Fine-Tuning Vision Transformers for Image Classification

The project focuses on fine-tuning vision transformers (ViT) for image classification using the Hugging Face Transformers library. It begins by installing and setting up the necessary packages and environment. The "beans" dataset is then loaded, and an exploration of the dataset is conducted, showcasing an example image and its corresponding label.

We proceed to access label information and convert label indices to human-readable strings. Additionally, we define a function to display a grid of example images from different classes.

We initialize the ViT feature extractor and display its configuration. Then, we process the dataset and apply a transformation function to prepare it for training. The project utilizes a data collator and an accuracy metric for evaluation. We load a pretrained ViT model and set training configurations.

Then, we train the model and conclude the notebook with an evaluation of the model's performance on the validation dataset, logging relevant metrics. Overall, the project provides a comprehensive pipeline for fine-tuning ViT models on image classification tasks.