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Prompt Engineering On Imagenet

Metrics

Harmonic mean

Results

Performance results of various models on this benchmark

Model Name	Harmonic mean	Paper Title	Repository
PromptKD	77.62	PromptKD: Unsupervised Prompt Distillation for Vision-Language Models
ProMetaR	74.09	Prompt Learning via Meta-Regularization
MaPLe	73.47	MaPLe: Multi-modal Prompt Learning
PromptSRC	74.01	Self-regulating Prompts: Foundational Model Adaptation without Forgetting
RPO	74.00	Read-only Prompt Optimization for Vision-Language Few-shot Learning
Customized Ensemble	75.49	Beyond Sole Strength: Customized Ensembles for Generalized Vision-Language Models
CLIP	70.22	Learning Transferable Visual Models From Natural Language Supervision
MetaPrompt	74.02	Learning Domain Invariant Prompt for Vision-Language Models
MMRL	74.45	MMRL: Multi-Modal Representation Learning for Vision-Language Models
HPT	74.17	Learning Hierarchical Prompt with Structured Linguistic Knowledge for Vision-Language Models
DePT	74.02	DePT: Decoupled Prompt Tuning	-
HPT++	74.24	HPT++: Hierarchically Prompting Vision-Language Models with Multi-Granularity Knowledge Generation and Improved Structure Modeling
CoCoOp	73.10	Conditional Prompt Learning for Vision-Language Models
CoPrompt	74.33	Consistency-guided Prompt Learning for Vision-Language Models

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