YugiVit

Vision transformer project for unsupervised learning of visual representations from Yu-Gi-Oh! card images. Implements and compares masked autoencoder and contrastive learning approaches, with experiments in embedding analysis, image retrieval, and card-type representation quality. Built from scratch in PyTorch, including the transformer architecture, training pipelines, data augmentation, and evaluation tools.