README.md

---
license: other
license_name: kaputt-defect-dataset-terms
license_link: LICENSE
pretty_name: Kaputt Defect Dataset (KDD)
task_categories:
  - image-classification
size_categories:
  - 100K<n<1M
tags:
  - anomaly-detection
  - defect-detection
  - visual-inspection
  - industrial
  - logistics
gated: auto
extra_gated_heading: Access the Kaputt Defect Dataset (KDD)
extra_gated_description: >-
  Access to the Kaputt Defect Dataset is granted after you accept its Terms of
  Use. By requesting access you agree to share your Hugging Face username and
  email address with the dataset authors.
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  The Kaputt Defect Dataset (KDD) is subject to the Creative Commons
  CC BY-NC-ND 4.0 license, with additional Superseding Terms that take
  precedence where they conflict with the Creative Commons license. The full,
  binding text is in the LICENSE file in this repository. In summary: you agree
  to use the KDD solely for education and scientific research in the field of
  computer vision; it is provided "as is" with no warranties; you are solely
  responsible for lawful use of any third-party intellectual property it may
  contain; disputes are adjudicated in King County, Washington. You must read
  and accept the full Terms of Use (LICENSE) before access is granted.
extra_gated_fields:
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      - Education
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extra_gated_button_content: Accept terms and request access
---

# Kaputt: A Large-Scale Dataset for Visual Defect Detection

![Paper carousel](assets/paper-carousel.jpg)

## Abstract

We present a novel large-scale dataset for defect detection in a logistics
setting. Recent work on industrial anomaly detection has primarily focused on
manufacturing scenarios with highly controlled poses and a limited number of
object categories. Existing benchmarks like MVTec-AD (Bergmann et al., 2021) and
VisA (Zou et al., 2022) have reached saturation, with state-of-the-art methods
achieving up to 99.9% AUROC scores. In contrast to manufacturing, anomaly
detection in retail logistics faces new challenges, particularly in the
diversity and variability of object pose and appearance. Leading anomaly
detection methods fall short when applied to this new setting. To bridge this
gap, we introduce a new benchmark that overcomes the current limitations of
existing datasets. With over 230,000 images (and more than 29,000 defective
instances), it is 40 times larger than MVTec and contains more than 48,000
distinct objects. To validate the difficulty of the problem, we conduct an
extensive evaluation of multiple state-of-the-art anomaly detection methods,
demonstrating that they do not surpass 56.96% AUROC on our dataset. Further
qualitative analysis confirms that existing methods struggle to leverage normal
samples under heavy pose and appearance variation. With our large-scale dataset,
we set a new benchmark and encourage future research towards solving this
challenging problem in retail logistics anomaly detection. The dataset is
available for download under https://www.kaputt-dataset.com.

![Teaser](assets/kaputt-teaser-large.jpg)

- **Venue:** IEEE/CVF International Conference on Computer Vision (ICCV), October 2025, Honolulu, Hawaii, USA
- **Paper:** https://arxiv.org/abs/2510.05903
- **Project page:** https://www.kaputt-dataset.com

## Authors

Sebastian Höfer¹, Dorian Henning¹, Artemij Amiranashvili¹, Douglas Morrison¹,
Mariliza Tzes¹, Ingmar Posner¹˒², Marc Matvienko¹, Alessandro Rennola¹, Anton Milan¹.

¹ Amazon &nbsp;&nbsp; ² University of Oxford

<table border="0" cellspacing="0" cellpadding="6"><tr>
<td valign="middle"><img src="assets/amazon-logo.png" alt="Amazon" width="84" height="28"></td>
<td valign="middle"><img src="assets/FTR_logo-2.webp" alt="Fulfillment Technologies &amp; Robotics" width="93" height="28"></td>
<td valign="middle"><img src="assets/iccv-navbar-logo.svg" alt="ICCV" width="102" height="28"></td>
</tr></table>

## Citation

```bibtex
@inproceedings{kaputt2025,
  title = {Kaputt: A Large-Scale Dataset for Visual Defect Detection},
  author = {H{\"o}fer, Sebastian and Henning, Dorian and Amiranashvili, Artemij and Morrison, Douglas and Tzes, Mariliza and Posner, Ingmar and Matvienko, Marc and Rennola, Alessandro and Milan, Anton},
  booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
  month = {October},
  year = {2025},
  address = {Honolulu, Hawaii, USA},
  publisher = {IEEE}
}
```

## License

This dataset is released under **CC BY-NC-ND 4.0 with additional Superseding
Terms that take precedence** where they conflict with the Creative Commons
license. See the [LICENSE](blob/main/LICENSE) file for the full, binding text.

Use is permitted solely for education and scientific research in the field of
computer vision. Per Clause 7 of the Terms, you may not remove the terms from
the dataset.

## Acknowledgements

We thank our collaborators in Amazon's operations, hardware and software
engineering, as well as our annotation teams. Their invaluable contributions to
hardware development, software implementation, data collection, and labeling
efforts were essential to the success of this work.