A PyTorch implementation of an image classification system based on the DINOv3 (self-DIstillation with NO labels) vision transformer. This project provides a complete training pipeline with distributed data parallel (DDP) support, advanced data augmentation, and multiple loss functions including supervised contrastive learning.

Train a DINOv3-based model to classify cystoscopy images of the bladder into 4 classes (2 cancer classes and 2 non-cancer classes).