#U-Net

This PyTorch-based U-Net implementation enhances high-definition image segmentation, particularly for challenges like Kaggle's Carvana Image Masking. Featuring Docker for straightforward deployment and mixed precision optimization, the model boasts a Dice coefficient of 0.988423 across vast test sets. The project supports diverse segmentation applications, such as medical and portrait, and offers seamless training and inference with Weights & Biases for live training progress. Pretrained models are accessible for swift application.

This tutorial demonstrates deep learning techniques for ultrasound nerve segmentation using Keras, detailing data pre-processing, model architecture, and training strategies. It guides users through data preparation and model definition, aligning with the Kaggle competition framework, while emphasizing Keras's modular and experimental-friendly nature.

This PyTorch-based implementation supports 3D and 2D U-Nets, including variants like Residual 3D U-Net enhanced with Squeeze-and-Excitation blocks. It's designed for precise semantic segmentation and regression on medical and biological datasets. The tool accommodates multi-channel inputs, various loss functions for imbalanced data, and integrates smoothly with NVIDIA GPUs to boost training efficiency. With YAML configuration, it offers adaptable training and prediction processes across different platforms, such as Windows and OS X. Discover examples demonstrating its precision in cell segmentation, beneficial for enhancing research results.