image_id
stringlengths 2
6
| image
imagewidth (px) 160
800
| mean_score
float32 1.81
8.6
| total_votes
int32 80
531
| rating_counts
sequencelengths 10
10
|
|---|---|---|---|---|
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End of preview. Expand
in Dataset Viewer.
AVA-Huggingface
This repository contains a Hugging Face dataset built from the AVA (Aesthetic Visual Analysis) dataset. The dataset includes images along with their aesthetic scores, total votes, and rating distributions. The data is prepared by filtering out images with fewer than 50 votes and stratifying them based on the computed mean aesthetic score.
Dataset Overview
- Image ID: Unique identifier for each image.
- Image: The actual image loaded from disk.
- Mean Score: The average aesthetic score computed from the rating counts.
- Total Votes: The total number of votes for the image.
- Rating Counts: The distribution of ratings (scores 1 through 10).
Preprocessing Steps
- Parsing: The
AVA.txtfile is parsed to extract the rating counts for each image. - Filtering: Images with fewer than 50 total votes are excluded.
- Stratification: The filtered images are stratified into 10 bins based on their mean aesthetic score.
- Dataset Creation: The data is then converted into a Hugging Face dataset with custom features for direct use with Hugging Face’s tools.
Train/Test Split Note
Important: The train and test splits provided in this repository are generated using a simple range-based selection:
dataset_full_dict = DatasetDict({
"train": hf_dataset_full.select(range(int(0.9 * len(hf_dataset_full)))),
"test": hf_dataset_full.select(range(int(0.1 * len(hf_dataset_full)), len(hf_dataset_full)))
})
This split is not the same as any official train/test split from the original AVA dataset. It is only meant to facilitate experiments and should not be considered as a validated split for rigorous evaluations.
How to Use
You can load the dataset directly using the Hugging Face datasets library:
from datasets import load_dataset
dataset = load_dataset("trojblue/Huggingface")
print(dataset)
(a stratified subset for model testing is also available here):
Citation
If you use this dataset in your research, please consider citing the original work:
@inproceedings{murray2012ava,
title={AVA: A Large-Scale Database for Aesthetic Visual Analysis},
author={Murray, N and Marchesotti, L and Perronnin, F},
booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
pages={3--18},
year={2012}
}
License
Please refer to the license of the original AVA dataset and ensure that you adhere to its terms when using this subset.
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