MNIST Handwritten Digit Classification with PyTorch

This project demonstrates a simple implementation of a deep learning model for classifying handwritten digits from the MNIST dataset using the PyTorch library. The MNIST dataset is a widely-used benchmark dataset in the field of computer vision.

Project Overview

The goal of this project is to train a convolutional neural network (CNN) model to accurately classify handwritten digits from the MNIST dataset. The model is built using PyTorch, a popular deep learning framework, and trained using the Adam optimizer.

The project involves the following steps:

Loading and preprocessing the MNIST dataset
Designing and building a CNN model architecture
Training the model on the training data
Evaluating the model's performance on the test data
Saving and loading the trained model
Performing inference on new images

Requirements

Python (3.x)
PyTorch (1.x)
torchvision
PIL

Installation

Clone the repository:

git clone https://github.com/your-username/mnist-classification-pytorch.git

Install the required dependencies:

pip install -r requirements.txt

Usage

Prepare the dataset:

The MNIST dataset will be automatically downloaded and preprocessed during the first run of the script. However, if you want to specify a different data directory or adjust any preprocessing parameters, you can modify the configuration in the script.

Train the model:

Run the training script to train the model. You can adjust hyperparameters such as the number of epochs, learning rate, and batch size in the script.

Evaluate the model:

After training, the model's performance on the test set will be evaluated automatically, and the accuracy score will be displayed.

Perform inference:

You can use the trained model to make predictions on new images by running the inference script and providing the path to the image file.

RafayKhattak/Digit-Classification-Pytorch

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