https://github.com/f4str/digit-recognizer
Classifiers for recognizing MNIST handwritten digits
https://github.com/f4str/digit-recognizer
gui machine-learning mnist neural-network python pytorch tkinter
Last synced: 3 months ago
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Classifiers for recognizing MNIST handwritten digits
- Host: GitHub
- URL: https://github.com/f4str/digit-recognizer
- Owner: f4str
- License: mit
- Created: 2020-02-11T04:11:07.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2022-05-23T06:39:03.000Z (about 3 years ago)
- Last Synced: 2025-01-26T06:26:25.081Z (4 months ago)
- Topics: gui, machine-learning, mnist, neural-network, python, pytorch, tkinter
- Language: Python
- Homepage:
- Size: 280 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Digit Recognizer
Simple implementations of different types of neural network classifiers and training scripts for the MNIST data set using `PyTorch`. Also includes a GUI using `Tkinter` to draw digits and predict accordingly.
## Installation
Clone the repository.
```bash
git clone https://github.com/f4str/digit-recognizer
```
Change directories into the cloned repository.
```bash
cd digit-recognizer
```
Install Python and create a virtual environment.
```bash
python3 -m venv venv
source venv/bin/activate
```
Install the dev dependencies using pip.
```bash
pip install -e .[dev]
```
## Usage
### Model Training
First train a model with the desired name and arguments.
```bash
python digit_recognizer/train.py --name {NAME} [...args]
```
This will create and train a model in `saved_models/{NAME}` based on the arguments provided. To view information on the available arguments, pass the `-h` flag.
### Model Evaluation
A trained model can be evaluated if desired. Ensure the name and arguments matches the saved model exactly.
```bash
python digit_recognizer/test.py --name {NAME} [...args]
```
To view information on the available arguments, pass the `-h` flag.
### GUI Canvas
Launch the GUI which will create a canvas to draw and recognize digits using the saved model. Ensure the name and arguments matches the saved model exactly.
```bash
python digit_recognizer/gui.py --name {NAME} [...args]
```
To view information on the available arguments, pass the `-h` flag.
In the canvas, draw by clicking and holding left click. To erase any segment, click and hold right click. To clear the entire screen, click the clear button. After drawing a digit, click the predict button. A prediction will be displayed along with the confidence percentage.
## Development
The `tox` library is used to run all tests and code formatting. This is automatically installed with the dev requirements. The available options are as follows.
* Run linting checks using `flake8`.
```bash
tox -e lint
```
* Run type checks using `mypy`.
```bash
tox -e type
```
* Run unit tests `pytest`.
```bash
tox -e test
```
* Run all three of the tests above.
```bash
tox
```
* Format the code using `black` and `isort` to comply with linting conventions.
```bash
tox -e format
```
Upon pull request, merge, or push to the `master` branch, the three tests with `tox` will be run using GitHub Actions. The workflow will fail if any of the tests fail. See `.github/workflows/python-package.yml` for more information on how the CI works.