# PatchCore Defect Detection — Metal Nut

## Model Description

This is an anomaly detection model trained 
on the MVTec AD metal nut category. It detects 
surface defects in manufactured components including 
scratches, bends, colour contamination, and 
orientation errors — without ever being trained on 
defective examples.

## Performance

| Metric | Score |
|--------|-------|
| Image AUROC | 0.9976 |
| Pixel AUROC | 0.9868 |

## Training Data

Trained on 220 defect-free images from the 
[MVTec AD dataset](https://www.mvtec.com/company/research/datasets/mvtec-ad) 
metal nut category.

## Algorithm

PatchCore (Roth et al., CVPR 2022) using 
WideResNet50 backbone with:
- Feature layers: layer2, layer3
- Coreset sampling ratio: 0.1
- Number of neighbours: 9
- Calibrated threshold: 0.577

## Threshold Calibration

Threshold calibrated at 99th percentile of good 
part scores:
- Good part pass rate: 95% (21/22)
- Defect detection rate: 96% (89/93)

## Usage

This model is deployed as a Streamlit web 
application. See the associated Space for the 
live demo.

## Citation

Roth, K., Pemula, L., Zepeda, J., Scholkopf, B., 
Brox, T., & Gehler, P. (2022). Towards Total Recall 
in Industrial Anomaly Detection. CVPR 2022.