How to Build an Automated Multimodal Data Labeling Pipeline

Roboflow Workflows is a low-code platform designed for building multi-step computer vision applications in a browser-based editor, allowing users to deploy their creations through various methods, including the Roboflow API and Dedicated Deployment. One key application of Workflows is in creating systems for automatic data labeling, which supports active learning by saving model predictions to datasets for training future AI models. The guide walks users through setting up a Workflow using a foundation model, YOLO World, which can perform zero-shot object detection to auto-label images without the need for pre-existing training data. Users can visualize results using bounding box components and upload predictions to a Roboflow dataset for further use. The Workflow, once configured, not only automates image labeling but also enables users to refine their models by applying filters like confidence thresholds. Through this process, the platform facilitates the development of scalable and adaptable computer vision solutions, supporting a range of tasks from object detection to multimodal model training.