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Military MODSIM is often focused on training humans. Many of the same simulators and tools used in human training can also generate imagery to train AI and ML systems. recently delivered an in-depth presentation at MODSIM World 2024 on how synthetic computer vision data can help modeling and simulation professionals overcome AI training challenges for this purpose.  

The team shared their expertise on the issues facing data scientists today in training vision-based AI / ML applications with easily accessible, quality imagery and the best practices for using synthetic data to solve those problems effectively. Now, you can use these tips and insights to help accelerate your computer vision projects. 

Read’s whitepaper Synthetic Computer Vision Data helps Overcome AI Training Challenges” now.  

What Will You Take Away from This Presentation? 

There is no question that artificial intelligence and machine learning have become essential tools for commercial and government organizations to hold a competitive edge. AI and ML systems need physically accurate simulated imagery to adapt to constantly changing real-world scenarios and information, whether that be innovative camouflaging techniques, rare edge cases, or new military vehicles and maritime vessels. This requires access to massive amounts of training data with complete annotation, which can be a significant obstacle for AI and ML engineers. In many ways, synthetic data is the solution and expert guidance on implementing this asset into your computer vision workflows is vital to boosting their performance. 

Examples of simulated satellite, aerial, multi-modal security, X-ray, medical, and non-visible sensor imagery generated with synthetic data from the platform.

In this whitepaper, you will learn about: 

  • Common problems with AI training data 
  • How synthetic data can fill the gaps in real datasets 
  • Distinct types of synthesized data: Generative AI vs. simulation-based synthetic data 
  • Examples of synthetic data applications for satellite imagery, manufacturing, and more 
  • Best practices for using synthetic data 
  • Predictions for the future of synthetic data in vision-based modeling and simulation 

Putting These Tips into Action 

The team has vast expertise in optimizing the performance of computer vision projects with synthetic data. Here are the best ways to benefit from that experience to enhance your AI and ML training programs quickly:

  • Request a no-commitment consultation with our synthetic data experts. A specialist will listen to your needs, advise on the right synthetic data solution for you, and share a demonstration of synthetic data generation and application based on your use case.
  • Try’s synthetic data generation Platform as a Service free for 30 days. Explore our Sample Gallery and several examples of synthetic data generation channels and multi-modal imagery rendering for different applications.

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