How Frontier AI Labs Acquire Visual Training Data for Multimodal Models

Build a strong visual training set by combining sources: CLIP-filtered web crawls, open datasets like COCO and LAION-5B, licensed stock libraries, human-collected and annotated footage, and synthetic images or VLM-generated labels. Start with task-specific datasets on Roboflow Universe, label and review in Roboflow Annotate, and bring in Roboflow Labs to collect the rare, real-world data your model is still missing. Training frontier multimodal models (opens in a new tab) requires massive amounts of high-quality visual data, including images, videos, documents, diagrams, and 3D environments. This data teaches models to recognize objects, understand scenes, interpret visual information, follow instructions, and reason about the physical world. However, acquiring visual data at frontier-model scale presents significant engineering and operational challenges. AI labs cannot rely on a single source and instead combine multiple data acquisition strategies. Each strategy provides different types of visual information and supervision. This guide explores how frontier AI labs acquire visual training data through web crawling, public datasets, commercial partnerships, human-collected and annotated data, and synthetic data generation, including the technologies and real-world examples behind each approach. For the specialized, real-world data that web crawls and public datasets miss, Roboflow Labs (opens in a new tab) helps collect, annotate, and…