Prompt Engineering On Imagenet V2

Métriques

Top-1 accuracy %

Résultats

Résultats de performance de divers modèles sur ce benchmark

		Paper Title
HPT++	65.31	HPT++: Hierarchically Prompting Vision-Language Models with Multi-Granularity Knowledge Generation and Improved Structure Modeling
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
PromptSRC	64.35	Self-regulating Prompts: Foundational Model Adaptation without Forgetting
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

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Prompt Engineering On Imagenet V2 | SOTA | HyperAI