README.md

---
annotations_creators: []
language: en
license: cc-by-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: >



  ![image/png](dataset_preview.jpg)



  This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 5354
  samples.


  ## Installation


  If you haven't already, install FiftyOne:


  ```bash

  pip install -U fiftyone

  ```


  ## 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)

  ```
---

# Dataset Card for MVTec AD

<!-- Provide a quick summary of the dataset. -->




![image/png](dataset_preview.jpg)


This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 5354 samples.

## Installation

If you haven't already, install FiftyOne:

```bash
pip install -U fiftyone
```

## 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)
```


## 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-4.0

### Dataset Sources

<!-- Provide the basic links for the dataset. -->

- **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)


## Dataset Creation


### Source Data

Data downloaded and converted from [MVTec website](https://www.mvtec.com/company/research/datasets/mvtec-ad)

## Citation

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

```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](https://huggingface.co/jamarks)