👀YOLO is the first neural network recognized objects in real time on mobile devices. Due to the absence of “for”cycles in layers architecture, it provides high speed and accuracy of recognition in one pass.
The first version of YOLO was offered in 2016, and today, in May 2021, the 5th version has already been released. At the moment of the YOLO family provide the best results of real-time object detection.
YOLO works faster than R-CNN because it splits the image into a constant number of cells, instead of highlighting regions and calculating a solution for each of them. Now YOLO is not so good in recognition of objects with complex shapes or a group of small objects due to the insufficient number of candidates for the margins.
Nevertheless, in December 2020, Scaled YOLO v4 showed the best results (55.8% AP) on the Microsoft COCO dataset among peers, overtaking the Google EfficientDet D7x / DetectoRS neural network or SpineNet-190 (self-learning on additional data), Amazon Cascade in accuracy -RCNN, ResNest200 Microsoft RepPoints v2 and Facebook RetinaNet SpineNet-190. These results were achieved in the conditions of an optimal ratio of speed and accuracy from 15 FPS to 1774 FPS.