LibreYOLO

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⭐ 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).

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.

LibreYOLO Detection Example

Installation & Quick start

pip install libreyolo

from libreyolo import LibreYOLO, SAMPLE_IMAGE

model = LibreYOLO("LibreYOLO9t.pt")
result = model(SAMPLE_IMAGE, save=True)

Optional pip extras

The base install covers YOLOv9 and the other detection models, plus training and inference. Add an extra in brackets when you need a heavier model family (for example RF-DETR, which needs transformers) or an export backend. Comma-separate to combine, e.g. pip install "libreyolo[rfdetr,onnx]":

Group	Extras
Export	`onnx`, `tensorrt`, `openvino`, `ncnn`, `tflite` (alias: `litert`), `coreml`
Models	`rfdetr`, `vlm`, `sam`, `openvocab`, `clip`, `gaze`
Training	`lora`, `plots`, `tensorboard`, `mlflow`, `wandb`
Everything	`pip install "libreyolo[all]"`

For the full list of extras and per-backend notes, see the docs.

To install from source (development, or to track unreleased changes):

git clone https://github.com/LibreYOLO/libreyolo.git
cd libreyolo
pip install -e .

A plain clone checks out release, the stable branch whose code matches these docs. For the latest unreleased work, switch to the integration branch with git checkout dev.

Flagship models

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.

Detection models

LibreYOLO is a YOLO library first. The table below covers the main detection families. ✓ supported. Empty cells are not currently supported.

Model family	Inference	Training	Export formats
Detection	Segmentation	Pose	ONNX	TorchScript	TensorRT	OpenVINO	NCNN	TFLite (LiteRT)
⭐ YOLOv9	✓			✓	✓	✓	✓	✓	✓	✓
⭐ RF-DETR	✓	✓	✓	✓	✓	✓	✓	✓		✓
YOLOX	✓			✓	✓	✓	✓	✓	✓	✓
YOLO-NAS	✓		✓	✓	✓	✓	✓	✓	✓
YOLOv9-E2E	✓			✓	✓	✓	✓	✓	✓
YOLOv9-P2	✓			✓	✓	✓	✓	✓	✓
YOLOv7	✓			✓	✓	✓	✓	✓	✓
D-FINE	✓	✓		✓	✓	✓	✓	✓
DEIM	✓			✓	✓	✓	✓	✓
DEIMv2	✓			✓	✓	✓	✓	✓
RT-DETR	✓			✓	✓	✓	✓	✓
RT-DETRv2	✓			✓	✓	✓	✓	✓
RT-DETRv4	✓			✓	✓	✓	✓	✓
EC	✓	✓	✓	✓	✓	✓	✓	✓
RTMDet	✓	✓		✓	✓	✓	✓	✓
PicoDet	✓			✓	✓	✓	✓	✓	✓

Beyond detection

The same three-line API covers a lot more than boxes: the checkpoint name selects the task. These families are extras on top of the core library.

All other tasks and their models

Instance segmentation (promptable): SAM, SAM 2, SAM 3, MobileSAM, EdgeTAM, PicoSAM3, EoMT
Semantic segmentation: SegFormer, PIDNet, EoMT, DINOv2
Panoptic segmentation: EoMT
Oriented boxes (OBB): RF-DETR
Classification: ResNet, ConvNeXt, MobileNetV4, EfficientNetV2, DINOv2, CLIP, SigLIP2
Point detection: FOMO, LocateAnything
Depth estimation: Depth Anything 3, Depth Anything V2, ZipDepth
Image restoration & super-resolution: NAFNet, Real-ESRGAN, SwinIR
Background removal (matting): BiRefNet
OCR: PP-OCR
Gaze estimation: L2CS
Open-vocabulary & VLM detection: Grounding DINO, OWLv2, OmDet-Turbo, OV-DEIM, Florence-2, Kosmos-2, Qwen3-VL, InternVL3, LFM2-VL, SmolVLM2, LocateAnything

License

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.

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