Teachable Machine Web Interface

Experiment with Teachable Machine using the web interface.

Train using your custom footage

Now use the data that you collected previously and the teachable machine website to train your custom model.

Export the Model

To run the model on the Pi, you need to have a a quantized, tensor flow lite model. All the hard work of training and converting the model (these are quite computationally intensive operations that could not reasonably be done on the Pi).

Put the Model on the Pi

You will have downloaded two files:

  • labels.txt: A file that puts a label onto the category numbers that come out from the model
  • model.tflite: The layers of tensors that run in the interpreter.

These should both be put into the models folder, under a new folder that is just for your model files.

Open predict.py on the Pi

> nano predict.py

Find the part where the models are defined. It is a list of tuples. Add a new tuple to the end of the list with a name for your model and the directory it is stored in, for example

models = [fdafsd
          ("New Model", "new_model/")]

Now you can run the prediction program and test out your new model.