This tutorial demonstrates how to use Label Studio, an open-source data labeling tool, and Hub, a dataset format for AI, to simplify semantic segmentation projects. Semantic segmentation involves attributing a class to each pixel of an image, such as identifying whether a pixel belongs to the "smile" or "non-smiling" class in this case. The tutorial uses the GENKI-4K subset containing 4000 face images labeled as smiling or non-smiling by human coders. After filtering for smiling images and labeling smiles using Label Studio, the resulting dataset is saved to Hub Storage. Finally, a semantic segmentation model (UNet) is trained on this data, with results showing how pixels where probabilities exceed a certain threshold are considered "smiling."