What we do
To make autonomous driving as safe as possible we evaluate data for neural networks.
2D
Semantic Segmentation
Aims to make the class prediction for an entire input image. To reach this goal each pixel is labeled with the specified color of its object.
Full Image Tagging
Full image tags provide general information about an input image such as environment and weather conditions.
Bounding Boxes
Rectangles to enclose a specific object or region. Provides tracking of different classes like cars, pedestrians or signs.
Object Tagging
Object tags provide specific information about an input image. Every object has its own requirements that need to be considered.
Skeleton Annotations
Visualizes human movements for a static input image by annotating different body parts. Is used for predicting motion sequence of a pedestrian.
Road Model Segmentation And Tagging
Aims to make the lane prediction for an entire input image. To reach this goal each lane is labeled with the specified color.
3D
Semantic Segmentation
Aims to make the class prediction in 3D LiDAR space. To reach this goal each point is labeled with the specified color of its object.
Bounding Boxes And Object Tagging
Cuboids to enclose a specific object or region in 3D LiDAR space. Object tags provide specific information about a LiDAR cloud. Every object has its own requirements that need to be considered.
A few things we’re great at
World-class QA Tooling
Our tools have been refined based on years of experience in data QA for Machine Learning.
An uncompromising focus on usability paired with a plethora of automated tests enable us to delivery fast while flexibly adapting to changing requirements.
Independent QA
We are not associated to any labeling company, and never will be. We are independent and provide objective metrics for your labeling process.
QA Made in Germany
We act as a cultural translator to your foreign labeling partners. All of our employees are based in Germany.
Evaluation
2D Bounding Box
3D LiDAR Bounding Box
