# Install and configure ai-vision-engine

Follow the source instructions for **ai-vision-engine**. Read the prerequisites and configuration steps before running an example.

## Install

Python 3.11 or 3.12 and [uv](https://docs.astral.sh/uv/) are required. Install FFmpeg for film export.

```bash
git clone https://github.com/qentrah/ai-vision-engine.git
cd ai-vision-engine
uv sync --extra dev
uv run ai-vision image path/to/image.jpg
```

Official detector weights download automatically on first use. Models are cached locally and excluded from Git. After weights are cached, inference can run offline. Keep the same working directory when using relative model paths. Optional models have separate caches; setting `HF_HOME` before the first run changes the Transformers cache.

```bash
uv sync --extra dev --extra ocr --extra scene --extra face
uv run ai-vision --ocr --pose --face --depth --segmentation image path/to/street.jpg
uv run ai-vision --ocr --ocr-languages ar en image path/to/arabic-sign.jpg
uv run ai-vision --config configs/default.yaml image path/to/street.jpg
```

Flags precede the command. Enabled optional components initialize once per engine. Errors propagate with a traceback; requested failures are not concealed as empty predictions.

## Check the installation

After setup, continue with the [first example](/docs/up-to-code-ai-vision-engine/quick-start-guide). If a command fails, compare the runtime version, working directory, and required configuration with the original README before changing dependencies.

## Source and help

- [GitHub repository](https://github.com/qentrah/ai-vision-engine)
- [Original README](https://github.com/qentrah/ai-vision-engine/blob/main/README.md)
- [Issues and existing reports](https://github.com/qentrah/ai-vision-engine/issues)

The catalog identifies the license as **AGPL-3.0**. Read the repository license before redistributing source or assets.