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Models/ViT-B/16

ViT-B/16

Reported on 7 benchmarks across 5 tasks · 4 papers · 3 SOTA

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

Computer Vision4 results

Person Re-IdentificationonSoccerNet-v2
Rank-1· 2022-06-06
81.5
SOTA
Sports Re-ID: Improving Re-Identification Of Players In Broadcast Videos Of Team Sports arXiv:2206.02373
Person Re-IdentificationonSoccerNet-v2
mAP· 2022-06-06
86
SOTA
Sports Re-ID: Improving Re-Identification Of Players In Broadcast Videos Of Team Sports arXiv:2206.02373
Image ClassificationonImageNet
GFLOPs· 2024-09-16
16.87
best: 1478 (InternImage-H)
Kolmogorov-Arnold Transformer arXiv:2409.10594
Image ClassificationonImageNet
Top 1 Accuracy· 2024-09-16
79.1
best: 88.3 (Unicom (ViT-L/14@336px) (Finetuned))
Kolmogorov-Arnold Transformer arXiv:2409.10594

Methodology2 results

Zero-Shot LearningonCOCO-MLT
Average mAP· uses extra data· 2021-02-26
60.17
SOTA
Learning Transferable Visual Models From Natural Language Supervision arXiv:2103.00020
Remote SensingonFireRisk
Accuracy (%)· 2023-03-13
63.31
best: 65.29 (MAE (ViT-B/16))
FireRisk: A Remote Sensing Dataset for Fire Risk Assessment with Benchmarks Using Supervised and Self-supervised Learning arXiv:2303.07035

Computer Code1 result

Remote Sensing Image ClassificationonFireRisk
Accuracy (%)· 2023-03-13
63.31
best: 65.29 (MAE (ViT-B/16))
FireRisk: A Remote Sensing Dataset for Fire Risk Assessment with Benchmarks Using Supervised and Self-supervised Learning arXiv:2303.07035