The MVTec AD 2 Dataset: Advanced Scenarios for Unsupervised Anomaly Detection

In recent years, performance on existing anomaly detection benchmarks like MVTec AD and VisA has started to saturate in terms of segmentation AU-PRO, with state-of-the-art models often competing in the range of less than one percentage point. This lack of discriminatory power prevents a meaningful comparison of models and thus hinders progress of the field, especially when considering the inherent stochastic nature of machine learning results. We present MVTec AD 2, a collection of eight anomaly detection scenarios with more than 8000 high-resolution images. It comprises challenging and highly relevant industrial inspection use cases that have not been considered in previous datasets, including transparent and overlapping objects, dark-field and back light illumination, objects with high variance in the normal data, and extremely small defects. We provide comprehensive evaluations of state-of-the-art methods and show that their performance remains below 60% average AU-PRO. Additionally, our dataset provides test scenarios with lighting condition changes to assess the robustness of methods under real-world distribution shifts. We host a publicly accessible evaluation server that holds the pixel-precise ground truth of the test set (this https URL). All image data is available atthis https URL.

@article{heckler-kram2025_2503.21622,
  title={ The MVTec AD 2 Dataset: Advanced Scenarios for Unsupervised Anomaly Detection },
  author={ Lars Heckler-Kram and Jan-Hendrik Neudeck and Ulla Scheler and Rebecca König and Carsten Steger },
  journal={arXiv preprint arXiv:2503.21622},
  year={ 2025 }
}