How to Use Multiple Models to Label Datasets with Autodistill

In the context of computer vision and AI, combining multiple domain-specific models for data labeling and training can lead to enhanced accuracy and efficiency, outperforming larger general-purpose models in specific use cases. The guide explores how to leverage the expertise of specialized models, such as person or vehicle detection, to label datasets and train a new model, using tools like Autodistill for automated annotation. The process involves creating or downloading a dataset, labeling it with specialized models, and then training a combined model, which is evaluated against both the specialized models and larger offerings like COCO, Google Cloud Vision, and AWS Rekognition. The results demonstrate that a combined model can achieve higher accuracy and faster inference times than both specialized and large-scale models, while offering the flexibility to be hosted locally or through a managed API, thereby underscoring the practical benefits of integrating domain-specific expertise in model development.