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Welcome to micromind's documentation!
=====================================

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    :target: https://www.python.org/downloads/

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    :target: https://github.com/fpaissan/micromind/blob/main/LICENSE

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    :target: https://pypi.org/project/micromind/

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 <https://pytorch.org>`_ 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.

Installation
------------

Using Pip
~~~~~~~~~

First of all, install `Python 3.8 or later <https://www.python.org>`_. Open a terminal and run:

.. code:: shell

    pip install micromind

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

.. code:: shell

    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 <https://micromind.readthedocs.org/en/latest/micromind.html#micromind.core.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.

.. code-block:: python

   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:

.. code-block:: python

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


Here is the link to the Python `file <https://github.com/micromind-toolkit/micromind/blob/mm_refactor/examples/mind.py>`_ inside our repository that illustrates how to use the MicroMind class.

.. toctree::
   :maxdepth: 2
   :caption: Contents:

   modules


Indices and tables
==================

* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`


.. toctree::
   :maxdepth: 2
   :caption: How To Contribute

   contribution