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Models

The central part of an ML pipeline is the model. The first step of creating a Rune is finding (or training) a Machine Learning model that matches your application. Right now, we support TFlite and tfjs models (onnx support coming soon).

You have two options:

  1. Choose a pre-trained model Several pre-trained tflite/tfjs models are available for download from the TF Hub. You can choose and start playing with the models from the TF Hub.
  2. Custom Model You can go on training a model by yourself. After getting the desired accuracy, convert a TensorFlow model into the tflite with the TensorFlow Lite Converter.

There are various techniques using which one can optimize a model to reduce the memory footprint without losing accuracy.

  • Quantization
  • Pruning
  • Clustering It will help deploy simple yet powerful models on extremely low-power, low-cost microcontrollers at the network edge.

You can find more details on converting a Tensorflow model to tflite here.

We have created a few Colab Notebooks to show how to train a model from scratch and converted them into tflite.

  • MicroSpeech: a Microspeech model for keyword spotting classification on the edge.
  • Mask-Detection: a model that detects whether a person is wearing a mask or not.