Fine-grained Image Classification on FGVC (CUB-200, Stanford Dogs, Stanford Cars, NABirds) (test)
90.06Average Top-1 AccuracyLoCA (r16)
Evaluation Results
| Method | Links | |
|---|---|---|
| LoCA (r16)Backbone=ConvNeXt-B, Number of Trainable Parameters (M)=3.702026.07 | 90.06 | |
| CABackbone=ConvNeXt-B, Number of Trainable Parameters (M)=6.132026.07 | 89.28 | |
| LoRABackbone=ConvNeXt-B, Number of Trainable Parameters (M)=17.582026.07 | 88.18 | |
| FSFBackbone=ConvNeXt-B, Number of Trainable Parameters (M)=1.112026.07 | 88.04 | |
| CoLoRABackbone=ConvNeXt-B, Number of Trainable Parameters (M)=4.572026.07 | 86.11 | |
| LoCA (r16)Backbone=ResNet-50, Number of Trainable Parameters (M)=1.942026.07 | 83.67 | |
| CABackbone=ResNet-50, Number of Trainable Parameters (M)=2.232026.07 | 83.48 | |
| FSFBackbone=ResNet-50, Number of Trainable Parameters (M)=2.522026.07 | 81.07 | |
| LoRABackbone=ResNet-50, Number of Trainable Parameters (M)=2.432026.07 | 80.96 | |
| FFTBackbone=ConvNeXt-B, Number of Trainable Parameters (M)=87.872026.07 | 79.73 | |
| LPBackbone=ConvNeXt-B, Number of Trainable Parameters (M)=0.312026.07 | 77.55 | |
| CoLoRABackbone=ResNet-50, Number of Trainable Parameters (M)=0.992026.07 | 76.44 | |
| FFTBackbone=ResNet-50, Number of Trainable Parameters (M)=24.142026.07 | 75.73 | |
| BiasBackbone=ConvNeXt-B, Number of Trainable Parameters (M)=0.442026.07 | 64.98 | |
| BiasBackbone=ResNet-50, Number of Trainable Parameters (M)=0.672026.07 | 48.85 | |
| LPBackbone=ResNet-50, Number of Trainable Parameters (M)=0.622026.07 | 45.39 |