MNIST Image Classification

Week-5 Assignment for ERAv2

Overview

This repository uses MNIST dataset for classification of handwritten digits from 0 to 9. Convolutional neural network (CNN) has been used with PyTorch library. The project separates files for model definition, utility functions and the main training/testing scripts for clarity.

Files Description

model.py: Defines the CNN architecture (Net class) used for digit classification.

utils.py: Utility functions for data transformation, training, and testing procedures.

s5_execution.ipynb: The main script that does data loading, model training, testing, and visualization of results.

Setup and Requirements

Before running the project, ensure you have Python 3.x installed along with the following packages:

• PyTorch
• torchvision
• matplotlib
• tqdm

You can install the dependencies using the following command:

pip install torch torchvision matplotlib tqdm

Usage

To use this project, follow these steps:

• Clone the repository to your local machine or Google Colab environment.
• Ensure all dependencies are installed.
• Execute different code blocks within s5_execution.ipynb in jypeter notebook or Google Colab.