How to Auto-Label Image Data with Gemini 3.7 in Roboflow

Roboflow Auto Label uses Gemini 3.7 Flash to accelerate object-detection dataset annotation by identifying user-specified class names in images and drawing bounding boxes, processing up to 1,000 images per credit. Gemini relies only on class names rather than visual descriptions, assigns all returned boxes full confidence, and produces boxes rather than segmentation masks, making human review necessary to correct missed, duplicate, loose, or misclassified annotations. The tutorial demonstrates cloning unlabeled aquarium images into a private Roboflow object-detection project, adding classes such as fish, jellyfish, shark, and stingray, previewing Gemini’s output, labeling a full batch, and approving corrected images for dataset versioning, training, or export. Gemini is positioned for common, easily named objects that need bounding boxes, while SAM 3 is recommended when precise masks or richer descriptive prompts are needed; both can be combined and should be reviewed before use in model training.

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