# 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

```python
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

```bash
git clone https://github.com/Libre-YOLO/libreyolo.git
cd libreyolo
pip install -e .
```

```{toctree}
:maxdepth: 2
:caption: User Guide

getting-started
inference
training
explainability
```

```{toctree}
:maxdepth: 2
:caption: API Reference

api/index
```

## Indices and tables

- {ref}`genindex`
- {ref}`modindex`
- {ref}`search`