Training YOLOv8 with Synthetic Data Using Falcon

Can you really train a YOLOv8 model using 1000 synthetic data images? You can if it's done right!
Learn the Duality workflow for crafting, collecting, and using synthetic data from our FalconEditor simulation software to train an AI model that works on real-world data.
This video will cover:
✅ Components of an effective simulation scenario
✅ Elements of successfully crafted synthetic data
✅ Using the synthetic data to train a YOLOv8 model
✅ Testing the model on real world images
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[@emailformosa]

What if you have a custom object that requires detection agnostic of texturization? For instance, my company makes a widget and our widget comes in a variety of patterned surface finishes and colors. Can you train a model to focus on dimensional accuracy for classification while ignoring textures?

[@dualityAI]

You for sure can use Falcon to train a model to detect an object with different textures. We recommend creating a scenario that randomly switches an asset's texture in each dataset image to create the needed variety.
It is important with synthetic data to be intentional and to match the physical world. We recommend validating the textures so that they are indistinguishable to the model from the physical world. See our blog for more info: www.duality.ai/blog/ml-synthetic-data-model
You'll also probably have to use a larger dataset than you would if the texture was consistent to accommodate the variety, but the nice thing about Falcon is that you can set it up and just let it run!