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홈뉴스연구 논문튜토리얼데이터셋백과사전SOTALLM 모델GPU 랭킹컨퍼런스
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한국어
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  1. 홈
  2. SOTA
  3. 프롬프트 엔지니어링
  4. Prompt Engineering On Imagenet A

Prompt Engineering On Imagenet A

평가 지표

Top-1 accuracy %

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름
Top-1 accuracy %
Paper TitleRepository
HPT50.85Learning Hierarchical Prompt with Structured Linguistic Knowledge for Vision-Language Models
MaPLe50.90MaPLe: Multi-modal Prompt Learning
PromptSRC50.90Self-regulating Prompts: Foundational Model Adaptation without Forgetting
CoCoOp50.63Conditional Prompt Learning for Vision-Language Models
POMP51.6Prompt Pre-Training with Twenty-Thousand Classes for Open-Vocabulary Visual Recognition
MMRL51.20MMRL: Multi-Modal Representation Learning for Vision-Language Models
CoPrompt50.50Consistency-guided Prompt Learning for Vision-Language Models
CLIP47.77Learning Transferable Visual Models From Natural Language Supervision
HPT++51.18HPT++: Hierarchically Prompting Vision-Language Models with Multi-Granularity Knowledge Generation and Improved Structure Modeling
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한국어

소개

회사 소개데이터셋 도움말

제품

뉴스튜토리얼데이터셋백과사전

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