Search is not available for this dataset
image_id stringlengths 3 6 | image dict | mean_score float32 1.88 8.06 | label int64 0 7 | total_votes int32 82 536 | rating_counts sequencelengths 10 10 | edge_density float64 0 110 | focus_measure float64 0 80.3k | texture_score float64 0 12.9 | noise_level float64 0 283 | saturation float64 0 255 | contrast float64 0 123 | brightness float64 0 251 | avg_dynamic_range float64 0 255 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
111875 | {
"bytes": [
255,
216,
255,
224,
0,
16,
74,
70,
73,
70,
0,
1,
2,
0,
0,
100,
0,
100,
0,
0,
255,
219,
0,
67,
0,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
... | 5.807693 | 4 | 208 | [
1,
2,
7,
34,
53,
50,
28,
19,
7,
7
] | 14.880221 | 2,196.818058 | 5.259637 | 46.870226 | 0 | 42.886864 | 21.468673 | 131 |
287132 | {"bytes":"/9j/4AAQSkZJRgABAgEBLAEsAAD/2wBDAAEBAQEBAQEBAQECAQECAgQCAgICAgUEBAMEBgUHBgYFBgYHCAoIBwcKBw(...TRUNCATED) | 4.852174 | 3 | 230 | [
7,
12,
22,
52,
63,
42,
19,
8,
3,
2
] | 11.387538 | 480.711189 | 5.813421 | 21.925127 | 226.214123 | 18.895327 | 13.280957 | 48 |
712748 | {"bytes":"/9j/4AAQSkZJRgABAQEAYABgAAD/4RlxRXhpZgAASUkqAAgAAAACADEBAgALAAAAJgAAAGmHBAABAAAAMgAAAMIAAA(...TRUNCATED) | 4.763514 | 3 | 148 | [
1,
5,
17,
33,
54,
29,
6,
2,
1,
0
] | 23.626694 | 1,880.813717 | 7.240996 | 43.368349 | 0 | 84.733927 | 123.448427 | 241 |
60556 | {"bytes":"/9j/4AAQSkZJRgABAgAAZABkAAD/2wBDAAICAgICAgICAgIDAgICAwQDAgIDBAQEBAQEBAQGBAUFBQUEBgYHBwcHBw(...TRUNCATED) | 5.918239 | 4 | 159 | [
0,
3,
5,
14,
41,
43,
34,
11,
5,
3
] | 25.026914 | 2,677.117259 | 9.124476 | 51.740866 | 108.502583 | 71.23629 | 122.115746 | 206 |
522313 | {"bytes":"/9j/4AAQSkZJRgABAgAAZABkAAD/7AARRHVja3kAAQAEAAAAVwAA/+4ADkFkb2JlAGTAAAAAAf/bAIQAAgEBAQEBAg(...TRUNCATED) | 5.065089 | 4 | 169 | [
2,
1,
5,
39,
74,
33,
10,
3,
1,
1
] | 16.332575 | 261.539394 | 4.069031 | 16.17218 | 51.599924 | 48.972526 | 108.205363 | 156 |
446002 | {"bytes":"/9j/4AAQSkZJRgABAQEAZABkAAD/2wBDAAUDBAQEAwUEBAQFBQUGBwwIBwcHBw8LCwkMEQ8SEhEPERETFhwXExQaFR(...TRUNCATED) | 5.46875 | 4 | 224 | [
5,
3,
9,
44,
63,
42,
32,
19,
2,
5
] | 46.462977 | 2,599.48225 | 8.210077 | 50.985118 | 108.795023 | 66.279666 | 63.391887 | 220 |
114431 | {"bytes":"/9j/4AAQSkZJRgABAgEBLAEsAAD/2wBDAAICAgICAgICAgIDAgICAwQDAgIDBAUEBAQEBAUGBQUFBQUFBgYHBwgHBw(...TRUNCATED) | 4.824268 | 3 | 239 | [
1,
4,
21,
89,
58,
40,
16,
8,
1,
1
] | 35.828913 | 6,380.135816 | 9.256748 | 79.875752 | 184.384799 | 72.622031 | 51.566247 | 251 |
600099 | {"bytes":"/9j/4AAQSkZJRgABAgAAZABkAAD/7AARRHVja3kAAQAEAAAAUwAA/+IMWElDQ19QUk9GSUxFAAEBAAAMSExpbm8CEA(...TRUNCATED) | 4.647482 | 3 | 139 | [
1,
3,
18,
48,
41,
14,
9,
4,
1,
0
] | 13.319756 | 1,374.333822 | 7.042367 | 37.072009 | 201.891466 | 28.326082 | 33.539508 | 73 |
800388 | {"bytes":"/9j/4AAQSkZJRgABAgEBLAEsAAD/wAARCAGAAoADAREAAhEBAxEB/9sAhAAGBAUGBQQGBgUGBwcGCAoRCwoJCQoVDx(...TRUNCATED) | 4.650602 | 3 | 166 | [
3,
6,
19,
42,
62,
22,
9,
1,
1,
1
] | 60.128784 | 12,427.099847 | 9.940293 | 111.476903 | 149.065255 | 71.100525 | 99.678841 | 223 |
245172 | {"bytes":"/9j/4AAQSkZJRgABAQEAZABkAAD/2wBDAAUDBAQEAwUEBAQFBQUGBwwIBwcHBw8LCwkMEQ8SEhEPERETFhwXExQaFR(...TRUNCATED) | 6.621622 | 5 | 222 | [
0,
1,
3,
12,
29,
63,
54,
37,
16,
7
] | 57.064766 | 2,423.080847 | 8.739488 | 49.224799 | 139.405057 | 48.521076 | 73.801702 | 170 |
End of preview. Expand in Data Studio
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
- Subset Selection: Images were extracted from
trojblue/AVA-aesthetics-10pct-min50-10bins, ensuring a minimum of 50 samples per bin. - Efficient Local Export: Images were stored locally using a multi-threaded approach to speed up processing.
- Metric Calculation: Various computer vision metrics were computed using
cv2_metricsfromprocslib, including sharpness, contrast, and other image quality indicators. - 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. 🚀
- Downloads last month
- 40