SKY ENGINE AI provides a Synthetic Data Cloud that generates multimodal synthetic data for training deep learning models in computer vision, significantly reducing the need for real-world image acquisition. This technology enhances model accuracy by up to 4150% and accelerates AI development cycles by up to 3340 times, addressing the challenges of data scarcity and high costs in various industries such as automotive, healthcare, and robotics.
Funding
$9.2M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
CCFounders
Product
Problem
Training computer vision models requires large, high-quality datasets, which are often expensive and time-consuming to acquire, especially when covering rare or edge cases. The scarcity of labeled data can significantly hinder the development and accuracy of AI applications across various industries.
Solution
SKY ENGINE AI offers a Synthetic Data Cloud platform that generates multimodal synthetic data to train deep learning models for computer vision, reducing reliance on real-world image acquisition. The platform uses 3D generative AI to create virtual environments and automatically annotate synthetic data with labels and ground truths. This approach allows for the creation of diverse datasets that cover edge cases and rare scenarios, improving model accuracy and accelerating AI development. The platform also provides tools for domain adaptation, ensuring that models trained on synthetic data perform well in real-world applications.
Target Audience
The primary target audience includes AI developers and data scientists in industries such as automotive, robotics, manufacturing, healthcare, defense, electronics, and UAV/drones, who need large, diverse, and accurately labeled datasets for training computer vision models.
Features
- 3D generative AI for creating virtual worlds and synthetic data.
- Physics-based rendering shaders tailored to sensor fusion.
- Automated synthetic data annotation with labels and ground truths.
- Domain adaptation tools for transferring models to real-world scenarios.
- Pre-trained deep neural networks for various computer vision tasks.
- Self-balancing data capabilities for numerical differentiations and sampling from evolving distributions.
- Multi-GPU and network-level adaptive deep learning and task scheduler.
- GPU memory-level integration with PyTorch and TensorFlow.