U-Net for Object Segmentation

This project centered on the design and implementation of a U-Net neural network for the segmentation of objects in images from the SYNTHIA dataset, which is widely used in autonomous driving research. The architecture was tailored to capture fine-grained details and spatial relationships within the images, allowing for precise identification and segmentation of objects such as vehicles, pedestrians, and traffic signs. Techniques like data augmentation, multi-scale feature extraction, and loss function optimization were applied to enhance model performance. The project achieved competitive results and demonstrated the potential of U-Net architectures for real-world applications in computer vision.