Object detection is a computer vision task that involves predicting the presence of one or more objects, along with their classes and bounding boxes. YOLO (You Only Look Once) is a state of art Object Detector which can perform object detection in real-time with a good accuracy.

The first three YOLO versions have been released in 2016, 2017 and 2018 respectively. However, in 2020, within only a few months of period, three major versions of YOLO have been released named YOLO v4, YOLO v5 and PP-YOLO. The release of YOLO v5 has even made a controversy among the people in machine learning community.

Additionally, this has caused a dilemma in the minds of people who are going to start their machine learning projects. In this article, we will discuss the reason for these many new YOLO releases, while emphasizing their originality, authorship, performance and the major improvements, helping people to choose the most appropriate version for their project.

How YOLO evolved

YOLO has been first introduced in 2016 and it was a milestone in object detection research due to its capability of detecting objects in real-time with a better accuracy.

It was proposed by Joseph Redmon, a graduate from the University of Washington. The paper describing YOLO won the the OpenCV People’s Choice Award at the Conference on Computer Vision and Pattern Recognition (CVPR) in 2016.

#machine-learning #yolo #yolov5 #yolov4 #object-detection #deep learning