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Introduction

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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

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