Building Training Data for Autonomous Dr...

Building training data for computer vision models that can detect and recognize foreground objects in images like trees, pedestrians and bicyclists is one thing. But building training data for autonomous driving—which must see everything going on in the scene from the objects to the environment itself—is quite another. Say you have a photo of a street scene with multiple cars, pedestrians, trees, sky, road, etc. How do you label objects that aren’t as simple to define, like the gaps in the trees with sky in between them? What about defining the percentage of the sky in an image of the same road when the leaves are in full bloom vs. when they’ve fallen to the ground? Or what about snow on the ground: do you tag the snow, the ground, or both? It challenged Mighty AI to reconsider its entire image-labeling workflow. In this presentation, Mighty AI Founder & CTO Daryn Nakhuda will drill into the challenges of creating semantic segmentation masks, including workflow design and annotation tools, as well as how to perform these tasks at scale.