Prompt Engineering On Imagenet R
Metriken
Top-1 accuracy %
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Vergleichstabelle
| Modellname | Top-1 accuracy % |
|---|---|
| maple-multi-modal-prompt-learning | 76.98 |
| prompt-pre-training-with-twenty-thousand-1 | 77.9 |
| learning-hierarchical-prompt-with-structured | 77.38 |
| consistency-guided-prompt-learning-for-vision | 77.51 |
| self-regulating-prompts-foundational-model | 77.80 |
| conditional-prompt-learning-for-vision | 76.18 |
| learning-transferable-visual-models-from | 73.96 |
| hpt-hierarchically-prompting-vision-language | 77.52 |
| mmrl-multi-modal-representation-learning-for | 77.53 |