MONET: A Massive, Open, Non-redundant and Enriched Text-to-image dataset
Training large text-to-image models requires high-quality, curated datasets with diverse content and detailed captions. Yet the cost and complexity of collecting, filtering, deduplicating, and re-captioning such corpora at scale hinders open and reproducible research in the field. We introduce MONET, an open Apache 2.0 dataset of approx. 104.9M image--text pairs collected from 2.9B raw pairs across heterogeneous open sources through successive stages of safety filtering, domain-based filtering, exact and near-duplicate removal, and re-captioning with multiple vision-language models covering short to long-form descriptions, and further augmented with synthetically generated samples. Each i...
| Authors | Benjamin Aubin, Gonzalo Iñaki Quintana, Onur Tasar et al. |
|---|---|
| Primary category | cs.CV |
| Published | 20 May 2026 |
| Last updated at source | 20 May 2026 |
Models and datasets referencing this paper
- jasperai/monetDataset
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