Synthetic Data Generation
Synthetic data, in the form of simulated imagery, is a very cost effective and successful way of generating the large amounts of imagery to replace the need for real-world imagery when training, AI/ML models, design and system validation and product visualization.
Primary Use Case
Synthetic data generation is a critical step in training AI / ML models. By creating high-quality, diverse datasets that mimic real-world data, you can overcome data scarcity and quality issues, reduce costs, and improve model performance and accuracy. Our tools and techniques ensure that the synthetic data is tailored to your specific AI / ML tasks, allowing you to train your models with confidence.
Annotation of real-world data is both time consuming and costly. Synthetic Data offers the ability
to receive programmatic
annotation of your datasets to
be used in training and
validation scenarios. Synthetic data sets can be created with bounding box and semantic segmentation annotation.
Randomization is crucial in computer vision for enhancing the performance, efficiency, and the robustness of a machine learning model.Synthetic data provides the ability to quickly and efficiently generate training sets with large amounts of randomization.
Additional Use Cases
Design and System
Synthetic data generation is a crucial step in the design and system validation process. By creating high-quality, diverse datasets that mimic real-world scenarios, you can simulate and test your systems under a wide range of conditions, reducing the need for physical prototypes and experiments. This not only saves time and resources but also ensures that your systems are more reliable, efficient, and secure.
Synthetic data generation is a valuable tool for product visualization and assembly instructions. By creating photorealistic, 3D renders of your products, you can showcase your products in a variety of settings and lighting conditions, giving customers a realistic view of the product. These images can be used for both marketing purposes as well as instructional documentation for your products.
Synthetic Data Generation Examples
Photorealistic Rendering Experience
We have 15+ years of photorealistic rendering experience.
We specialize in utilizing CAD models and creating our synthetic data sets to get the most accurate representations of your objects.
Computer Vision Experience
We have 40+ years of Computer Vision experience.
Synthetic data is far more cost effective than capturing and annotating real-world data.