Implementation of U-Net for Image Segmentation

Image segmentation, a crucial task in computer vision, entails breaking down an image into distinct, meaningful segments. These segments represent different objects, boundaries, or areas of interest within the image. The primary aim of image segmentation is to simplify image representation, making it easier to analyze and extract valuable information.

In this project, we’ll unravel the intricacies of image segmentation tasks and explore the state-of-the-art U-Net deep learning architecture. We’ll take a quick look at various types of image segmentation tasks and dive deep into understanding U-Net’s architecture. We’ll also learn to implement this cutting-edge segmentation method from scratch, using the powerful combination of TensorFlow and Keras. The skills learned in this project can be seamlessly adapted for future deep learning projects.