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.
Citation:
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. LAION-5B: An open large-scale dataset for training next generation image-text models. Advances in Neural Information Processing Systems 35 (NeurIPS 2022), 25278-25294
Further information:
LAION-5B NeurIPS paper (Neural Information Processing Systems, one of the strongest machine learning international venues): https://openreview.net/forum?id=M3Y74vmsMcY
NeurIPS 2022 Outstanding Paper Award: https://blog.neurips.cc/2022/11/21/announcing-the-neurips-2022-awards/
Press releases:
https://www.helmholtz.ai/themenmenue/news-events/news/news/article/30569/index.html
https://blogs.helmholtz.de/research-field-information/2023/04/06/kuenstliche-intelligenz-fuer-alle/
Top-10 most influential papers 2022 – LAION-5B NeurIPS paper is inside
https://www.paperdigest.org/2023/01/most-influential-nips-papers-2023-01/
Open Source releases: https://github.com/mlfoundations/open_clip,
https://huggingface.co/laion