Prompt Engineering On Imagenet S
Métriques
Top-1 accuracy %
Résultats
Résultats de performance de divers modèles sur ce benchmark
Tableau comparatif
| Nom du modèle | Top-1 accuracy % |
|---|---|
| hpt-hierarchically-prompting-vision-language | 49.28 |
| conditional-prompt-learning-for-vision | 48.75 |
| mmrl-multi-modal-representation-learning-for | 49.17 |
| consistency-guided-prompt-learning-for-vision | 49.43 |
| prompt-pre-training-with-twenty-thousand-1 | 49.8 |
| maple-multi-modal-prompt-learning | 49.15 |
| learning-transferable-visual-models-from | 46.15 |
| self-regulating-prompts-foundational-model | 49.55 |
| learning-hierarchical-prompt-with-structured | 49.36 |