Binary Image Classification using Deep Learning from Scratch

Abstract: Over the last several years, deep learning has seen a considerable increase in its usage and applications. One of the most basic algorithms in either machine learning or deep learning is image classification. Numerous businesses, like Microsoft and Google, use deep learning in their products, including search engines, Google Lenses, and other areas of technology. The enhancements and technical advancements in this area specifically serve to enhance the model's performance. The categorization of numerous photographs into two different groups is the focus of this study. In this study, the data is initially preprocessed to ensure that it can be easily inserted into the deep learning neural network model. Without using any pre-existing deep learning frameworks like Tensor Flow, the architecture and neural networks needed for this project are designed from the ground up. In this study, we essentially use deep learning neural networks that were built from scratch to attempt to discriminate between a cat and a dog. Each and every cost and activation function is calculated by hand. We build a two-layer neural network to divide the photos into two groups that meet our needs. For the training data, the developed model's accuracy is 98%, while for the test data, it is over 80%. If the data is suitably pre-processed, this deep learning model may also be used to classify other images.