Application of neural networks of Siamese architecture in problems of classifying products of various categories on supermarket shelves

This paper presents a study on the application of Siamese architecture neural networks in problems of classifying various food products on the shelves of department stores. Siamese networks are a special class of neural network architectures that combine two convolutional subnets. This type of neural networks is often used in object matching problems and has an important advantage over traditional convolutional neural networks, namely the absence of the need for a large amount of training data. During the work, we generated our own data set, including five different product categories. As a result, it was possible to achieve a tonality of 97.5% during training.