MusiCorpus supplies 1,309 pages of real historical handwritten music with transcriptions and annotations, the largest such resource for training optical music recognition systems under realistic conditions.
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YOLOv12: Attention-Centric Real-Time Object Detectors
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abstract
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.
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representative citing papers
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citing papers explorer
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SARES-DEIM: Sparse Mixture-of-Experts Meets DETR for Robust SAR Ship Detection
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Training-Free Tunnel Defect Inspection and Engineering Interpretation via Visual Recalibration and Entity Reconstruction
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Visual Prototype Conditioned Focal Region Generation for UAV-Based Object Detection
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Scale-Gest: Scalable Model-Space Synthesis and Runtime Selection for On-Device Gesture Detection
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A Self-Evolving Defect Detection Framework for Industrial Photovoltaic Systems
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Unified Unsupervised and Sparsely-Supervised 3D Object Detection by Semantic Pseudo-Labeling and Prototype Learning
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Edge Assisted Multi-Camera Vehicle Tracking Framework for Real-Time and Scalable Deployment
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SpikeDet: Better Firing Patterns for Accurate and Energy-Efficient Object Detection with Spiking Neural Networks
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