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Prompt Engineering On Oxford Iiit Pet Dataset

Metrics

Harmonic mean

Results

Performance results of various models on this benchmark

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

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Prompt Engineering On Oxford Iiit Pet Dataset | SOTA | HyperAI