Urban Scene Understanding Using Deep Learning and Image Segmentation Techniques

This project is a computer vision and deep learning assignment that uses the Cityscapes dataset, which contains urban scene images, to perform image segmentation or clustering. The code first imports essential libraries like PyTorch, NumPy, and Matplotlib for building and training neural networks, processing images, and visualizing results. It then extracts the dataset from a ZIP file and prepares it for use. The project likely involves creating a custom PyTorch dataset, applying image transformations, and using models or clustering techniques (like K-Means) to analyze or segment images into meaningful regions. Overall, the code aims to automate understanding of city scenes by grouping pixels or features that represent different objects such as roads, cars, or buildings.