Before we end up diving down a rabbit hole of possible upsampling algorithms, let's get back to our goal for the chapter. Per figure 13.6, step 1 is to get familiar with a foundational segmentation...

Architeture please

Extracted text: Before we end up diving down a rabbit hole of possible upsampling algorithms, let's get back to our goal for the chapter. Per figure 13.6, step 1 is to get familiar with a foundational segmentation algorithm called U-Net. The U-Net architecture is a design for a neural network that can produce pixel- wise output and that was invented for segmentation. As you can see from the highlight in figure 13.6, a diagram of the U-Net architecture looks a bit like the letter U, which explains the origins of the name. We also immediately see that it is quite a bit more complicated than the mostly sequential structure of the classifiers we are familiar with. We'll see a more detailed version of the U-Net architecture shortly, in figure 13.7, and learn exactly what cach of those components is doing. Once we understand the model architecture, we can work on training one to solve our segmentation task.