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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 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.