LibreYOLO Documentation¶
LibreYOLO is an open-source, MIT-licensed implementation of YOLO object detection models. It provides a clean, independent codebase for training and inference.
Note
While this codebase is MIT licensed, pre-trained weights converted from other repositories may inherit their original licenses (often AGPL-3.0).
Features¶
🚀 Supported Models: Full support for YOLOv8 and YOLOv11 architectures
📦 Unified API: Simple, consistent interface for loading and using different YOLO versions
🛠️ Training Engine: Built-in support for training models on custom datasets
⚖️ MIT License: Permissive licensing for the codebase
🔄 Weight Conversion: Tools to convert weights from Ultralytics format
🔍 Explainability: Built-in CAM methods (GradCAM, EigenCAM, etc.)
Quick Start¶
from libreyolo import LIBREYOLO
# Load a model (auto-detects v8 vs v11)
model = LIBREYOLO(model_path="weights/libreyolo8n.pt", size="n")
# Run inference
detections = model(image="path/to/image.jpg", save=True)
# Access results
for det in detections:
print(f"Detected with confidence {det['scores']}")
Installation¶
git clone https://github.com/Libre-YOLO/libreyolo.git
cd libreyolo
pip install -e .