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Prompt Engineering On Stanford Cars 1

Metrics

Harmonic mean

Results

Performance results of various models on this benchmark

Model Name	Harmonic mean	Paper Title	Repository
DePT	77.79	DePT: Decoupled Prompt Tuning	-
HPT	75.57	Learning Hierarchical Prompt with Structured Linguistic Knowledge for Vision-Language Models
PromptKD	83.13	PromptKD: Unsupervised Prompt Distillation for Vision-Language Models
PromptSRC	76.58	Self-regulating Prompts: Foundational Model Adaptation without Forgetting
CLIP	68.65	Learning Transferable Visual Models From Natural Language Supervision
MMRL	78.06	MMRL: Multi-Modal Representation Learning for Vision-Language Models
RPO	74.69	Read-only Prompt Optimization for Vision-Language Few-shot Learning
ProMetaR	76.72	Prompt Learning via Meta-Regularization
HPT++	75.59	HPT++: Hierarchically Prompting Vision-Language Models with Multi-Granularity Knowledge Generation and Improved Structure Modeling
MetaPrompt	75.48	Learning Domain Invariant Prompt for Vision-Language Models
CoCoOp	72.01	Conditional Prompt Learning for Vision-Language Models
MaPLe	73.47	MaPLe: Multi-modal Prompt Learning
CoPrompt	75.66	Consistency-guided Prompt Learning for Vision-Language Models

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