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This file is generated from the sagemaker-2017-07-24.normal.json service model. */ using System; using System.Collections.Generic; using System.Xml.Serialization; using System.Text; using System.IO; using System.Net; using Amazon.Runtime; using Amazon.Runtime.Internal; namespace Amazon.SageMaker.Model { /// /// Configures how labels are consolidated across human workers and processes output data. /// public partial class AnnotationConsolidationConfig { private string _annotationConsolidationLambdaArn; /// /// Gets and sets the property AnnotationConsolidationLambdaArn. /// /// The Amazon Resource Name (ARN) of a Lambda function implements the logic for annotation /// consolidation and to process output data. /// /// /// /// This parameter is required for all labeling jobs. For built-in /// task types, use one of the following Amazon SageMaker Ground Truth Lambda function /// ARNs for AnnotationConsolidationLambdaArn. For custom labeling workflows, /// see Post-annotation /// Lambda. /// /// /// /// Bounding box - Finds the most similar boxes from different workers based on /// the Jaccard index of the boxes. /// /// /// /// Image classification - Uses a variant of the Expectation Maximization approach /// to estimate the true class of an image based on annotations from individual workers. /// /// /// /// Multi-label image classification - Uses a variant of the Expectation Maximization /// approach to estimate the true classes of an image based on annotations from individual /// workers. /// /// /// /// Semantic segmentation - Treats each pixel in an image as a multi-class classification /// and treats pixel annotations from workers as "votes" for the correct label. /// /// /// /// Text classification - Uses a variant of the Expectation Maximization approach /// to estimate the true class of text based on annotations from individual workers. /// /// /// /// Multi-label text classification - Uses a variant of the Expectation Maximization /// approach to estimate the true classes of text based on annotations from individual /// workers. /// /// /// /// Named entity recognition - Groups similar selections and calculates aggregate /// boundaries, resolving to most-assigned label. /// /// /// /// Video Classification - Use this task type when you need workers to classify /// videos using predefined labels that you specify. Workers are shown videos and are /// asked to choose one label for each video. /// /// /// /// Video Frame Object Detection - Use this task type to have workers identify /// and locate objects in a sequence of video frames (images extracted from a video) using /// bounding boxes. For example, you can use this task to ask workers to identify and /// localize various objects in a series of video frames, such as cars, bikes, and pedestrians. /// /// /// /// Video Frame Object Tracking - Use this task type to have workers track the /// movement of objects in a sequence of video frames (images extracted from a video) /// using bounding boxes. For example, you can use this task to ask workers to track the /// movement of objects, such as cars, bikes, and pedestrians. /// /// /// /// 3D Point Cloud Object Detection - Use this task type when you want workers /// to classify objects in a 3D point cloud by drawing 3D cuboids around objects. For /// example, you can use this task type to ask workers to identify different types of /// objects in a point cloud, such as cars, bikes, and pedestrians. /// /// /// /// 3D Point Cloud Object Tracking - Use this task type when you want workers /// to draw 3D cuboids around objects that appear in a sequence of 3D point cloud frames. /// For example, you can use this task type to ask workers to track the movement of vehicles /// across multiple point cloud frames. /// /// /// /// 3D Point Cloud Semantic Segmentation - Use this task type when you want workers /// to create a point-level semantic segmentation masks by painting objects in a 3D point /// cloud using different colors where each color is assigned to one of the classes you /// specify. /// /// /// /// Use the following ARNs for Label Verification and Adjustment Jobs /// /// /// /// Use label verification and adjustment jobs to review and adjust labels. To learn more, /// see Verify /// and Adjust Labels . /// /// /// /// Semantic Segmentation Adjustment - Treats each pixel in an image as a multi-class /// classification and treats pixel adjusted annotations from workers as "votes" for the /// correct label. /// /// /// /// Semantic Segmentation Verification - Uses a variant of the Expectation Maximization /// approach to estimate the true class of verification judgment for semantic segmentation /// labels based on annotations from individual workers. /// /// /// /// Bounding Box Adjustment - Finds the most similar boxes from different workers /// based on the Jaccard index of the adjusted annotations. /// /// /// /// Bounding Box Verification - Uses a variant of the Expectation Maximization /// approach to estimate the true class of verification judgement for bounding box labels /// based on annotations from individual workers. /// /// /// /// Video Frame Object Detection Adjustment - Use this task type when you want /// workers to adjust bounding boxes that workers have added to video frames to classify /// and localize objects in a sequence of video frames. /// /// /// /// Video Frame Object Tracking Adjustment - Use this task type when you want /// workers to adjust bounding boxes that workers have added to video frames to track /// object movement across a sequence of video frames. /// /// /// /// 3D Point Cloud Object Detection Adjustment - Use this task type when you want /// workers to adjust 3D cuboids around objects in a 3D point cloud. /// /// /// /// 3D Point Cloud Object Tracking Adjustment - Use this task type when you want /// workers to adjust 3D cuboids around objects that appear in a sequence of 3D point /// cloud frames. /// /// /// /// 3D Point Cloud Semantic Segmentation Adjustment - Use this task type when /// you want workers to adjust a point-level semantic segmentation masks using a paint /// tool. /// /// /// [AWSProperty(Required=true, Max=2048)] public string AnnotationConsolidationLambdaArn { get { return this._annotationConsolidationLambdaArn; } set { this._annotationConsolidationLambdaArn = value; } } // Check to see if AnnotationConsolidationLambdaArn property is set internal bool IsSetAnnotationConsolidationLambdaArn() { return this._annotationConsolidationLambdaArn != null; } } }