This project leverages the CARLA simulator to create a dataset for semantic segmentation of urban driving scenes. Semantic segmentation is a crucial task in autonomous driving, where the goal is to classify each pixel in an image into predefined categories such as roads, sidewalks, vehicles, pedestrians, etc. Using the high-fidelity simulation environment of CARLA, we can generate realistic annotated data to train and evaluate segmentation models.
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