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Prompt Engineering On Imagenet A

Métriques

Top-1 accuracy %

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	Top-1 accuracy %	Paper Title	Repository
HPT	50.85	Learning Hierarchical Prompt with Structured Linguistic Knowledge for Vision-Language Models
MaPLe	50.90	MaPLe: Multi-modal Prompt Learning
PromptSRC	50.90	Self-regulating Prompts: Foundational Model Adaptation without Forgetting
CoCoOp	50.63	Conditional Prompt Learning for Vision-Language Models
POMP	51.6	Prompt Pre-Training with Twenty-Thousand Classes for Open-Vocabulary Visual Recognition
MMRL	51.20	MMRL: Multi-Modal Representation Learning for Vision-Language Models
CoPrompt	50.50	Consistency-guided Prompt Learning for Vision-Language Models
CLIP	47.77	Learning Transferable Visual Models From Natural Language Supervision
HPT++	51.18	HPT++: Hierarchically Prompting Vision-Language Models with Multi-Granularity Knowledge Generation and Improved Structure Modeling

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