MobileNet-3D V1 is characterized by depthwise separable convolutions. The full convolutional operator is split into two layers. The first layer is called a depthwise convolution, which performs lightweight filtering by applying a single convolutional filter per input channel. This is realized by having a "ChannelGroups" setting equal to the number of input channels:

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The second layer is a 1⨯1⨯1 convolution called a pointwise convolution, which is responsible for building new features through computing linear combinations of the input channels:

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Remove the last layers of the trained net so that the net produces a vector representation of an image:

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Get a set of videos:

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Visualize the features of a set of videos:

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Use the pre-trained model to build a classifier for telling apart images from two action classes not present in the dataset. Create a test set and a training set:

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Remove the last layers from the pre-trained net:

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Create a new net composed of the pre-trained net followed by a linear layer and a softmax layer:

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Train on the dataset, freezing all the weights except for those in the "Linear" new layer (use TargetDevice -> "GPU" for training on a GPU):

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Perfect accuracy is obtained on the test set:

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Inspect the number of parameters of all arrays in the net:

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Obtain the total number of parameters:

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Obtain the layer type counts:

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Display the summary graphic:

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Export the net to the ONNX format:

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Get the size of the ONNX file:

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Check some metadata of the ONNX model:

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Import the model back into the Wolfram Language. However, the NetEncoder and NetDecoder will be absent because they are not supported by ONNX:

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