Turn domain knowledge into useful labels. Review suggested annotations, refine boundaries, and resolve uncertain examples together.
Annotation starts with agreement. Two people can look at the same image and draw different boundaries. Bring examples and class definitions into the discussion before asking your team to label an entire collection.
Assisted annotations give reviewers a starting point. Your team still decides which labels make sense, where boundaries need adjustment, and when an image is too uncertain to include. Human review is part of the workflow.
Inspect proposed bounding boxes and regions.
A reviewed annotation set is more useful when the team understands its limits. Keep track of difficult examples and use them to ask better questions during model evaluation, rather than hiding uncertainty in a single accuracy number.
Review the distribution of labeled classes.
Annotation studio is part of the connected workflow: prepare, annotate, select, train, and deploy. Your team can revisit an earlier step when evaluation reveals something new. The product illustrations explain the workflow without uploading a dataset.