iMerit has labeled, annotated, enriched and segmented over 100 million images and videos that power Computer Vision algorithms. iMerit’s delivery team works with complex edge cases and nuanced taxonomies to give clients an extra advantage.
Computer Vision
iMerit collaborates to deploy AI and Machine Learning in Autonomous Technology, Geospatial Technology, Medical AI, and other industries by enriching, annotating and labeling data.
Services
Bounding Boxes
iMerit Computer Vision experts use rectangular box annotation to illustrate objects and train data, enabling algorithms to identify and localize objects during the ML processes.
Polygon Annotation
Expert annotators plot points on each vertex of the target object. Polygon annotation allows all of the object’s exact edges to be annotated, regardless of shape.
Semantic Segmentation
Images are segmented into component parts, by the iMerit team, and then annotated. iMerit Computer Vision experts detect desired objects within images at the pixel level.
PanOptic Segmentation
Coupling instance and semantic segmentation, iMerit enrichment teams identify the pixels in images as belonging to a class and identify what instances of that class they belong to.
LiDAR Annotation
iMerit teams label images and videos in 360 degree visibility, captured by multi-sensor cameras, in order to build accurate, high-quality, ground truth datasets for use cases including autonomous vehicles.
"iMerit's data annotation enables us to build accurate models and actionable insights for hundreds of MLB pitchers"
Steven Cadavid, President, KinaTrax
"iMerit's incredibly knowledgeable data labelers are part of our team and are the right people for our enrichment efforts."
Director of Data Products, Research Institute of Autonomous Driving
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The need for speed in high-quality data annotation has never been greater. iMerit combines the best of predictive and automated annotation technology with world-class data annotation and subject matter experts to deliver the data you need to get to production, fast.
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