| --- |
| license: cc-by-nc-sa-4.0 |
| library_name: onnx |
| pipeline_tag: image-classification |
| tags: |
| - anomaly-detection |
| - patchcore |
| - mvtec-ad |
| - onnx |
| - industrial-inspection |
| datasets: |
| - mvtec-ad |
| --- |
| |
| # PatchCore on MVTec AD (ONNX, runs in the browser) |
|
|
| From-scratch PyTorch reimplementation of **PatchCore** (Roth et al., *Towards Total Recall in Industrial Anomaly |
| Detection*, CVPR 2022), exported to ONNX. Fitted on **defect-free images only**; defects are found as patches that |
| are far from every patch seen on good parts. |
|
|
| **Live demo (in your browser, no server):** [yandyassine/patchcore-defect-detection](https://huggingface.co/spaces/yandyassine/patchcore-defect-detection) · |
| **Code & write-up:** [COMPUTER-VISION-PROJECT-LIBRARY / 13](https://github.com/yandouziyassine/COMPUTER-VISION-PROJECT-LIBRARY/tree/main/13-anomaly-detection-patchcore) |
|
|
| ## Results (MVTec AD test set, image level) |
|
|
| | Category | Test images | Image AUROC | F1 at the shipped threshold | |
| |---|---|---|---| |
| | bottle | 83 | 1.000 | 1.000 | |
| | metal_nut | 115 | 0.999 | 0.995 | |
| | grid | 78 | 0.977 | 0.974 | |
| |
| These numbers were re-measured **with the ONNX files in this repo** (fp16-stored weights), and match the PyTorch |
| model exactly at this precision. |
| |
| ## Files |
| |
| | File | What it does | Size | |
| |---|---|---| |
| | `backbone.onnx` | image `(1,3,224,224)` → patch features `(784, 1536)`: WideResNet-50-2 layers 2+3, 3×3 local averaging | 50 MB | |
| | `knn_<category>.onnx` | features → anomaly map `(224,224)` + image score; memory bank (10 % greedy coreset) baked in | 50–64 MB | |
| | `thresholds.json` | per-category decision threshold (best F1 on the test set) | | |
|
|
| Weights are stored as float16 and cast to float32 inside the graph, so the math runs in float32. |
|
|
| ## Use |
|
|
| Preprocessing: resize the shorter side to 256, center-crop 224, ImageNet mean/std normalization. |
|
|
| ```python |
| import numpy as np, onnxruntime as ort |
| bb = ort.InferenceSession("backbone.onnx") |
| knn = ort.InferenceSession("knn_bottle.onnx") |
| feats = bb.run(None, {"image": x})[0] # x: float32 (1,3,224,224) |
| amap, score = knn.run(None, {"features": feats}) |
| defect = score > 1.9728 # thresholds.json["bottle"]["threshold"] |
| ``` |
|
|
| ## Limitations |
|
|
| - One model per product; a photo of another object will look anomalous everywhere (by design). |
| - Thresholds were picked on the MVTec test set itself, so the F1 column is optimistic; AUROC is threshold-free. |
| - Trained at 224 px; very small defects can be missed. |
|
|
| ## License |
|
|
| Memory banks are derived from MVTec AD, licensed **CC BY-NC-SA 4.0**: non-commercial use only. Backbone weights: |
| torchvision ImageNet WideResNet-50-2. |
|
|
