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Poster
LAION-5B: An open large-scale dataset for training next generation image-text models
Christoph Schuhmann · Romain Beaumont · Richard Vencu · Cade Gordon · Ross Wightman · Mehdi Cherti · Theo Coombes · Aarush Katta · Clayton Mullis · Mitchell Wortsman · Patrick Schramowski · Srivatsa Kundurthy · Katherine Crowson · Ludwig Schmidt · Robert Kaczmarczyk · Jenia Jitsev

Wed Nov 30 02:00 PM -- 04:00 PM (PST) @ Hall J #1012
in Poster Session 4 »

Groundbreaking language-vision architectures like CLIP and DALL-E proved the utility of training on large amounts of noisy image-text data, without relying on expensive accurate labels used in standard vision unimodal supervised learning. The resulting models showed capabilities of strong text-guided image generation and transfer to downstream tasks, while performing remarkably at zero-shot classification with noteworthy out-of-distribution robustness. Since then, large-scale language-vision models like ALIGN, BASIC, GLIDE, Flamingo and Imagen made further improvements. Studying the training and capabilities of such models requires datasets containing billions of image-text pairs. Until now, no datasets of this size have been made openly available for the broader research community. To address this problem and democratize research on large-scale multi-modal models, we present LAION-5B - a dataset consisting of 5.85 billion CLIP-filtered image-text pairs, of which 2.32B contain English language. We show successful replication and fine-tuning of foundational models like CLIP, GLIDE and Stable Diffusion using the dataset, and discuss further experiments enabled with an openly available dataset of this scale. Additionally we provide several nearest neighbor indices, an improved web-interface for dataset exploration and subset generation, and detection scores for watermark, NSFW, and toxic content detection.

Keywords: reproducibility · CLIP · multi-modal learning · open source · large-scale datasets
[ Paper]  [ Slides]  [ Poster]  [ OpenReview] 

Author Information

Christoph Schuhmann (LAION e.V.)
Romain Beaumont
Richard Vencu (Simplilearn)
Cade Gordon (University of California, Berkeley)
Ross Wightman (Wedge Labs)

https://twitter.com/wightmanr

Mehdi Cherti (forschungszentrum jülich)
Theo Coombes
Aarush Katta
Clayton Mullis
Mitchell Wortsman (University of Washington, Allen Institute for Artificial Intelligence)
Patrick Schramowski (Tu Darmstadt)
Srivatsa Kundurthy
Katherine Crowson (stability.ai)
Ludwig Schmidt (University of Washington)
Robert Kaczmarczyk (Technische Universität München)

Co-founer of LAION AI and epidemiological researcher and resident doctor (dermatology) at the Technical University of Munich, Germany.

Jenia Jitsev (Juelich Supercomputing Center (JSC), LAION)

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