Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Lehr, Jan | - |
| dc.contributor.author | Philipps, Jan | - |
| dc.contributor.author | Sargsyan, Alik | - |
| dc.contributor.author | Botschen, Maximilian | - |
| dc.contributor.author | Gevorgyan, Shoghik | - |
| dc.contributor.author | Paust, Anna-Maria | - |
| dc.contributor.author | Pape, Martin | - |
| dc.contributor.author | Nguyen Hoang, Viet | - |
| dc.date.accessioned | 2024-12-17T08:24:25Z | - |
| dc.date.available | 2024-07-09T10:06:29Z | - |
| dc.date.available | 2024-12-17T08:24:25Z | - |
| dc.date.issued | 2024-01-15 | - |
| dc.identifier.uri | https://fordatis.fraunhofer.de/handle/fordatis/363.2 | - |
| dc.identifier.uri | http://dx.doi.org/10.24406/fordatis/305.2 | - |
| dc.description.abstract | VIADUCT data set provides 49 categories of industrial objects with corresponding 135 defect categories. | en |
| dc.language.iso | en | en |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en |
| dc.subject | anomaly detection | en |
| dc.subject | industrial anomaly detection | en |
| dc.subject | automated optical inspection | en |
| dc.subject | quality control | en |
| dc.subject | visual inspection | en |
| dc.subject.ddc | DDC::600 Technik, Medizin, angewandte Wissenschaften | en |
| dc.subject.ddc | DDC::600 Technik, Medizin, angewandte Wissenschaften::670 Industrielle Fertigung | en |
| dc.subject.ddc | DDC::000 Informatik, Informationswissenschaft, allgemeine Werke::000 Informatik, Wissen, Systeme | en |
| dc.title | VIADUCT: Multisector data set for Visual Industrial Anomaly Detection | en |
| dc.type | Image | en |
| dc.contributor.funder | Bundesministerium für Bildung und Forschung BMBF (Deutschland) | en |
| dc.description.technicalinformation | The data set consists exclusively of PNG files. Each category is divided into the sub-followers train, val, test and ground truth. Only defect-free images are in the train and val directory. In the test directory are the sub-folders for each defect category and the category "good" (defect-free). The annotations (also called ground truth or labels) for the images in the defect category directories are located in the ground truth directory mentioned above. The so-called anomaly maps are binary and mark with white on black pixels where the defect is in the image. | en |
| fordatis.group | Produktion | en |
| fordatis.institute | IPK Institut für Produktionsanlagen und Konstruktionstechnik | en |
| fordatis.project.fhgid | 10-12204-2200-00001 | en |
| fordatis.rawdata | false | en |
| fordatis.sponsorship.projectid | 01DK20098 | en |
| fordatis.sponsorship.projectname | Verbundprojekt: VIADUCT - Aufwandsverkleinerung von KI Anwendungen in der Industrie durch Reduzierung von Trainingsdaten | en |
| fordatis.sponsorship.projectacronym | VIADUCT | en |
| fordatis.date.start | 2020-09-01 | - |
| fordatis.date.end | 2023-02-28 | - |
| Appears in Collections: | Institut für Produktionsanlagen und Konstruktionstechnik IPK | |
Files in This Item:
Version History
| Version | Item | Date | Summary |
|---|---|---|---|
| 2 | fordatis/363.2 | 2024-12-16 19:20:46.174 | Ergänzung des Objekts: 20 teabag.zip |
| 1 | fordatis/363 | 2024-07-09 12:06:29.0 |
This item is licensed under a Creative Commons License
