Domain-Shift-Dataset of Defects on Metallic Surfaces (MSD-Shift)
| Zugehörige Institution(en) am KIT | Institut für Produktionstechnik (WBK) |
| Publikationstyp | Forschungsdaten |
| Publikationsdatum | 29.06.2022 |
| Erstellungsdatum | 01.04.2022 - 06.06.2022 |
| Identifikator | DOI: 10.5445/IR/1000147763 KITopen-ID: 1000147763 |
| Lizenz | Creative Commons Namensnennung – Nicht kommerziell – Keine Bearbeitungen 4.0 International |
| Vorab online veröffentlicht am | 08.06.2022 |
| Schlagwörter | Transfer Learning, Domain Shift, Domain Generalization, Domain Adaption, Machine Learning, Deep Learning, Metallic Surfaces, Defects |
| Liesmich | The dataset maps two different surfaces (domains) from mechanical engineering (Surfaces of Ball Screw Drives (BSD); Surface of Metallic Semi-finished Products (SEV)). The domains each contain image data with and image data without surface defects. The surfaces differ, but the defect features are similar across the domains. The dataset is thus suitable for investigating questions in the context of domain shift, domain generalization, and transfer learning. The dataset is structured as follows: BSD with defect (5240); BSD without defect (1896) SEV with defect (2018); SEV without defect (21806). Attention: The Domains are not balanced over classes. The SEV images are each 224x224 pixel RGB PNG files. THE BSD images are each 150x150 pixel RGB PNG files. The SEV data are excerpts from the Severstal dataset. |
| Art der Forschungsdaten | Dataset |