Prompt Engineering On Imagenet V2
Métriques
Top-1 accuracy %
Résultats
Résultats de performance de divers modèles sur ce benchmark
Nom du modèle | Top-1 accuracy % | Paper Title | Repository |
|---|---|---|---|
| CoCoOp | 64.07 | Conditional Prompt Learning for Vision-Language Models | |
| MaPLe | 64.07 | MaPLe: Multi-modal Prompt Learning | |
| POMP | 63.8 | - | - |
| CLIP | 60.83 | Learning Transferable Visual Models From Natural Language Supervision | |
| HPT | 65.25 | Learning Hierarchical Prompt with Structured Linguistic Knowledge for Vision-Language Models | |
| MMRL | 64.47 | MMRL: Multi-Modal Representation Learning for Vision-Language Models | |
| HPT++ | 65.31 | HPT++: Hierarchically Prompting Vision-Language Models with Multi-Granularity Knowledge Generation and Improved Structure Modeling | |
| PromptSRC | 64.35 | Self-regulating Prompts: Foundational Model Adaptation without Forgetting |
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