Label verification and adjustment
When the labels on a dataset need to be validated, Amazon SageMaker Ground Truth provides functionality to have workers verify that labels are correct or to adjust previous labels. These types of jobs fall into two distinct categories:
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Label verification — Workers indicate if the existing labels are correct, or rate their quality, and can add comments to explain their reasoning. Workers will not be able to modify or adjust labels.
If you create a 3D point cloud or video frame label adjustment or verification job, you can choose to make label category attributes (not supported for 3D point cloud semantic segmentation) and frame attributes editable by workers.
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Label adjustment — Workers adjust prior annotations and, if applicable, label category and frame attributes to correct them.
The following Ground Truth built-in task types support adjustment and verification labeling jobs:
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Bounding box
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Semantic segmentation
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3D point cloud object detection, 3D point cloud object tracking, and 3D point cloud semantic segmentation
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All video frame object detection and video frame object tracking task types — bounding box, polyline, polygon and keypoint
Tip
For 3D point cloud and video frame labeling verification jobs, it is recommended that you add new label category attributes or frame attributes to the labeling job. Workers can use these attribute to verify individual labels or the entire frame. To learn more about label category and frame attributes, see Worker user interface (UI) for 3D point cloud and Worker user interface (UI) for video frame.
You can start a label verification and adjustment jobs using the SageMaker AI console or the API.
Cautions and considerations
To get expected behavior when creating a label verification or adjustment job, carefully verify your input data.
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If you are using image data, verify that your manifest file contains hexadecimal RGB color information.
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To save money on processing costs, filter your data to ensure you are not including unwanted objects in your labeling job input manifest.
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Add required Amazon S3 permissions to ensure your input data is processed correctly.
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.
Color information requirements for semantic segmentation jobs
To properly reproduce color information in verification or adjustment tasks, the tool requires hexadecimal RGB color information in the manifest (for example, #FFFFFF for white). When you set up a Semantic Segmentation verification or adjustment job, the tool examines the manifest to determine if this information is present. If it can't find it,Amazon SageMaker Ground Truth displays an error message and the ends job setup.
In prior iterations of the Semantic Segmentation tool, category color information wasn't output in hexadecimal RGB format to the output manifest. That feature was introduced to the output manifest at the same time the verification and adjustment workflows were introduced. Therefore, older output manifests aren't compatible with this new workflow.
Filter your data before starting the job
Amazon SageMaker Ground Truth processes all objects in your input manifest. If you have a partially labeled data set, you might want to create a custom manifest using an Amazon S3 Select query on your input manifest. Unlabeled objects individually fail, but they don't cause the job to fail, and they might incur processing costs. Filtering out objects you don't want verified reduces your costs.
If you create a verification job using the console, you can use the filtering tools provided there. If you create jobs using the API, make filtering your data part of your workflow where needed.
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