딥러닝 탐구생활

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..

논문 출처 : https://arxiv.org/abs/2004.10934 YOLOv4: Optimal Speed and Accuracy of Object DetectionThere are a huge number of features which are said to improve Convolutional Neural Network (CNN) accuracy. Practical testing of combinations of such features on large datasets, and theoretical justification of the result, is required. Some features operatearxiv.org IntroductionCNN 기반 객체 감지기는 주로 추천 시스템에..

논문 출처 : https://arxiv.org/abs/1804.02767 YOLOv3: An Incremental ImprovementWe present some updates to YOLO! We made a bunch of little design changes to make it better. We also trained this new network that's pretty swell. It's a little bigger than last time but more accurate. It's still fast though, don't worry. At 320x320 YOLOv3arxiv.org YOLOv3(You Only Look Once version 3)는 Joseph Redmon과 Ali ..