Use the Smart Labeler in Custom Vision on the latest trained iteration to auto-suggest labels for faster annotation is correct.

The Use the Smart Labeler in Custom Vision on the latest trained iteration to auto-suggest labels for faster annotation workflow leverages predictions from a recently trained iteration to propose tags and bounding boxes so human reviewers can accept or refine suggestions rather than label every image from scratch. This integration inside Custom Vision directly uses the model you have already trained and so it provides the fastest, lowest friction way to accelerate annotation while staying within Azure services.

Manually tag every photo to guarantee label quality is incorrect because manually labeling each image will meet quality goals but it does not speed up the process. Manual tagging is reliable but it is the slowest option and it does not leverage automated suggestions that reduce human effort.

Attempt to apply the Smart Labeler on a brand new untrained Custom Vision project to label images immediately is incorrect because the Smart Labeler depends on predictions from a trained iteration. Without a trained model the suggestions will be poor or unavailable and you will not get the quick, accurate auto-labeling that the Smart Labeler provides.

Use Azure Machine Learning data labeling workflows or the Visual Object Tagging Tool to speed up annotations is incorrect in this context because although Azure Machine Learning labeling workflows can help organize and manage labeling tasks, they do not provide the same out of the box, iteration‑based auto-suggestion inside Custom Vision. The Visual Object Tagging Tool is an older open source tool that is not actively maintained and so it is less likely to be the fastest supported choice on newer Azure workflows or exams.

Try to train the detection model without any human labeled images by using unsupervised methods is incorrect because unsupervised methods do not produce reliable, production ready object detection labels quickly. Object detection usually requires curated labeled data and attempting to avoid human labeling entirely will not meet the project timeline or accuracy needs.

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