Most AI teams have a consensus that a data-centric approach gives better results compared to the model-centric approach in vision AI development.
However, for multiple reasons, teams can not opt for it because of-
1. Not enough data available
2. Synthetic data will not be suitable
3. Have enough real-world data however labeling them is expensive
The most common challenge is the latter one - data labeling is expensive.
We, at Labellerr started to journey to solve the 3rd challenge as I mentioned above and now with multiple iterations, we have developed a workflow that includes automation with human components to reduce the cost while maintaining quality.
So that more teams can adapt the data-centric approach to their model development.
Read our detailed article on the topic, here we discuss the approach, importance, and Labellerr's role to enable teams to adopt.
https://lnkd.in/gj6U9Fny
#datacentricai #computervision #visionai #imagelabeling
Bridging Vision & Execution in Design & Construction for Over 20 Years | SVP, Business Development, Willdan
8moHow can this technique enhance production workflows across industries, Rahul Singhal?