Classification of gravure printed patterns using convolutional neural networks (Python code)
| datacite.relation.isSupplementTo | https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/3841 | |
| dc.contributor.author | Rothmann-Brumm, Pauline | |
| dc.date.accessioned | 2023-05-16T11:26:33Z | |
| dc.date.accessioned | 2023-06-13T13:58:31Z | |
| dc.date.available | 2023-05-16T11:26:33Z | |
| dc.date.available | 2023-06-13T13:58:31Z | |
| dc.date.created | 2023 | |
| dc.date.issued | 2023-05-16 | |
| dc.description | This dataset contains Python code ('code_DeepLearn_ImgClass.zip') for automated classification of gravure printed patterns from the [HYPA-p](https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/3841) dataset. The developed algorithm performs supervised deep learning of convolutional neural networks (CNNs) on labeled data ('CNN_dataset.zip'), i.e. selected, labeled 'S-subfields' from the HYPA-p dataset. 'CNN_dataset.zip' is a subset from the images in the folder 'labeled_data.zip', which can be created with the provided Python code. PyTorch is used as a deep learning framework. The Python code yields trained CNNs, which can be used for automated classification of unlabeled data from the HYPA-p dataset. Well-known, pre-trained network architectures like Densenet-161 or MobileNetV2 are used as a starting point for training. Several trained CNNs are included in this submission, see 'trained_CNN_models.zip'. Further information can be found in the dissertation of Pauline Rothmann-Brumm (2023) and in the provided README-file. | de_DE |
| dc.identifier.uri | https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/3838 | |
| dc.identifier.uri | https://doi.org/10.48328/tudatalib-1147 | |
| dc.language.iso | en | de_DE |
| dc.rights.license | CC-BY-NC-4.0 (https://creativecommons.org/licenses/by-nc/4.0) | |
| dc.subject | Python code | de_DE |
| dc.subject | pattern classification | de_DE |
| dc.subject | hydrodynamic pattern formation | de_DE |
| dc.subject | deep learning | de_DE |
| dc.subject | convolutional neural networks | de_DE |
| dc.subject | gravure printing | de_DE |
| dc.subject.classification | 4.21-03 | |
| dc.subject.classification | 4.22-03 | |
| dc.subject.classification | 4.31-03 | |
| dc.subject.ddc | 660 | |
| dc.subject.ddc | 620 | |
| dc.title | Classification of gravure printed patterns using convolutional neural networks (Python code) | de_DE |
| dc.type | Text | de_DE |
| dc.type | Software | de_DE |
| dc.type | Image | de_DE |
| dc.type | Model | de_DE |
| dcterms.accessRights | openAccess | |
| person.identifier.orcid | 0000-0002-8220-0676 | |
| tuda.history.classification | Version=2020-2024;403-03 Mechanische Verfahrenstechnik | |
| tuda.history.classification | Version=2020-2024;404-03 Strömungsmechanik | |
| tuda.history.classification | Version=2020-2024;405-03 Beschichtungs- und Oberflächentechnik | |
| tuda.project | DFG | SFB1194 | TP C01 Dörsam | |
| tuda.unit | TUDa |
Files
Original bundle
1 - 5 of 5
| Name | Description | Size | Format | |
|---|---|---|---|---|
| README_DeepLearn_ImgClass.txt | 6.62 KB | Plain Text | ||
| code_DeepLearn_ImgClass.zip | 66.46 KB | ZIP-Archivdateien | ||
| CNN_dataset.zip | 4.6 GB | ZIP-Archivdateien | ||
| labeled_data.zip | 10.93 GB | ZIP-Archivdateien | ||
| trained_CNN_models.zip | 741.15 MB | ZIP-Archivdateien |