Object Detection.
Unrestricted.
The MIT-licensed training and inference engine for state-of-the-art YOLO models. Built for commercial applications, free from AGPL restrictions.
from libreyolo import LIBREYOLO
# Load any YOLO architecture
model = LIBREYOLO("yolov11.pt")
# Run inference
results = model.predict("image.jpg")
# That's it. No boilerplate. Just Python.The Licensing Landscape
Not all open-source is equal. Understand the difference before you ship.
Typical YOLO Implementations
The Standard Approach
- AGPL-3.0Copyleft License
- Viral clause: Your code may need to be open-sourced
- Proprietary use requires expensive licensing
- Legal review recommended before deployment
The Libre Engine
Libre-YOLO
- MITPermissive License
- No copyleft: Your code stays private
- Commercial use: Fully permitted, no fees
- Safe to merge into any codebase
Built for Real Products
Technical excellence meets legal clarity. Everything you need to ship with confidence.
Clean Room Implementation
Zero lineage from restrictive repositories. A fresh codebase built from research papers, not copied code.
Unified Architecture
Run v8, v11, and future architectures with a single, stable API. One engine, all models.
Deep Inspection
Debug your model's 'brain' with native feature map visualization. See what the network sees.
Production Ready
Optimized inference paths, ONNX export, and deployment guides for edge and cloud.
Native Python
No complex dependencies or build steps. pip install and go. Works where you work.
Hardware Agnostic
CPU, CUDA, MPS (Apple Silicon), and more. Train and deploy anywhere.
Start Building Today
No licensing negotiations. No legal reviews. Just install and ship.
$ pip install libreyolo