YOLOv12: Attention-Centric Real-Time Object Detectors
Enhancing the network architecture of the YOLO framework has been crucial for
a long time, but has focused on CNN-based improvements despite the proven
superiority of attention mechanisms in modeling capabilities. This is because
attention-based models cannot match the speed of CNN-based models. This paper
proposes an attention-centric YOLO framework, namely YOLOv12, that matches the
speed of previous CNN-based ones while harnessing the performance benefits of
attention mechanisms. YOLOv12 surpasses all popular real-time object detectors
in accuracy with competitive speed. For example, YOLOv12-N achieves 40.6% mAP
with an inference latency of 1.64 ms on a T4 GPU, outperforming advanced
YOLOv10-N / YOLOv11-N by 2.1%/1.2% mAP with a comparable speed. This advantage
extends to other model scales. YOLOv12 also surpasses end-to-end real-time
detectors that improve DETR, such as RT-DETR / RT-DETRv2: YOLOv12-S beats
RT-DETR-R18 / RT-DETRv2-R18 while running 42% faster, using only 36% of the
computation and 45% of the parameters. More comparisons are shown in Figure 1.
67b608ca13df25808fbc22ae
Yunjie Tian
67b608ca13df25808fbc22af
Qixiang Ye
67b608ca13df25808fbc22b0
David Doermann
YOLOv12: Attention-Centric Real-Time Object Detectors
Enhancing the network architecture of the YOLO framework has been crucial for
a long time, but has focused on CNN-based improvements despite the proven
superiority of attention mechanisms in modeling capabilities. This is because
attention-based models cannot match the speed of CNN-based models. This paper
proposes an attention-centric YOLO framework, namely YOLOv12, that matches the
speed of previous CNN-based ones while harnessing the performance benefits of
attention mechanisms. YOLOv12 surpasses all popular real-time object detectors
in accuracy with competitive speed. For example, YOLOv12-N achieves 40.6% mAP
with an inference latency of 1.64 ms on a T4 GPU, outperforming advanced
YOLOv10-N / YOLOv11-N by 2.1%/1.2% mAP with a comparable speed. This advantage
extends to other model scales. YOLOv12 also surpasses end-to-end real-time
detectors that improve DETR, such as RT-DETR / RT-DETRv2: YOLOv12-S beats
RT-DETR-R18 / RT-DETRv2-R18 while running 42% faster, using only 36% of the
computation and 45% of the parameters. More comparisons are shown in Figure 1.
A new YOLO framework, YOLOv12, integrates attention mechanisms to achieve high accuracy and competitive speed, outperforming existing models like YOLOv10/11-N and RT-DETR/RT-DETRv2.
YOLO framework attention mechanisms CNN mAP inference latency GPU RT-DETR