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Image Classification on ImageNet (val) (Baseline vs Pruned Accuracy & FLOPs Reduction)

81.4Top-1 Accuracy (Baseline)Swin-T
69.096872.290975.48578.6791Oct 15, 2021Apr 1, 2022Sep 17, 2022Mar 5, 2023Aug 21, 2023Feb 6, 2024Jul 24, 2024

Evaluation results

MethodLinks
Swin-TArch.=Swin-T2024.0781.4——4.49—
X-PrunerArch.=Swin-T2024.0781.480.7-0.73.2—
STEPArch.=Swin-T2024.0781.477.2-4.23.5—
PASSArch.=Swin-T2024.0781.480.9-0.53.4—
ViT-B/16Arch.=ViT-B/162024.0781.07——17.6—
DepGraphArch.=ViT-B/162024.0781.0779.17-1.910.4—
PASSArch.=ViT-B/162024.0781.0779.77-1.310.7—
VIT-B/16Arch.=ViT-B/162023.0181.07———17.6
DepGraph+EMAArch.=ViT-B/162023.0181.0779.58-1.39—10.4
DepGraphArch.=ViT-B/162023.0181.0779.17-1.9—10.4
GFPArch.=ResNeXt-502024.0777.9777.53-0.442.11—
GFPArch.=NeXt-502023.0177.9777.53-0.44—2.11
CP-ViTArch.=ViT-B/162023.0177.9177.36-0.55—11.7
ResNeXt-50Arch.=ResNeXt-502024.0777.62——4.27—
DepGraphArch.=ResNeXt-502024.0777.6276.48-1.142.09—
PASSArch.=ResNeXt-502024.0777.6277.21-0.412.01—
ResNeXt-50Arch.=NeXt-502023.0177.62———4.27
DepGraphArch.=NeXt-502023.0177.6276.48-1.14—2.09
SSSArch.=ResNeXt-502024.0777.5774.98-2.592.43—
SSSArch.=NeXt-502023.0177.5774.98-2.59—2.43
TASArch.=ResNet-502023.0177.4676.2-1.26—2.31
GFPArch.=ResNet-502023.0176.7976.42-0.37—2.04
TaylorArch.=ResNet-502023.0176.1874.5-1.68—2.25
ResNet-50Arch.=ResNet-502023.0176.15———4.13
SFPArch.=ResNet-502023.0176.1574.61-1.54—2.4
FPGMArch.=ResNet-502023.0176.1575.5-0.65—2.38
CCPArch.=ResNet-502023.0176.1575.5-0.65—2.11
DepGraphArch.=ResNet-502023.0176.1575.83-0.32—1.99
GReg-2Arch.=ResNet-502023.0176.1375.36-0.77—2.77
SSSArch.=ResNet-502023.0176.1274.18-1.94—2.82
AutoSlimArch.=ResNet-502023.0176.175.6-0.5—2
SlimableArch.=ResNet-502023.0176.174.9-1.2—2.3
GFPArch.=Mob-v22023.0175.7469.16-6.58—0.15
AOFP-C1Arch.=ResNet-502023.0175.3475.630.29—2.58
MetaArch.=Mob-v22023.0174.768.2-6.5—0.14
DenseNet-121Arch.=DenseNet-1212023.0174.44———2.86
DepGraph-1.38GArch.=DenseNet-1212023.0174.4473.98-0.46—1.37
DepGraph-0.58GArch.=DenseNet-1212023.0174.4470.13-4.31—0.57
PSP-1.38GArch.=DenseNet-1212023.0174.3574.05-0.3—1.38
PSP-0.58GArch.=DenseNet-1212023.0174.3570.34-4.01—0.58
SFPBackbone=ResNet34, Dynamic=N2021.1073.9271.832.0941.1—
FPGMBackbone=ResNet34, Dynamic=N2021.1073.9272.541.3841.1—
LCCLBackbone=ResNet34, Dynamic=Y2021.1073.4272.990.4324.8—
TaylorBackbone=ResNet34, Dynamic=N2021.1073.3172.830.4822.25—
FTWTBackbone=ResNet34, Dynamic=Y, r=0.972021.1073.373.250.0525.86—
FTWTBackbone=ResNet34, Dynamic=Y, r=0.952021.1073.372.790.5137.77—
FTWTBackbone=ResNet34, Dynamic=Y, r=0.932021.1073.372.171.1347.42—
ResNet18Backbone=ResNet34, Dynamic=N2021.1073.369.763.5450.04—
FTWTBackbone=ResNet34, Dynamic=Y, r=0.922021.1073.371.711.5952.24—
ThiNetArch.=ResNet-502023.0172.8872.04-0.84—2.44
Mob-v2Arch.=Mob-v22023.0171.87———0.33
DepGraphArch.=Mob-v22023.0171.8768.46-3.41—0.15
FBSBackbone=ResNet18, Dynamic=Y2021.1070.768.22.549.49—
SFPBackbone=ResNet18, Dynamic=N2021.1070.2867.13.1841.8—
LCCLBackbone=ResNet18, Dynamic=Y2021.1069.9866.333.6534.6—
FTWTBackbone=ResNet18, Dynamic=Y, r=0.912021.1069.7667.492.2751.56—
MobileNetV1-75Backbone=MobileNetV1, Dynamic=N2021.1069.76672.7642.85—
PFP-BBackbone=ResNet18, Dynamic=N2021.1069.7465.654.0943.12—
FTWTBackbone=MobileNetV1, Dynamic=Y, r=12021.1069.5769.66-0.0941.07—
NetAdaptArch.=Mob-v22023.01—70——0.24