Fine-Tune ViT for Image Classification with 🤗 Transformers

Hugging Face Blog · 4 years ago

Vision Transformer models divide images into patches and embed them as tokens, enabling transformer architectures to perform image classification tasks similar to how they process text in NLP. The tutorial demonstrates fine-tuning Google's vit-base-patch16-224-in21k model on the beans dataset, achieving 98.5% validation accuracy after 4 epochs of training with a batch size of 16. Users can apply the same approach to other image classification datasets using the Hugging Face Transformers and Datasets libraries with the provided code templates.