MPDD (Metal Parts Defect Detection Dataset)

Introduced by Jezek et al. in Deep learning-based defect detection of metal parts: evaluating current methods in complex conditions

MPDD is a dataset aimed at benchmarking visual defect detection methods in industrial metal parts manufacturing. It consists of more than 1000 images with pixel-precise defect annotation masks. The dataset is 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.

Papers


Paper Code Results Date Stars

Dataset Loaders


No data loaders found. You can submit your data loader here.

Tasks


Similar Datasets


License


  • Unknown

Modalities


Languages