This repository implements U-Net, a fully convolutional neural network architecture, for semantic image segmentation tasks. The model takes 512x512 input images and outputs pixel-level segmentation masks using a sigmoid activation function. It uses Keras functional API for flexible architecture experimentation and includes data augmentation via Keras ImageDataGenerator to compensate for limited training data. The implementation targets biomedical image segmentation, originally from the ISBI challenge.