ISP-AD: The Industrial Screen Printing Anomaly Detection Dataset

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, 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)
• Dataset: Zenodo Repository (CC BY-NC-SA 4.0)
• GitHub Repository: p4ulk/isp-ad (PyTorch & Anomalib Pipelines)

---

🎓 Citation & Attribution

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

@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}
}