This tutorial guides you through building, training, and testing a UNETR (U-Net with Transformers) model for face segmentation into 11 classes using TensorFlow and Python. Learn the complete workflow from loading the LAPA dataset for face parts segmentation to constructing the UNETR architecture that combines Vision Transformers (ViT) for multi-class classification. Follow along as the instructor demonstrates model training and shows how to run inference and visualize segmentation masks. The 27-minute video is organized into clear sections covering introduction and demo, installation requirements, model building and training, and model testing. Access the complete code through the provided link and explore additional computer vision and image segmentation tutorials available on the instructor's blog and YouTube playlists.