Prompt Engineering on ImageNet-R

Metric: Top-1 accuracy % (higher is better)

LeaderboardDataset

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#	Model↕	Top-1 accuracy %▼	Extra Data	Paper	Date↕	Code
1	POMP	77.9	No	Prompt Pre-Training with Twenty-Thousand Classes...	2023-04-10	Code
2	PromptSRC	77.8	No	Self-regulating Prompts: Foundational Model Adap...	2023-07-13	Code
3	MMRL	77.53	No	MMRL: Multi-Modal Representation Learning for Vi...	2025-03-11	Code
4	HPT++	77.52	No	HPT++: Hierarchically Prompting Vision-Language ...	2024-08-27	Code
5	CoPrompt	77.51	No	Consistency-guided Prompt Learning for Vision-La...	2023-06-01	Code
6	HPT	77.38	No	Learning Hierarchical Prompt with Structured Lin...	2023-12-11	Code
7	MaPLe	76.98	No	MaPLe: Multi-modal Prompt Learning	2022-10-06	Code
8	CoCoOP	76.18	No	Conditional Prompt Learning for Vision-Language ...	2022-03-10	Code
9	CLIP	73.96	No	Learning Transferable Visual Models From Natural...	2021-02-26	Code

#1POMPSOTA
77.9
Top-1 accuracy %· 2023-04-10
Prompt Pre-Training with Twenty-Thousand Classes for Open-Vocabulary Visual Recognition Code
#2PromptSRC
77.8
Top-1 accuracy %· 2023-07-13
Self-regulating Prompts: Foundational Model Adaptation without Forgetting Code
#3MMRL
77.53
Top-1 accuracy %· 2025-03-11
MMRL: Multi-Modal Representation Learning for Vision-Language Models Code
#4HPT++
77.52
Top-1 accuracy %· 2024-08-27
HPT++: Hierarchically Prompting Vision-Language Models with Multi-Granularity Knowledge Generation and Improved Structure Modeling Code
#5CoPrompt
77.51
Top-1 accuracy %· 2023-06-01
Consistency-guided Prompt Learning for Vision-Language Models Code
#6HPT
77.38
Top-1 accuracy %· 2023-12-11
Learning Hierarchical Prompt with Structured Linguistic Knowledge for Vision-Language Models Code
#7MaPLeSOTA
76.98
Top-1 accuracy %· 2022-10-06
MaPLe: Multi-modal Prompt Learning Code
#8CoCoOPSOTA
76.18
Top-1 accuracy %· 2022-03-10
Conditional Prompt Learning for Vision-Language Models Code
#9CLIPSOTA
73.96
Top-1 accuracy %· 2021-02-26
Learning Transferable Visual Models From Natural Language Supervision Code