Achieved 93.84% accuracy on the FoodVision Mini dataset by developing a Vision Transformer (ViT) model, integrating EfficientNet-B2 for feature enhancement. Leveraged transfer learning from pretrained ViTs and optimised training using PyTorch, improving food classification performance significantly.