Why Image Labeling Is the Hidden Cost of Vision AI (and What to Do About It)

The promise of computer vision in manufacturing is well documented: sharper defect detection, reduced downtime, and measurable gains in yield. But behind most stalled deployments is a more mundane reality—manual annotation.

Our latest white paper takes a hard look at one of the least glamorous, most time-consuming steps in deep learning: labeling your images. It outlines ten intelligent tools that dramatically reduce that bottleneck, transforming annotation from a manual burden into a scalable, model-driven workflow.

Drawing from real-world use cases and platform data, it explores:

• Why labeling 10% of your dataset may be all you need
• How predictive labeling accelerates outcomes while improving model accuracy
• Ways to detect mislabels and tackle dataset imbalance without starting over
• The role of foundation models like SAM and AI-driven augmentation tools
• What separates industrial-grade annotation pipelines from lab experiments

This is a sober look at what actually works when deploying vision AI in high-stakes environments and 10 Intelligent tools simplify your workflow without corrupting the results.