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English(EN) Real-Time Object Detection Meets DINOv3

DEIMv2目标检测模型集成DINOv3特性以提升性能

研究人员推出了一款名为DEIMv2的目标检测模型,该模型集成了DINOv3的特性,以提升性能和效率。这款新模型涵盖了从X到Atto的八种尺寸,可满足包括GPU、边缘设备和移动平台在内的各种部署需求。DEIMv2通过优化参数数量和性能,取得了最先进的成果,其最大的DEIMv2-X版本在参数更少的情况下,准确率超越了之前的模型。紧凑型的DEIMv2-S模型也在COCO数据集上设定了新的基准,在参数量低于1000万的情况下,AP超过50。 AI

影响 在各种模型尺寸和部署场景下,为目标检测设定了新的最先进成果。

排序理由 该集群包含一篇详细介绍新模型发布和基准测试结果的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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DEIMv2目标检测模型集成DINOv3特性以提升性能

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该集群包含一篇详细介绍新模型发布和基准测试结果的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Shihua Huang, Yongjie Hou, Longfei Liu, Xuanlong Yu, Xi Shen ·

    实时物体检测遇上 DINOv3

    arXiv:2509.20787v5 Announce Type: replace Abstract: Driven by the simple and effective Dense O2O, DEIM demonstrates faster convergence and enhanced performance. In this work, we extend it with DINOv3 features, resulting in DEIMv2. DEIMv2 spans eight model sizes from X to Atto, co…