Whoa, 1.3MB

Take all of this with a grain of salt.

○ The AI model+weights are only 7kb but we need Tensorflow to use it; ○ Tensorflow-Core + Tensorflow-Layers are a ~600kb download.

BUT

At Filtrou.me we HAD to use Tensorflow to get facial keypoins anyway. So, in THIS app, I was able to save 1.3mb of wasm and js.

I have no idea about code size necessary to implement SolvePnP on pure JS.

Some implementations are just ~400 lines of C++ but they used the Eigen library. And God knows what library Eigen uses and how many LOCs Eigen has…

And it’s just better to sit and watch a model train than to implement linear algebra operations from scratch in javascript.

Watching a model train.

How to train the AI

I won’t go into much detail here because I trained it all using Jupyter Notebook and described how it works there. You can run the entire program (data generation and then the model training) on a Google Colab in less than 15 minutes (with GPU enabled).

Take a look

Results

Given the way Filtrou.me works, as I explaiend in my last blog post , I was able to train the neural network on a very small domain. That is, face-api.js configured as I use it (lowest resolution and with the faster/more innacurate network) won’t recognize faces more than 1 meter away.

So I could train the network with data generated within this radius.

And as the user will be looking at the camera I also generated images where the face is mostly looking at the camera.

I used a LOT of noise on top of the training data and this made it work on real life, pretty well.

And, of course, to avoid overfitting (it would work on generated data but not in real life) the best I could do is have a very simple network and normalize/scale the features.

I tried to use dropout, SELU as activation, have a deeper network or add more neuron per layer but it would all eventually decrease performance on the real world.

Take away

If you’re doing something for the web and downloading an external dependency to run some calculations is expensive, try to train a simple neural network to solve it.

See it in action

Doubts?