Classification of gravure printed patterns using convolutional neural networks (Python code)
Date
2023Author
Description
This dataset contains Python code ('code_DeepLearn_ImgClass.zip') for automated classification of gravure printed patterns from the HYPA-p 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.
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.
Subject
Python code;pattern classification;hydrodynamic pattern formation;deep learning;convolutional neural networks;gravure printingDFG subject classification
4.21-03 Mechanische Verfahrenstechnik4.22-03 Strömungsmechanik
4.31-03 Beschichtungs- und Oberflächentechnik
URI
https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/3838https://doi.org/10.48328/tudatalib-1147
Related third party funded projects
DFG | SFB1194 | TP C01 DörsamRelated Resources
- Is supplement to: https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/3841
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Except where otherwise noted, this item's license is described as Creative Commons Attribution-NonCommercial 4.0