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Prompt Engineering On Oxford Iiit Pet Dataset

Metrics

Harmonic mean

Results

Performance results of various models on this benchmark

Model Name	Harmonic mean	Paper Title	Repository
CLIP	94.12	Learning Transferable Visual Models From Natural Language Supervision
HPT++	96.91	HPT++: Hierarchically Prompting Vision-Language Models with Multi-Granularity Knowledge Generation and Improved Structure Modeling
RPO	96.05	Read-only Prompt Optimization for Vision-Language Few-shot Learning
MaPLe	96.58	MaPLe: Multi-modal Prompt Learning
DePT	96.37	DePT: Decoupled Prompt Tuning	-
HPT	96.71	Learning Hierarchical Prompt with Structured Linguistic Knowledge for Vision-Language Models
ProMetaR	96.49	Prompt Learning via Meta-Regularization
MetaPrompt	96.26	Learning Domain Invariant Prompt for Vision-Language Models
PromptSRC	96.30	Self-regulating Prompts: Foundational Model Adaptation without Forgetting
CoPrompt	96.87	Consistency-guided Prompt Learning for Vision-Language Models
MMRL	96.74	MMRL: Multi-Modal Representation Learning for Vision-Language Models
CoCoOp	96.43	Conditional Prompt Learning for Vision-Language Models
PromptKD	97.15	PromptKD: Unsupervised Prompt Distillation for Vision-Language Models

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