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| title: MVTec Anomaly Detection Demo | |
| emoji: π | |
| colorFrom: blue | |
| colorTo: blue | |
| sdk: gradio | |
| sdk_version: "4.44.0" | |
| app_file: app.py | |
| pinned: false | |
| license: mit | |
| # MVTec Anomaly Detection Demo | |
| Upload an image or pick a sample to detect anomalies using PaDiM / PatchCore / FastFlow models trained on the MVTec AD benchmark. | |
| ## Models | |
| - **PaDiM** β Patch Distribution Modeling (multivariate Gaussian per patch) | |
| - **PatchCore** β Memory bank + K-Nearest Neighbors | |
| - **FastFlow** β Normalising flows on CNN features | |
| ## Dataset | |
| [MVTec Anomaly Detection Dataset](https://www.mvtec.com/company/research/datasets/mvtec-ad) | |