STEGO is a deep learning research project implementing an unsupervised semantic segmentation approach. The method learns to segment images into meaningful categories without requiring labeled training data by distilling feature correspondences from a pre-trained vision transformer. The implementation uses PyTorch and includes training, evaluation pipelines, and a Colab demo for reproducing the ICLR 2022 paper results.