Image Classification On Dtd
Métriques
Accuracy
Résultats
Résultats de performance de divers modèles sur ce benchmark
Tableau comparatif
| Nom du modèle | Accuracy |
|---|---|
| vision-models-are-more-robust-and-fair-when | 80.5 |
| self-supervised-learning-by-estimating-twin-1 | 76.6 |
| an-evolutionary-approach-to-dynamic | 81.0 |
| transboost-improving-the-best-imagenet | 76.49 |
| with-a-little-help-from-my-friends-nearest | 75.5 |
| radam-texture-recognition-through-randomized-1 | 84.0 |
| a-continual-development-methodology-for-large | 82.23 |
| bamboo-building-mega-scale-vision-dataset | 81.9 |
| task-arithmetic-in-the-tangent-space-improved | 90.0 |
| non-binary-deep-transfer-learning-for | 79.79 |
| non-binary-deep-transfer-learning-for | 66.8 |