Measuring the Effect of Data Augmentation in a CNN-Based Deep Neural Network Model

Traditional classification methods have difficulty in meeting the changing needs according tothe ever-increasing data piles. With the development of processors with high performance as memory andprocessing capabilities, deep learning-based methods have been widely used. A large amount of data isneeded to train a deep learning-based model, which is a computational science field. CIFAR-10, whichcontains images of 10 different objects in the world, is a benchmark dataset used effectively in imageidentification and classification. The proposed deep learning-based models should be tested in a computerenvironment in order to be used in real life. The proposed model performs the testing process with imagesthat it has never encountered during the training phase. In this article, a deep learning model is proposedthat performs classification on the CIFAR-10 dataset, which contains images of objects in the world. Aneffective classification method has been developed by removing the overfitting effect, if any, on theproposed model. Proposed model, classification process was carried out both with and without dataaugmentation. The data set used was expanded with random crop, scale transformation, vertical andhorizontal flipping data augmentation techniques. In the experimental studies, there was a big differencebetween the performance of the process using the data augmentation technique and the process without anyaugmentation. Using different augmentation techniques together or individually did not improve modelperformance. Proposed model achieved success rates of 91.93%, 93.63% and 90.49%, respectively,including train accuracy, precision, recall. According to the results obtained, it can be said that the studyhas achieved results that can compete with the literature.