---
license: cc-by-nc-sa-4.0
task_categories:
- image-classification
- image-segmentation
tags:
- industrial-inspection
- anomaly-detection
- computer-vision
- synthetic-data
- defect-synthesis
- unsupervised-learning
- supervised-learning
- self-supervised-learning
- defect-detection
- mvtec-ad
- industrial-dataset
size_categories:
- 100K<n<1M
modalities:
- images
---

# ISP-AD: The Industrial Screen Printing Anomaly Detection Dataset

![ISP-AD Dataset Overview](title_figure.png)

The **ISP-AD Dataset** is a large-scale industrial visual anomaly detection benchmark designed for unsupervised, self-supervised, and supervised learning. It features subtle, weakly contrasted surface defects embedded within structured screen-printed patterns with high permitted design variability.

Comprising **559,049** samples, ISP-AD is one of the **largest publicly available industrial anomaly detection datasets to date**, providing both synthetic and real defects collected directly from the factory floor.

It is intended to foster research on **industrially applicable anomaly detection approaches** including:
* Novel **training strategies using real and synthetic defects** as proposed in the accompanying [Paper](https://link.springer.com/article/10.1007/s10845-025-02778-z                            ), as well as **unsupervised methods**.
* **Zero-shot** or **few-shot defect synthesis** approaches.
* **Emerging vision foundation models** for zero-shot testing or auxiliary fine-tuning for domain adaptation on challenging textured industrial patterns.

---

## 📊 Dataset Structure & Modalities

Image patches are provided in **256×256 px** and **512×512 px** resolutions (RGB and grayscale) across **three distinct optical imaging modalities** from a real-world screen-printing process.

---

## 📈 Benchmarks
Comprehensive image- and pixel-level evaluation benchmarks for state-of-the-art (SOTA) unsupervised anomaly detection methods—including **EfficientAD**, **U-Flow**, **GLASS**, and **DDAD**—are provided in the accompanying paper.

---

## 📂 Dataset Resources

The dataset is publicly available on **Zenodo** under the **CC BY-NC-SA 4.0** license.
Additional information regarding dataset splits, minimal working examples, and evaluation pipelines can be found at:

* **Paper**: [Springer Journal of Intelligent Manufacturing (2026)](https://link.springer.com/article/10.1007/s10845-025-02778-z                            )
* **Dataset**: [Zenodo Repository (CC BY-NC-SA 4.0)](https://doi.org/10.5281/zenodo.14911042                            )
* **GitHub Repository**: [p4ulk/isp-ad (PyTorch & Anomalib Pipelines)](https://github.com/p4ulk/isp-ad)

---

## 🎓 Citation & Attribution

If you integrate the ISP-AD dataset or the associated code into your scientific research, please cite the primary reference:

```bibtex
@article{krassnig2026isp,
  title={ISP-AD: A large-scale real-world dataset for advancing industrial anomaly detection with synthetic and real defects},
  author={Krassnig, Paul Josef and Gruber, Dieter Paul},
  journal={Journal of Intelligent Manufacturing},
  pages={1--26},
  year={2026},
  publisher={Springer}
}
```