Bring your images together and create synthetic variations. Explore different lighting, backgrounds, and edge cases before you start labeling.
Before training, look at the situations your dataset actually represents. A large collection of similar images can still leave a model unprepared for everyday variation. The dataset studio gives your team a place to review that starting point.
Use synthetic examples to explore changes in lighting, angle, and background. Domain experts should review whether those changes are plausible for the task. More variation is useful when it reflects the conditions a model will encounter.
Define the conditions you want to explore.
Move a reviewed dataset into annotation with its context intact. Describe the objects and edge cases your reviewers should look for, so the next stage starts with a common understanding rather than another disconnected folder.
Agree on which images belong in the working set.
Dataset 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.