README.md

---
dataset_info:
  features:
  - name: image_id
    dtype: string
  - name: image
    struct:
    - name: bytes
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    - name: path
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  - name: mean_score
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  - name: label
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  - name: total_votes
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  - name: rating_counts
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  - name: edge_density
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  - name: focus_measure
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  - name: texture_score
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  - name: noise_level
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  - name: saturation
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  - name: contrast
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  - name: brightness
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  - name: avg_dynamic_range
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  splits:
  - name: train
    num_bytes: 2737038380
    num_examples: 20437
  download_size: 2710920619
  dataset_size: 2737038380
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---

# AVA Subset with Metrics

This dataset is a processed subset of the **AVA (Aesthetic Visual Analysis) dataset**, derived from **trojblue/AVA-aesthetics-10pct-min50-10bins**. It includes a selection of images alongside computed **visual quality metrics**.

## **Derivation Process**
1. **Subset Selection**: Images were extracted from `trojblue/AVA-aesthetics-10pct-min50-10bins`, ensuring a minimum of 50 samples per bin.
2. **Efficient Local Export**: Images were stored locally using a multi-threaded approach to speed up processing.
3. **Metric Calculation**: Various **computer vision metrics** were computed using `cv2_metrics` from `procslib`, including sharpness, contrast, and other image quality indicators.
4. **Data Merging**: The computed metrics were merged back into the dataset, providing additional insights beyond aesthetic scores.

## **Usage**
This dataset is ideal for:
- Training models that incorporate both **aesthetic scores and image quality metrics**.
- Analyzing relationships between **image structure and subjective ratings**.
- Benchmarking computer vision models on real-world **aesthetic quality assessment**.

The dataset is publicly available for research and model development. 🚀