YOLO v5 model architecture [Explained]

YOLO is a state of the art, real-time object detection algorithm created by Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi in 2015 and was pre-trained on the COCO dataset. It uses a single neural network to process an entire image. The image is divided into regions and the algorithm predicts probabilities and bounding boxes for each region. YOLO is well-known for its speed and accuracy and it has been used in many applications like: healthcare, security surveillance and self-driving cars. Since 2015 the Ultralytics team has been working on improving this model and many versions since then have been released.