Image Classification on CUB (Accuracy and Feature Dimensions)
87AccuracyBaseline Swin Transformer small
Evaluation results
| Method | Links | |||
|---|---|---|---|---|
| Baseline Swin Transformer smallBackbone=Swin Transformer small2025.11 | 87 | 768 | 768 | |
| Baseline Resnet50Backbone=ResNet-502025.11 | 86.6 | 2,048 | 2,048 | |
| CHiQPMBackbone=Swin Transformer small2025.11 | 85.9 | 50 | 5 | |
| SLDD-ModelBackbone=Swin Transformer small2025.11 | 85.3 | 50 | 5 | |
| CHiQPMBackbone=ResNet-502025.11 | 85.3 | 50 | 5 | |
| QPMBackbone=ResNet-502025.11 | 85.1 | 50 | 5 | |
| QPMBackbone=Swin Transformer small2025.11 | 85 | 50 | 5 | |
| Q-SENNBackbone=ResNet-502025.11 | 84.6 | 50 | 5 | |
| SLDD-ModelBackbone=ResNet-502025.11 | 84.5 | 50 | 5 | |
| PIP-NetBackbone=ResNet-502025.11 | 82 | 731 | 12 | |
| ProtoPoolBackbone=ResNet-502025.11 | 79.4 | 202 | 202 | |
| glm-saga5Backbone=ResNet-502025.11 | 78 | 809 | 5 | |
| glm-saga5Backbone=Swin Transformer small2025.11 | 76.5 | 572 | 5 |