CNN Hyperparameters Optimization Using Random Search For Image Classification

In the last decade, deep learning in general and Convolutional Neural Networks (CNN) have achieved a human level and outstanding performance in almost all computer-based systems such as classification(text, images, and videos). Building an efficient CNN architecture and finding the most suitable layers requires time. In addition, tuning and setting CNN parameters such as the number of filters, size of filters, Dense rate, Dropout value, etc., has an essential effect on the overall performance of the CNN model. This paper investigated the effects of CNN hyperparameter optimizations using Random Search. To achieve our goals, we selected to investigate the hyperparameter optimizations of MobileNetV2 and VGG19 models when used for image classification. Here, we have combined four mask detection datasets of approximately 6K, 12K, 4k, and 4k  images into one dataset. Overall, the results of the experimental works showed that using the hyperparameter optimization technique improved the overall performance of both MobileNetV2 and VGG19 models. In addition, VGG19 outperformed MobileNetV2 across Accuracy, Precision, recall, and F1 score evaluation matrixes.