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Models/ROADMAP (ResNet-50)

ROADMAP (ResNet-50)

Reported on 7 benchmarks across 2 tasks · 1 paper

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

Computer Vision6 results

Image RetrievalonSOP
R@1· 2021-10-01
83.1
best: 91.2 (Unicom+ViT-L@336px)
Robust and Decomposable Average Precision for Image Retrieval arXiv:2110.01445
Image RetrievalonCUB-200-2011
R@1· 2021-10-01
68.5
best: 79.2 (CGD (MG/SG))
Robust and Decomposable Average Precision for Image Retrieval arXiv:2110.01445
Image RetrievaloniNaturalist
R@1· 2021-10-01
69.1
best: 88.9 (Unicom+ViT-L@336px)
Robust and Decomposable Average Precision for Image Retrieval arXiv:2110.01445
Image RetrievaloniNaturalist
R@16· 2021-10-01
91.3
best: 95.9 (Recall@k Surrogate loss (ViT-B/16))
Robust and Decomposable Average Precision for Image Retrieval arXiv:2110.01445
Image RetrievaloniNaturalist
R@32· 2021-10-01
93.9
best: 97.2 (Recall@k Surrogate loss (ViT-B/16))
Robust and Decomposable Average Precision for Image Retrieval arXiv:2110.01445
Image RetrievaloniNaturalist
R@5· 2021-10-01
83.1
best: 92.1 (Recall@k Surrogate loss (ViT-B/16))
Robust and Decomposable Average Precision for Image Retrieval arXiv:2110.01445

Methodology1 result

Metric LearningonStanford Online Products
R@1· 2021-10-01
83.1
best: 91.2 (Unicom+ViT-L@336px)
Robust and Decomposable Average Precision for Image Retrieval arXiv:2110.01445