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Manage QA processes
How Segments.ai helps to streamline QA, how to set up a linting process and which additional features can be leveraged
The platform ensures short feedback loops through the following design choices:
- 1.Each sample is annotated by 1 labeler. A sample is either an individual image or point cloud, or a sequence of images, point clouds or multiple sensors, depending on the dataset settings - see Sample.
- 2.Labelers are not able to manually select samples to label first. Through the "Start Labeling" workflow, samples are presented for them.
- 3.After a reviewer has rejected a sample, the original labeler has to correct the rejected sample before being able to continue labeling any other samples in the queue. The rejected sample will appear in the beginning of the labeler's queue. For more details about the label queue, see Label queue mechanics
- 4.After a labeler has corrected a rejected sample, the original reviewer has to review the corrected sample. The corrected sample will appear in the beginning of the reviewer's queue.
Linting is the process of performing static analysis to flag erroneous patterns. For example, one would want to programmatically ...
- 1.Identify cuboids with unexpected dimensions or positions
- 2.Spot too small segmentation masks or uncover unlabeled pixels
- 3.Observe movement errors in sequences
- 4.Flag incorrect categories
Using the API/SDK & webhooks system, it is straightforward to set up such linting process and verify labels against expected properties:
Enable the ratings functionality in the dataset and leave star-based ratings
Leave star-based ratings
Add a "Verified" label status, intended to support an additional QA round of all labels with status "Reviewed"
Enable ratings and add a "Verified" label status in the dataset settings