MPDD2 is a dataset aimed at benchmarking visual defect detection methods in industrial metal parts manufacturing. It consists of more than 700 images, which are divided into the training subset with anomaly-free samples and the validation subset that contains both normal and anomalous samples. The dataset can be downloaded at the following link.
See the full paper at the following link
If you use the dataset in this repository, please cite
@INPROCEEDINGS{9943437,
author={Jezek, Stepan and Jonak, Martin and Burget, Radim and Dvorak, Pavel and Skotak, Milos},
booktitle={2022 14th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)},
title={Anomaly detection for real-world industrial applications: benchmarking recent self-supervised and pretrained methods},
year={2022},
volume={},
number={},
pages={64-69},
doi={10.1109/ICUMT57764.2022.9943437}
}
Stepan Jezek: Stepan.Jezek1@vut.cz Radim Burget: burgetrm@vut.cz
This work was supported by project "Defectoscopy of painted parts using automatic adaptation of neural networks", FW03010273, Technology Agency of the Czech Republic