MNIST Classification Project

A PyTorch implementation of MNIST digit classification that achieves >95% accuracy in one epoch with less than 25,000 parameters.

Model Architecture

• Input Layer: 28x28x1
• Conv1: 8 filters with BatchNorm and ReLU
• Conv2: 16 filters with BatchNorm and ReLU
• Conv3: 20 filters with BatchNorm and ReLU
• MaxPooling layers
• Fully Connected Layer: 10 outputs
• Total Parameters: <25,000

Key Features

• Achieves >95% accuracy in 1 epoch
• Lightweight architecture (<25K parameters)
• Uses BatchNormalization for faster convergence
• Implements dropout for regularization

GitHub Actions Tests

The CI/CD pipeline automatically verifies:

1. Model has less than 25,000 parameters
2. Achieves accuracy greater than 95% in one epoch

Setup and Training

1. Install dependencies: