Classification of gravure printed patterns using convolutional neural networks (Python code)
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Date
2023-05-16
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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.
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Except where otherwise noted, this license is described as CC-BY-NC 4.0 - Attribution-NonCommercial 4.0 International