Part Two of Mindtech’s Synthetic Data Series Reveals How to Get the Ideal Mix of Synthetic and Real Data to Boost AI Accuracy

**Mindtech Launches Second Part in Synthetic Data Series Revealing How to Solve the Tricky Issue of Striking the Right Balance between Synthetic with Real World Training Data**

*Synthetic Data Guide Provides Industry Analysis on Synthetic Data, Plus Practical Tips for Data Engineers Featuring Real-World Use Cases for Data Training**

Mindtech Global, developer of the world’s leading platform for the creation of synthetic data for training AI, has released part two of its synthetic data guide aimed at tackling visual AI’s training problems. It’s a how-to for resolving issues related to combining real-world images with computer-generated ones to optimise the performance of their AI model.

AI developers have become increasingly aware that feeding an AI network a compound of data generated by real-world and synthetic images is a sure-fire way to boost a network’s precision.

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What’s less clear is exactly how much of each to include, and how to decide. There’s no simple answer or magic formula, according to Chris Longstaff, VP Product Management, Mindtech Global. Instead, teams need to ask themselves a series of questions about what they’re trying to achieve.

Longstaff said, “While it is of course up to the machine vision developer to decide how good is good enough, we’d recommend a starting point of around 90% synthetic data to 10% real — as there is both research validating this and empirical evidence this ratio has performed well in a great many use cases.

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“But while Mindtech’s data generation platform, Chameleon, lets people create a great deal of synthetic data quickly, that choice must always be led by the needs of the problem visual AI developers are trying to solve.”

Mindtech Global is the developer of the world’s leading end-to-end ‘synthetic’ data creation platform for the training of AI vision systems. The company’s Chameleon platform is a step change in the way AI vision systems are trained, helping computers understand and predict human interactions in applications ranging across retail, smart home, healthcare and smart city.

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[To share your insights with us, please write to sghosh@martechseries.com]