An application improving the accuracy of image classification

Abstract

There have been various research approaches to the problem of image classification so far. For image data containing kinds of objects in the wild, many machine learning algorithms give unreliable results. Meanwhile, deep learning networks are appropriate for big data, and they can deal with the problem effectively. Therefore, this paper aims to build an application combining a ResNet model and image manipulation to improve the accuracy of classification. The classifier performs the training phases on CIFAR-10 in a feasible time. In addition, it achieves around 93% accuracy of the test data. This result is better than that of some recently published studies.