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Watch the tutorial and read on to discover how to navigate this critical aspect of machine learning and video annotation.
Setting Up Your Project
The first step is initializing your annotation project. After creating a Project and adding a Task, you assign Jobs to your Annotators. These jobs contain images for annotation. In our demonstration video, we've intentionally introduced errors for educational purposes—such as labeling "dogs" as "cats".
Switching Roles for Quality Assurance (QA)
When the annotator has completed their tasks, it's time for Quality Assurance. To show how this works, we'll switch back to the Project owner's account to initiate the QA process.
Assigning a QA specialist to review the annotations is a breeze. Just invite the person to your project and assign them to the specific job. Then change the status of the Job to "Validation".
Review and Issue Tracking
The person assigned as QA will log in and have access to the QA interface which has been designed specifically for issues reporting and tracking. It lacks the typical annotation tools but includes an "Issue tracker" icon.
QA will go through each annotation to identify errors. Once found, QA creates an issue and submits it. CVAT also provides predefined issues for common errors, saving time and ensuring consistency.
Navigating and Resolving Issues
After the QA specialist completes their review, we’ll go back to the annotator’s account and interface to see how the reported issues look. The annotator can easily navigate through the list of issues and correct the errors. After all is done, the annotator saves the work, making the annotations complete and ready for future use.
And that’s it!
Happy Annotating!
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