File size: 1,234 Bytes
258878c c461b65 258878c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 | # 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. |