# YOLO-Type Neural Networks in the Process of Adapting Mathematical Graphs to the Needs of the Blind
## 2 Development of YOLO Neural Networks
Since 2020, a solution called PP-YOLO has been developed, and its versions PP-YOLO [38], PP-YOLO2 [39], and PP-YOLOE [40] have been published in the AvXiv journal. The changes made in this regard naturally had an impact on the entire official distribution of the main YOLO network. All changes, features, and architectural modifications in these variants have been described in their respective cited papers. The latest versions published so far are YOLOv9 [41], YOLOv10 [42], and YOLOv11 [36; 43]. The changes to the first version were mainly architectural and involved the introduction of two frameworks: PGI (Programmable Gradient Information) and GELAN (Generalized Efficient Layer Aggregation Network), which made it possible to reduce the number of computations and parameters compared to the previous YOLOv8 version. The scaled models for YOLOv9 included five variants with AP and AP\({}_{50}\) values ranging from 38.3% and 53.1% for the basic T variant to 55.6% and 72.8% for the most complex E variant. The second version, like its predecessors, focused on architectural changes but also eliminated the NMS algorithm in post-processing, which allowed the training process to be sped up. In place of the algorithm, dual label assignment was proposed. This allowed for the obtainment of an AP value at the level of 38.5% for the basic model YOLOv10-N and 54.4% for the most complex YOLOv10-X. The authors provided a total of six scaled versions. The latest version was also developed by Ultralytics and featured architectural changes, improved accuracy with fewer parameters, and optimizations for speed and efficiency. The authors provided five scale models as in YOLOv5 and YOLOv8, which were nano, small, medium, large, and extra-large. The AP for each of them was 39.5%, 47.0%, 51.5%, 53.4%, and 54.7%, respectively. All three architectures have been described in detail in their respective papers [41; 42; 43]. All of the average precision values that are described and cited are given in Table 1. \begin{table}
\begin{tabular}{c c c c} \hline
**YOLO Version** & **Scaled Models** & **AP** & **AP\({}_{\bf 50}\)** \\ \hline YOLOv1 & - & 63.4\% & - \\ \hline YOLOv2 & - & 78.6\% & - \\ \hline YOLOv3 & - & 36.2\% & 60.6\% \\ \hline YOLOv4 & - & 43.5\% & 65.7\% \\ \hline  & n & - & 45.7\% \\  & s & - & 56.8\% \\ YOLOv5 & m & - & 64.1\% \\  & l & - & 67.3\% \\  & x & - & 68.9\% \\ \hline  & N & - & 37.5\% \\ YOLOv6 & S & - & 45.0\% \\  & M & - & 50.0\% \\  & L & - & 52.8\% \\ \hline  & t & 38.7\% & 56.7\% \\ YOLOv7 & 7 & 51.4\% & 69.7\% \\  & x & 53.1\% & 71.2\% \\ \hline  & n & 37.3\% & - \\  & s & 44.9\% & - \\ YOLOv8 & m & 50.2\% & - \\  & l & 52.9\% & - \\  & x & 53.9\% & - \\ \hline  & t & 38.3\% & 53.1\% \\  & s & 46.8\% & 63.4\% \\ YOLOv9 & m & 51.4\% & 68.1\% \\  & c & 53.0\% & 70.2\% \\  & e & 55.6\% & 72.8\% \\ \hline \end{tabular}
\end{table} 
Table 1: Summarization of AP parameters for the main YOLO versions. The main measure used to assess the performance of the YOLO neural networks is the AP metric, sometimes referred to as mAP (Mean Average Precision) [44]. Its value is calculated taking into account three factors including the average precision of all the categories included in the dataset and allows for the evaluation of different configurations and models. The first factor can be described as precision and recall and determines the relationship between the accuracy of positive predictions and the actual number of objects correctly recognized. A large number of items being simultaneously identified may result in some of them being misclassified, which will inevitably affect the result obtained. To account for this factor, the AP metric takes into consideration the precision-recall curve. The second component focuses on assessing the effectiveness of recognizing objects from multiple categories in a single image and can be summarized as handling multiple object classes. This factor works as previously described in the general characterization of the AP metric, but the important aspect is that it considers all categories separately.