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