Welcome to micromind’s documentation!

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This is the official repository of micromind, a toolkit that aims to bridge two communities: artificial intelligence and embedded systems. micromind is based on PyTorch and provides exportability for the supported models in ONNX, Intel OpenVINO, and TFLite.

Key Features

  • Smooth flow from research to deployment;

  • Support for multimedia analytics recipes (image classification, sound event detection, etc);

  • Detailed API documentation;

  • Tutorials for embedded deployment.


Using Pip

First of all, install Python 3.8 or later. Open a terminal and run:

pip install micromind

for the basic install. To install micromind with the full exportability features, run

pip install micromind[conversion]

Basic how-to

If you want to launch a simple training on an image classification model, you just need to define a class that extends MicroMind, defining the modules you want to use, such as a PhiNet, the forward method of the model and the way in which to calculate your loss function. micromind takes care of the rest for you.

class ImageClassification(MicroMind):
   def __init__(self, *args, **kwargs):
      super().__init__(*args, **kwargs)

      self.modules["classifier"] = PhiNet(
         (3, 32, 32), include_top=True, num_classes=10

   def forward(self, batch):
      return self.modules["classifier"](batch[0])

   def compute_loss(self, pred, batch):
      return nn.CrossEntropyLoss()(pred, batch[1])

Afterwards, you can export the model in the format you like best between ONNX, TFLite and OpenVINO, just run this simple code:

m = ImageClassification()
m.export("output_onnx", "onnx", (3, 32, 32))

Here is the link to the Python file inside our repository that illustrates how to use the MicroMind class.

Indices and tables