We’re excited to introduce pre-labels validation, a powerful way to make your labeling more accurate, cheaper and faster by injecting your pre-annotations. This fundamentally changes the labeling process by letting our annotators focus on accepting, refining or fixing the pre-labels rather than annotating an image from scratch.
Example use cases:
- Reviewing and correcting your model output
- Changing annotation classes for a label schema change, e.g. breaking a dent class to big-dent and small-dent
- Improving an open source labeled dataset by treating the data as pre-labels
- Reviewing annotation output from your third party labeling teams
With each model iteration, the amount of labeled data required often increases exponentially — presenting a clear incentive for ML teams to increase labeling efficiency with pre-labels.
Read more on our blog here.