metadata
language: en
license: cc-by-nc-sa-4.0
tags:
- anomaly-detection
- computer-vision
- industrial-inspection
- patchcore
- mvtec-ad
datasets:
- foersben/mvtec-ad
metrics:
- roc_auc
- f1
library_name: pytorch
pipeline_tag: image-feature-extraction
Industrial Anomaly Detection: PatchCore (MVTec AD Bottle)
Model Overview
- Architecture: PatchCore (Roth et al., CVPR 2022)
- Backbone: Frozen
Wide-ResNet-50-2(Layers 2 & 3 concatenation, D = 1536 channels) - Memory Bank: 10% Greedy k-Center minimax coreset subsampling (16,386 prototypes)
- Evaluation Split: Official MVTec AD Bottle hold-out test set (83 images: 20 good, 63 defect with ground-truth masks)
Empirical Benchmark Results (Tesla T4 GPU)
| Metric | Target | Result |
|---|---|---|
| Image-level AUROC | >= 0.980 | 1.0000 (100% Perfect Separation) |
| Pixel-level AUROC | >= 0.960 | 0.9848 |
| AUPRO (FPR <= 0.30) | >= 0.920 | 0.9376 |
| Mean Latency | < 100 ms | 26.31 ms |
| Median Latency (p50) | — | 26.25 ms |
| 95th Percentile Latency (p95) | < 80 ms | 27.51 ms |
Threshold Methodology Disclosure
- Oracle In-Sample Threshold: Threshold tau = 17.6255 yields F1 = 1.0000 on the 83 test images. Disclosed as an empirical upper bound.
- Operational Calibration (Recommended): In production lines without defect samples, establish threshold on a held-out nominal validation split (
train/good) at the 99th percentile (target FAR <= 1.0%).
Hardware & Limitations
- Hardware: Evaluated on NVIDIA Tesla T4 GPU (15.0 GB VRAM, Turing architecture).
- Domain Scope: Specialized for clear rigid glassware bottles. Requires a separate nominal prototype memory bank for opaque steel surfaces or textiles.