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Half of this dataset is composed of the 643 OntoUML images [from the OntoUML Catalogue](https://doi.org/10.1007/978-3-031-17995-2_1), and the other half comprised 321 randomly selected class diagrams and 322 randomly selected non-UML images from the [dataset of the paper “Multiclass classification of four types of UML diagrams from images using deep learning"](https://doi.org/10.5281/zenodo.4595957
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). The idea behind it was to have equal parts OntoUML and non-OntoUML images. In the paper it is referred to as the “binary dataset”.
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license: apache-2.0
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Half of this dataset is composed of the 643 OntoUML images [from the OntoUML Catalogue](https://doi.org/10.1007/978-3-031-17995-2_1), and the other half comprised 321 randomly selected class diagrams and 322 randomly selected non-UML images from the [dataset of the paper “Multiclass classification of four types of UML diagrams from images using deep learning"](https://doi.org/10.5281/zenodo.4595957
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). The idea behind it was to have equal parts OntoUML and non-OntoUML images. In the paper it is referred to as the “binary dataset”.
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See: https://github.com/SimeonKaishev/OntoUML_IMG_Converter
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license: apache-2.0
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