Fine-Grained Visual Classification on Stanford Dogs (test)
92Top-1 AccRLRR
Evaluation Results
| Method | Links | ||
|---|---|---|---|
| RLRRBackbone=ViT-B/16, Pre-training=ImageNet-21k, Params. (M)=0.47, AugReg=false2024.03 | 92 | — | |
| ARCBackbone=ViT-B/16, Pre-training=ImageNet-21k, Params. (M)=0.25, AugReg=false2024.03 | 91.9 | — | |
| BiasBackbone=ViT-B/16, Pre-training=ImageNet-21k, Params. (M)=0.28, AugReg=false2024.03 | 91.2 | — | |
| MP-FGVCBackbone=ViT-B-162023.09 | 91 | — | |
| LoRABackbone=ViT-B/16, Pre-training=ImageNet-21k, Params. (M)=0.44, AugReg=false2024.03 | 91 | — | |
| PRISBackbone=CNN2023.09 | 90.7 | — | |
| VPT-ShallowBackbone=ViT-B/16, Pre-training=ImageNet-21k, Params. (M)=0.25, AugReg=false2024.03 | 90.7 | — | |
| IELTBackbone=ViT-B-16, reproduced_from_source=true2023.09 | 90.6 | — | |
| TransFGBackbone=ViT-B-16, reproduced_from_source=true2023.09 | 90.5 | — | |
| MSHQPBackbone=CNN2023.09 | 90.4 | — | |
| API-NetBackbone=CNN2023.09 | 90.3 | — | |
| VPT-DeepBackbone=ViT-B/16, Pre-training=ImageNet-21k, Params. (M)=0.85, AugReg=false2024.03 | 90.2 | — | |
| RLRR*Backbone=ViT-B/16, Pre-training=ImageNet-21k, Params. (M)=0.47, AugReg=true2024.03 | 90 | — | |
| AdapterBackbone=ViT-B/16, Pre-training=ImageNet-21k, Params. (M)=0.41, AugReg=false2024.03 | 89.8 | — | |
| SSFBackbone=ViT-B/16, Pre-training=ImageNet-21k, Params. (M)=0.39, AugReg=true2024.03 | 89.6 | — | |
| Full fine-tuningBackbone=ViT-B/16, Pre-training=ImageNet-21k, Params. (M)=85.98, AugReg=false2024.03 | 89.4 | — | |
| SIM-TransBackbone=ViT-B-16, reproduced_from_source=true2023.09 | 89.2 | — | |
| MRDMNBackbone=CNN2023.09 | 89.1 | — | |
| ARC*Backbone=ViT-B/16, Pre-training=ImageNet-21k, Params. (M)=0.25, AugReg=true2024.03 | 89.1 | — | |
| CALBackbone=CNN2023.09 | 88.7 | — | |
| Linear probingBackbone=ViT-B/16, Pre-training=ImageNet-21k, Params. (M)=0.18, AugReg=false2024.03 | 86.2 | — | |
| SPT-DeepBackbone=ViT-B, Pre-training Method=MoCo-v3, Pre-training Dataset=ImageNet-1K, Tuning Depth=Deep2024.02 | 85.84 | — | |
| FDLBackbone=CNN2023.09 | 84.9 | — | |
| SPT-ShallowBackbone=ViT-B, Pre-training Method=MoCo-v3, Pre-training Dataset=ImageNet-1K, Tuning Depth=Shallow2024.02 | 84.17 | — | |
| PC-DenseNet-161backbone=DenseNet-161, pairwise_confusion=true2017.05 | 83.75 | 2.57 | |
| GateVPTBackbone=ViT-B, Pre-training Method=MoCo-v3, Pre-training Dataset=ImageNet-1K2024.02 | 83.37 | — | |
| VPT-DeepBackbone=ViT-B, Pre-training Method=MoCo-v3, Pre-training Dataset=ImageNet-1K, Tuning Depth=Deep2024.02 | 83.33 | — | |
| PC-BilinearCNNbackbone=VGG-16, pairwise_confusion=true2017.05 | 83.04 | 0.91 | |
| SPT-DeepBackbone=ViT-B, Pre-training Method=MAE, Pre-training Dataset=ImageNet-1K, Tuning Depth=Deep2024.02 | 82.23 | — | |
| BilinearCNNbackbone=VGG-162017.05 | 82.13 | — | |
| VPT-ShallowBackbone=ViT-B, Pre-training Method=MoCo-v3, Pre-training Dataset=ImageNet-1K, Tuning Depth=Shallow2024.02 | 81.97 | — | |
| FullBackbone=ViT-B, Pre-training Method=MoCo-v3, Pre-training Dataset=ImageNet-1K, Tuning Depth=Full2024.02 | 81.19 | — | |
| DenseNet-161backbone=DenseNet-1612017.05 | 81.18 | — | |
| Krause et al.2017.05 | 80.6 | — | |
| Zhang et al.2017.05 | 80.43 | — | |
| FullBackbone=ViT-B, Pre-training Method=MAE, Pre-training Dataset=ImageNet-1K, Tuning Depth=Full2024.02 | 80.38 | — | |
| SPT-ShallowBackbone=ViT-B, Pre-training Method=MAE, Pre-training Dataset=ImageNet-1K, Tuning Depth=Shallow2024.02 | 80.01 | — | |
| GateVPTBackbone=ViT-B, Pre-training Method=MAE, Pre-training Dataset=ImageNet-1K2024.02 | 78.9 | — | |
| VPT-DeepBackbone=ViT-B, Pre-training Method=MAE, Pre-training Dataset=ImageNet-1K, Tuning Depth=Deep2024.02 | 78.83 | — | |
| VPT-ShallowBackbone=ViT-B, Pre-training Method=MAE, Pre-training Dataset=ImageNet-1K, Tuning Depth=Shallow2024.02 | 77.07 | — | |
| PANDStudent=ResNet-182026.02 | 74.98 | — | |
| PANDStudent=MobileNet-V22026.02 | 74.52 | — | |
| PC-ResNet-50backbone=ResNet-50, pairwise_confusion=true2017.05 | 73.35 | 3.43 | |
| VL2LiteStudent=MobileNet-V22026.02 | 73.28 | — | |
| VL2LiteStudent=ResNet-182026.02 | 73.14 | — | |
| KDStudent=MobileNet-V22026.02 | 73.02 | — | |
| ResNet-50backbone=ResNet-502017.05 | 69.92 | — | |
| RKDStudent=MobileNet-V22026.02 | 69.45 | — | |
| RKDStudent=ResNet-182026.02 | 69.03 | — | |
| KDStudent=ResNet-182026.02 | 68.8 | — | |
| w/o KDStudent=MobileNet-V22026.02 | 68.28 | — | |
| w/o KDStudent=ResNet-182026.02 | 67.37 | — |