원문 YOLOv4: Optimal Speed and Accuracy of Object Detection Link : https://arxiv.org/abs/2004.10934v1 YOLOv4: Optimal Speed and Accuracy of Object Detection There 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 fea..
원문 YOLOv3: An Incremental Improvement Link : https://arxiv.org/abs/1804.02767 YOLOv3: An Incremental Improvement We 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 YOLOv3 arxiv.org Introduction yolov..
원문 YOLO9000: Better, Faster, Stronger Link : https://arxiv.org/abs/1612.08242 YOLO9000: Better, Faster, Stronger We introduce YOLO9000, a state-of-the-art, real-time object detection system that can detect over 9000 object categories. First we propose various improvements to the YOLO detection method, both novel and drawn from prior work. The improved model, YOLOv2, arxiv.org Introduction 해당 논문에..
원문 You Only Look Once:Unified, Real-Time Object Detection (2016) Link : https://arxiv.org/abs/1506.02640 You Only Look Once: Unified, Real-Time Object Detection We present YOLO, a new approach to object detection. Prior work on object detection repurposes classifiers to perform detection. Instead, we frame object detection as a regression problem to spatially separated bounding boxes and associa..
RUP라는 창업 프로젝트를 진행하면서 yolo를 사용한 Real Time Object Detection을 해야되는 상황이 생겼다. 그래서 yolo를 darknet을 이용해 환경을 구축했는데 darknet을 로컬 환경에 바로 구축하면 python 패키지의 버전문제, cuda 버전문제등 에러가 발생할 수 있으므로 docker를 이용해 환경구축을 했다. Dockerfile Dockerfile을 이용해 환경을 구축했고 해당 가상환경은 ubuntu 20.04 버전을 이용하였다. FROM nvidia/cuda:11.4.2-cudnn8-devel-ubuntu20.04 ARG DEBIAN_FRONTEND=noninteractive RUN apt-get update && \ apt-get upgrade -y && \ a..
