| title: LibreYOLO | |
| emoji: 🔍 | |
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| # LibreYOLO | |
| **MIT-licensed open-source computer vision.** | |
| Inference and training for a wide range of detection models, behind one familiar Python and CLI interface. | |
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| ## What is LibreYOLO? | |
| LibreYOLO is an MIT-licensed computer vision library with inference and training support for a variety of models. It provides a familiar high-level Python and CLI interface and reads common YOLO-format datasets, so existing workflows port over with minimal changes. There is no AGPL anywhere in the dependency chain, so you can use it in closed-source or commercial products. | |
| ```python | |
| from libreyolo import LibreYOLO, SAMPLE_IMAGE | |
| model = LibreYOLO("LibreYOLO9t.pt") | |
| result = model(SAMPLE_IMAGE, save=True) | |
| ``` | |
| The flagship families are **YOLOv9** (CNN) and **RF-DETR** (transformer) for detection and segmentation. The weights hosted in this organization auto-download on first use. | |
| ## Try it in your browser | |
| No install required. Run detection, segmentation, pose, and depth on your own images in the live demo: | |
| **[LibreYOLO live demo (Gradio)](https://huggingface.co/spaces/LibreYOLO/libreyolo-demo)** | |
| ## Links | |
| [Website](https://www.libreyolo.com/) · [GitHub](https://github.com/LibreYOLO/libreyolo) · [Hugging Face](https://huggingface.co/LibreYOLO) · [Benchmarks (Vision Analysis)](https://www.visionanalysis.org/) · [LinkedIn](https://www.linkedin.com/company/libreyolo/) · [Reddit](https://www.reddit.com/r/LibreYOLO/) | |
| --- | |
| MIT-licensed open-source computer vision. If you find it useful, a ⭐ on [GitHub](https://github.com/LibreYOLO/libreyolo) helps a lot. | |