See the official website: https://autovi.utc.fr Modern industrial production lines must be set up with robust defect inspection modules that are able to withstand high product variability. This means that in a context of industrial production, new defects that are not yet known may appear, and must therefore be identified. On industrial production lines, the typology of potential defects is vast (texture, part failure, logical defects, etc.). Inspection systems must therefore be able to detect non-listed defects, i.e. not-yet-observed defects upon the development of the inspection system. To solve this problem, research and development of unsupervised AI algorithms on real-world data is required. Renault Group and the Université de technologie de Compiègne (Roberval and Heudiasyc Laboratories) have jointly developed the Automotive Visual Inspection Dataset (AutoVI), the purpose of which is to be used as a scientific benchmark to compare and develop advanced unsupervised anomaly detection algorithms under real production conditions. The images were acquired on Renault Group's automotive production lines, in a genuine industrial production line environment, with variations in brightness and lighting on constantly moving components. This dataset is representative of actual data acquisition conditions on automotive production lines. The dataset contains 3950 images, split into 1530 training images and 2420 testing images. The evaluation code can be found at https://github.com/phcarval/autovi_evaluation_code. DisclaimerAll defects shown were intentionally created on Renault Group's production lines for the purpose of producing this dataset. The images were examined and labeled by Renault Group experts, and all defects were corrected after shooting. LicenseCopyright © 2023-2024 Renault Group This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. To view a copy of the license, visit https://creativecommons.org/licenses/by-nc-sa/4.0/. For using the data in a way that falls under the commercial use clause of the license, please contact us. AttributionPlease use the following for citing the dataset in scientific work: Carvalho, P., Lafou, M., Durupt, A., Leblanc, A., & Grandvalet, Y. (2024). The Automotive Visual Inspection Dataset (AutoVI): A Genuine Industrial Production Dataset for Unsupervised Anomaly Detection [Dataset]. https://doi.org/10.5281/zenodo.10459003 ContactIf you have any questions or remarks about this dataset, please contact us at philippe.carvalho@utc.fr, meriem.lafou@renault.com, alexandre.durupt@utc.fr, antoine.leblanc@renault.com, yves.grandvalet@utc.fr. Changelog v1.0.0 Cropped engine_wiring, pipe_clip and pipe_staple images Reduced tank_screw, underbody_pipes and underbody_screw image sizes v0.1.1 Added ground truth segmentation maps Fixed categorization of some images Added new defect categories Removed tube_fastening and kitting_cart Removed duplicates in pipe_clip