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Prompt Engineering On Caltech 101

Metrics

Harmonic mean

Results

Performance results of various models on this benchmark

Model Name	Harmonic mean	Paper Title	Repository
ProMetaR	96.16	Prompt Learning via Meta-Regularization
HPT++	96.96	HPT++: Hierarchically Prompting Vision-Language Models with Multi-Granularity Knowledge Generation and Improved Structure Modeling
MaPLe	96.02	MaPLe: Multi-modal Prompt Learning
CoCoOp	95.84	Conditional Prompt Learning for Vision-Language Models
PromptSRC	96.02	Self-regulating Prompts: Foundational Model Adaptation without Forgetting
HPT	96.65	Learning Hierarchical Prompt with Structured Linguistic Knowledge for Vision-Language Models
CLIP	95.40	Learning Transferable Visual Models From Natural Language Supervision
CoPrompt	96.55	Consistency-guided Prompt Learning for Vision-Language Models
MetaPrompt	96.32	Learning Domain Invariant Prompt for Vision-Language Models
RPO	96.03	Read-only Prompt Optimization for Vision-Language Few-shot Learning
PromptKD	97.77	PromptKD: Unsupervised Prompt Distillation for Vision-Language Models
MMRL	96.68	MMRL: Multi-Modal Representation Learning for Vision-Language Models
DePT	96.28	DePT: Decoupled Prompt Tuning	-

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