Image Classification for Binary Classes Using Deep Convolutional Neural Network: An Experimental Study

Convolutional neural networks (CNNs) have proved itself a well-built model for image recognition in these modern computing days. Inclined by CNN's successes, we present an elaborative experimental assessment of CNN on image classification using a newly fabricated dataset of high-resolution images belonging to two different classes. The dataset partitioned into two distinct categories of high-resolution images of cats and dogs. This chapter presents an extensive experimental study of training size on training and validation accuracy and loss. We designed a fine-tuned predictive two-class image classification model for a large training size, which achieved a training accuracy of 100%, with validation accuracy close to 99.13%.