LAION-5B
Open Source Image-Text Pair Dataset
LAION-5B is currently the largest open-source image-text pair dataset, containing 5.85 billion images and corresponding text descriptions (ALT text), constructed by the LAION organization. The data is extracted from Common Crawl for image-text pairs and filtered for similarity using the CLIP model. It is widely used for training models like Stable Diffusion, DALL-E, and visual language models like CLIP.
Dataset Highlights
The largest open-source multimodal dataset in history, driving the generative AI revolution
Unprecedented Scale
5.85 billion image-text pairs, currently the largest open-source multimodal dataset, providing a solid data foundation for large-scale visual language model training.
CLIP Filtering Ensures Quality
Each data point is calculated for image-text similarity scores using the CLIP model, ensuring semantic consistency and high-quality alignment between images and text descriptions.
Multilingual Coverage
Includes English subset (2.32 billion), multilingual subset (2.26 billion), and no language label subset (1.27 billion), meeting the needs of multilingual multimodal research.
Foundation for Generative AI
Iconic models such as Stable Diffusion and OpenCLIP are trained based on LAION-5B, serving as the core data source driving the text-to-image revolution.
Rich Metadata
Provides rich metadata such as CLIP embedding vectors, NSFW safety scores, and watermark detection tags, supporting multidimensional data filtering and analysis.
Community Driven
Built collaboratively by the international research community, fully open-source and accessible, anyone can freely obtain and use it, promoting the democratization of AI.
Applicable Scenarios
From text-to-image to cross-modal understanding, covering all scenarios of multimodal AI
Text-to-Image Model Training
Train text-to-image generation models like Stable Diffusion and DALL-E to achieve high-quality image generation
CLIP / Visual Language Models
Train visual language models like CLIP and OpenCLIP based on image-text pairs through contrastive learning
Large-Scale Image Retrieval
Build a large-scale image search system based on semantics, supporting text queries for images
Multimodal Research
Cutting-edge research directions such as zero-shot classification, visual question answering (VQA), and cross-modal understanding
Data Preview
The following are example entries from the LAION-5B dataset, each containing an image URL, text description, and metadata
{"url": "https://example.com/image1.jpg", "text": "A golden retriever playing in autumn leaves", "similarity": 0.312, "width": 1024, "height": 768}
{"url": "https://example.com/image2.jpg", "text": "Modern architecture with glass facades reflecting sunset", "similarity": 0.287, "width": 1920, "height": 1080}
{"url": "https://example.com/image3.jpg", "text": "Fresh vegetables and herbs arranged on wooden cutting board", "similarity": 0.341, "width": 800, "height": 600}
3 Steps to Get Started Quickly
Quickly integrate LAION-5B into your multimodal AI workflow from browsing to training
Browse Dataset
View details of the LAION-5B dataset on the Ace Data Cloud platform and select the desired subset (English / Multilingual / No Language Label).
Download Data
Download the shard data files in Parquet format, or use the img2dataset tool to bulk download the corresponding image resources.
Load and Train
Load the data into your multimodal training workflow and start training text-to-image models, CLIP, or other vision-language models.
Start Exploring LAION-5B Data
5.85 billion image-text pairs, CC BY 4.0 open license, available now. Whether you are training generative models or conducting multimodal research, LAION-5B is an indispensable data resource.