Real-Time Object Detection Meets DINOv3
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
68da43a90177a6054b013c66
Shihua Huang
68da43a90177a6054b013c67
Yongjie Hou
68da43a90177a6054b013c68
Longfei Liu
68da43a90177a6054b013c69
Xuanlong Yu
68da43a90177a6054b013c6a
Xi Shen
Real-Time Object Detection Meets DINOv3
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
DEIMv2, an extended version of DEIM with DINOv3 features, achieves superior performance-cost trade-offs across diverse deployment scenarios, including GPU, edge, and mobile, by using Spatial Tuning Adapter and HGNetv2 with pruning.
Dense O2O MAL DETRs YOLO DINOv3 Spatial Tuning Adapter STA HGNetv2 depth and width pruning Dense O2O AP COCO