03-23
    35.5
    SOTA
    The effectiveness of MAE pre-pretraining for billion-scale pretrainingarXiv:2303.13496
  • Image ClassificationoniNaturalist 2018 - 5-shot
    Top 1 Accuracy· uses extra data· 2023-03-23
    72.8
    SOTA
    The effectiveness of MAE pre-pretraining for billion-scale pretrainingarXiv:2303.13496
  • Image ClassificationoniNaturalist 2018 - 10-shot
    Top 1 Accuracy· uses extra data· 2023-03-23
    80.3
    SOTA
    The effectiveness of MAE pre-pretraining for billion-scale pretrainingarXiv:2303.13496
  • Few-Shot Image ClassificationoniNaturalist 2018 - 1-shot
    Top 1 Accuracy· uses extra data· 2023-03-23
    35.5
    SOTA
    The effectiveness of MAE pre-pretraining for billion-scale pretrainingarXiv:2303.13496
  • Few-Shot Image ClassificationoniNaturalist 2018 - 5-shot
    Top 1 Accuracy· uses extra data· 2023-03-23
    72.8
    SOTA
    The effectiveness of MAE pre-pretraining for billion-scale pretrainingarXiv:2303.13496
  • Few-Shot Image ClassificationoniNaturalist 2018 - 10-shot
    Top 1 Accuracy· uses extra data· 2023-03-23
    80.3
    SOTA
    The effectiveness of MAE pre-pretraining for billion-scale pretrainingarXiv:2303.13496
  • Zero-Shot Transfer Image ClassificationonFood-101
    Top 1 Accuracy· 2023-03-23
    96.2
    SOTA
    The effectiveness of MAE pre-pretraining for billion-scale pretrainingarXiv:2303.13496
  • Image ClassificationonImageNet V2
    Top 1 Accuracy· uses extra data· 2023-03-23
    83
    best: 84.63 (Model soups (BASIC-L))
    The effectiveness of MAE pre-pretraining for billion-scale pretrainingarXiv:2303.13496
  • Image ClassificationonObjectNet
    Top-1 Accuracy· uses extra data· 2023-03-23
    75.8
    best: 82.7 (CoCa)
    The effectiveness of MAE pre-pretraining for billion-scale pretrainingarXiv:2303.13496
  • Image ClassificationonImageNet - 5-shot
    Top 1 Accuracy· uses extra data· 2023-03-23
    81.5
    best: 82.78 (ViT-MoE-15B (Every-2))
    The effectiveness of MAE pre-pretraining for billion-scale pretrainingarXiv:2303.13496
  • Image ClassificationonImageNet - 10-shot
    Top 1 Accuracy· uses extra data· 2023-03-23
    83.7
    best: 84.6 (MAWS (ViT-6.5B))
    The effectiveness of MAE pre-pretraining for billion-scale pretrainingarXiv:2303.13496
  • Image ClassificationonImageNet - 1-shot
    Top 1 Accuracy· uses extra data· 2023-03-23
    62.1
    best: 68.66 (ViT-MoE-15B (Every-2))
    The effectiveness of MAE pre-pretraining for billion-scale pretrainingarXiv:2303.13496
  • Few-Shot Image ClassificationonImageNet - 5-shot
    Top 1 Accuracy· uses extra data· 2023-03-23
    81.5
    best: 82.78 (ViT-MoE-15B (Every-2))
    The effectiveness of MAE pre-pretraining for billion-scale pretrainingarXiv:2303.13496
  • Few-Shot Image ClassificationonImageNet - 10-shot
    Top 1 Accuracy· uses extra data· 2023-03-23
    83.7
    best: 84.6 (MAWS (ViT-6.5B))
    The effectiveness of MAE pre-pretraining for billion-scale pretrainingarXiv:2303.13496
  • Few-Shot Image ClassificationonImageNet - 1-shot
    Top 1 Accuracy· uses extra data· 2023-03-23
    62.1
    best: 68.66 (ViT-MoE-15B (Every-2))
    The effectiveness of MAE pre-pretraining for billion-scale pretrainingarXiv:2303.13496
  • Zero-Shot Transfer Image ClassificationonImageNet
    Accuracy (Private)· 2023-03-23
    82.1
    best: 88.5 (M2-Encoder)
    The effectiveness of MAE pre-pretraining for billion-scale pretrainingarXiv:2303.13496