This repository implements Swin-Unet, a vision transformer architecture adapted for medical image segmentation tasks. It uses a Swin Transformer encoder combined with a U-Net style decoder to perform semantic segmentation on medical imaging datasets such as Synapse and ACDC. The model is trained using PyTorch with configurable hyperparameters including batch size, learning rate, and image size.