Accurately define lane lines in drivable areas for vehicle perception models.
Given the raw data, we recognize lane markings by annotating lines on the objects of interest.
Well-defined different kinds of lanes for ego car, bicycle, opposite direction traffic, divergence etc.
Proprietary Quality Assurance
Our engineers work so hard to fulfill all your custom requirements. Starting from supporting various sensor data formats to building complex annotation tools. We also set up unique data sharing modes so that you can focus on building and optimizing ML models.
With rigour quality checks, free repetition allowance, high-quality ground truth annotation and end-to-end project management we're confident you'll love us.
Definition, Process Excellence and Transparency are the three pillars of our accuracy promise.
Full suite of annotation tools with the help of project consulting, dedicated project management and tech support.
Playment uses machine learning models to perform semi-automatic labeling at a fraction of the cost of manual labeling.
“We were very impressed with Playment’s ability to grasp complex requirements and quickly build custom tools to support it. Our dedicated engagement manager was very helpful in sharing his domain expertise to formulate the right solution for our team.”
“Playment's fully managed approach has been critical in factoring the variability and scaling up our annotation requirements. Thanks to their tools and skilled workforce, we are extremely satisfied by the quality and turnaround time they have provided. I would highly recommend Playment.”
“Playment's quality annotations helped us focus our efforts towards building perception system for our robots. Their flexibility to seamlessly incorporate our feedback and evolving requirements have made them a trusted partner.”
“Quality annotations by Playment have helped us achieve higher accuracy of our models in a very short time. Flexible solutions, QA process, and dedicated project manager helped us have peace of mind. The team was able to experience a real off-loading of annotation needs.”