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Annotate objects in urban and street environments that are relevant to the autonomous vehicles.
Build huge volumes of road scenes that enables you to train and detect moving object detection for autonomous driving.
Playment's state of the art tools enables data labeling from multi-sensor configuration of camera, lidar and radar.
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. Jitesh(Engagement Manager) was very helpful with sharing his domain expertise to formulate the solution.
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 to achieve higher accuracy of our models in a very short time. Flexible solutions, QA process and dedicated project manager helped us to have a good peace of mind. Annotation work was off-loaded in the true sense.