How to Train and Deploy a Vision Transformer (ViT) Classification Model

Vision Transformers (ViTs) are a cutting-edge model architecture for image classification, utilizing the Transformer model, which is prevalent in both computer vision and natural language processing. The guide outlines the process of training a ViT model using Roboflow to classify defects in juice boxes, such as loose straws or broken wrappers. Starting with dataset preparation, users can either fork a pre-labeled dataset from Roboflow Universe or upload their own data. Once data is annotated and labeled, a dataset version is generated to train the model. The guide then details training the model using Roboflow's platform and deploying it with Roboflow Inference, allowing custom logic and workflows to be built for model deployment. The article emphasizes exploring the Roboflow Workflows editor to create and experiment with different deployment strategies, highlighting the utility of ViTs in automating quality assurance tasks.