Real-Time Object Detection Meets DINOv3
Authors: name: Shihua Huang; name: Yongjie Hou; name: Longfei Liu; name: Xuanlong Yu; name: Xi Shen
Published: 2025-09-25 06:14:00

Abstract: Benefiting from the simplicity and effectiveness of Dense O2O and MAL, DEIM
has become the mainstream training framework for real-time DETRs, significantly
outperforming the YOLO series. In this work, we extend it with DINOv3 features,
resulting in DEIMv2. DEIMv2 spans eight model sizes from X to Atto, covering
GPU, edge, and mobile deployment. For the X, L, M, and S variants, we adopt
DINOv3-pretrained or distilled backbones and introduce a Spatial Tuning Adapter
(STA), which efficiently converts DINOv3's single-scale output into multi-scale
features and complements strong semantics with fine-grained details to enhance
detection. For ultra-lightweight models (Nano, Pico, Femto, and Atto), we
employ HGNetv2 with depth and width pruning to meet strict resource budgets.
Together with a simplified decoder and an upgraded Dense O2O, this unified
design enables DEIMv2 to achieve a superior performance-cost trade-off across
diverse scenarios, establishing new state-of-the-art results. Notably, our
largest model, DEIMv2-X, achieves 57.8 AP with only 50.3 million parameters,
surpassing prior X-scale models that require over 60 million parameters for
just 56.5 AP. On the compact side, DEIMv2-S is the first sub-10 million model
(9.71 million) to exceed the 50 AP milestone on COCO, reaching 50.9 AP. Even
the ultra-lightweight DEIMv2-Pico, with just 1.5 million parameters, delivers
38.5 AP, matching YOLOv10-Nano (2.3 million) with around 50 percent fewer
parameters. Our code and pre-trained models are available at
https://github.com/Intellindust-AI-Lab/DEIMv2