If it's not revealing too much, can you tell us a bit more about how you got the necessary training data? And any tips on optimizing models for on-device ML?
Sorry, I unfortunately can't reveal too much about the training data. As for tips on optimizing models for device, choosing a fully convolutional architecture is almost a requirement as any substantial fully connected layer is going to take up too much space. For convolutional layers you want to use more efficient versions like depthwise separable convolutions. Using quantization is a pretty easy way to reduce the size of the model without sacrificing too much performance as well.