Research on industrial anomaly detection is limited by the availability of datasets that capture imperfect imaging conditions and complex defect characteristics. To fill this gap, we present the Industrial Screen Printing Anomaly Detection dataset (ISP-AD), which represents a real-world industrial use case in screen printing. It features subtle, weakly contrasted surface defects embedded within structured patterns with high permitted design variability. Comprising a total of 559.049 samples this dataset is the largest publicly available industrial anomaly detection dataset to date, enabling both unsupervised and supervised training scenarios. ISP-AD consists of: 312.674 fault-free samples 246.375 defective samples: 245.664 synthetic defects 711 real defects Designed to advance research in unsupervised, self-supervised, and supervised anomaly detection, ISP-AD provides a benchmark for evaluating defect detection methods under realistic industrial conditions. This dataset accompanies the paper: ISP-AD: A Large-Scale Real-World Dataset for Advancing Industrial Anomaly Detection with Synthetic and Real Defects submitted to the Journal of Intelligent Manufacturing Detailed information on dataset generation, specifications, data splits and intended use cases can be found in the accompanying paper. The dataset structure is visualized in the README.md file. The preprint version is publicly available on arXiv: https://arxiv.org/abs/2503.04997 License This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC-BY-NC-SA 4.0) License. To view a copy of the license, visit https://creativecommons.org/licenses/by-nc-sa/4.0/ This dataset is provided for non-commercial research and educational purposes only. It does not claim ownership of any copyrighted designs, registered industrial designs, or proprietary elements depicted in the images. The creators assume no responsibility for third-party misuse or potential claims related to intellectual property rights. Attribution If you use this dataset in scientific work, please cite the paper as the primary reference: Krassnig, P. J., & Gruber, D. P. (2025). ISP-AD: A Large-Scale Real-World Dataset for Advancing Industrial Anomaly Detection with Synthetic and Real Defects. arXiv preprint. https://arxiv.org/abs/2503.04997 Additionally, if referencing the dataset directly, please cite: Krassnig, P. J., Haselmann, M., Kremnitzer, M., & Gruber, D. P. (2025). The Industrial Screen Printing Anomaly Detection Dataset (ISP-AD). Zenodo. https://doi.org/10.5281/zenodo.14911043 Contact For dataset-related questions, please contact: paul.krassnig@pccl.at; dieter.gruber@pccl.at Acknowledgements The research work was performed within the COMET-project: Deep online learning for highly adaptable polymer surface inspection systems (project-no.: 879785) at the Polymer Competence Center Leoben GmbH (PCCL, Austria) within the framework of the COMET-program of the Federal Ministry for Climate Action, Environment, Energy, Mobility, Innovation and Technology and the Federal Ministry for Digital and Economic Affairs and with contributions by Burg Design GmbH. The PCCL is funded by the Austrian Government and the State Governments of Styria, Lower Austria and Upper Austria.