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ZENODO
Dataset . 2025
License: CC BY
Data sources: ZENODO
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2025
License: CC BY
Data sources: ZENODO
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2025
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2025
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2025
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2025
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2025
License: CC BY
Data sources: Datacite
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Dataset for DCCN

Authors: hyc711;

Dataset for DCCN

Abstract

Dataset Description This archive contains training materials and a minimal dataset for image aesthetic quality assessment based on two widely-used datasets: AVA and CUHK-PQ. 1. AVA Dataset (Aesthetic Visual Analysis) The AVA dataset consists of approximately 250,000 images, each annotated with a distribution of aesthetic scores (from 1 to 10). In our experiments, we used the complete AVA dataset for training to achieve optimal performance. However, due to the large file size (~30GB), it is not included in this archive. Instead, we provide a minimal version of the dataset with around 30,000 selected images and a merged CSV file, which can be used for lightweight training and testing. Please note that models trained on this smaller subset may perform worse than those trained on the full dataset. You can download the full AVA dataset using this tool:https://github.com/imfing/ava_downloader 2. CUHK-PQ Dataset The CUHK-PQ dataset is organized into two folders: high/ – High aesthetic quality images low/ – Low aesthetic quality images The folder structure itself serves as ground truth labels, making the dataset straightforward to use in binary classification tasks. In this project, the CUHK-PQ dataset was used to train and evaluate a binary aesthetic classifier. Archive Contents minimal_dataset_for_ava.csv – Combined label file for AVA subset (with train/val/test split) AVA/ – Folder with selected AVA training images README.md – Dataset usage instructions A sample image showing the training result visualization

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average