Building Quality Models for Autonomous Driving
This ebook aims to give you a deeper understanding of what “quality” might mean for your autonomous vehicle use case, and how you can start measuring it. You’ll learn how you can effectively balance time versus quality — starting small, iterating, and scaling slowly but deliberately — to set your organization up for the quickest past to accuracy.
Is Your Training Data Veering You Off Course?
Good annotation and testing practices are the foundations of building a great model. But understanding what constitutes quality data is a tricky question. While “as accurate as possible” seems the obvious answer, this thinking can bog down any team in an endless cycle of manual annotation, training, more annotation, and retraining.
- An explainer on why managing noise is critical for autonomous driving models (and which noise to safely ignore)
- How to set data quality requirements that meet your needs
- How to define and measure data quality for annotation
- How to guarantee data quality