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# 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`