Aurora Corpus
The dataset everything else is trained on
An 18M-image illustration corpus with tags, captions and quality bands, plus the whole filtering pipeline that produced it.
- Category
- Data
- Status
- Active
- Language
- Python
- Licence
- CC-BY-4.0
- Stars
- 980
- Since
- 2026
A dataset without its pipeline is an artefact you cannot audit. Aurora publishes both: the parquet shards, and the filter chain that decided what got in.
The chain
- Crawl and archive with source attribution retained.
- Perceptual dedup across the whole corpus.
- Tagging, captioning and aesthetic scoring.
- Quality banding, published as a column rather than applied as a deletion.
The last point matters. Filters are opinions. Shipping the score instead of the deletion lets someone else disagree with ours.
Status
Active. Growing roughly a million rows a month.
Writing
Filters are opinions, so ship the score
On building Aurora: why every quality filter in the pipeline emits a column instead of deleting a row, and what that costs in storage.
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