When we delve into the world of image segmentation, one model stands out above the rest – U-Net. Originally introduced in 2015 by Ronneberger et al., U-Net was designed specifically for medical image segmentation tasks. However, its versatility soon became evident as researchers began to apply it to a wide range of semantic segmentation tasks.

But that’s not all U-Net is capable of. This neural network architecture can also be used for super resolution, transforming low-resolution images into high-definition ones, as well as for image generation, creating images from random noise.

In this article, we will take a deep dive into the intricacies of U-Net and how you can implement it from scratch using PyTorch. Figure 1 displays the complete U-Net architecture, and it’s easy to see how this network earned its name with its distinct U-like shape.

Join me as we explore the power and potential of U-Net in revolutionizing image segmentation and beyond.