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arXiv · 2503.21622

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

Lars Heckler-Kram·Jan-Hendrik Neudeck·Ulla Scheler·Rebecca König·Carsten Steger
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Abstract

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 (https://benchmark.mvtec.com/). All image data is available at https://www.mvtec.com/company/research/datasets/mvtec-ad-2.

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Lars Heckler-Kram, Jan-Hendrik Neudeck, Ulla Scheler, Rebecca König, Carsten Steger. 2025-03-27. The MVTec AD 2 Dataset: Advanced Scenarios for Unsupervised Anomaly Detection. https://doi.org/10.1007/s11263-026-02743-0

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