Data

Generative Line Drawings Dataset

Monash University
Jon McCormack (Aggregated by)
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.26180/19119548.v1&rft.title=Generative Line Drawings Dataset&rft.identifier=https://doi.org/10.26180/19119548.v1&rft.publisher=Monash University&rft.description=This is a collection of 257 generative art images created using a line drawing algorithm. Each image also includes a numeric score assigned by the generative artist who created the system. The score ranges from 0-5 and the higher the value the better this artist liked this image (in terms of its aesthetics).The images are in png format. The ratings are in a csv file with the number corresponding to the 4 digit number in the image name.The dataset is described in more detail in the forthcoming paper: J. McCormack & C. Cruz Gambardella: Quality-diversity for aesthetic evolution, EvoMUSART Conference Proceedings, 2022.&rft.creator=Jon McCormack&rft.date=2022&rft_rights=CC-BY-NC-ND-4.0&rft_subject=Generative art&rft_subject=Aesthetics&rft_subject=Artificial Intelligence and Image Processing&rft_subject=Electronic Media Art&rft.type=dataset&rft.language=English Access the data
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This is a collection of 257 generative art images created using a line drawing algorithm. Each image also includes a numeric score assigned by the generative artist who created the system. The score ranges from 0-5 and the higher the value the better this artist liked this image (in terms of its aesthetics).

The images are in png format. The ratings are in a csv file with the number corresponding to the 4 digit number in the image name.

The dataset is described in more detail in the forthcoming paper: J. McCormack & C. Cruz Gambardella: Quality-diversity for aesthetic evolution, EvoMUSART Conference Proceedings, 2022.

Issued: 2022-02-04

Created: 2022-02-04

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Aesthetics | Artificial Intelligence and Image Processing | Electronic Media Art | Generative art |

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