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Prompt Engineering On Oxford 102 Flower

Metrics

Harmonic mean

Results

Performance results of various models on this benchmark

Model Name	Harmonic mean	Paper Title	Repository
CLIP	74.83	Learning Transferable Visual Models From Natural Language Supervision
PromptSRC	85.95	Self-regulating Prompts: Foundational Model Adaptation without Forgetting
CoPrompt	85.71	Consistency-guided Prompt Learning for Vision-Language Models
HPT	87.16	Learning Hierarchical Prompt with Structured Linguistic Knowledge for Vision-Language Models
MetaPrompt	84.52	Learning Domain Invariant Prompt for Vision-Language Models
ProMetaR	86.70	Prompt Learning via Meta-Regularization
DePT	86.46	DePT: Decoupled Prompt Tuning	-
CoCoOp	81.71	Conditional Prompt Learning for Vision-Language Models
RPO	84.50	Read-only Prompt Optimization for Vision-Language Few-shot Learning
HPT++	85.85	HPT++: Hierarchically Prompting Vision-Language Models with Multi-Granularity Knowledge Generation and Improved Structure Modeling
PromptKD	90.24	PromptKD: Unsupervised Prompt Distillation for Vision-Language Models
MaPLe	82.56	MaPLe: Multi-modal Prompt Learning
MMRL	86.78	MMRL: Multi-Modal Representation Learning for Vision-Language Models

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