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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: