Start a label verification or adjustment job (API) - Amazon SageMaker AI
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Start a label verification or adjustment job (API)

Start a label verification or adjustment job by chaining a successfully completed job or starting a new job from scratch using the CreateLabelingJob operation. The procedure is almost the same as setting up a new labeling job with CreateLabelingJob, with a few modifications. Use the following sections to learn what modifications are required to chain a labeling job to create an adjustment or verification labeling job.

When you create an adjustment or verification labeling job using the Ground Truth API, you must use a different LabelAttributeName than the original labeling job. The original labeling job is the job used to create the labels you want adjusted or verified.

Important

The label category configuration file you identify for an adjustment or verification job in LabelCategoryConfigS3Uri of CreateLabelingJob must contain the same labels used in the original labeling job. You can add new labels. For 3D point cloud and video frame jobs, you can add new label category and frame attributes to the label category configuration file.

Bounding Box and Semantic Segmentation

To create a bounding box or semantic segmentation label verification or adjustment job, use the following guidelines to specify API attributes for the CreateLabelingJob operation.

  • Use the LabelAttributeName parameter to specify the output label name that you want to use for verified or adjusted labels. You must use a different LabelAttributeName than the one used for the original labeling job.

  • If you are chaining the job, the labels from the previous labeling job to be adjusted or verified will be specified in the custom UI template. To learn how to create a custom template, see Create Custom Worker Task Templates.

    Identify the location of the UI template in the UiTemplateS3Uri parameter. SageMaker AI provides widgets that you can use in your custom template to display old labels. Use the initial-value attribute in one of the following crowd elements to extract the labels that need verification or adjustment and include them in your task template:

    • crowd-semantic-segmentation—Use this crowd element in your custom UI task template to specify semantic segmentation labels that need to be verified or adjusted.

    • crowd-bounding-box—Use this crowd element in your custom UI task template to specify bounding box labels that need to be verified or adjusted.

  • The LabelCategoryConfigS3Uri parameter must contain the same label categories as the previous labeling job.

  • Use the bounding box or semantic segmentation adjustment or verification lambda ARNs for PreHumanTaskLambdaArn and AnnotationConsolidationLambdaArn:

    • For bounding box, the adjustment labeling job lambda function ARNs end with AdjustmentBoundingBox and the verification lambda function ARNs end with VerificationBoundingBox.

    • For semantic segmentation, the adjustment labeling job lambda function ARNs end with AdjustmentSemanticSegmentation and the verification lambda function ARNs end with VerificationSemanticSegmentation.

3D point cloud and video frame

  • Use the LabelAttributeName parameter to specify the output label name that you want to use for verified or adjusted labels. You must use a different LabelAttributeName than the one used for the original labeling job.

  • You must use the human task UI Amazon Resource Name (ARN) (HumanTaskUiArn) used for the original labeling job. To see supported ARNs, see HumanTaskUiArn.

  • In the label category configuration file, you must specify the label attribute name (LabelAttributeName) of the previous labeling job that you use to create the adjustment or verification labeling job in the auditLabelAttributeName parameter.

  • You specify whether your labeling job is a verification or adjustment labeling job using the editsAllowed parameter in your label category configuration file identified by the LabelCategoryConfigS3Uri parameter.

    • For verification labeling jobs, you must use the editsAllowed parameter to specify that all labels cannot be modified. editsAllowed must be set to "none" in each entry in labels. Optionally, you can specify whether or not label categories attributes and frame attributes can be adjusted by workers.

    • Optionally, for adjustment labeling jobs, you can use the editsAllowed parameter to specify labels, label category attributes, and frame attributes that can or cannot be modified by workers. If you do not use this parameter, all labels, label category attributes, and frame attributes will be adjustable.

    To learn more about the editsAllowed parameter and configuring your label category configuration file, see Label category configuration file schema.

  • Use the 3D point cloud or video frame adjustment lambda ARNs for PreHumanTaskLambdaArn and AnnotationConsolidationLambdaArn for both adjustment and verification labeling jobs:

    • For 3D point clouds, the adjustment and verification labeling job lambda function ARNs end with Adjustment3DPointCloudSemanticSegmentation, Adjustment3DPointCloudObjectTracking, and Adjustment3DPointCloudObjectDetection for 3D point cloud semantic segmentation, object detection, and object tracking respectively.

    • For video frames, the adjustment and verification labeling job lambda function ARNs end with AdjustmentVideoObjectDetection and AdjustmentVideoObjectTracking for video frame object detection and object tracking respectively.

Ground Truth stores the output data from a label verification or adjustment job in the S3 bucket that you specified in the S3OutputPath parameter of the CreateLabelingJob operation. For more information about the output data from a label verification or adjustment labeling job, see Label verification and adjustment data in the output manifest.