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README.md

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+---
+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.