Fine-grained Image Classification on Herbarium (test)
14.9Top-1 Test ErrorFull FT
Evaluation Results
| Method | Links | |
|---|---|---|
| Full FTBackbone=SWIN-L, % Trainable parameters=100%, No backbone backpropagation=false2023.03 | 14.9 | |
| InCABackbone=SWIN-L, % Trainable parameters=3.7%, No backbone backpropagation=true2023.03 | 18.2 | |
| LoRABackbone=SWIN-L, % Trainable parameters=0.8%, No backbone backpropagation=false2023.03 | 18.4 | |
| Full FTBackbone=ViT-L/162023.03 | 18.8 | |
| LoRABackbone=ViT-L/162023.03 | 19.2 | |
| InCABackbone=ViT-L/162023.03 | 21.1 | |
| AdaLNBackbone=SWIN-L, % Trainable parameters=0.1%, No backbone backpropagation=false2023.03 | 21.2 | |
| VPTBackbone=ViT-L/162023.03 | 21.4 | |
| InCA (last)Backbone=SWIN-L, % Trainable parameters=3.7%, No backbone backpropagation=true2023.03 | 23 | |
| InCA (last)Backbone=ViT-L/162023.03 | 24.6 | |
| In. MLP-3Backbone=SWIN-L, % Trainable parameters=2.8%, No backbone backpropagation=true2023.03 | 24.9 | |
| MLP-3Backbone=SWIN-L, % Trainable parameters=2.8%, No backbone backpropagation=false2023.03 | 27.6 | |
| AdaLNBackbone=ViT-L/162023.03 | 27.9 | |
| BitFitBackbone=ViT-L/162023.03 | 28.3 | |
| In. LPBackbone=SWIN-L, % Trainable parameters=0.1%, No backbone backpropagation=true2023.03 | 29.2 | |
| In. MLP-3Backbone=ViT-L/162023.03 | 29.5 | |
| BitFitBackbone=SWIN-L, % Trainable parameters=0.1%, No backbone backpropagation=false2023.03 | 29.7 | |
| In. LPBackbone=ViT-L/162023.03 | 32.6 | |
| LPBackbone=SWIN-L, % Trainable parameters=0.1%, No backbone backpropagation=false2023.03 | 34 | |
| MLP-3Backbone=ViT-L/162023.03 | 36.4 | |
| LPBackbone=ViT-L/162023.03 | 39.8 |