Requirements to create verification and adjustment labeling jobs

To create a label verification or adjustment job, you must satisfy the following criteria.

• For non streaming labeling jobs: The input manifest file you use must contain the label attribute name (LabelAttributeName) of the labels that you want adjusted. When you chain a successfully completed labeling job, the output manifest file is used as the input manifest file for the new, chained job. To learn more about the format of the output manifest file Ground Truth produces for each task type, see Labeling job output data.

  For streaming labeling jobs: The Amazon SNS message you sent to the Amazon SNS input topic of the adjustment or verification labeling job must contain the label attribute name of the labels you want adjusted or verified. To see an example of how you can create an adjustment or verification labeling job with streaming labeling jobs, see this Jupyter Notebook example in GitHub.

• The task type of the verification or adjustment labeling job must be the same as the task type of the original job unless you are using the Image Label Verification task type to verify bounding box or semantic segmentation image labels. See the next bullet point for more details about the video frame task type requirements.

• For video frame annotation verification and adjustment jobs, you must use the same annotation task type used to create the annotations from the previous labeling job. For example, if you create a video frame object detection job to have workers draw bounding boxes around objects, and then you create a video object detection adjustment job, you must specify bounding boxes as the annotation task type. To learn more video frame annotation task types, see Task types.

• The task type you select for the adjustment or verification labeling job must support an audit workflow. The following Ground Truth built-in task types support adjustment and verification labeling jobs: bounding box, semantic segmentation, 3D point cloud object detection, 3D point cloud object tracking, and 3D point cloud semantic segmentation, and all video frame object detection and video frame object tracking task types — bounding box, polyline, polygon and keypoint.