Zero Shot Transfer Image Classification On 5
Métriques
Accuracy (Private)
Résultats
Résultats de performance de divers modèles sur ce benchmark
Tableau comparatif
| Nom du modèle | Accuracy (Private) |
|---|---|
| altclip-altering-the-language-encoder-in-clip | 69.5 |
| lit-zero-shot-transfer-with-locked-image-text | 79.4 |
| eva-clip-improved-training-techniques-for | 82.1 |
| eva-clip-18b-scaling-clip-to-18-billion | 87.3 |
| scaling-vision-transformers-to-22-billion | 90.1 |
| pali-a-jointly-scaled-multilingual-language | 88.0 |
| learning-transferable-visual-models-from | 77.2 |
| pali-a-jointly-scaled-multilingual-language | 44.7 |
| combined-scaling-for-zero-shot-transfer | 85.6 |
| scaling-up-visual-and-vision-language | 75.8 |
| Modèle 11 | 86.4 |
| internvl-scaling-up-vision-foundation-models | 83.8 |
| coca-contrastive-captioners-are-image-text | 90.2 |