Quality assurance
Quality Assurance
We deliver quality metrics and KPIs for AI datasets, adapted to your needs for any industry.
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- AUTOMOTIVE
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- SURVEILLANCE
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- AGRICULTURE
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- PRODUCTION
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- HEALTH CARE
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- RETAIL CHECKOUT
OUR APPROACH
For AI, flawed, incomplete or unfitting training data leads to bad performance and low reliability! Finding the issues inside an immense volume of data is a challenging task if you don’t have the right tools in place to support you.
Incenda AI’s QA solution ensures that your labels reach an outstanding level of quality – at scale, for immense data volumes.
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.
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.