# Field Data Quality Assessment

VIMS instance for data-quality.

## Related Article
[/insights/crowdsourced-data-quality/](https://landvex.com/insights/crowdsourced-data-quality/)

## Anomaly Classes
- blurry_image
- poor_lighting
- wrong_angle
- missing_context
- gps_inaccurate

## Quick Start

1. Add training images to `data/raw/`
2. Annotate using LabelImg (YOLO format)
3. Run preprocessing: `python src/detector.py`
4. Train model: `python src/detector.py --train`
5. Run inference: `python src/detector.py --predict data/test/image.jpg`

## API

Once deployed, access via:
- REST: `POST /api/data-quality/predict`
- WebSocket: `ws://host/ws/data-quality/alerts`