Convolutional Neural Network using MNIST dataset for Digit Recognition

Official repository: https://github.com/MartinBraquet/ml-digits-recognition.

Test online: https://martinbraquet.com/index.php/solo_page_digits_recognition.

Installation from PyPI

pip install ml-digits-recognition

Usage

from ml_digits_recognition import drawing
drawing.run()

Installation from Source

For basic usage:

pip install -e "."

For development:

pip install -e ".[dev]"

Documentation

Click here for a full description.

Visualization of the convolutional neural network:

nn_visualization.ipynb

Training

Train the model and save it as model.pt.

ml_digits_recognition_training.ipynb

Accuracy vs epochs.

Loss vs epochs.

Test

Test in Jupiter Notebook. The model can be loaded from the training above in model.pt or from the default precise model in model_precise.pt.

ml_digits_recognition_test.ipynb

Test in Python.

python src/ml_digits_recognition/drawing.py

Tools

Draw a digit and save it as a PNG file.

user_input_drawing.ipynb

CI / CD

Each commit on the main branch is subject to a CI test.

A new version is released to PyPI when a tag is created. TODO: Set version in pyproject.toml only, then trigger a new PyPI release (and git tag) by updating the version and pushing into main. The CD should thus check on each push on main, if the package is more recent than the last tag, then build and deploy new release. This is faster as one only needs to update locally the version in pyproject.toml in the same commit as the fixes for the new release. The rest is automated, so it can all be done from a local IDE. The version can be accessible at other places if desired. For example in __init__.py:

import importlib.metadata
__version__ = importlib.metadata.version(__package__)

Issues / Bug reports / Feature requests

Please open an issue.

Contributions

Contributions are welcome. Please check the outstanding issues and feel free to open a pull request.

Contributors