Data enhancement analysis for deep-based image classification

Previous works have shown that the effectiveness and reliability of CNN used in computer vision is closely dependent on whether the dataset is adequate. In this paper, we illustrated how data augmentation, a technique which expands original datasets through transformations, could help improve CNN models’ performance and prevent overfitting through several experiments. After importing two extremely small datasets found on Kaggle, we carried out experiments by comparing the performance of models employing and not employing data augmentation techniques, while keeping track of their training accuracy, validation accuracy and their final test accuracy respectively. Eventually, noticeable improvement could be witnessed when employing appropriate data augmentation techniques on small datasets in our experiments, as such methods could generate a more comprehensive and sufficient dataset to help models learn the key features.