building U-Net Model on a self-driving car dataset.

building a U-Net Model, a type of CNN designed for quick, precise image segmentation, and using it to predict a label for every single pixel in an image - in this case, an image from a self-driving car dataset.

Build your own U-Net Explain the difference between a regular CNN and a U-net Implement semantic image segmentation on the CARLA self-driving car dataset Apply sparse categorical crossentropy for pixelwise prediction