LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs
This provides a foundational resource for researchers and developers working on multi-modal AI, enabling training of models like CLIP from scratch without proprietary data.
The authors tackled the lack of large-scale public datasets for training multi-modal language-vision models by creating and releasing LAION-400M, a dataset of 400 million image-text pairs filtered using CLIP, along with embeddings and indices for efficient similarity search.
Multi-modal language-vision models trained on hundreds of millions of image-text pairs (e.g. CLIP, DALL-E) gained a recent surge, showing remarkable capability to perform zero- or few-shot learning and transfer even in absence of per-sample labels on target image data. Despite this trend, to date there has been no publicly available datasets of sufficient scale for training such models from scratch. To address this issue, in a community effort we build and release for public LAION-400M, a dataset with CLIP-filtered 400 million image-text pairs, their CLIP embeddings and kNN indices that allow efficient similarity search.