title: YOLOv12: Attention-Centric Real-Time Object Detectors
summary: 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.
paper_authors._id: 67b608ca13df25808fbc22ae
paper_authors.name: Yunjie Tian
paper_authors._id: 67b608ca13df25808fbc22af
paper_authors.name: Qixiang Ye
paper_authors._id: 67b608ca13df25808fbc22b0
paper_authors.name: David Doermann
paper_title: YOLOv12: Attention-Centric Real-Time Object Detectors
paper_summary: 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.
paper_ai_keywords: attention mechanisms CNN-based improvements real-time object detectors mAP (mean Average Precision) inference latency GPU T4 GPU parameter-efficient end-to-end real-time detectors DETR (Detection Transformer) RT-DETR (Real-Time DETR) computational requirements