Pytorch-UNet
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.
