Label Data, Train, and Deploy a Vision Model in One Command

Autodistill is an open-source utility that streamlines the process of labeling data, training, and deploying computer vision models in a single command using a command line interface. It leverages foundation models like CLIP, Grounding DINO, and Segment Anything for automatic image labeling, which are then used to train smaller, task-specific models such as YOLOv8 for detection and segmentation or ViT for classification. Despite the efficiency of foundation models, they are resource-intensive and not suitable for all use cases, particularly those involving brand-specific or uncommon objects. The guide details the steps for installing and using Autodistill, including collecting image data through platforms like Roboflow, defining an ontology for labeling, and deploying the trained model for inference. While Autodistill simplifies and accelerates the model training process, users must consider the limitations of foundation models and adjust their approach accordingly to ensure effective results.