Human-Centric Efficiency in Image Annotation for Autonomous Driving
At Sama, we are constantly exploring ways to boost efficiency to deliver high quality training data at scale. In this talk, we present a solution that we have developed to speed up polygonal instance segmentation using machine learning. This is especially relevant for projects involving autonomous vehicles, where it is typical to apply instance segmentation to label scenes comprising hundreds of frames, each with multiple objects (vehicles, pedestrians, traffic signs, etc.) We have found our machine learning assisted annotation tool to not only significantly reduces the variability over annotators but also boost polygon drawing efficiency by 300% compared to manual only annotation.
With a background in Computer Science, Abha leads the Customer Success Engineering team at Sama. The team is responsible for managing technical relationships with customers and prospects to understand their business needs, ideate upon them, and manage the implementation and communication of the solutions developed. Within the company, she wears many hats: from building complex solutions to deliver high quality data for complex workflows, to improving internal processes, and troubleshooting the in-house SamaHub platform. Her mission is to design and architect solutions that help clients achieve their larger ML vision while creating a positive social impact.