The text discusses DATACOMP, a dataset testbed featuring 12.8 billion image-text pairs from Common Crawl. Researchers can use it to design filtering techniques, curate data, and assess datasets for improving multimodal models. DATACOMP-1B achieves a 3.7 percentage point improvement over OpenAI’s CLIP ViT-L/14 in zero-shot accuracy on ImageNet. Access the Paper, Code, and Project for more details.
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DATACOMP: Advancing AI through Multimodal Datasets
Introduction
Multimodal datasets combine different data types, such as images and text, to advance artificial intelligence. Researchers from Apple and the University of Washington have introduced DATACOMP, a multimodal dataset testbed that contains 12.8 billion pairs of images and text data from Common Crawl.
Practical Solutions and Value
DATACOMP serves as a platform for researchers to design and evaluate new filtering techniques, curate data, and assess datasets. By using standardized CLIP training with downstream testing, researchers can improve dataset design for multimodal models.
The platform facilitates the study of scaling trends across varying resources and compute scales, providing valuable insights for AI model development and benchmarking.
DATACOMP-1B, the best baseline, surpasses OpenAI’s CLIP ViT-L/14 by 3.7 percentage points in zero-shot accuracy on ImageNet, showcasing its efficacy and potential impact on AI model performance.
Practical Implementation
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