| --- |
| annotations_creators: [] |
| language: en |
| license: cc-by-nc-sa-4.0 |
| size_categories: |
| - 1K<n<10K |
| task_categories: |
| - image-classification |
| - image-segmentation |
| task_ids: [] |
| pretty_name: MVTec AD |
| tags: |
| - fiftyone |
| - image |
| - image-classification |
| - image-segmentation |
| - anomaly-detection |
| dataset_summary: > |
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| This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 5354 |
| samples. |
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| If you haven't already, install FiftyOne: |
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| ```bash |
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| pip install -U fiftyone |
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| ``` |
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| ```python |
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| import fiftyone as fo |
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| import fiftyone.utils.huggingface as fouh |
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| dataset = fouh.load_from_hub("Voxel51/mvtec-ad") |
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| session = fo.launch_app(dataset) |
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| ``` |
| --- |
| |
| # Dataset Card for MVTec AD |
|
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| <!-- Provide a quick summary of the dataset. --> |
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| This dataset originates from MVTec but is provided in a different format. You can easily load it using [FiftyOne](https://github.com/voxel51/fiftyone) |
| The total number of samples remains the same as the original: 5,354. |
|
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| ## Installation |
|
|
| If you haven't already, install FiftyOne: |
|
|
| ```bash |
| pip install -U fiftyone |
| ``` |
|
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| ## Usage |
|
|
| ```python |
| import fiftyone as fo |
| import fiftyone.utils.huggingface as fouh |
| |
| # Load the dataset |
| # Note: other available arguments include 'max_samples', etc |
| dataset = fouh.load_from_hub("Voxel51/mvtec-ad") |
| |
| # Launch the App |
| session = fo.launch_app(dataset) |
| ``` |
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| ## Dataset Details |
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| ### Dataset Description |
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| MVTec AD is a dataset for benchmarking anomaly detection methods with a focus on industrial inspection. It contains over 5000 high-resolution images divided into fifteen different object and texture categories. Each category comprises a set of defect-free training images and a test set of images with various kinds of defects as well as images without defects. |
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| Pixel-precise annotations of all anomalies are also provided. |
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| The data is released under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0). |
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| In particular, it is not allowed to use the dataset for commercial purposes. If you are unsure whether or not your application violates the non-commercial use clause of the license, please contact the dataset's authors. |
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| If you have any questions or comments about the dataset, feel free to contact the dataset's authors via email at re-request@mvtec.com |
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| - **Language(s) (NLP):** EN |
| - **License:** CC BY-NC-SA 4.0 |
|
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| ### Dataset Sources |
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| <!-- Provide the basic links for the dataset. --> |
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| - **Dataset Homepage** https://www.mvtec.com/company/research/datasets/mvtec-ad |
| - **Demo:** https://try.fiftyone.ai/datasets/mvtec-ad/samples |
| - **Paper:** [The MVTec Anomaly Detection Dataset: A Comprehensive Real-World |
| Dataset for Unsupervised Anomaly Detection](https://link.springer.com/content/pdf/10.1007/s11263-020-01400-4.pdf) |
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| ## Dataset Creation |
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| ### Source Data |
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| Data downloaded and converted from [MVTec website](https://www.mvtec.com/company/research/datasets/mvtec-ad) |
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| ## Citation |
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| <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> |
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| **BibTeX:** |
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| ```bibtex |
| |
| @article{Bergmann2021MVTecAnomalyDetection, |
| title={The MVTec Anomaly Detection Dataset: A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection}, |
| author={Bergmann, Paul and Batzner, Kilian and Fauser, Michael and Sattlegger, David and Steger, Carsten}, |
| journal={International Journal of Computer Vision}, |
| volume={129}, |
| number={4}, |
| pages={1038--1059}, |
| year={2021}, |
| doi={10.1007/s11263-020-01400-4} |
| } |
| |
| @inproceedings{Bergmann2019MVTecAD, |
| title={MVTec AD — A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection}, |
| author={Bergmann, Paul and Fauser, Michael and Sattlegger, David and Steger, Carsten}, |
| booktitle={IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, |
| pages={9584--9592}, |
| year={2019}, |
| doi={10.1109/CVPR.2019.00982} |
| } |
| ``` |
|
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| ## Dataset Card Authors |
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| [Jacob Marks](https://huggingface.co/jamarks) |
