FlySight and DMI Benchmark Synthetic Data, Achieving Training Results at the Level to the Renowned Singapore Maritime Dataset.
From fusing virtual scenarios with real-world data to rigorously validating the “GAP” between the virtual simulations created by DMI and actual real-world scenarios, the core objective of this collaboration is to ascertain how closely DMI’s virtual representations can mirror reality. This enhances the fidelity and applicability of virtual simulations.
The essence of this collaboration lies in accurately determining and minimizing the discrepancies between synthetic datasets and real-world observations. Through a meticulous comparison process, this initiative aims to refine the precision of virtual models, ensuring they closely align with real-world data. This endeavor is crucial for fields that rely on exacting standards of data validation and model reliability, providing a concrete basis for the application of virtual models in real-world scenarios.
“The results show that real and synthetic data models perform in comparable ways (MAP 31 % vs 32%) whereas the “hybrid model” behaves considerably better (MAP 39%).
The first thing that comes to mind when someone asks us to develop an AI method is: where do we get the data for training and testing? My first choice is always to look for a public dataset. However, I often encounter the same questions: Is the data sufficient in quantity? Is there enough variety in the data? Considering privacy and ownership, can I use this data for training and testing my algorithm? What if I need more data? Synthetic data generation has the potential to address all these issues and many more. Our tests with DMI have shown that this is not just a possibility for the future—it is a reality right now. We are very interested in continuing this collaboration and exploring new scenarios and contexts” says Niccolò Camarlinghi, Head of research, FlySight.
Source and images @FlySight
