| Dataset Name | SOTA Method | Metric | Trend | ||
|---|---|---|---|---|---|
| EuroSAT | TTC | Accuracy64.1 | 109 | 16d ago | |
| UCF101 | C-TPT | Accuracy75.1 | 81 | 16d ago | |
| Stanford Cars | MMAL-Net | Accuracy95 | 74 | 16d ago | |
| Aircraft | CAP | Top-1 Acc94.9 | 72 | 5d ago | |
| Caltech101 | TPT | Accuracy95.9 | 60 | 16d ago | |
| Pets | MTA | Accuracy93.7 | 53 | 16d ago | |
| Food101 | SigLIP2-B/16-256 | Top-1 Acc94.7 | 52 | 20d ago | |
| DTD | R-TPT | Clean Accuracy54 | 41 | 16d ago | |
| Oxford Flowers 102 | DINOv3-B | Accuracy99.69 | 41 | 20d ago | |
| SUN397 | CLIP | Top-1 Accuracy78.4 | 39 | 2mo ago | |
| FGVC Aircraft | ResNet-50 (ReLabel-trained) | Accuracy88.89 | 39 | 16d ago | |
| DTD | LoRA | Accuracy86.58 | 38 | 5d ago | |
| Cars | MTA | Accuracy78.4 | 37 | 16d ago | |
| Describable Textures Dataset (DTD) | C-TPT | Accuracy55.4 | 37 | 16d ago | |
| Oxford-IIIT Pets | OmniVec2 | Top-1 Accuracy99.6 | 37 | 20d ago | |
| Aircraft | MTA | Accuracy32.7 | 30 | 16d ago | |
| Pets (test) | GRPO | Accuracy70.7 | 29 | 2mo ago | |
| SUN397 (novel classes) | TCPT | ECE0.77 | 28 | 3mo ago | |
| EuroSAT (novel classes) | DAC | Expected Calibration Error3.33 | 28 | 3mo ago | |
| UCF101 (base classes) | TCPT | Accuracy84.68 | 27 | 3mo ago | |
| EuroSAT base classes | Temp. Scal. | Accuracy93.6 | 27 | 3mo ago | |
| DTD (base classes) | ZS-Norm | Accuracy81.02 | 27 | 3mo ago | |
| SUN397 (base classes) | TCPT | Accuracy81.68 | 27 | 3mo ago | |
| FGVCAircraft (base classes) | Temp. Scal. | Accuracy42 | 27 | 3mo ago | |
| Food101 (base classes) | ZS-Norm | Accuracy91.71 | 27 | 3mo ago |
