MVTec AD — A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection
Abstract
The detection of anomalous structures in natural image data is of utmost importance for numerous tasks in the field of computer vision. The development of methods for unsupervised anomaly detection requires data on which to train and evaluate new approaches and ideas. We introduce the MVTec Anomaly Detection (MVTec AD) dataset containing 5354 high-resolution color images of different object and texture categories. It contains normal, i.e., defect-free, images intended for training and images with anomalies intended for testing. The anomalies manifest themselves in the form of over 70 different types of defects such as scratches, dents, contaminations, and various structural changes. In addition, we provide pixel-precise ground truth regions for all anomalies. We also conduct a thorough evaluation of current state-of-the-art unsupervised anomaly detection methods based on deep architectures such as convolutional autoencoders, generative adversarial networks, and feature descriptors using pre-trained convolutional neural networks, as well as classical computer vision methods. This initial benchmark indicates that there is considerable room for improvement. To the best of our knowledge, this is the first comprehensive, multi-object, multi-defect dataset for anomaly detection that provides pixel-accurate ground truth regions and focuses on real-world applications.
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The MVTec AD and BeanTech AD datasets are extensively utilized to evaluate anomaly detection algorithms.
“These datasets are extensively utilized to evaluate anomaly detection algorithms, offering diverse testing scenarios and data for this research.”
From An Unsupervised Method for Industrial Image Anomaly Detection with Vision Transformer-Based Autoencoder (2024) · cites this paper as [22]
The MVTec AD dataset is tailored for unsupervised image anomaly detection tasks and simulates real-world industrial scenarios.
“the MVTec AD dataset is tailored for unsupervised image anomaly detection tasks. Simulating real-world industrial scenarios”
From An Unsupervised Method for Industrial Image Anomaly Detection with Vision Transformer-Based Autoencoder (2024) · cites this paper as [22]
The MVTec AD dataset comprises 5354 high-resolution color images (3629 normal and 1725 anomalous), with resolutions of 700 × 700 or 1024 × 1024 pixels, spanning 15 categories (5 texture and 10 object) and covering over 70 types of artificially induced defects.
“Encompassing 5354 high-resolution color images (3629 normal and 1725 anomalous), resolutions are either 700 × 700 or 1024 × 1024 pixels. Spanning 15 categories (5 texture and 10 object), the dataset covers over 70 types of artificially induced defects”
From An Unsupervised Method for Industrial Image Anomaly Detection with Vision Transformer-Based Autoencoder (2024) · cites this paper as [22]
Unlike CIFAR-10, each category has normal and abnormal samples in MVTec-AD.
“Unlike CIFAR-10, each category has normal and abnormal samples in MVTec-AD.”
From A Unified Model for Multi-Class Anomaly Detection (2022) · cites this paper as [4]
UniAD could handle all 15 categories in MVTec-AD.
“Our UniAD could handle all 15 categories in MVTec-AD, hence would be more suitable for real scenes.”
From A Unified Model for Multi-Class Anomaly Detection (2022) · cites this paper as [4]
Without query embedding, meaning the encoder embeddings are directly input to the decoder, the performance is the worst.
“Without query embedding, meaning the encoder embeddings are directly input to the decoder, the performance is the worst.”
From A Unified Model for Multi-Class Anomaly Detection (2022) · cites this paper as [4]
The query embedding is of vital significance.
“Tab. 5a verifies our assertion that the query embedding is of vital significance.”
From A Unified Model for Multi-Class Anomaly Detection (2022) · cites this paper as [4]
All 15 categories are handled by a unified model.
“All 15 categories are handled by a unified model.”
From A Unified Model for Multi-Class Anomaly Detection (2022) · cites this paper as [4]
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- Authors
- Bergmann, P., Fauser, M., Sattlegger, D., & Steger, C.
- Affiliations
- MVTec Software GmbH
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 9584-9592
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2948982773
- Cited by
- 2,203
- References
- 27
- Citations by year
- 2026: 502 · 2025: 551 · 2024: 447 · 2023: 321 · 2022: 170 · 2021: 146 · 2020: 62 · 2019: 4
- FWCI
- 138.57
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Keywords
- unsupervised anomaly detection, defect detection, convolutional autoencoder, generative adversarial networks, pre-trained convolutional neural networks, benchmark evaluation, real-world images
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