Jetraw AI — Custom solution for synthetic data generation based on sensor model

Create data that resembles the natural pixel distribution

Generate multiple image-label pairs from a single dataset

Emulate sensor performance across different scenarios

part of research groups

meet jetraw ai

Unlike traditional data augmentation, Jetraw AI’s synthetic data leverages advanced sensor models to replicate original pixel distributions, ensuring physically plausible and high-quality outputs

Seamlessly enrich dataset to address data scarcity

Lower costs related to data acquisition and manual labelling

Improve model accuracy, normalisation, and robustness to different settings

easy to use

Boost dataset, enhance model performance in 3 simple steps

1

Start with a small, high-quality dataset

2

Adjust parameters to emulate various optical settings and conditions (e.g. illumination, camera quality, motion blur)

3

Automatically create synthetic images representing a desired scenario, and apply to your model for training

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partnerships

We are a part of the image standards to define the future of data-centric AI

World Health Organization and the International Telecommunication Union

QUAREP-LiMi —Biomedical image quality standard

Croissant ML DB format with Google, NASA, Harvard, and others

FAQ

Things to know about Jetraw AI

How does it improve my model?

Jetraw AI enhances your model’s performance by making it more resilient to natural variations, such as changes in illumination, increased noise, and motion blur. By exposing your model to data that accurately simulates these real-world fluctuations, Jetraw AI helps your model better handle diverse and challenging conditions, ultimately improving its robustness and accuracy.

Does it help improve the quality of my data?

Yes, you can easily enrich your dataset with high-quality data, without the need for expensive acquisitions or manual labeling.

How is it different from synthetic data generation or augmentation tools? Why use it instead of open source ones?

Unlike generative models, Jetraw AI uses physics-based principles instead of machine learning to create new data samples. This method produces data that accurately reflects real-world conditions without the need for extensive training. While traditional augmentation tools manipulate existing datasets, Jetraw AI begins with a small, high-quality dataset and generates data with varying physical properties—such as illumination, noise, and motion blur—while preserving the pixel distribution of real acquisitions. The result is data that is both diverse and highly realistic.

Is it open-source or paid? How can I get started?

Jetraw AI is a commercial solution that you can implement on CPU for automatic data generation and continuous model training. To get started, simply reach out to us. We’ll discuss your needs and requirements, provide an estimate, and if we agree to proceed, we’ll build a custom solution tailored to your specifications, which you can then integrate into your system.

Is it generated with classical ML model or deep learning?

No, Jetraw AI does not rely on classical machine learning models or deep learning techniques. Instead, it uses physics-based principles to generate data. This method allows Jetraw AI to create highly realistic data that accurately represents real-world conditions, without the need for traditional machine learning or deep learning models.

Can I use it on CPU/GPU?

Off-the-shelf for CPU only at the moment, GPU support could be provided upon request.

Can I integrate it in my training pipeline?

Yes, Jetraw AI can be seamlessly integrated into your training pipeline. It’s designed to be compatible with existing workflows, allowing you to easily incorporate the high-quality, physics-based data generated by Jetraw AI into your model training process. This integration enhances the robustness and accuracy of your models by providing realistic and diverse data.

Can I generate images on-the-fly?

Yes, whenever a real image is loaded, a synthetic one is automatically generated with random parameters.

publications