*Introducing YOLOv12: Attention-Centric Real-Time Object Detectors* 🚀 The Future of Real-Time Object Detection is here. YOLOv12 takes real-time detection to the next level by integrating attention mechanisms while maintaining the speed of CNN-based models. This breakthrough closes the gap between accuracy and latency, making it a game-changer for AI applications. 🔥 *𝐖𝐡𝐲 𝐘𝐎𝐋𝐎𝐯12 𝐒𝐭𝐚𝐧𝐝𝐬 𝐎𝐮𝐭* ✅ First-ever attention-centric YOLO with CNN-level speed ✅ Unmatched balance between performance and efficiency ✅ Outperforms YOLOv7, YOLOv8, and Gold-YOLO across all scales 🚀 *𝐊𝐞𝐲 𝐀𝐜𝐡𝐢𝐞𝐯𝐞𝐦𝐞𝐧𝐭𝐬* ⚡️ YOLOv12-N: 40.6% mAP at 1.64ms latency on T4 GPU → +2.1% mAP over YOLOv10-N ⚡️ YOLOv12-S: 42% faster than RT-DETR-R18 with only 36% compute & 45% parameters ⚡️ Best-in-class latency-accuracy and FLOPs-accuracy trade-offs With YOLOv12, real-time object detection is faster, smarter, and more efficient than ever. Get ready to explore the next evolution.🚀🔍 https://www.arxiv.org/abs/2502.12524 Checkout: https://www.linkedin.com/posts/nirajlunavat_yolov12-activity-7298360460993568769-eHiK