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PROJECT

# Vision Transformer for Image Classification

In this project, we’ll use transfer learning to fine-tune a Vision Transformer (ViT) model for classifying images from the MNIST dataset in Python using the Transformers library. We’ll use the Matplotlib library to visualize our data and evaluate our model using the scikit-learn library.

You will learn to:

Load an image classification dataset from Hugging Face Hub.

Perform exploratory data analysis and create meaningful visualizations.

Preprocess image data for Vision Transformers (ViT).

Download a pretrained Vision Transformer (ViT) model from Hugging Face Hub.

Fine-tune Vision Transformer (ViT) on the dataset.

Evaluate the model using the scikit-learn library.

Skills

Computer Vision

Deep Learning

Data Visualization

Transformer Models

Prerequisites

Hands-on experience with Python

Basic understanding of machine learning

Basic understanding of Transformers

Technologies

Python

Matplotlib

Torchvision

Hugging Face

Scikit-learn

Project Description

In this project, we’ll train an image classifier to recognize the digit present in the image. The images will contain a single digit ranging from 0 to 9. We’ll use a Vision Transformer (ViT) as the image classifier. This project will teach us the steps to fine-tune a ViT.

We’ll load the dataset using the Datasets library and visualize the image data using Matplotlib. We’ll perform data preprocessing and augmentation, followed by splitting the data into train, validation, and test sets. We’ll then download a pretrained ViT model from Hugging Face Hub and fine-tune it on our dataset using the Transformers library. We’ll finally evaluate our model using the F1 score metric in the scikit-learn library.

Project Tasks

1

Introduction

Task 0: Get Started

Task 1: Import Libraries

Task 2: Load the Dataset

Task 3: Visualize the Dataset

2

Set Up a Training for the Model

Task 4: Create a Mapping of Class Names to Index

Task 5: Load the Preprocessor for the Dataset

Task 6: Define Data Augmentations

Task 7: Implement Data Transformation

Task 8: Collate the Function for DataLoader

Task 9: Create a Model

3

Model Training

Task 10: Define a Metric for the Model

Task 11: Set Up Trainer Arguments

Task 12: Create a Trainer Object

Task 13: Evaluate the Model Before Training

Task 14: Train the Model

Task 15: Visualize the Performance in TensorBoard

4

Model Evaluation

Task 16: Evaluate the Model

Task 17: Set Up the Confusion Matrix

Task 18: Save the Model and Metrics

Task 19: Set Up an Inference for the Model

Congratulations!