Silera provides a vision data engine that automatically generates photorealistic, labeled defect datasets for training computer vision models. This synthetic data creation process eliminates the need for extensive manual data collection or factory downtime. The platform enables rapid model accuracy improvement across industrial inspection, infrastructure monitoring, and material recovery applications.
Funding
Funding not disclosed
Founders
Product
Problem
Developing and deploying computer vision models often requires extensive real-world data collection and annotation, which can be costly, time-consuming, and pose logistical challenges. The need for diverse and accurately labeled datasets can hinder the rapid development and deployment of AI-powered vision systems.
Solution
Silera provides a platform for generating photorealistic synthetic data to train and optimize vision AI models. The platform leverages procedural 3D simulation and domain randomization techniques to produce auto-labeled datasets, reducing the reliance on real-world data. By enabling closed-loop AI testing in a browser, Silera facilitates rapid iteration and fine-tuning of models, accelerating deployment and reducing costs. The generated synthetic data covers a wide range of scenarios and edge cases, enhancing the robustness and accuracy of vision models in dynamic, real-world environments.
Target Audience
Silera targets companies developing computer vision models for applications such as robotics, automation, waste management, agriculture, and other industries requiring robust and accurate vision-based AI.
Features
- Procedural 3D simulation for generating diverse synthetic datasets
- Domain randomization to enhance model generalization and robustness
- Auto-labeling of synthetic data, eliminating manual annotation efforts
- Cloud-based platform for scalable AI testing and simulation
- Pre-trained vision models for out-of-the-box solutions
- Tools for fine-tuning models with minimal real-world data
- Support for various vision-based AI applications, including robotics, waste management, and agriculture