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RealSim™ technology improves computer vision model performance with limited real-world data.

Training Data augmented by Generative AI

Technology

RealSim™ stimulates perception model learning with Generative AI.

RealSim™ is an offline, post-processing technology based on generative AI. Apply realistic textures onto existing training datasets to improve realism, increase variation and introduce alternative objects into a scene.

RealSim generative AI targetting road surface variations
Pre-trained for sample generation

RealSim™ is a series of pre-trained models, already capable of replicating many real-world contexts. After several years of development, it offers highly realistic augmentations in regular RGB camera data logged from vehicles, drones and robotics.

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Get in touch to request sample data augmentation - free of charge. 

Synthetic image example for manufacturing components

Replace up to 80%* of your real-world annotated data with RealSim™ 

*when joint training using a ratio of 20% real-world training data and 80% RealSimâ„¢ augmented data, achieve comparable performance in image segmentation (mIoU)

80% infographic, RealSim can improve accuracy of object detection models by up to 80%

Founded through research, driven by market.

A spin-off from Chalmers University of Technology

We're part of the innovative startup scene in Gothenburg, Sweden. Our purpose is to bring the latest advancements in Generative AI to market - driven by real market needs.

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