Dataset for DCCN

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.

3. Code

The code package contains implementations for training and evaluating models on both the AVA and CUHK-PQ datasets.
This code enables reproduction of the experimental results presented in this study.

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

• result_ava.png – Sample visualization of model performance on the AVA dataset

• result_pq.png – Sample visualization of model performance on the CUHK-PQ dataset

• Source code for training and evaluating models on AVA and CUHK-PQ datasets