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.
Pixel-precise annotations of all anomalies are also provided. More information can be found in our corresponding paper.
Please note: License Terms
The data is released under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0).
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 us via the form below.
For your convenience, we provide a download link for the whole dataset, as well as links for each object category.
For each object, the data consist of three folders:
- 'train', which contains the (defect-free) training images
- 'test', which contains the test images
- 'ground_truth', which contains the pixel-precise annotations of anomalous regions
Download the whole dataset (4.9 GB)
Download each object category separately:
If you use this dataset in scientific work, please cite our papers:
Paul Bergmann, Kilian Batzner, Michael Fauser, David Sattlegger, Carsten Steger: The MVTec Anomaly Detection Dataset: A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection;
in: International Journal of Computer Vision, January 2021. [pdf]
Paul Bergmann, Michael Fauser, David Sattlegger, Carsten Steger: MVTec AD – A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection;
in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2019. [pdf]