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Prompt Engineering On Imagenet S

Metrics

Top-1 accuracy %

Results

Performance results of various models on this benchmark

Model Name	Top-1 accuracy %	Paper Title	Repository
HPT++	49.28	HPT++: Hierarchically Prompting Vision-Language Models with Multi-Granularity Knowledge Generation and Improved Structure Modeling	-
CoCoOp	48.75	Conditional Prompt Learning for Vision-Language Models	-
MMRL	49.17	MMRL: Multi-Modal Representation Learning for Vision-Language Models	-
CoPrompt	49.43	Consistency-guided Prompt Learning for Vision-Language Models	-
POMP	49.8	Prompt Pre-Training with Twenty-Thousand Classes for Open-Vocabulary Visual Recognition	-
MaPLe	49.15	MaPLe: Multi-modal Prompt Learning	-
CLIP	46.15	Learning Transferable Visual Models From Natural Language Supervision	-
PromptSRC	49.55	Self-regulating Prompts: Foundational Model Adaptation without Forgetting	-
HPT	49.36	Learning Hierarchical Prompt with Structured Linguistic Knowledge for Vision-Language Models	-

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Prompt Engineering On Imagenet S | SOTA | HyperAI