Resource retrieval

Get the pre-trained net:

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Basic usage

Classify an image:

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The prediction is an Entity object, which can be queried:

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Get a list of available properties of the predicted Entity:

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Obtain the probabilities of the 10 most likely entities predicted by the net:

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An object outside the list of the ImageNet classes will be misidentified:

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Obtain the list of names of all available classes:

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Feature extraction

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

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Get a set of images:

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Visualize the features of a set of images:

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Visualize convolutional weights

Extract the weights of the first convolutional layer in the trained net:

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Show the dimensions of the weights:

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Visualize the weights as a list of 24 images of size 3⨯3:

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Transfer learning

Use the pre-trained model to build a classifier for telling apart images of sunflowers and roses. 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 "linearNew" layer (use TargetDevice -> "GPU" for training on a GPU):

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Accuracy obtained on the test set:

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Net information

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 to ONNX

Export the net to the ONNX format:

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Get the size of the ONNX file:

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The size is similar to the byte count of the resource object:

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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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