Good ground truth labels are the foundation of building a great model, but overly-accurate annotations quickly run up costs and slow down turnaround; you may in fact run out of budget before you get representative data! That’s why awareness of what constitutes good quality ground truth is critical to how you optimally define and design your annotation processes, and allot your resources.
This comprehension is at the root of how you’ll get the best performance out of your model and ensure your project’s production-readiness.
The Challenges and Solutions of Data Annotation in Computer Vision
We want to help you get that understanding by sharing our insights on ground truth label quality. We at Sama are in an ideal position to provide this knowledge thanks to our long history of providing multiple Tier 1s and OEMs with optimally-annotated data for their autonomous driving applications. This work has provided us with broad and deep experience in overcoming data labeling challenges, so we know how to save time and money on annotation without compromising quality. That’s why we’ve published an ebook that provides a deeper, more practical conception of what “quality” might mean as well as data labeling best practices.
Here’s what our ebook covers:
- How noise is one of the critical issues for models that power autonomous driving (and which noise can be safely ignored)
- How to set data quality requirements that meet your model’s needs
- How to define and measure data quality for your annotation process
- How to guarantee data quality