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ResearchFormerly WizWand

Image Classification on CIFAR-100 10-shot (test)

87.39Top-1 AccEVA
25.021241.213157.40573.5969Nov 11, 2024Nov 29, 2024Dec 17, 2024Jan 5, 2025Jan 23, 2025Feb 10, 2025Mar 1, 2025
Updated 8mo ago

Evaluation results

MethodLinks
EVAModel=BEIT-L/14, Pre-training Dataset=CLIP OpenAI, IN-22K, IN-1K, Evaluation Protocol=Linear Probing2024.1187.39
SABackbone=ViT-Large, Shot-count=10-shot2025.0385.45
Split LearningBackbone=ViT-Large, Shot-count=10-shot2025.0383.66
Linear ProbingBackbone=ViT-Large, Shot-count=10-shot2025.0383.28
Fine TuningBackbone=Swin-L2025.0382.65
Fine TuningBackbone=ViT-Large, Shot-count=10-shot2025.0381.82
SABackbone=Swin-L2025.0380.96
Offsite TuningBackbone=Swin-L2025.0380.68
Offsite TuningBackbone=ViT-Large, Shot-count=10-shot2025.0380.11
Linear ProbingBackbone=Swin-L2025.0379.83
ScaleKDModel=ViT-B/16, Pre-training Dataset=IN-1K, Evaluation Protocol=Linear Probing2024.1177.52
CLIPModel=ViT-B/16, Pre-training Dataset=CLIP OpenAI, IN-12K, IN-1K, Evaluation Protocol=Linear Probing2024.1177.37
CLIPModel=ViT-B/16, Pre-training Dataset=LAION-300M, Evaluation Protocol=Linear Probing2024.1177.21
CLIPModel=ViT-B/16, Pre-training Dataset=LAION-2B, IN-12K, IN-1K, Evaluation Protocol=Linear Probing2024.1176.77
Split LearningBackbone=Swin-L2025.0373.59
CLIPModel=ViT-B/16, Pre-training Dataset=LAION-2B, IN-1K, Evaluation Protocol=Linear Probing2024.1172.34
From-scratchModel=ViT-B/16, Pre-training Dataset=IN-1K, Evaluation Protocol=Linear Probing2024.1166.77
LN-TUNEBackbone=ViT-Large, Shot-count=10-shot2025.0347.97
LN-TUNEBackbone=Swin-L2025.0327.42