Dataset Card for MVTec AD

This is a FiftyOne dataset with 5354 samples.

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 [email protected]

Language(s) (NLP): en
License: cc-by-4.0

Dataset Sources

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

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

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