Synthetic Image Data

A.I. revolutionizes how we use visual information.

A.I. enables businesses and researchers to automate tasks which until now could only be performed by human beings. Humans, despite their unique capabilities, have a tendency to fatigue and making mistakes.

There are countless examples where so much visual information is collected that human processing capacities are overwhelmed or are just not useful: Identifying product defects on production lines in real-time, teaching drones to fly from A to B without crashing into the next tree, or analyzing countless images of planet earth delivered by satellites every day.

At the same time, global data production is increasing massively every year. Think of all the food-cat-baby-selfie images and videos you receive on your smartphone! The problem is: This does not help in most cases.

A.I. - or more precisely: Machine learning algorithms - need lots of data to learn. For a production-ready solution, this often amounts to hundreds of thousands, if not millions of images.

A.I. needs huge amounts of data.

Real-life data is expensive.
Or not available.

Only a fraction of collected data can be used for A.I. - most lacks quality & variety, has the wrong content, is heavily biased or is protected by privacy regulation. The remaining data has to be annotated and cleaned in a mostly manual process. The result: A.I. projects are time consuming, extremely costly and full of uncertainty.

the data revolution

Creating Powerful Training Data

At Promethion, we replace real-life images with „artificial“ synthetic data. We use our library of simulated objects, materials, surfaces and environments to generate visual datasets for A.I. training at scale.

Our procedurally generated images are able to store multiple layers of relevant information, enhancing the precision and explanatory power of our A.I. applications.

Better data for better A.I.

„Garbage in - garbage out“ - an A.I. solution can only be as good as the training data. We create consistently high image quality even for complex scenarios at unlimited scale.

Perfect annotation

Next to data collection, arguably the most time consuming tasks in A.I. projects are data classification and labelling - especially if a high level of accuracy is required. Our data is pre-labelled by nature with pixel-level precision.

Data diversity

We recreate natural diversity, cover edge cases and rare events to solve highly specific challenges and avoid the bias problems of real-life datasets. Our data offers full flexibility in image perspectives, environmental conditions and camera specifications.

Faster development

We avoid spending the valuable time of A.I. teams with months of data preparation. Most synthetic datasets can be created within days and model performance can be consistently improved.

Ethics & privacy

Protecting personal data is one of the major benefits of using synthetic data. It is fully GDPR/CCPS compliant and contains no personally identifiable information or has other proprietary limitations.

... and save the planet

Did you know that training of a single A.I. model consumes so much energy that the CO2 emission footprint is equivalent to the total lifetime value of several cars? With synthetic data, high model performance can be achieved with far less data - reducing the need for massive computing power.