| Task Name | Dataset Name | SOTA Result | Trend | |
|---|---|---|---|---|
| Image Classification | Food101 (test) | Accuracy96.38 | 87 | |
| Class-Incremental Learning | Food101-LT rho=100 (test) | Accuracy36.84 | 48 | |
| Image Classification | Food101 Base-to-new generalization | Accuracy (Base Classes)93.75 | 47 | |
| Multimodal Classification | FOOD101 UPMC (train test) | Accuracy94.2 | 36 | |
| Fine-grained Classification | Food101 | Accuracy98.6 | 30 | |
| Image Classification | Food101 novel classes | ECE0.0074 | 29 | |
| Fine-grained classification | Food101 (base classes) | Accuracy91.71 | 27 | |
| Error Detection | Food101 | AuROC95.06 | 27 | |
| Image Classification | Food101 | Top-1 Accuracy96.38 | 24 | |
| Image Classification | Food101 H (harmonic mean) | Accuracy91.38 | 16 | |
| Image Classification | Food101 Base | Top-1 Accuracy91.63 | 16 | |
| Fine-grained classification | Food101 novel classes | MCE0.25 | 15 | |
| Image Classification | Food101 5-way 5-shot (test) | Accuracy (%)79.59 | 13 | |
| Image Classification | Food101 5-way 2-shot | Accuracy64.18 | 13 | |
| Image Classification | Food101 New classes | Accuracy92.05 | 12 | |
| Fine-grained category recognition and content assessment | Food101 | Score75 | 12 | |
| Classification | Food101 | Robust Accuracy57.84 | 10 | |
| Object Recognition | Food101 | MiniGPT-4 Score64.16 | 10 | |
| Image Classification | Food101 | Accuracy (Clean)83.89 | 9 | |
| Image Classification | Food101 Pathological Non-IID (test) | Accuracy92.73 | 9 | |
| Image Classification | Food101 4-shot | Accuracy91.4 | 8 | |
| Zero-shot Image Classification | Food101 | Accuracy (Clean)74.33 | 7 | |
| k-NN Image Classification | Food101 | Accuracy80.3 | 7 | |
| Class-conditioned generation | Food101 | FID6.05 | 7 | |
| Image Classification | Food101N | Accuracy85.61 | 7 |