Machine Learning Models Efficiency Analysis for Image Classification Problem

Kirill Smelyakov , Yaroslav Honchar , Oleksandr Bohomolov and Anastasiya Chupryna

Kharkiv National University of Radio Electronics, 14 Nauky Ave., Kharkiv, 61166, Ukraine

The article is devoted to the analysis of the effectiveness of the application of modern machine learning models of convolutional neural networks, which are used for image classification. To conduct such an analysis, an actual dataset is selected and divided into training, validation, and test subsets in a standard proportion. The dataset which is selected consists of images of birds. Classification efficiency indicators are determined. ResNet and EfficientNet V2 neural networks are trained using a full training cycle and Transfer Learning technology on frozen and free weights. Pytorch framework is used to train ResNet model and Tensorflow framework is used to train EfficientNet V2 model. The effectiveness of the use of neural networks is evaluated. The evaluation is done by analyzing popular classification metrics, such as precision, recall, and f1 score. The results of experiments are given, along with conclusions and practical recommendations on the use of machine learning models