| Task Name | Dataset Name | SOTA Result | Trend | |
|---|---|---|---|---|
| Image Classification | Pets | Accuracy99.75 | 320 | |
| Fine-grained Classification | Pets | Accuracy93.7 | 58 | |
| Image Classification | Pets (test) | Accuracy98.22 | 58 | |
| Image Classification | Pets | Top-1 Accuracy95.4 | 54 | |
| Fine-grained classification | Pets (test) | Accuracy85.9 | 42 | |
| Image Classification | Pets | Average Set Size2.01 | 36 | |
| Model Selection | Pets | Weighted Kendall's Tau0.841 | 36 | |
| Fine-grained classification | Pets | Top-1 Accuracy85.79 | 33 | |
| Image Classification | Pets | Accuracy94.5 | 33 | |
| Fine-grained classification | Pets | Clean Accuracy88.3 | 31 | |
| Image Classification | Pets | Accuracy91.6 | 30 | |
| Image-to-image retrieval | Pets | mAP82.3 | 30 | |
| Image Classification | Pets | Class Accuracy (CA)89.56 | 28 | |
| Multi-view crowd counting | PETS 2009 (test) | MAE3.29 | 27 | |
| Image Classification | Pets | Base Accuracy96.8 | 27 | |
| Multi-Object Tracking | PETS 2009 (S2.L1) | MOTA97.8 | 26 | |
| Classification | Pets | Accuracy93.5 | 24 | |
| Classification | Pets | AURC0.221 | 23 | |
| Image Classification | Pets | Clean Top-1 Accuracy93.7 | 22 | |
| Clustering | Pets | NMI93.7 | 21 | |
| Feature Inversion | Pets | SSIM52.1 | 20 | |
| Multi-view Crowd Counting | PETS 2009 | MAE2.97 | 15 | |
| Fine-Grained Visual Categorization | Pets-37 | cACC86.5 | 15 | |
| Backdoor Attack | Pets | CAD-8.2 | 13 | |
| Transferability Estimation | Pets | Weighted Kendall's tau0.792 | 13 |