In my last post, I covered some of the difficulties of working with custom neural networks in Lens Studio. This time, I’ll show you what it looks like, with a step-by-step guide for building and implementing your own custom neural network in a Snap Lens.
By the end of this post, you’ll know how to:
For a more in-depth look at working with Fritz AI Studio, you can check out our Quickstart Guide; or, for an applied use case, read through our end-to-end cat detector tutorial.
Image segmentation models (like other computer vision models) require a lot of labeled data for training. We could always collect and manually annotate data, but that can be incredibly time consuming, since we’d need to do this for thousands of images.
Fortunately, we can get started with a much smaller number of images, thanks to the synthetic data generation tool in Fritz AI Studio. This tool allows users to:
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