Attribute timeline: catch errors faster
Tracking attribute changes across sequences shouldn’t feel like detective work. Yet that’s been the reality. To check if [...]
Introducing warnings: catch issues before they matter
We’ve added a new Warning System to Segments.ai that helps teams catch common annotation mistakes early, before they [...]
A new timeline for tracks: more clarity, less guesswork
We’ve redesigned the track timeline in Segments to make it easier to see what’s changing and where. The [...]
Improved Issue Tracking: Less Friction, More Precision
When you're labeling complex multi-sensor data, issue tracking isn’t a luxury. Yet, our previous functionality was limited. [...]
Solving Multi-Sensor Labeling Challenges in Robotics and Automotive
How to build ground-truth data, specifically for autonomous systems in the robotics and automotive industries? Otto, CEO [...]
Voxel51 <> Segments.ai integration
Machine learning models are only as good as the data they’re trained on. The common saying “garbage in, [...]
Late vs early sensor fusion: a comparison
Sensor fusion is the process of combining data from multiple sensors (e.g. multiple cameras, lidars, and radars) to [...]
Best practices for ML teams: working with annotation providers and platforms
Computer vision technology has revolutionized multiple industries, from autonomous vehicles to advanced robotics. The foundation of these innovations [...]
Building your internal data labeling software
Annotation or labeling tooling has multiple use cases. Think about labelling datasets to use for your ML [...]
What data do you need for multi-sensor labeling?
In autonomous driving and robotics, combining camera images with lidar point cloud data can greatly improve the perception [...]
