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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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Prompt Engineering On Oxford 102 Flower | SOTA | HyperAI