Selection of a Neural Network Model Based on the Hierarchy Process Analysis Method

Abstract

This article discusses the selection of the optimal neural network model YOLOv8 (YOLOv8s, YOLOv8l, YOLOv8x, YOLOv8m, YOLOv8n) using the hierarchy process analysis method, which allows structuring and systematizing complex decisions based on multi-criteria assessments. The main focus is on identifying and comparative analysis of the most significant criteria for assessing the effectiveness of neural network models, such as training time, as well as the Precision, Recall and F1-score metrics. These metrics play a key role in computer vision tasks, especially when it comes to object detection. During the study, pairwise comparison matrices were constructed, which allow not only to visually represent the relative importance of each of the selected parameters, but also to quantitatively assess their impact on the overall effectiveness of the model. The process of forming pairwise comparison matrices includes the opinion of experts in the field of machine learning and computer vision, which ensures a high degree of reliability of the results. After processing the data and performing calculations, including weighting each criterion, priorities for alternative YOLOv8 models were derived. As a result of the calculations, it was revealed that the YOLOv8n neural network model has the highest priority among all the alternatives evaluated. This emphasizes its superiority compared to other modifications of this model.