Developing a Handwritten Digits Classifier with PyTorch

Project Overview

This project involves building a convolutional neural network (CNN) using PyTorch to classify handwritten digits from the MNIST dataset. The goal is to achieve high accuracy in identifying digits from 0 to 9, leveraging deep learning techniques.

Table of Contents

• Project Overview
• Installation
• Dataset
• Project Structure
• Usage
• Results
• Contributing

Installation

To get started, clone the repository and install the necessary dependencies.

git clone https://github.com/Ghadeer52/Handwritten-Digits-Classifier-with-PyTorch.git
cd Handwritten-Digits-Classifier-with-PyTorch
pip install -r requirements.txt

Dataset

The dataset used for this project is the MNIST dataset, which contains 60,000 training images and 10,000 testing images of handwritten digits.

Project Structure

• notebooks/: Jupyter notebooks containing the analysis and model training code.
• data/: Directory where the dataset is stored.
• models/: Saved models and training checkpoints.
• scripts/: Python scripts for data loading, model training, and evaluation.
• README.md: Project documentation.

Usage

To train the model and make predictions, follow these steps:

Data Loading: Load the dataset for training and testing.

python scripts/load_data.py

Model Training: Train the CNN using the provided notebook.

jupyter notebook notebooks/train_model.ipynb

Evaluation: Evaluate the trained model on the test set.

python scripts/evaluate_model.py

Results

The project achieves high accuracy in classifying handwritten digits. Detailed results and model performance metrics are available in the results/ directory.

Contributing

Contributions are welcome! Please submit a pull request or open an issue to discuss any changes or improvements.