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October 2, 2025Open Access

ISP-AD: A Large-Scale Real-World Dataset for Advancing Industrial Anomaly Detection with Synthetic and Real Defects

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Incorporating weakly labeled real defects enhances generalization in anomaly detection models.
The ISP-AD dataset is the largest publicly available collection of industrial defect images, featuring synthetic and real samples.
Experiments reveal that training on synthetic defects establishes a baseline for integrating real defective samples.
The dataset encourages research on unsupervised, self-supervised, and supervised learning approaches in real-world scenarios.

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New dataset improves anomaly detection with synthetic and real defects, highlighting industrial applications.

Krassnig et al. (2025) studied this question.

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