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English(EN) YOLO-PEFT: Parameter-Efficient Fine-Tuning on YOLO Family

YOLO-PEFT框架增强了实时目标检测器的微调性能

研究人员开发了YOLO-PEFT,一个旨在改进YOLO等实时目标检测模型参数高效微调(PEFT)的新框架。与可能因检测器复杂结构而失败的通用PEFT方法不同,YOLO-PEFT采用一种感知结构的方来规划适配器放置,确保兼容性并避免静默失败。该方法在YOLO11s和YOLO12s上实现了比完整微调更高的mAP分数,并在YOLO11上将峰值训练内存减少了近44%,尽管训练时间有所延长。 AI

影响 该框架有望提高大型目标检测模型的微调效率和有效性,从而加速其在实时应用中的部署。

排序理由 该集群描述了一篇详细介绍目标检测模型微调新框架的最新研究论文。

在 Hugging Face Daily Papers 阅读 →

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YOLO-PEFT框架增强了实时目标检测器的微调性能

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报道来源 [2]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    YOLO-PEFT:YOLO系列模型的参数高效微调

    Generic parameter-efficient fine-tuning (PEFT) methods transferred from language models can fail silently on real-time detectors, whose heterogeneous operators and detection-specific components impose placement constraints absent from regular Transformer stacks. We propose YOLO-P…

  2. arXiv cs.CV TIER_1 English(EN) · Xu Lin, WenJie Nie, Jinlong Peng, Weifu Fu, YueXiao Ma, Xiawu Zheng, Yong Liu ·

    YOLO-PEFT:YOLO系列模型的参数高效微调

    arXiv:2608.07051v1 Announce Type: new Abstract: Generic parameter-efficient fine-tuning (PEFT) methods transferred from language models can fail silently on real-time detectors, whose heterogeneous operators and detection-specific components impose placement constraints absent fr…