Fine-grained Image Classification on EuroSAT (test)
0.9Top-1 Test ErrorLoRA
Evaluation Results
| Method | Links | |
|---|---|---|
| LoRABackbone=ViT-L/162023.03 | 0.9 | |
| Full FTBackbone=ViT-L/162023.03 | 1 | |
| VPTBackbone=ViT-L/162023.03 | 1.1 | |
| InCABackbone=ViT-L/162023.03 | 1.2 | |
| BitFitBackbone=ViT-L/162023.03 | 1.4 | |
| In. MLP-3Backbone=ViT-L/162023.03 | 1.5 | |
| AdaLNBackbone=ViT-L/162023.03 | 1.5 | |
| InCA (last)Backbone=ViT-L/162023.03 | 1.9 | |
| In. LPBackbone=ViT-L/162023.03 | 2.1 | |
| MLP-3Backbone=ViT-L/162023.03 | 2.5 | |
| LPBackbone=ViT-L/162023.03 | 3.7 | |
| Full FTBackbone=SWIN-L, % Trainable parameters=100%, No backbone backpropagation=false2023.03 | 70 | |
| LoRABackbone=SWIN-L, % Trainable parameters=0.8%, No backbone backpropagation=false2023.03 | 90 | |
| AdaLNBackbone=SWIN-L, % Trainable parameters=0.1%, No backbone backpropagation=false2023.03 | 110 | |
| InCABackbone=SWIN-L, % Trainable parameters=3.7%, No backbone backpropagation=true2023.03 | 150 | |
| In. MLP-3Backbone=SWIN-L, % Trainable parameters=2.8%, No backbone backpropagation=true2023.03 | 160 | |
| BitFitBackbone=SWIN-L, % Trainable parameters=0.1%, No backbone backpropagation=false2023.03 | 170 | |
| MLP-3Backbone=SWIN-L, % Trainable parameters=2.8%, No backbone backpropagation=false2023.03 | 220 | |
| InCA (last)Backbone=SWIN-L, % Trainable parameters=3.7%, No backbone backpropagation=true2023.03 | 240 | |
| In. LPBackbone=SWIN-L, % Trainable parameters=0.1%, No backbone backpropagation=true2023.03 | 270 | |
| LPBackbone=SWIN-L, % Trainable parameters=0.1%, No backbone backpropagation=false2023.03 | 370 |