Fine Grained Image Classification On Oxford 1
Métriques
Accuracy
Résultats
Résultats de performance de divers modèles sur ce benchmark
Tableau comparatif
| Nom du modèle | Accuracy |
|---|---|
| omnivec2-a-novel-transformer-based-network | 99.6 |
| scaling-up-visual-and-vision-language | 96.19% |
| autoaugment-learning-augmentation-policies | 88.98% |
| transformer-in-transformer | 95.0% |
| bamboo-building-mega-scale-vision-dataset | 95.1% |
| dinov2-learning-robust-visual-features | 96.7 |
| efficientnet-rethinking-model-scaling-for | 95.4% |
| omnivec-learning-robust-representations-with | 99.2 |
| autoformer-searching-transformers-for-visual | 94.9% |
| neural-architecture-transfer | - |
| towards-fine-grained-image-classification | 96.28 |
| fixing-the-train-test-resolution-discrepancy | 94.8% |
| vision-models-are-more-robust-and-fair-when | 85.3% |
| fine-grained-visual-classification-via-2 | 95.28% |