"Computer vision teams face an uncomfortable reality. Even as annotation costs continue to rise, research consistently shows that teams annotate far more data than they actually need. Sometimes teams annotate the wrong data entirely, contributing little to model improvements."
"Most teams never develop systematic approaches to selecting which data needs annotation in the first place. This is largely because annotation often remains siloed from data curation and model evaluation, making it impossible to act on the full picture."
"The conventional approach treats annotation as an isolated workflow: Collect data, export to a labeling platform, wait for humans to label data, import labels, discover problems, go back to the annotation vendor, and repeat."
Computer vision teams often annotate excessive data, leading to rising costs and wasted resources. Research indicates that 95% of data annotations are ineffective. Many teams lack systematic data selection methods, resulting in high error rates and missed opportunities for model improvement. Safety-critical models, like those for autonomous vehicles, require precise annotations, yet teams frequently label redundant data while overlooking critical edge cases. The conventional annotation workflow is fragmented, creating bottlenecks and inefficiencies in the development process.
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