딥러닝 탐구생활

논문 출처: https://arxiv.org/abs/2405.14458 YOLOv10: Real-Time End-to-End Object DetectionOver the past years, YOLOs have emerged as the predominant paradigm in the field of real-time object detection owing to their effective balance between computational cost and detection performance. Researchers have explored the architectural designs, optimarxiv.org Abstract최근 몇 년 동안 YOLO는 real-time object detec..

논문 출처 : https://arxiv.org/abs/2207.02696 YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectorsYOLOv7 surpasses all known object detectors in both speed and accuracy in the range from 5 FPS to 160 FPS and has the highest accuracy 56.8% AP among all known real-time object detectors with 30 FPS or higher on GPU V100. YOLOv7-E6 object detector (56 FPSarxiv.org Ab..

논문 출처 : https://arxiv.org/abs/2209.02976 YOLOv6: A Single-Stage Object Detection Framework for Industrial ApplicationsFor years, the YOLO series has been the de facto industry-level standard for efficient object detection. The YOLO community has prospered overwhelmingly to enrich its use in a multitude of hardware platforms and abundant scenarios. In this technical reportarxiv.org Abstract빠르게 발전..