Why synthetic data?

Synthetic image data has come a long way and the use of synthetic training data has proven to have a number of advantages, both technically and commercially.
Our customers report experiencing a smoother data acquisition process during challenging projects.

Save time, effort and costs

The process of capturing and labeling real image data is a time and effort intensive approach.
In times where wages are rising and qualified personnel are scarce, manual or half automated data acquisition may not be the most efficient utilization of your resources.
Most importantly, this activity incurs linear costs - so consider the actual + opportunity cost incurred when your team creates a real image dataset of a few hundred or thousand images.
If this scenario sounds familiar, we urge you to rethink your approach - using our methodology, your team invests a fraction of the time and can focus on activities that generate value.
Contact us to explore the possibility of saving resources.

No more biased datasets

How do you acquire sufficient images of edge cases? e.g. defects.
“Production lines are not designed to produce defects”, said one of our partners.
We solve this challenge with our synthetic image offering. As an example in the area of defect detection, we offer synthetic images representing a variety of defects on various materials, cracks, scratches, blow-holes, discolorations to name a few.
Our ever-growing library of domain specific features combined with the domain expert Q&A enables us to generate images with the finest degree of realism.

Overcome the constraints of real data

To acquire a balanced dataset of real images, that correctly represents the distribution and variances of an object and the scene, is a highly demanding task. To attempt this within competitive project timelines results in delays and algorithms being trained on biased data.
Enter synthetic image data.
Our technology and image generation process follows the principles of Structured Domain Randomization. In this approach, we address the gap in a balanced data distribution and generate images that represent objects, features and their environments including all the relevant variances within real parametric ranges.

Limitations on real data due to privacy policies

Real image data is an asset for industrial organizations and a basis for competitive advantage.
This limits the potential to create generic deep learning models.
With our synthetic image data generated from scratch, you own the data and have the possibility to create and continously improve generic deep learning models for particular use cases, without the need for specific customers' data.