Semantic Segmentation with UNet++

Semantic segmentation is the process of dividing an image into multiple segments, each of which corresponds to a specific object or region of interest. This is achieved by assigning a class label to each pixel in the image, based on its visual features and context. Semantic segmentation is commonly used in computer vision applications such as autonomous driving, object detection, and image editing. UNet is an extension of the original UNet architecture for semantic segmentation tasks. It is a fully convolutional neural network that consists of an encoder and a decoder, which are connected by a series of skip connections.