Python code: Classification of in situ high speed videos of the gravure printing fluid splitting process using deep learning
| datacite.relation.cites | https://doi.org/10.25534/tudatalib-191.2 | |
| datacite.relation.cites | ISBN/978-1-83864-483-3 | |
| datacite.relation.cites | https://github.com/PacktPublishing/PyTorch-Computer-Vision-Cookbook | |
| datacite.relation.cites | https://github.com/ladisk/pyMRAW | |
| datacite.relation.isCitedBy | https://doi.org/10.1007/s11998-022-00687-x | |
| dc.contributor.author | Brumm, Pauline | |
| dc.contributor.author | Ciotta, Nicola | |
| dc.contributor.author | Sauer, Hans Martin | |
| dc.contributor.author | Blaeser, Andreas | |
| dc.contributor.author | Doersam, Edgar | |
| dc.date.accessioned | 2022-08-26T23:12:57Z | |
| dc.date.available | 2022-08-26T23:12:57Z | |
| dc.date.created | 2022 | |
| dc.date.issued | 2022-08-26 | |
| dc.description | The files that are made available here are all the key components that are needed to recreate the automated classification of high speed videos of the gravure printing fluid splitting process using deep learning. The purpose of this was to create a tool that can distinguish the distinct regimes of pattern formation. This could ultimately lead to a better understanding of the fluid splitting process and a complete map of pattern formation regimes. The zip-file "Python_code" contains the Python script that was used to extract PNG frames from a video dataset created by Julian Schäfer in 2019 ([DOI: 10.25534/tudatalib-191.2](https://doi.org/10.25534/tudatalib-191.2)). The result of this frame extraction is provided as "Frame_dataset_PNG_8_bit.zip". Additionally, "Python_code.zip" contains the scripts for training the deep learning models and using them for inference. All trained models for the associated publication can be found in the zip-file "models". Further information can be found in the README-file and in the publication: Pauline Brumm, Nicola Ciotta, Hans Martin Sauer, Andreas Blaeser, Edgar Dörsam, 2022. Deep learning study of induced stochastic pattern formation in the gravure printing fluid splitting process, Journal of Coatings Technology and Research. [DOI: 10.1007/s11998-022-00687-x](https://doi.org/10.1007/s11998-022-00687-x) | de_DE |
| dc.identifier.uri | https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/3552 | |
| dc.identifier.uri | https://doi.org/10.48328/tudatalib-938 | |
| dc.language.iso | en | de_DE |
| dc.rights.license | CC-BY-NC-4.0 (https://creativecommons.org/licenses/by-nc/4.0) | |
| dc.subject | gravure printing | de_DE |
| dc.subject | fluid splitting | de_DE |
| dc.subject | Python code | de_DE |
| dc.subject | deep learning | de_DE |
| dc.subject | video classification | de_DE |
| dc.subject | pattern formation | de_DE |
| dc.subject | machine learning | de_DE |
| dc.subject | viscous fingering | 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 | Python code: Classification of in situ high speed videos of the gravure printing fluid splitting process using deep learning | 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 | |
| person.identifier.orcid | 0000-0001-6291-1967 | |
| person.identifier.orcid | #PLACEHOLDER_PARENT_METADATA_VALUE# | |
| person.identifier.orcid | #PLACEHOLDER_PARENT_METADATA_VALUE# | |
| person.identifier.orcid | 0000-0002-4338-1777 | |
| 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
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| Name | Description | Size | Format | |
|---|---|---|---|---|
| README.TXT | 4.69 KB | Plain Text | ||
| Python_code.zip | 31.4 KB | ZIP-Archivdateien | ||
| results.zip | 1.48 MB | ZIP-Archivdateien | ||
| maps.zip | 376.8 MB | ZIP-Archivdateien | ||
| class_probabilities.zip | 153.17 KB | ZIP-Archivdateien | ||
| Frame_dataset_PNG_8_bit.zip | 732.68 MB | ZIP-Archivdateien | ||
| models.zip | 1.08 GB | ZIP-Archivdateien |