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mnist

★ 64 Open GitHub ↗

PyTorch sandbox for testing convolutional networks, ResNets, and other architectures on MNIST digits.

// repository documentation

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# 🚀 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)
[![Ultralytics Discord](https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue)](https://discord.com/invite/ultralytics)
[![Ultralytics Forums](https://img.shields.io/discourse/users?server=https%3A%2F%2Fcommunity.ultralytics.com&logo=discourse&label=Forums&color=blue)](https://community.ultralytics.com/)
[![Ultralytics Reddit](https://img.shields.io/reddit/subreddit-subscribers/ultralytics?style=flat&logo=reddit&logoColor=white&label=Reddit&color=blue)](https://reddit.com/r/ultralytics)

## 📗 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.
- `scipy`: Used by `train.py` to load the bundled MATLAB `.mat` MNIST files.
- `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.
- `h5py`: Provides HDF5 file support for reading large v7.3 `.mat` files in `train_xview_classes.py`.
- `tqdm`: Progress bars used by `detect.py` and `train_resnet.py`.

## 🏃‍♂️ Run

To start [training](https://docs.ultralytics.com/modes/train/) on the MNIST digits dataset, install the dependencies and execute `train.py` from the repository root:

```bash
pip3 install -U -r requirements.txt
python3 train.py
```

The default script trains `ConvNetb` for 20 epochs using the bundled `data/MNISTtrain.mat` and `data/MNISTtest.mat` files, which were initially curated from Yann LeCun's [MNIST database](http://yann.lecun.com/exdb/mnist/). Additional experimental entry points include `train_resnet.py`, `train_sandd.py`, and `train_xview_classes.py`.

## 💡 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!

[![Ultralytics open-source contributors](https://raw.githubusercontent.com/ultralytics/assets/main/im/image-contributors.png)](https://github.com/ultralytics/ultralytics/graphs/contributors)

## 📄 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/agpl-v3) open-source license encourages collaborative learning and knowledge sharing. Please refer to the [LICENSE](https://github.com/ultralytics/mnist/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

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)!

mnist

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