Metadata-Version: 2.4
Name: deeplabcut2yolo
Version: 2.2.5
Summary: Convert DeepLabCut dataset to YOLO format
Author-email: Sira Pornsiriprasert <code@psira.me>
Maintainer-email: Sira Pornsiriprasert <code@psira.me>
License-Expression: GPL-3.0-only
Project-URL: Homepage, https://github.com/p-sira/deeplabcut2yolo
Project-URL: Repository, https://github.com/p-sira/deeplabcut2yolo
Project-URL: Issues, https://github.com/p-sira/deeplabcut2yolo/issues
Project-URL: Documentation, https://p-sira.github.io/deeplabcut2yolo/
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: pyyaml
Requires-Dist: ruamel-yaml
Dynamic: license-file

# deeplabcut2yolo
**Convert DLC to YOLO,**\
**Lightning-fast and hassle-free.**

[![License: GPL v3](https://img.shields.io/badge/license-GPLv3-red.svg)](https://www.gnu.org/licenses/gpl-3.0)
[![PyPI Package Version](https://img.shields.io/pypi/v/deeplabcut2yolo?label=pypi%20package&color=a190ff)](https://pypi.org/project/deeplabcut2yolo/)
[![Package Total Downloads](https://img.shields.io/pepy/dt/deeplabcut2yolo)](https://pepy.tech/projects/deeplabcut2yolo)
[![Documentation](https://img.shields.io/badge/docs-passing-default)](https://p-sira.github.io/deeplabcut2yolo/)

**deeplabcut2yolo** facilitates training [DeepLabCut datasets](https://benchmark.deeplabcut.org/datasets.html) on [YOLO](https://docs.ultralytics.com/) models. Deeplabcut2yolo automatically converts DeepLabCut (DLC) labels to COCO-like format compatible with YOLO, while providing customizability for more advanced users, so you can spend your energy on what matters!

![Results from d2y](d2y-trimouse.jpg "DLC Tri-mouse dataset converted for YOLO training")
*All DeepLabCut datasets belong to their respective owner under CC BY-NC 4.0. This particular image is the training data for YOLO, converted using deeplabcut2yolo from the Tri-Mouse dataset (Lauer et al., 2022).*

## Quick Start
```python
import deeplabcut2yolo as d2y

# In its simplest form,
d2y.convert("./deeplabcut-dataset/")

# To also generate data.yml
d2y.convert(
    dataset_path,
    train_paths=train_paths,
    val_paths=val_paths,
    skeleton_symmetric_pairs=skeleton_symmetric_pairs,
    data_yml_path="data.yml",
    class_names=class_names,
    verbose=True,
)
```

To install deeplabcut2yolo using pip:
```
pip install deeplabcut2yolo
```

For more information, see [examples](https://github.com/p-sira/deeplabcut2yolo/tree/main/examples) and [documentation](https://p-sira.github.io/deeplabcut2yolo/).

## Features
- Automatically detect default DeepLabCut dataset structure
- Vectorized label conversion
- Support single- and multi-animal projects
- Convenient data.yml generation function for YOLO models

## Contribution
You can contribute to deeplabcut2yolo by making pull requests. Currently, these are high-priority features:
- Testing module and test cases
- Documentation

## Citation
Citation is not required but is greatly appreciated. If this project helps you, 
please cite using the following APA-style reference

> Pornsiriprasert, S. (2025). *Deeplabcut2yolo: A Python Library for Converting DeepLabCut Dataset to YOLO Format* (Version 2.2.5) [Computer software]. GitHub. https://github.com/p-sira/deeplabcut2yolo/

or this BibTeX entry.

```
@software{deeplabcut2yolo,
    author = {{Pornsiriprasert, S}},
    title = {Deeplabcut2yolo: A Python Library for Converting DeepLabCut Dataset to YOLO Format},
    url = {https://github.com/p-sira/deeplabcut2yolo/},
    version = {2.2.5},
    publisher = {GitHub},
    year = {2025},
    month = {5},
}
```
