A Step-by-Step Guide to Quality Training Data for Computer Vision
Building a high-performing model takes time, money, and the right set of tools. But most importantly, it takes expertise.
It takes expertise to set project parameters and establish proper annotation criteria for your use case. It takes expertise to get to ground truth. And then, it takes expertise to ensure that these things translate into high-quality training data and a data pipeline that can scale with your model.
That’s where we specialize. In our most recent piece, we walk through the four prioritized phases to quality training data, explain the significance of specific metrics for quality, and discuss how our Customer Success team partners with you to ensure the quality you need.
We’ll dive deep for each phase, covering:
- Quality Requirements and Annotation Criteria
- Workforce Training and Platform Configuration
- Quality Assurance and Quality Control
- Iterating and Scaling the Process
We’ve established quality management best practices and a reliable pipeline for quality training data based on our experience labeling tens of millions of images, video, text, and audio records alongside our customers.
At Alegion, it’s not just the platform that supports your quality training data needs, it’s our whole team of experts, dedicated to your success.