# YOLO-Type Neural Networks in the Process of Adapting Mathematical Graphs to the Needs of the Blind
## 2 Development of YOLO Neural Networks
The architecture was changed from Darknet-19 to Darknet-53, consisting of 53 convolutional layers, while reducing the number of max-pooling layers to 0. The third version of the network achieved an AP score of 36.2% and an AP\({}_{50}\) of 60.6%. These results are lower than those obtained in previous versions, but this is due to the change in the benchmark dataset from PASCAL VOC to Microsoft COCO, which has since become the default. Along with this change, a new AP\({}_{50}\) measure has been introduced that sets the threshold for the IoU parameter to 0.5. The detailed architecture of this version was also presented in the aforementioned paper [25]. The next version was published 2 years later, in 2020, in the journal ArXiv [26] by [PERSON], [PERSON], and [PERSON]. Since the article did not have the same authors as the previous versions, the community was initially skeptical about the new version and did not know if it was the official version. After some time, the presented concept was accepted as YOLOv4 and as a continuation of the evolution of the network, because it maintained all the characteristics of its predecessors while improving the training process and parameter optimization. Improvements included additional data augmentation methods, allowing the combination of four images to be analyzed out of their standard context during the training process, and the use of genetic algorithms for hyperparameter optimization. The basic architecture remained the same, as the Darknet-53 was also chosen, but a few modifications were made to better adapt it to the overall YOLO standards. The average precision metric for this network version on the MS COCO dataset was 43.5% for AP and 65.7% for AP\({}_{50}\). The detailed architecture of this version was also described in the mentioned paper [26]. A fifth version appeared a few months later, authored by Ultalytics founder and CEO [PERSON], but this time, it was not published but only appeared on Github [27]. Among the new features of YOLOv5 was the creation of mobile network versions for iOS and Android. In addition, the entire solution has been scaled and subdivided into networks of five different sizes so that they can be configured according to the user's needs and the capabilities of the environment in which they will be deployed. The main difference between this version and YOLOv4 is the foundation, because instead of being developed on Darknet, Pytorch was used as the baseline. The different versions were named according to their complexity. They included nano, small, medium, large, and extra-large. The average precision for each subdivision is currently 45.7%, 56.8%, 64.1%, 67.3%, and 68.9%, respectively. In addition, Yolov5 version 6.0 was released in 2021, introducing increased accuracy, optimized runtime performance, and five new complexity models. They include nano6, small6, medium6, large6, and extra-large6. The average accuracy for each new subdivision is currently 54.4%, 63.7%, 69.3%, 71.3%, and 72.7%, respectively. The architecture of this version is also available in the Github repository [27]. One year after the publication of YOLOv4, Scaled-YOLOv4 [28] was presented at CVPR 2021 (2021 IEEE Conference on Computer Vision and Pattern Recognition), where a new concept was introduced that allowed the model to be scaled. It gave the developer of a given model the choice of whether he or she prefers higher precision at the expense of speed, or lower accuracy at high speed. This is well illustrated by the AP parameter, which was 22% for the YOLOv4-tiny version focused on reducing detection time, while it was 56% for the higher-precision YOLOv4-large version. The architecture of this version was also described in detail in the mentioned paper [28]. Further development has led to new approaches being published on ArXiv: YOLOR [29] in May 2021 and YOLOX [30] in July 2021. In both cases, modifications of the basic principles have been explored.