# Crowdsourced Field Verification

VIMS instance for crowdsourced-verification.

## Related Article
[/insights/ground-truth-crowdsourced-verification/](https://landvex.com/insights/ground-truth-crowdsourced-verification/)

## Anomaly Classes
- verification_complete
- discrepancy_found
- location_mismatch
- quality_issue
- new_construction

## 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/crowdsourced-verification/predict`
- WebSocket: `ws://host/ws/crowdsourced-verification/alerts`