Case Study: Digital Divide Data helps an automotive AI company scale vehicle recognition annotation with V7

Digital Divide Data scales automotive image labeling to 200,000/week with V7

The customer, Digital Divide Data, faced the challenge of scaling its automotive AI development to recognize global vehicle types and license plates with over 99.9% accuracy. They required a dedicated team to annotate up to 200,000 images per week efficiently without sacrificing data quality or hindering their development timeline. To meet this need, they partnered with vendor V7 and utilized its Darwin annotation platform and services.

The solution implemented by V7, in collaboration with its annotation partner Digital Divide Data, massively streamlined data production. Using V7's scalable tools and DDD's expert team, they achieved a remarkable annotation accuracy of over 99.9%. This partnership enabled the customer to rapidly scale their weekly output from 35,000 to 200,000 images, significantly accelerating their model development and helping them outpace competitors.

Digital Divide Data