Import pretrained ONNX network


Import a pretrained network from ONNX™ (Open Neural Network Exchange).

net = importONNXNetwork(modelfile,'OutputLayerType',outputtype) imports a pretrained network from the ONNX (Open Neural Network Exchange) file modelfile and specifies the output layer type of the imported network.

This function requires the Deep Learning Toolbox™ Converter for ONNX Model Format support package. If this support package is not installed, then the function provides a download link.


net = importONNXNetwork(modelfile,'OutputLayerType',outputtype,'Classes',classes) additionally specifies the classes for a classification network.


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Download and install the Deep Learning Toolbox Converter for ONNX Model Format support package.

Type importONNXNetwork at the command line.


If Deep Learning Toolbox Converter for ONNX Model Format is not installed, then the function provides a link to the required support package in the Add-On Explorer. To install the support package, click the link, and then click Install. Check that the installation is successful by importing the network from the model file 'cifarResNet.onnx' at the command line. If the support package is installed, then the function returns a DAGNetwork object.

modelfile = 'cifarResNet.onnx';
classes = ["airplane" "automobile" "bird" "cat" "dee" "dog" "frog" "horse" "ship" "truck"];
net = importONNXNetwork(modelfile,'OutputLayerType','classification','Classes',classes)
net = 

  DAGNetwork with properties:

         Layers: [77×1 nnet.cnn.layer.Layer]
    Connections: [85×2 table]

Import a residual neural network trained on the CIFAR-10 data set. Specify the file containing the ONNX network, its output type, and its output classes.

modelfile = 'cifarResNet.onnx';
classes = ["airplane" "automobile" "bird" "cat" "deer" "dog" "frog" "horse" "ship" "truck"];
net = importONNXNetwork(modelfile, ...
    'OutputLayerType','classification', ...
net = 
  DAGNetwork with properties:

         Layers: [77×1 nnet.cnn.layer.Layer]
    Connections: [85×2 table]

Analyze the imported network.


Input Arguments

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Name of ONNX model file containing the network, specified as a character vector or a string scalar. The file must be in the current folder, in a folder on the MATLAB® path, or you must include a full or relative path to the file.

Example: 'cifarResNet.onnx'

Type of the output layer that the function appends to the end of the imported network, specified as 'classification', 'regression', or 'pixelclassification'. Using 'pixelclassification' appends a pixelClassificationLayer object (requires Computer Vision Toolbox™).

Example: 'regression'

Classes of the output layer, specified as a categorical vector, string array, cell array of character vectors, or 'auto'. If Classes is 'auto', then the software sets the classes to categorical(1:N), where N is the number of classes. If you specify a string array or cell array of character vectors str, then the software sets the classes of the output layer to categorical(str,str).

Data Types: char | categorical | string | cell

Output Arguments

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Pretrained network, returned as DAGNetwork object.


  • If the ONNX network contains a layer that Deep Learning Toolbox Converter for ONNX Model Format does not support, then the function returns an error message. In this case, you can still use importONNXLayers to import the network architecture and weights.

    importONNXNetwork supports the following ONNX operators, with some limitations:

    • Add

    • AveragePool

    • BatchNormalization

    • Clip

    • Concat

    • Conv

    • ConvTranspose

    • Div

    • Dropout

    • Flatten

    • Gemm

    • GlobalAveragePool

    • Identity

    • LRN

    • LSTM

    • LeakyRelu

    • MaxPool

    • MatMul

    • Mul

    • PRelu

    • Relu

    • Reshape

    • Sigmoid

    • Softmax

    • Sub

    • Sum

    • Tanh

    • ImageScaler

  • To use a pretrained network for prediction or transfer learning on new images, you must preprocess your images in the same way the images that were used to train the imported model were preprocessed. Most common preprocessing steps are resizing images, subtracting image average values, and converting the images from BGR images to RGB.

    • To resize images, use imresize. For example, imresize(image,[227,227,3]).

    • To convert images from RGB to BGR format, use flip. For example, flip(image,3).

    For more information on preprocessing images for training and prediction, see Preprocess Images for Deep Learning.

Compatibility Considerations

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Not recommended starting in R2018b


[1] Open Neural Network Exchange.

[2] ONNX.

Introduced in R2018a