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Models/MAWS (ViT-H)

MAWS (ViT-H)

Reported on 8 benchmarks across 3 tasks · 1 paper

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

Computer Vision8 results

Image ClassificationonObjectNet
Top-1 Accuracy· uses extra data· 2023-03-23
72.6
best: 82.7 (CoCa)
The effectiveness of MAE pre-pretraining for billion-scale pretraining arXiv:2303.13496
Image ClassificationonImageNet - 5-shot
Top 1 Accuracy· uses extra data· 2023-03-23
79.8
best: 82.78 (ViT-MoE-15B (Every-2))
The effectiveness of MAE pre-pretraining for billion-scale pretraining arXiv:2303.13496
Image ClassificationonImageNet - 10-shot
Top 1 Accuracy· uses extra data· 2023-03-23
82.5
best: 84.6 (MAWS (ViT-6.5B))
The effectiveness of MAE pre-pretraining for billion-scale pretraining arXiv:2303.13496
Image ClassificationonImageNet - 1-shot
Top 1 Accuracy· uses extra data· 2023-03-23
57.1
best: 68.66 (ViT-MoE-15B (Every-2))
The effectiveness of MAE pre-pretraining for billion-scale pretraining arXiv:2303.13496
Few-Shot Image ClassificationonImageNet - 5-shot
Top 1 Accuracy· uses extra data· 2023-03-23
79.8
best: 82.78 (ViT-MoE-15B (Every-2))
The effectiveness of MAE pre-pretraining for billion-scale pretraining arXiv:2303.13496
Few-Shot Image ClassificationonImageNet - 10-shot
Top 1 Accuracy· uses extra data· 2023-03-23
82.5
best: 84.6 (MAWS (ViT-6.5B))
The effectiveness of MAE pre-pretraining for billion-scale pretraining arXiv:2303.13496
Few-Shot Image ClassificationonImageNet - 1-shot
Top 1 Accuracy· uses extra data· 2023-03-23
57.1
best: 68.66 (ViT-MoE-15B (Every-2))
The effectiveness of MAE pre-pretraining for billion-scale pretraining arXiv:2303.13496
Zero-Shot Transfer Image ClassificationonImageNet
Accuracy (Private)· 2023-03-23
81.1
best: 88.5 (M2-Encoder)
The effectiveness of MAE pre-pretraining for billion-scale pretraining arXiv:2303.13496