LibreYOLO

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An MIT-licensed computer vision library. Detection, segmentation, pose, depth, OCR and a dozen more tasks behind one small API, with training and export included rather than sold separately. Reads common YOLO-format datasets, so existing workflows port over with minimal changes.

Install

pip install libreyolo

from libreyolo import LibreYOLO, SAMPLE_IMAGE

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

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

One API, seventeen tasks

The same three lines run every task. Only the checkpoint changes.

from libreyolo import LibreYOLO

LibreYOLO("LibreYOLO9t.pt")("street.jpg", save=True)             # detection
LibreYOLO("LibreDeepLabv3mv3-sem.pt")("street.jpg", save=True)   # semantic segmentation
LibreYOLO("LibreHRNetw32-pose.pt")("street.jpg", save=True)      # pose
LibreYOLO("LibreMiDaSs-depth.pt")("street.jpg", save=True)       # depth
LibreYOLO("LibreFeyNobgl-matte.pt")("portrait.jpg", save=True)   # background removal
LibreYOLO("LibreRTDETRv2n-obb.pt")("aerial.jpg", save=True)      # oriented boxes

Sources are not just files. Point it at a webcam, an RTSP stream, a video, a directory, a YouTube URL or your screen:

libreyolo predict --model yolo9-t --source 0 --show          # webcam
libreyolo predict --model yolo9-t --source rtsp://camera/1   # network camera
libreyolo predict --model yolo9-t --source screen            # screen capture

What ships

Per-family sizes, checkpoints and parity evidence live in the model reference.

Train

from libreyolo import LibreYOLO

model = LibreYOLO("LibreYOLO9t.pt")
model.train(data="dataset.yaml", epochs=100, imgsz=640)

libreyolo train --model yolo9-t --data dataset.yaml --epochs 100

Multi-GPU, LoRA, layer freezing, distillation, from-scratch training, and TensorBoard, MLflow, Weights & Biases, Comet, ClearML, Neptune and DVCLive logging are all supported. See the training guide.

Export and deploy

Twelve formats: ONNX, TorchScript, TensorRT, OpenVINO, CoreML, Core AI, TFLite (LiteRT), NCNN, MNN, RKNN, Paddle and ExecuTorch. Plus NVIDIA Triton serving and DeepStream config generation.

libreyolo export --model yolo9-t --format onnx

Support varies by family and task, see the export matrix.

Documentation

• Docs covers install, tasks, models, training, prediction, export and the CLI
• Benchmarks for independent numbers
• CHANGELOG.md for what changed

License

• Code: MIT License.
• Weights: pre-trained weights may inherit licensing from their original source, and not all of them are permissive. Check the license on the specific Hugging Face repo before you use one commercially. Every LibreYOLO Hugging Face model states its license.