# Consensus Engine Validation

VIMS instance for consensus-engine.

## Related Article
[/insights/how-the-consensus-engine-works/](https://landvex.com/insights/how-the-consensus-engine-works/)

## Anomaly Classes
- low_confidence
- high_variance
- outlier_detected
- conflict_unresolved
- validation_passed

## 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/consensus-engine/predict`
- WebSocket: `ws://host/ws/consensus-engine/alerts`