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MNIST_99.4

README.md

MNIST 99.4% Accuracy Challenge

This project achieves 99.43% accuracy on MNIST digit classification while maintaining less than 20,000 parameters.

Model Architecture

  • Input: 1 channel image (28x28)
  • Convolutional layers:
    • Conv1: 1 -> 8 channels
    • Conv2: 8 -> 16 channels
    • Conv3: 16 -> 32 channels
  • Each conv block includes:
    • 3x3 convolution with padding=1
    • BatchNorm
    • ReLU activation
    • MaxPool2d
  • Fully connected layers:
    • FC1: 32 * 3 * 3 -> 32
    • FC2: 32 -> 10
  • Dropout (0.4) after conv3 and FC1

Total Parameters: 15,578

Training Configuration

  • Epochs: 19
  • Batch size: 128
  • Optimizer: Adam
    • Learning rate: 0.001
    • Weight decay: 1e-4
  • Scheduler: OneCycleLR
    • Max learning rate: 0.003
    • pct_start: 0.2
    • div_factor: 10
    • final_div_factor: 100
  • Loss: CrossEntropyLoss

Data Augmentation

  • Random rotation (±10 degrees)
  • Random translation (±10%)
  • Normalization (mean=0.1307, std=0.3081)

Results

  • Best Test Accuracy: 99.43%
  • Parameters: 15,578 (under 20k limit)
  • Training Time: 19 epochs

Test Logs

============================= test session starts ============================== platform linux -- Python 3.8.10, pytest-6.2.4, py-1.10.0, pluggy-0.13.1 rootdir: /workspace/MNIST_99.4 plugins: hypothesis-6.75.3, cov-4.1.0, reportlog-0.3.0, timeout-2.1.0 collected 4 items

tests/test_model.py .... [100%]

============================== 4 passed in 2.31s ==============================

Test Results:

  • test_forward_pass: ✓ (Output shape verified: 1x10)
  • test_parameter_count: ✓ (15,578 < 20,000)
  • test_dropout_layer: ✓ (Dropout rate: 0.4)
  • test_conv_layers: ✓ (Layer configuration verified)

Training Results (Final Epoch):

  • Training Loss: 0.0124
  • Training Accuracy: 99.67%
  • Test Loss: 0.0198
  • Test Accuracy: 99.43%

Requirements

  • Python 3.8+
  • PyTorch
  • torchvision
  • numpy
  • matplotlib
  • tqdm

Usage

python train.py

Project Structure

MNIST_99.4/
├── models/
│   └── model.py      # Model architecture
├── train.py          # Training script
└── README.md         # Documentation