How to Build a Computer Vision Active Learning Workflow

James Gallagher's guide explores constructing an active learning workflow for computer vision using Roboflow Workflows, a visual editor for building and deploying computer vision applications. The guide outlines the process of using active learning to enhance model performance by gathering real-time data and utilizing model predictions as labels for new training datasets. This method not only improves model accuracy over time but also mitigates the effects of data drift. The workflow is demonstrated through a yard management model that detects shipping containers and other features, with successful model predictions being added to the dataset for future training. The guide emphasizes hands-on testing and deployment flexibility, allowing users to run workflows on various platforms such as the Roboflow cloud or edge devices like NVIDIA Jetson. Gallagher also highlights the ease of using pre-built functions in Roboflow Workflows without needing to write code, making it accessible for iterative development and active learning pipeline setup.