Enterprise data management tools and powerful project analytics to help you manage your way to better datasets.
Deliver more accurate annotations, ensure consistency, and boost model performance with quality control tools embedded into the annotation workflow. Multi-step QA, interactive feedback mechanisms and consensus ensure quality standards are maintained and empower you to build the best benchmark datasets for automation or external labeling teams.
“Manual QA allowed us to...increase image data accuracy to 90% by decreasing the number of potential issues on one submitted label from upwards of ten to one or fewer. Rejected labels automatically inform annotators that they need to be revised...increasing work efficiency.”
Compare, remove inaccuracies, and consolidate annotation results from multiple labelers into new images or videos using our Python SDK.
Cross-check the accuracy of your automated and hand-labeled data with built-in review and approval mechanisms and integrated issue threads.
Discover insights faster and closely track project progress, team performance, validation efforts, and other key metrics with actionable analytics and reporting. Object and category analytics help you better understand your labeled data and inform improvements.
Configure optimal workflows with extensive workforce management and control over class and tool selection, including the number of annotations per class. Role-based access, granular permissions, and assignments ensure work is delegated effectively and that your data remains yours.