Metadata-Version: 2.4
Name: libreyolo
Version: 0.1.5
Summary: Libre YOLO - An open source YOLO library with MIT license.
Author: LibreYOLO Team
License: MIT
Project-URL: Homepage, https://github.com/Libre-YOLO
Project-URL: Repository, https://github.com/Libre-YOLO/libreyolo
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy>=1.19.0
Requires-Dist: Pillow>=8.0.0
Requires-Dist: torch>=1.7.0
Requires-Dist: torchvision>=0.11.0
Requires-Dist: PyYAML>=6.0
Requires-Dist: matplotlib>=3.3.0
Requires-Dist: requests>=2.25.0
Requires-Dist: opencv-python>=4.11.0.86
Requires-Dist: tqdm>=4.65.0
Requires-Dist: pycocotools>=2.0.0
Provides-Extra: onnx
Requires-Dist: onnx>=1.14.0; extra == "onnx"
Requires-Dist: onnxsim>=0.4.0; extra == "onnx"
Requires-Dist: onnxscript>=0.1.0; extra == "onnx"
Requires-Dist: onnxruntime>=1.16.0; extra == "onnx"
Provides-Extra: rfdetr
Requires-Dist: rfdetr==1.4.1; extra == "rfdetr"
Requires-Dist: transformers<5; extra == "rfdetr"
Requires-Dist: timm>=0.9.0; extra == "rfdetr"
Requires-Dist: supervision>=0.19.0; extra == "rfdetr"
Provides-Extra: tensorrt
Requires-Dist: pycuda>=2022.1; extra == "tensorrt"
Provides-Extra: openvino
Requires-Dist: openvino>=2024.0.0; extra == "openvino"
Dynamic: license-file

# LibreYOLO

[![Documentation](https://img.shields.io/badge/docs-libreyolo.com-blue)](https://www.libreyolo.com/docs)
[![PyPI](https://img.shields.io/pypi/v/libreyolo)](https://pypi.org/project/libreyolo/)
[![License](https://img.shields.io/badge/license-MIT-green)](LICENSE)

MIT-licensed YOLO implementation supporting inference for YOLOv9 (t, s, m, c), YOLOX (nano, tiny, s, m, l, x), and RF-DETR (nano, small, base, medium, large).

Training support is under development

![LibreYOLO Detection Example](media/parkour_result.jpg)

## Installation

```bash
pip install libreyolo
```

## Quick Start

```python
from libreyolo import LIBREYOLO

# Auto-detect model version and size
model = LIBREYOLO("libreyoloXs.pt")
results = model(image="https://raw.githubusercontent.com/Libre-YOLO/libreyolo/main/media/parkour.jpg", save=True)

print(f"Detected {results['num_detections']} objects")
```

## Documentation

Full documentation at [libreyolo.com/docs](https://www.libreyolo.com/docs):

## License

- **Code:** MIT License
- **Weights:** Pre-trained weights may inherit licensing from the original source
