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---
dataset_info:
features:
- name: image_id
dtype: string
- name: image
struct:
- name: bytes
dtype: binary
- name: path
dtype: string
- name: mean_score
dtype: float32
- name: label
dtype: int64
- name: total_votes
dtype: int32
- name: rating_counts
sequence: int32
- name: edge_density
dtype: float64
- name: focus_measure
dtype: float64
- name: texture_score
dtype: float64
- name: noise_level
dtype: float64
- name: saturation
dtype: float64
- name: contrast
dtype: float64
- name: brightness
dtype: float64
- name: avg_dynamic_range
dtype: float64
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. 🚀
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