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A course exercise implementing a pre-trained U-Net model for semantic segmentation of satellite imagery, featuring data preprocessing, augmentation, model training, and performance evaluation with visualization scripts.
Unveiling the secrets of an ancient library buried by Mount Vesuvius, this Kaggle competition, supported by the Vesuvius Challenge organization, tasked participants with detecting ink from 3D X-ray scans of charred scrolls preserved in a Roman villa in Herculaneum.
NYU Deep Learning 2023 project for semantic segmentation in video sequences using dual-phase learning with U-Net and ConvLSTM, focusing on predicting segmentation masks from synthetic 3D shape videos.
Semantic segmentation is a type of computer vision technique that assigns a label to every pixel in an image. The label indicates the class of object that the pixel represents. Semantic segmentation is used in tasks such as self-driving cars, where it is important to know not only the boundaries of objects, but also what those objects are.