⭐ Support LibreYOLO. The best way to help is to star the repo. Feel free to open an issue if you encounter problems or have suggestions, and code contributions are very welcome (see CONTRIBUTING.md). We are also looking for sponsors to donate GPU resources to the project. If you or your company can help, please reach out on LinkedIn.
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.
pip install libreyoloTo install the latest main in editable mode (for development or to track unreleased changes):
git clone https://github.com/LibreYOLO/libreyolo.git
cd libreyolo
pip install -e .For optional runtime and export dependencies such as ONNX Runtime, OpenVINO, TensorRT, NCNN, and RF-DETR, see the full docs.
from libreyolo import LibreYOLO, SAMPLE_IMAGE
model = LibreYOLO("LibreYOLO9t.pt")
result = model(SAMPLE_IMAGE, save=True)LibreYOLO recommends these model families because they offer the best balance and receive the heaviest testing:
- YOLOv9 for CNN-based YOLO models.
- RF-DETR for transformer-based detection and segmentation.
✓ supported, exp experimental. Empty cells are not currently supported.
| Model family | Inference | Training | Export formats | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Detection | Segmentation | Pose | ONNX | TorchScript | TensorRT | OpenVINO | NCNN | ||
| YOLOv9 | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ||
| RF-DETR | ✓ | ✓ | exp | ✓ | ✓ | ✓ | |||
| YOLOX | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ||
| YOLOv9-E2E | ✓ | ✓ | |||||||
| YOLO-NAS | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |
| D-FINE | ✓ | exp | ✓ | ✓ | ✓ | ✓ | |||
| DEIM | ✓ | exp | ✓ | ✓ | ✓ | ✓ | |||
| DEIMv2 | ✓ | exp | ✓ | ✓ | ✓ | ✓ | |||
| RT-DETR | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |||
| RT-DETRv2 | ✓ | exp | |||||||
| RT-DETRv4 | ✓ | exp | |||||||
| PicoDet | ✓ | exp | |||||||
| EC | ✓ | ✓ | ✓ | exp | |||||
- Code: MIT License
- Weights: Pre-trained weights may inherit licensing from the original source. Check the license in the specific HF repo of weights that you are interested in. LibreYOLO HF models always have a license.
- v1.1.0 (2026-04-27): New model families (YOLO-NAS, D-FINE, RT-DETR), instance segmentation, ByteTrack tracking, video inference, and a brand-new CLI. See the release notes.