| --- |
| 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. |
|
|
