https://github.com/ultralytics/mnist

MNIST Sandbox for testing neural network architectures.
https://github.com/ultralytics/mnist

classification cnn machine-learning ml mnist neural-networks yolo

Last synced: 12 days ago
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MNIST Sandbox for testing neural network architectures.

• Host: GitHub
• URL: https://github.com/ultralytics/mnist
• Owner: ultralytics
• License: agpl-3.0
• Created: 2018-06-17T01:26:50.000Z (over 7 years ago)
• Default Branch: main
• Last Pushed: 2025-05-11T00:10:48.000Z (5 months ago)
• Last Synced: 2025-05-11T00:26:41.714Z (5 months ago)
• Topics: classification, cnn, machine-learning, ml, mnist, neural-networks, yolo
• Language: Python
• Homepage: https://ultralytics.com
• Size: 13.5 MB
• Stars: 5
• Watchers: 1
• Forks: 4
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          


# 🚀 Introduction

Welcome to the repository containing innovative software developed by Ultralytics 🧠. Our code is 🌟 **open-sourced and freely available for redistribution under the AGPL-3.0 license**. For more insight into our work and impact, head over to https://www.ultralytics.com.

[![Ultralytics Actions](https://github.com/ultralytics/mnist/actions/workflows/format.yml/badge.svg)](https://github.com/ultralytics/mnist/actions/workflows/format.yml)

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# 📗 Description

The repository at https://github.com/ultralytics/mnist is our dedicated playground for the [MNIST dataset](https://docs.ultralytics.com/datasets/classify/mnist/). 🖐 This repository houses sandbox code that allows for experimentation and training of different [neural network](https://www.ultralytics.com/glossary/neural-network-nn) architectures on the famous MNIST digit database.

![MNIST Examples](https://upload.wikimedia.org/wikipedia/commons/2/27/MnistExamples.png)

# 📦 Requirements

Ensure you have Python 3.7 or later installed on your machine. The following packages are required, and you can install them using pip with the provided command: `pip3 install -U -r requirements.txt`.

- `numpy`: A fundamental package for scientific computing in Python.

- `torch`: [PyTorch](https://pytorch.org/), an open-source machine learning library for Python.

- `torchvision`: A PyTorch package that includes datasets and model architectures for [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv).

- `opencv-python`: An open-source computer vision and [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) software library.

# 🏃‍♂️ Run

To start [training](https://docs.ultralytics.com/modes/train/) on the MNIST digits dataset, execute `train.py` from your Python environment. The training and test data are located in the `data/` folder and were initially curated by Yann LeCun (http://yann.lecun.com/exdb/mnist/).

```python

# Example snippet of train.py to showcase its usage.

# This will set up the environment for training a model on MNIST dataset.

# Import necessary libraries (Make sure they are installed as per requirements)

import torch

# Your training script will start here, initialize models, load data, etc.

# ...

# Start the training process

# ...

# Save your trained model

torch.save(model.state_dict(), "path_to_save_model.pt")

# Add suitable comments to each segment of your code for better understanding.

```

# 🤝 Contribute

We welcome contributions from the community! Whether you're fixing bugs, adding new features, or improving documentation, your input is invaluable. Take a look at our [Contributing Guide](https://docs.ultralytics.com/help/contributing/) to get started. Also, we'd love to hear about your experience with Ultralytics products. Please consider filling out our [Survey](https://www.ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey). A huge 🙏 and thank you to all of our contributors!

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# ©️ License

Ultralytics is excited to offer two different licensing options to meet your needs:

- **AGPL-3.0 License**: Perfect for students and hobbyists, this [OSI-approved](https://opensource.org/license/) open-source license encourages collaborative learning and knowledge sharing. Please refer to the [LICENSE](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) file for detailed terms.

- **Enterprise License**: Ideal for commercial use, this license allows for the integration of Ultralytics software and AI models into commercial products without the open-source requirements of AGPL-3.0. For use cases that involve commercial applications, please contact us via [Ultralytics Licensing](https://www.ultralytics.com/license).

# 📬 Contact Us

For bug reports, feature requests, and contributions, head to [GitHub Issues](https://github.com/ultralytics/mnist/issues). For questions and discussions about this project and other Ultralytics endeavors, join us on [Discord](https://discord.com/invite/ultralytics)!