Setting up the Neural Network

Overview

The U-Net architecture is a convolutional neural network (CNN) used for semantic segmentation tasks in image processing. It was developed for image segmentation but has since found applications in various other domains due to its effectiveness in handling datasets and its ability to generate precise segmentation masks.

The architecture is named U-Net due to its U-shaped design. It consists of two main parts: the contracting path (left side) and the expansive path (right side), which are connected by a central bottleneck. The pictorial view of the U-Net structure is as follows:

The contracting path is used for feature extraction and context capture through convolutional and pooling layers, leading to a bottleneck layer. The expansive path then utilizes upsampling layers and convolutional operations, ...