Update README.md

README.md

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   - split: test
     path: data/test-*
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+# 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)
+```
+
+## 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.