After team members complete data labeling, the labeling job creator can initiate acceptance to check labeling results. The acceptance can be initiated only when a labeling member has labeled data. Otherwise, the acceptance initiation button is unavailable.
By percentage: Sampling is performed based on a percentage for acceptance.
By quantity: Sampling is performed based on quantity for acceptance.
If you click Pass, set Rating to A, B, C, or D. Option A indicates the highest score. If you click Reject, enter your rejection reasons in the text box.
You can continue accepting tasks whose acceptance is not completed. For tasks for which an acceptance process is not initiated, the Continue Acceptance button is unavailable.
In the Labeling Progress pane in the Task Statistics tab, click Continue Acceptance to continue accepting jobs. The Real-Time Acceptance Report page is displayed. You can continue to accept the images that are not accepted.
After the continue acceptance is complete, click Stop Acceptance in the upper right corner. On the displayed page, view the acceptance status of the labeling job, such as the number of sampled files, configure parameters, and perform the acceptance. The labeling information is synchronized to the Labeled tab of the labeling job only after the acceptance is complete.
Once the labeled data is accepted, team members cannot modify the labeling information. Only the dataset creator can modify the labeling information.
Parameter |
Description |
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Modifying Labeled Data |
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Acceptance Scope |
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You can view the acceptance report of an ongoing or finished labeling job. Log in to the management console and choose Data Management > Label Data. On the Data Labeling page, select My Creations and click the name of a team labeling job. The job details page is displayed. In the upper right corner of the page, click Acceptance Report. In the displayed dialog box, view report details.
After a job is accepted, delete it if the labeling job is no longer used. After a job is deleted, the labeling details that are not accepted will be lost. However, the original data in the dataset and the labeled data that has been accepted are still stored in the corresponding OBS bucket.