Note

This is a Hugging Face dataset. Learn how to load datasets from the Hub in the Hugging Face integration docs.

Hugging Face

Dataset Card for MVTec AD#

image/png

This dataset originates from MVTec but is provided in a different format. You can easily load it using FiftyOne The total number of samples remains the same as the original: 5,354.

Installation#

If you haven’t already, install FiftyOne:

pip install -U fiftyone

Usage#

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)

Dataset Details#

Dataset Description#

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.

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 the dataset’s authors.

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

  • Language(s) (NLP): EN

  • License: CC BY-NC-SA 4.0

Dataset Sources#

Dataset Creation#

Source Data#

Data downloaded and converted from MVTec website

Citation#

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}
}

Dataset Card Authors#

Jacob Marks