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license: cc-by-nc-sa-4.0 |
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tags: |
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- anomaly-detection |
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- industrial-inspection |
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- computer-vision |
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- mvtec |
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- unsupervised-learning |
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--- |
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# MVTec Anomaly Detection Dataset (MVTec AD) |
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## Dataset description |
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The **MVTec Anomaly Detection (MVTec AD)** dataset is a large-scale real-world dataset for unsupervised anomaly detection in industrial inspection scenarios. |
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It contains high-resolution images of multiple object and texture categories, including normal samples and various defect types with pixel-level ground truth masks. |
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Official website: |
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https://www.mvtec.com/company/research/datasets/mvtec-ad |
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--- |
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## Dataset format |
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In this repository, the dataset is provided as a **single compressed archive**: |
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**`mvtec_anomaly_detection.tar.xz`** |
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After downloading, the archive must be extracted locally: |
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```bash |
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tar -xJf mvtec_anomaly_detection.tar.xz |
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``` |
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--- |
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## Dataset structure |
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```text |
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Each category follows the structure: |
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category/ |
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├── train/ |
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│ └── good/ |
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├── test/ |
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│ ├── good/ |
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│ └── defect_type/ |
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└── ground_truth/ |
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└── defect_type/ |
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``` |
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- Training set: only normal images |
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- Test set: normal and anomalous images |
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- Ground truth: pixel-level defect masks |
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--- |
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## Categories |
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The dataset includes objects and textures such as: |
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bottle, cable, capsule, carpet, grid, hazelnut, leather, metal_nut, pill, screw, tile, toothbrush, transistor, wood, zipper, and others. |
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--- |
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## License |
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This dataset is distributed under: |
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**Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International |
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(CC BY-NC-SA 4.0)** |
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You may: |
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- Share and redistribute the dataset |
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- Adapt and build upon the dataset |
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Under the conditions: |
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- Attribution required |
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- Non-commercial use only |
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- Share-alike under the same license |
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Full license text: |
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https://creativecommons.org/licenses/by-nc-sa/4.0/ |
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--- |
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## Citation |
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If you use this dataset in academic work, please cite: |
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```bibtex |
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@inproceedings{bergmann2019mvtec, |
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title={MVTec AD — A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection}, |
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author={Bergmann, Paul and Fauser, Michael and Sattlegger, David and Steger, Carsten}, |
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booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, |
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year={2019} |
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} |
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``` |
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--- |
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## Source and attribution |
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This dataset is originally provided by: |
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MVTec Software GmbH |
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[https://www.mvtec.com](https://www.mvtec.com) |
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All rights remain with the original authors. |
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--- |
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## Intended use |
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This dataset is intended for: |
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* Academic research |
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* Educational purposes |
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* Benchmarking anomaly detection algorithms |
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Commercial use is not permitted. |
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--- |
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## Disclaimer |
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This repository is not affiliated with MVTec Software GmbH. |
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It is provided only for research and educational purposes. |
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--- |
