---
license: cc-by-nc-sa-4.0
tags:
- anomaly-detection
- industrial-inspection
- computer-vision
- mvtec
- unsupervised-learning
---

# MVTec Anomaly Detection Dataset (MVTec AD)

## Dataset description

The **MVTec Anomaly Detection (MVTec AD)** dataset is a large-scale real-world dataset for unsupervised anomaly detection in industrial inspection scenarios.  
It contains high-resolution images of multiple object and texture categories, including normal samples and various defect types with pixel-level ground truth masks.

Official website:  
https://www.mvtec.com/company/research/datasets/mvtec-ad

---

## Dataset format

In this repository, the dataset is provided as a **single compressed archive**:

**`mvtec_anomaly_detection.tar.xz`**

After downloading, the archive must be extracted locally:

```bash
tar -xJf mvtec_anomaly_detection.tar.xz
```

---
## Dataset structure


```text
Each category follows the structure:
category/
├── train/
│ └── good/
├── test/
│ ├── good/
│ └── defect_type/
└── ground_truth/
└── defect_type/
```

- Training set: only normal images  
- Test set: normal and anomalous images  
- Ground truth: pixel-level defect masks  

---

## Categories

The dataset includes objects and textures such as:

bottle, cable, capsule, carpet, grid, hazelnut, leather, metal_nut, pill, screw, tile, toothbrush, transistor, wood, zipper, and others.

---

## License

This dataset is distributed under:

**Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International  
(CC BY-NC-SA 4.0)**

You may:

- Share and redistribute the dataset  
- Adapt and build upon the dataset  

Under the conditions:

- Attribution required  
- Non-commercial use only  
- Share-alike under the same license  

Full license text:  
https://creativecommons.org/licenses/by-nc-sa/4.0/

---

## Citation

If you use this dataset in academic work, please cite:

```bibtex
@inproceedings{bergmann2019mvtec,
  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={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2019}
}
```

---

## Source and attribution

This dataset is originally provided by:

MVTec Software GmbH
[https://www.mvtec.com](https://www.mvtec.com)

All rights remain with the original authors.

---

## Intended use

This dataset is intended for:

* Academic research
* Educational purposes
* Benchmarking anomaly detection algorithms

Commercial use is not permitted.

---

## Disclaimer

This repository is not affiliated with MVTec Software GmbH.
It is provided only for research and educational purposes.

---