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ResearchFormerly WizWand

Feature Importance Assessment on ImageNet 1k (val)

34.71Insertion ScoreGeneric
5.714813.242420.7728.2976Jul 6, 2025
Updated 8mo ago

Evaluation results

MethodLinks
GenericVariant=Swin, Model=Swin2-B2025.0734.7147.16
GenericVariant=Swin, Model=Swin-B2025.0733.7642.94
BeyondVariant=Swin, Model=Swin2-B2025.0731.3249.44
ViT-CXVariant=DeiT, Model=DeiT3-L-16/2242025.0726.1159.6
IIAModel=BeiTv1-B-16/224, Training Scheme=Self-supervised2025.0725.3447.5
Chefer-LRPModel=BeiTv1-B-16/224, Training Scheme=Self-supervised2025.0724.8247.04
IIAModel=ViT-B-16/224, Training Scheme=MAE2025.0724.5343.63
Chefer-LRPModel=ViT-B-16/224, Training Scheme=MAE2025.0724.4442.8
BeyondVariant=Swin, Model=Swin-B2025.0723.7350.39
GenericVariant=DeiT, Model=DeiT3-L-16/2242025.0722.162.73
ViT-CXModel=BeiTv1-B-16/224, Training Scheme=Self-supervised2025.0721.5854.06
IBAVariant=DeiT, Model=DeiT3-L-16/2242025.0721.0165.6
Chefer-LRPVariant=ViT, Model=ViT-L-16/2242025.0720.9753.81
IBAVariant=Swin, Model=Swin2-B2025.0720.9453.85
IBAVariant=ViT, Model=ViT-L-16/2242025.0720.8854.85
GenericVariant=ViT, Model=ViT-L-16/2242025.0720.1755.54
IIAVariant=ViT, Model=ViT-L-16/2242025.0720.1455.22
ViT-CXModel=ViT-B-16/224, Training Scheme=MAE2025.0720.0145.8
ViT-CXVariant=ViT, Model=ViT-L-16/2242025.0719.7657.38
BeyondModel=BeiTv1-B-16/224, Training Scheme=Self-supervised2025.0719.7551.82
BeyondVariant=DeiT, Model=DeiT3-L-16/2242025.0719.3464.27
BeyondVariant=ViT, Model=ViT-L-16/2242025.0718.9956.55
ViT-CXVariant=DeiT, Model=DeiT3-B-16/2242025.0718.8850.4
CoIBAVariant=Swin, Model=Swin2-B2025.0718.8855.77
ViT-CXVariant=DeiT, Model=DeiT-B-16/2242025.0718.2146.97
IBAVariant=Swin, Model=Swin-B2025.0718.0352.81
GenericVariant=ViT, Model=ViT-B-16/2242025.0717.9257.03
IIAVariant=DeiT, Model=DeiT3-L-16/2242025.0717.7555.93
Chefer-LRPVariant=ViT, Model=ViT-B-16/2242025.0717.6756.69
CoIBAVariant=DeiT, Model=DeiT3-L-16/2242025.0717.6467.2
ViT-CXVariant=DeiT, Model=DeiT-S-16/2242025.0717.5744.43
IBAVariant=ViT, Model=ViT-B-16/2242025.0717.2359.06
CoIBAVariant=Swin, Model=Swin-B2025.0717.0954.8
ViT-CXVariant=ViT, Model=ViT-B-16/2242025.0716.9458.17
IIAVariant=ViT, Model=ViT-B-16/2242025.0716.5658.7
BeyondModel=ViT-B-16/224, Training Scheme=MAE2025.0716.2348.09
BeyondVariant=ViT, Model=ViT-B-16/2242025.0715.8460.37
GenericVariant=DeiT, Model=DeiT3-B-16/2242025.0715.752.42
IBAModel=ViT-B-16/224, Training Scheme=MAE2025.0715.6648.9
CoIBAVariant=ViT, Model=ViT-L-16/2242025.0715.461.14
IIAVariant=DeiT, Model=DeiT3-B-16/2242025.0715.3852.7
GenericVariant=DeiT, Model=DeiT-B-16/2242025.0715.149.41
IBAVariant=DeiT, Model=DeiT3-B-16/2242025.0715.0753.32
ViT-CXVariant=ViT, Model=ViT-S-16/224, Strong Regularization=true2025.0714.9856.65
BeyondVariant=DeiT, Model=DeiT3-B-16/2242025.0714.7154.07
Chefer-LRPVariant=DeiT, Model=DeiT-B-16/2242025.0714.6348.79
GenericVariant=ViT, Model=ViT-S-16/224, Strong Regularization=true2025.0714.5656.08
BeyondVariant=DeiT, Model=DeiT-B-16/2242025.0714.4548.88
ViT-CXModel=ViT-B-16/224, Training Scheme=Dino2025.0714.0945.55
IBAModel=BeiTv1-B-16/224, Training Scheme=Self-supervised2025.0713.9659.87
IBAVariant=DeiT, Model=DeiT-B-16/2242025.0713.9550.18
CoIBAModel=ViT-B-16/224, Training Scheme=MAE2025.0713.7753.42
IIAVariant=ViT, Model=ViT-S-16/224, Strong Regularization=true2025.0713.6957.31
IIAVariant=DeiT, Model=DeiT-B-16/2242025.0713.6950.51
IBAVariant=ViT, Model=ViT-S-16/224, Strong Regularization=true2025.0713.5658.64
CoIBAModel=BeiTv1-B-16/224, Training Scheme=Self-supervised2025.0713.5162.45
BeyondVariant=ViT, Model=ViT-S-16/224, Strong Regularization=true2025.0713.558.47
CoIBAVariant=ViT, Model=ViT-B-16/2242025.0713.0162.58
CoIBAVariant=DeiT, Model=DeiT3-B-16/2242025.0712.9756.54
Chefer-LRPVariant=DeiT, Model=DeiT-S-16/2242025.0712.7647.51
GenericVariant=DeiT, Model=DeiT-S-16/2242025.0712.148.64
ViT-CXVariant=ViT, Model=ViT-T-16/224, Strong Regularization=true2025.0712.0845.09
CoIBAVariant=DeiT, Model=DeiT-B-16/2242025.0711.7953.96
IBAVariant=DeiT, Model=DeiT-S-16/2242025.0711.4347.87
BeyondVariant=ViT, Model=ViT-T-16/224, Strong Regularization=true2025.0711.2644.07
BeyondVariant=DeiT, Model=DeiT-S-16/2242025.0711.1348.88
CoIBAVariant=ViT, Model=ViT-S-16/224, Strong Regularization=true2025.0711.0863.35
IIAVariant=DeiT, Model=DeiT-S-16/2242025.0710.8949.76
CoIBAVariant=DeiT, Model=DeiT-S-16/2242025.079.5553.38
GenericVariant=ViT, Model=ViT-T-16/224, Strong Regularization=true2025.07948.95
IBAModel=ViT-B-16/224, Training Scheme=Dino2025.078.9149.45
IIAVariant=ViT, Model=ViT-T-16/224, Strong Regularization=true2025.078.549.75
BeyondModel=ViT-B-16/224, Training Scheme=Dino2025.078.1250.23
IBAVariant=ViT, Model=ViT-T-16/224, Strong Regularization=true2025.078.0249.22
Chefer-LRPModel=ViT-B-16/224, Training Scheme=Dino2025.077.6250.42
IIAModel=ViT-B-16/224, Training Scheme=Dino2025.077.4650.52
CoIBAVariant=ViT, Model=ViT-T-16/224, Strong Regularization=true2025.076.9754.23
CoIBAModel=ViT-B-16/224, Training Scheme=Dino2025.076.8353.68