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End of preview. Expand in Data Studio
monet_gdt labels
The labels used to train monet_gdt: mood, color and shape answers for 27,976 images, color triads for 77,410 images, and the id lists that make the build reproducible. Code: github.com/Telosnex/monet_gdt.
No images are included. Each row names its image by id; the manifest gives each image's source URL and license, and the repository's scripts download them.
Files
| File | Rows | Contents |
|---|---|---|
jev/mood.jsonl |
27,976 | 10 mood-word scores per image |
jev/shapes.jsonl |
27,976 | scores for 29 decorative shapes |
jev/colors.jsonl |
27,976 | the candidate colors offered, and the probability of each as primary, secondary and tertiary |
jev/*_repeat.jsonl |
1,342 each | the same questions asked again on test images (labeler consistency) |
jev/mood_population.jsonl |
1,500 | mood scores for wallpaper-like photos at 448 px (font calibration) |
jev/mood_res448.jsonl |
100 | test photos at 448 px (resolution check for the calibration) |
rule_triads.jsonl |
27,976 | the color model's targets in monet_gdt 2026.10.27, derived from colors.jsonl |
rule_2026_10_30/triads.jsonl |
77,410 | the color model's targets from monet_gdt 2026.10.30: {id, set, triad}, primary, secondary, tertiary hex. set is corpus (in the manifest) or matched (in selection_matched.jsonl) |
rule_2026_10_30/meta.json |
how the triads were made: monet_ui version, rule file sha256, quantizer, clusters, thumbnail | |
data/manifest.jsonl |
27,976 | the labeled images: id, split (train/val/test), part, sha256, size, source URL, license |
data/selection.jsonl |
27,976 | how the labeled images were chosen from MONET |
data/selection_matched.jsonl |
50,000 | wallpaper-like Megalith photos used only to train the student |
data/population_ids.json |
1,500 | the font-calibration photos |
data/res_check_ids.json |
100 | the resolution-check photos |
Every Jev label row has id, call, model (jev-1.14.0) and
prompt_sha256, the hash of the exact question in the repository's
prompts/.
The 2026.10.30 color triads come from code, not from Jev:
monet_ui 1.0.1's
MonetGdtRule20261030 on each image's 96 px thumbnail, with 128
QuantizerCelebi clusters. Anyone can make them again from the images.
In colors.jsonl, probabilities are keyed by hex color, which differs
per image, so read it as plain JSON lines rather than with datasets:
import json
rows = [json.loads(line) for line in open('jev/colors.jsonl')]
Images
- 14,400 Megalith-10m photos from Flickr, marked CC0, Public Domain Mark, "No known copyright restrictions" or US Government Work when crawled.
- 13,576 synthetic images from MONET (jasperai/monet), generated by Apache-2.0 models (FLUX.1-schnell, FLUX.2-klein-4B, Z-Image).
About 1% of the Flickr photos have since been removed from Flickr; the
build skips them. Per-source details: LICENSES.txt.
License
The labels and id lists: CC0 1.0. The images they refer to are not part of this dataset and keep their own licenses (above).
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