# AnomalyOS Model Card

## Model Details

### Model Description
AnomalyOS is an advanced anomaly detection system for surface defect detection. It combines patch-based deep learning (PatchCore), knowledge graphs, and retrieval-augmented generation with explainable AI techniques.

### Model Type
- **Primary**: Patch-based Convolutional Neural Network
- **Retrieval**: FAISS Vector Search + Knowledge Graph
- **Explainability**: Gradient-based + Attention Heatmaps

## Intended Use

### Primary Use Cases
- Surface defect detection in manufacturing
- Quality control automation
- Real-time anomaly detection

### Out-of-scope Use Cases
- Medical image analysis (without domain-specific validation)
- Safety-critical autonomous systems (without additional verification)

## Training Data

### Dataset
- **Source**: MVTec AD Dataset + Custom Industrial Data
- **Categories**: 15 object categories (bottle, carpet, wood, etc.)
- **Training Samples**: ~4,000 images per category
- **Image Resolution**: 256x256 to 1024x1024 pixels

### Data Processing
- Normalization: ImageNet statistics
- Augmentation: Random crops, flips, rotations
- Train/Val/Test Split: 70/15/15

## Model Performance

### Metrics
- **AUROC**: 0.95+ (average across categories)
- **Detection F1**: 0.92+ (at IoU >= 0.5)
- **Inference Time**: ~100ms per image (on GPU)

### Performance by Category
See detailed performance metrics in reports/performance_metrics.json

## Limitations

1. Performance may degrade on images with significant lighting variations
2. Requires object segmentation for optimal results
3. Not validated for extreme manufacturing conditions
4. Knowledge graph coverage depends on training data completeness

## Ethical Considerations

- Model predictions should always be validated by human experts
- Use should comply with data protection and privacy regulations
- Potential for automation bias - regular performance audits recommended

## Updates

- **Version**: 1.0.0
- **Last Updated**: 2024-03-31
- **Next Review**: 2024-09-30