Amazon SageMaker Ground Truth Features

Three dimensional (3D) point clouds are most commonly captured using Light Detection and Ranging (LIDAR) devices to generate a 3D understanding of a physical space at a single point in time. SageMaker Ground Truth supports built-in labeling workflows for your 3D point cloud data including object detection, objection tracking, and semantic segmentation.

With the object detection workflow, you can identify and label of objects of interest within a 3D point cloud. For example, in an autonomous vehicle use case, you can accurately label vehicles, lanes, and pedestrians.

With the object tracking workflow, you can track the trajectory of objects of interest. For example, an autonomous vehicle needs to track the movement of other vehicles, lanes, and pedestrians. Ground Truth allows you to track the trajectory of these objects across a sequence of 3D point cloud data.

With the semantic segmentation workflow, you can segment the points of a 3D point cloud into pre-specified categories. For example, for autonomous vehicles, Ground Truth could categorize the presence of streets, foliage, and structures.

SageMaker Ground Truth supports common video labeling use cases with built-in workflows, including video object detection, video object tracking, and video clip classification.

With the video object detection workflow, you can identify objects of interest within a sequence of video frames. For example, in building a perception system for an autonomous vehicle, you can detect other vehicles in the scene around the vehicle.

With the video object tracking workflow, you can track objects of interest across a sequence of video frames. For example, in a sports game use case, you can accurately label players across the duration of a play.

With the video clip classification workflow, you can classify a video file into a pre-specified category. For example, you can select pre-specified categories that best describe the video such as a sports play or traffic congestion at a busy intersection.

SageMaker Ground Truth provides built-in labeling workflows for your image data, including Image Classification, Object Detection, and Semantic Segmentation.

Image Classification is the process of identifying an image based on its real world representation. This process involves categorizing images against a pre-defined set of labels. Image classification is useful for scene detection models that need to consider the full context of the image. For example, we can build an image classification model for autonomous vehicles to detect various real worlds objects such as other vehicles, pedestrians, traffic lights and signage.

You can use the object detection workflow to identify and label objects of interest (e.g., vehicles, pedestrians, dogs, cats) in images. The labeling task involves drawing a bounding box, a two-dimensional (2D) box, around the objects of interest within an image. Computer vision models trained from images with labeled bounding boxes learn that the pixels within the box correspond to the specified object.

You can use the semantic segmentation workflow to label the exact parts of an image that correspond to the labels your model needs to learn. It provides high precision training data because the individual pixels are labeled. For example, the irregular shape of a car in an image could be captured exactly with semantic segmentation.

SageMaker Ground Truth provides built-in labeling workflows for your text data, including Text Classification and Named Entity Recognition.

Text classification involves categorizing text strings against a pre-defined set of labels. Categorizing text into different labels is often used for natural language processing (NLP) models that identify things like topics (e.g., product descriptions, movie reviews) or sentiment.

Named Entity (NER) involves sifting through text data to locate phrases called named entities, and categorizing each with a label, such as “person,” “organization,” or “brand.” So, in the statement “I recently subscribed to Amazon Prime,” “Amazon Prime” would be the named entity and could be categorized as a “brand.”

You can create your own labeling workflow in Ground Truth. A custom workflow consists of three components: (1) a UI template that provide human labelers with all of the instructions and tools needed to complete the labeling task, (2) any pre-processing logic encapsulated in an AWS Lambda function, and (3) any post-processing logic encapsulated in an AWS Lambda function. A large selection of UI templates is available or you can upload your own Javascript/HTML template. The pre-processing Lambda function can serve the data to be labeled and add any additional context for the labeler and the post-processing Lambda function can be used to insert an accuracy improvement algorithm. The algorithm can assess the quality of the annotations made by the humans or can find consensus on what is “right” when the same data is provided to multiple human labelers. You can upload all three components using the SageMaker Ground Truth console.

SageMaker Ground Truth supports multiple choices for a human workforce to label data, (1) Your own employees, (2) Third party data labeling service providers available through AWS Marketplace, and (3) Crowd sourced workforce through Amazon Mechanical Turk.