unet is a Keras implementation of the U-Net architecture for image segmentation.
The project addresses biomedical image segmentation by implementing the U-Net convolutional architecture using Keras functional API. The approach trains a deep neural network to learn pixel-level masks from input images, using binary crossentropy loss and sigmoid activation to produce segmentation outputs in the range [0, 1]. The implementation includes data augmentation via Keras ImageDataGenerator to expand a limited training dataset of 512x512 images.
This implementation suits developers working on segmentation tasks who want a straightforward Keras-based reference or starting point. The code is presented as Jupyter notebooks alongside a runnable main.py script, making it accessible for experimentation and learning. The project demonstrates the architecture on membrane segmentation from the ISBI challenge dataset and reports accuracy around 0.97 after training.
The project shows minimal ongoing development activity, with code presented in a tutorial format rather than as an actively maintained library. The repository appears to serve primarily as an educational reference implementation of the U-Net paper rather than as a production-ready tool receiving regular updates or community contributions.
