Meet us at Computer Vision and Pattern Recognition June 16th-20th 2019 Long Beach, CA

2D Bounding Boxes

For object detection and localization in images and videos.

  • Polygonal lines to best estimate lanes
  • Custom class list
  • Additional attributes
  • Lane estimation even with low visibility in image

We support diverse use-cases

2D bounding box annotations, bounding box annotation tool, bounding box for object detection, bounding box computer vision, bounding box labeling, image annotation

Object localization for Autonomous Vehicles

Extensively used to train autonomous driving perception models for pedestrians, traffic signs, lane obstacles, etc.

Object detection for Ecommerce

Used to train visual search machine learning models for recognition of various fashion accessories and furniture.

Damage detection for Insurance

Identification of car damage, roof damage or safety parameters from live world images to train machine learning models that detect the degree of damage for insurance claims.

Drone and Robot training

Labeled images for training smart surveillance drones and robots to identify a variety of objects.

Built for Enterprise

Proprietary Quality Assurance

Multi-level quality checks
We have proprietary and unique QA processes for every annotation constructed from experience of annotating millions of objects. QA process is a combination of
  • Annotator reputation engine.
  • Maker-checker and consensus models.
  • Automatic checks to catch systemic errors.
  • Critical manual checks with statistical sampling.
Creative training of annotators
We work with our customers to draft a comprehensive annnotation policy and transform it into easy to learn training material. Our training software enables our project managers to train a huge workforce of annotators even on nuanced labeling concepts and time to time policy changes.
Multi-level quality checks
We have proprietary and unique QA processes for every annotation constructed from experience of annotating millions of objects. QA process is a combination of
  • Annotator reputation engine.
  • Maker-checker and consensus models.
  • Automatic checks to catch systemic errors.
  • Critical manual checks with statistical sampling.
Verification tools for customers' QC teams
We believe in providing complete transparency to our customers on data accuracy metrics. Playment's customer tools and dashboard facilitates checking of labeled output, annotation level feedbacks by your QC teams.

End-to-end Tech Support

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.

Tech Support

Why choose Playment

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.

Guaranteed Quality

Definition, Process Excellence and Transparency are the three pillars of our accuracy promise.

Fully Managed Solution

Full suite of annotation tools with the help of project consulting, dedicated project management and tech support.

Competitive Cost

Playment uses machine learning models to perform semi-automatic labeling at a fraction of the cost of manual labeling.

Trusted by

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.
Nikola Noxon
Senior Engineer, Daimler
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.
Shmoolik Mangan
Algorithm Development Manager, Vayavision

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