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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
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!
Relevant Courses
Use the following content to review prerequisites or explore specific concepts in detail.