What is an annotation group?

An annotation group in the context of image datasets refers to the overarching category that includes all classes within a dataset, serving the purpose of organizing and labeling images in various ways depending on the training goals for a model. This concept is particularly useful when the same image needs to be labeled differently for different models, such as distinguishing between different board games and their respective pieces in an augmented reality application. Roboflow utilizes annotation groups to enhance dataset management by allowing images to have multiple annotations across various datasets while maintaining image count efficiency and enabling the correction of annotations across these datasets. Choosing an annotation group involves selecting a specific name that best represents all classes in a dataset, which aids in maintaining a scalable ontology as the dataset library expands. While generic terms like "object" or "thing" could be used, they might not be ideal for scalability in larger datasets, and selecting a more specific term is recommended to avoid future complications.