Fine-tune ViT for image classification using Hugging Face Transformers and ViTImageProcessor

AI Impact Summary

The post walks through end-to-end fine-tuning of a Vision Transformer on an image dataset using Hugging Face Transformers, reusing google/vit-base-patch16-224-in21k and ViTImageProcessor to align preprocessing. It shows dataset processing with datasets: loading beans, mapping transforms with ds.with_transform, and returning pixel_values tensors compatible with PyTorch, highlighting the importance of consistent input shapes (1, 3, 224, 224). Practical implications: to operationalize this, teams must provision GPUs, configure training with Trainer, and ensure preprocessing pipelines are version-stable to avoid drift between pretrained weights and inputs.