Prompt Engineering On Imagenet V2
Métriques
Top-1 accuracy %
Résultats
Résultats de performance de divers modèles sur ce benchmark
Tableau comparatif
| Nom du modèle | Top-1 accuracy % |
|---|---|
| conditional-prompt-learning-for-vision | 64.07 |
| maple-multi-modal-prompt-learning | 64.07 |
| Modèle 3 | 63.8 |
| learning-transferable-visual-models-from | 60.83 |
| learning-hierarchical-prompt-with-structured | 65.25 |
| mmrl-multi-modal-representation-learning-for | 64.47 |
| hpt-hierarchically-prompting-vision-language | 65.31 |
| self-regulating-prompts-foundational-model | 64.35 |