At launch, Adaptive Data for vision supports the image tasks teams depend on most: visual question answering, image captioning, visual reasoning, image classification, and document question answering.
Bring your data however it already lives. Adaptive Data accepts datasets built around images, supplied as URLs, embedded bytes, or references to files. Where you have them, it also takes the text that goes with each image: a question and answer, a caption, a label. You walk out with enhanced versions of those same datasets, in the format you already consume for text and documents, through the same API and Python SDK. And the capabilities you already rely on carry straight over to images:
Expand Your World: Grow a dataset across 242 languages and localizations, so the text paired with your images reaches the communities your model serves, not just the slice you started with.
Blueprint: Set the properties that matter to you, like tone, length, safety thresholds, and custom content policies. Every example you get back is shaped and enforced against them, automatically.
It's the same approach that has delivered an average 82% increase in data quality across 242 languages in text and documents. Now it works on images.
Adaptive Data multimodal consistently outperforms the baseline across all five vision datasets. Evaluated across multiple tasks spanning charts, finance, captioning, numerical, and spatial QA, adapted data has an average win rate of 67%. Adaptive Data doesn’t just improve your pipeline, it changes what’s possible across every vision task.