Welcome to micromind’s documentation!
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.
Installation
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.
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