What is YOLOv5
YOLO an acronym for ‘You only look once’, is an object detection algorithm that divides images into a grid system. Each cell in the grid is responsible for detecting objects within itself.
YOLO is one of the most famous object detection algorithms due to its speed and accuracy.
The History of YOLO
YOLOv5
Shortly after the release of YOLOv4 Glenn Jocher introduced YOLOv5 using the Pytorch framework.
The open source code is available on GitHub
Author: Glenn Jocher
Released: 18 May 2020
YOLOv4
With the original authors work on YOLO coming to a standstill, YOLOv4 was released by Alexey Bochoknovskiy, Chien-Yao Wang, and Hong-Yuan Mark Liao. The paper was titled YOLOv4: Optimal Speed and Accuracy of Object Detection
Author: Alexey Bochoknovskiy, Chien-Yao Wang, and Hong-Yuan Mark Liao
Released: 23 April 2020
yolov4-tiny ： https://arxiv.org/abs/2011.04244
code yolov4-tiny ：
https://github.com/bubbliiiing/yolov4-tiny-pytorch
YOLOv3
YOLOv3 improved on the YOLOv2 paper and both Joseph Redmon and Ali Farhadi, the original authors, contributed.
Together they published YOLOv3: An Incremental Improvement
The original YOLO papers were are hosted here
Author: Joseph Redmon and Ali Farhadi
Released: 8 Apr 2018
YOLOv2
YOLOv2 was a joint endevor by Joseph Redmon the original author of YOLO and Ali Farhadi.
Together they published YOLO9000:Better, Faster, Stronger
Author: Joseph Redmon and Ali Farhadi
Released: 25 Dec 2016
YOLOv1
YOLOv1 was released as a research paper by Joseph Redmon.
The paper was titled You Only Look Once: Unified, Real-Time Object Detection
Author: Joseph Redmon
Released: 8 Jun 2015