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Prompt Engineering On Imagenet R

Métriques

Top-1 accuracy %

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	Top-1 accuracy %	Paper Title	Repository
MaPLe	76.98	MaPLe: Multi-modal Prompt Learning	-
POMP	77.9	Prompt Pre-Training with Twenty-Thousand Classes for Open-Vocabulary Visual Recognition	-
HPT	77.38	Learning Hierarchical Prompt with Structured Linguistic Knowledge for Vision-Language Models	-
CoPrompt	77.51	Consistency-guided Prompt Learning for Vision-Language Models	-
PromptSRC	77.80	Self-regulating Prompts: Foundational Model Adaptation without Forgetting	-
CoCoOP	76.18	Conditional Prompt Learning for Vision-Language Models	-
CLIP	73.96	Learning Transferable Visual Models From Natural Language Supervision	-
HPT++	77.52	HPT++: Hierarchically Prompting Vision-Language Models with Multi-Granularity Knowledge Generation and Improved Structure Modeling	-
MMRL	77.53	MMRL: Multi-Modal Representation Learning for Vision-Language Models	-

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Prompt Engineering On Imagenet R | SOTA | HyperAI