Image Classification and Semantic Segmentation with Deep Learning

Deep Learning (DL) in convolutional networking systems (CNNs) is resourceful and revolutionizing in artificial intelligence (AI) and it will gradually change the world industrially and globally based on computer vision that will allow people to input Machine Learning (ML) models and output their given images and classify them based on different datasets and results since DL is a type of AI that imitates the way humans gain certain types of knowledge much easier and faster with neural networks having representation learning. However, there will be many challenging tasks such as image detection, recognition, and segmentation of objects in an unbounded environment that will be efficiently addressed by many types and methods of deep neural networks. In this paper, image classification is used to classify different images while segmentation is used to cluster (or bind) together parts of an image from the same object class to distinguish common patterns or results. Therefore, the aim is to train and test different DL models that will be able to produce images given from a modeled dataset, classify each part (whether it is for training or testing), and segment each of them in real time and compare them to see if they can be safely used in a technological environment. These methods will divide a dataset into groups or clusters to see if any similarities are minimized or maximized results produced in the form of line graphs that will showcase the accuracy and the losses for each modeled dataset.