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---
dataset_info:
features:
- name: image_id
dtype: string
- name: image
dtype: image
- name: mean_score
dtype: float32
- name: label
dtype:
class_label:
names:
'0': score_1
'1': score_2
'2': score_3
'3': score_4
'4': score_5
'5': score_6
'6': score_7
'7': score_8
- name: total_votes
dtype: int32
- name: rating_counts
sequence: int32
splits:
- name: train
num_bytes: 2668687812.28
num_examples: 20437
- name: validation
num_bytes: 695881906.17
num_examples: 5110
download_size: 3385167789
dataset_size: 3364569718.4500003
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
# AVA Aesthetics 10% Subset (min50, 10 bins)
This dataset is a curated 10% subset of the [AVA Aesthetics Dataset](https://github.com/christopher-beckham/AVA_dataset) (or the original AVA dataset as described in [Murray et al., 2012](#citation)). It includes images that have at least 50 total votes and have been stratified into 10 bins based on their computed mean aesthetic scores.
## Dataset Overview
- **Dataset Name:** AVA Aesthetics 10% Subset (min50, 10 bins)
- **Subset Size:** 10% of the original AVA dataset (after filtering for a minimum of 50 votes per image)
- **Filtering Criteria:** Only images with ≥50 votes were considered.
- **Stratification:** Images were binned into 10 equally spaced intervals across the score range (1 to 10), and a random 10% sample was selected from each bin.
- **Image Format:** JPEG (files with `.jpg` extension)
- **Data Fields:**
- `image_id`: Unique identifier of the image.
- `image`: The image file (loaded as an `Image` feature in Hugging Face Datasets).
- `mean_score`: The mean aesthetic score computed from the rating counts.
- `total_votes`: Total number of votes received by the image.
- `rating_counts`: A list representing the count of votes for scores 1 through 10.
## Dataset Creation Process
1. **Parsing the AVA.txt File:**
- Each line in `AVA.txt` contains metadata for an image including the image ID and counts for ratings 1 through 10.
- Total votes and the mean score are computed as:
- **Total Votes:** Sum of counts for ratings 1–10.
- **Mean Score:** \( \text{mean\_score} = \frac{\sum_{i=1}^{10} i \times \text{count}_i}{\text{total\_votes}} \).
2. **Filtering:**
- Images with fewer than 50 total votes are removed to ensure label stability and reduce noise.
3. **Stratification:**
- The images are grouped into 10 bins based on their mean score. This stratification helps maintain a balanced representation of aesthetic quality across the dataset.
4. **Sampling:**
- From each bin, 10% of the images are randomly selected to form the final subset.
5. **Conversion to Hugging Face Dataset:**
- The resulting data is transformed into a Hugging Face Dataset with the following fields: `image_id`, `image`, `mean_score`, `total_votes`, and `rating_counts`.
- The dataset is then split into train and test sets (default split: 90% train, 10% test).
## Intended Use Cases
- **Aesthetic Quality Assessment:** Developing models to predict image aesthetics.
- **Computer Vision Research:** Studying features associated with aesthetic judgments.
- **Benchmarking:** Serving as a balanced subset for rapid experimentation and validation of aesthetic scoring models.
## Limitations and Considerations
- **Subset Size:** Being only 10% of the original dataset, this subset may not capture the full diversity of the AVA dataset.
- **Sampling Bias:** The stratification and random sampling approach might introduce bias if certain bins are underrepresented.
- **Missing Files:** Some images may be absent if the corresponding JPEG file was not found in the provided directory.
- **Generalization:** Models trained on this subset should be evaluated on larger datasets or additional benchmarks to ensure generalization.
## How to Use
You can load the dataset directly using the Hugging Face `datasets` library:
```python
from datasets import load_dataset
dataset = load_dataset("trojblue/ava-aesthetics-10pct-min50-10bins")
print(dataset)
```
(The full dataset converted to Huggingface Datasets format is also available here):
- [trojblue/AVA-Huggingface](https://huggingface.co/datasets/trojblue/AVA-Huggingface)
## Citation
If you use this dataset in your research, please consider citing the original work:
```bibtex
@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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