Open AccessA comparison of conventional and deep learning methods of image classification
Author(s) -
Maryna Dovbnych,
Małgorzata Plechawska–Wójcik
Publication year - 2021
Publication title -
journal of computer sciences institute
Language(s) - English
Resource type - Journals
ISSN - 2544-0764
DOI - 10.35784/jcsi.2727
Subject(s) - mnist database , computer science , artificial intelligence , convolutional neural network , pattern recognition (psychology) , contextual image classification , deep learning , perceptron , image (mathematics) , task (project management) , multilayer perceptron , machine learning , layer (electronics) , artificial neural network , engineering , chemistry , systems engineering , organic chemistry
The aim of the research is to compare traditional and deep learning methods in image classification tasks. The conducted research experiment covers the analysis of five different models of neural networks: two models of multi–layer perceptron architecture: MLP with two hidden layers, MLP with three hidden layers; and three models of convolutional architecture: the three VGG blocks model, AlexNet and GoogLeNet. The models were tested on two different datasets: CIFAR–10 and MNIST and have been applied to the task of image classification. They were tested for classification performance, training speed, and the effect of the complexity of the dataset on the training outcome.