Amazon SageMaker Ground Truth Adds Auto-Segment Feature for Semantic Segmentation Labeling
Amazon SageMaker Ground Truth added the auto-segment feature to the semantic segmentation labeling user interface. This feature increases labeling throughput, improves accuracy, and mitigates labeler fatigue. It simplifies the task by automatically labeling areas of interest in an image with only minimal input. You can accept, undo, or correct the resulting output from auto-segment.
SageMaker Ground Truth helps you build highly accurate training datasets quickly. The service offers easy access to your own and third-party human labelers and provides them with built-in workflows and interfaces for labeling tasks. SageMaker Ground Truth offers a built-in labeling workflow and interface for semantic segmentation, which is a computer vision ML technique that involves assigning class labels to individual pixels in an image.
With this new feature, you can work up to ten times faster on semantic segmentation tasks. Instead of drawing a tightly fitting polygon or using the brush tool to capture an object in an image, you draw four points: one at the top-most, bottom-most, left-most, and right-most points of the object. Ground Truth takes these four points as input and uses the Deep Extreme Cut (DEXTR) algorithm to produce a tightly fitting mask around the object.
Learn more about this feature from our launch blog and documentation.