Quality assurance

Quality Assurance

We deliver QA for AI datasets, adapted to your needs for any industry.

OUR APPROACH

2D BOUNDING BOX

3D BOUNDING BOX

TRANSPARENT EVALUATION PROGRESS

We monitor and track the evaluation and labeling progress all the time to make sure you always know what is going on. 

Iteration 1
Evaluated Frames 90%
Iteration 2
Evaluated Frames 70%
Iteration 3
Evaluated Frames 20%

OUR RESULT

FRAME REJECTION RATE

Let us know what your target accuracy needs to be and we will adjust the workflow accordingly.

WE SUPPORT

IMAGE

SEQUENTIAL AND NON SEQUENTIAL

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.

OCR is a technology that specializes in recognizing all kinds of typed, handwritten or printed texts or characters and translating them into machine-encoded text.

Rectangles to enclose a specific object or region. Provides tracking of different classes like cars, pedestrians or signs.

Object tags provide specific information about an input image. Every object has its own requirements that need to be considered.

Full image tags provide general information about an input image such as environment and weather conditions.

Visualizes human movements for a static input image by annotating different body parts. Is used for predicting motion sequence of a pedestrian.

Aims to make the lane prediction for an entire input image. To reach this goal each lane is labeled with the specified color.

LIDAR

SEQUENTIAL AND NON SEQUENTIAL

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.

Object tags provide specific information about a LiDAR cloud. Every object has its own requirements that need to be considered.