Pretrained Networks from External Platforms

Import pretrained networks from external deep learning platforms

Import neural networks from TensorFlow™ 2, TensorFlow-Keras, PyTorch^®, the ONNX™ (Open Neural Network Exchange) model format, and Caffe. You can import networks interactively using the Deep Network Designer app or using command line functions. The app generates an import report which includes information about the import and if there are any required actions. For more information, see Pretrained Deep Neural Networks and Interoperability Between Deep Learning Toolbox, TensorFlow, PyTorch, and ONNX.

You must have support packages to run the import functions in Deep Learning Toolbox™. If the support package is not installed, each function provides a download link to the corresponding support package in the Add-On Explorer. A recommended practice is to download the support package to the default location for the version of MATLAB^® you are running. You can also directly download the support packages from the following links.

The importNetworkFromONNX function requires Deep Learning Toolbox Converter for ONNX Model Format. To download the support package, go to https://www.mathworks.com/matlabcentral/fileexchange/67296-deep-learning-toolbox-converter-for-onnx-model-format.
The importNetworkFromPyTorch function requires Deep Learning Toolbox Converter for PyTorch Models. To download the support package, go to https://www.mathworks.com/matlabcentral/fileexchange/111925-deep-learning-toolbox-converter-for-pytorch-models.
The importNetworkFromTensorFlow function requires Deep Learning Toolbox Converter for TensorFlow Models. To download the support package, go to https://www.mathworks.com/matlabcentral/fileexchange/64649-deep-learning-toolbox-converter-for-tensorflow-models.

Diagram showing the interoperability between Deep Learning Toolbox, TensorFlow, ONNX, and PyTorch.

Apps

Deep Network Designer

Design and visualize deep learning networks

Functions

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Network Import

TensorFlow Import

importNetworkFromTensorFlow Import TensorFlow network as MATLAB network (Since R2023b)

PyTorch Import

importNetworkFromPyTorch Import PyTorch network as MATLAB network (Since R2022b)

Caffe Import

`importCaffeNetwork`	Import pretrained convolutional neural network models from Caffe
`importCaffeLayers`	Import convolutional neural network layers from Caffe

ONNX Import

importNetworkFromONNX Import ONNX network as MATLAB network (Since R2023b)

Parameters Imported by `importONNXFunction`

`ONNXParameters`	Parameters of imported ONNX network for deep learning (Since R2020b)
`freezeParameters`	Convert learnable network parameters in `ONNXParameters` to nonlearnable (Since R2020b)
`unfreezeParameters`	Convert nonlearnable network parameters in `ONNXParameters` to learnable (Since R2020b)
`addParameter`	Add parameter to `ONNXParameters` object (Since R2020b)
`removeParameter`	Remove parameter from `ONNXParameters` object (Since R2020b)

Network Assembly

`dlnetwork`	Deep learning neural network
`functionLayer`	Function layer (Since R2021b)
`replaceLayer`	Replace layer in neural network
`addLayers`	Add layers to neural network
`removeLayers`	Remove layers from neural network

Topics

Import

Interoperability Between Deep Learning Toolbox, TensorFlow, PyTorch, and ONNX
Learn how to import networks from TensorFlow, PyTorch, and ONNX and use the imported networks for common Deep Learning Toolbox workflows. Learn how to export networks to TensorFlow and ONNX.
Tips on Importing Models from TensorFlow, PyTorch, and ONNX
Tips on importing Deep Learning Toolbox networks from TensorFlow, PyTorch, and ONNX.
Import PyTorch Model Using Deep Network Designer
This example shows how to import a PyTorch® model interactively by using the Deep Network Designer app. (Since R2023b)
Pretrained Deep Neural Networks
Learn how to download and use pretrained convolutional neural networks for classification, transfer learning and feature extraction.
Inference Comparison Between TensorFlow and Imported Networks for Image Classification
Perform prediction in TensorFlow with a pretrained network, import the network into MATLAB using importTensorFlowNetwork, and then compare inference results between TensorFlow and MATLAB networks.
Inference Comparison Between ONNX and Imported Networks for Image Classification
Perform prediction in ONNX with a pretrained network, import the network into MATLAB using importONNXNetwork, and then compare inference results between ONNX and MATLAB networks.
Classify Images in Simulink with Imported TensorFlow Network
Import a pretrained TensorFlow network using importTensorFlowNetwork, and then use the Predict block for image classification in Simulink^®.
Deploy Imported TensorFlow Model with MATLAB Compiler
Import third-party pretrained networks and deploy the networks using MATLAB Compiler™.
View Autogenerated Custom Layers Using Deep Network Designer
This example shows how to import a pretrained TensorFlow™ network and view the autogenerated layers in Deep Network Designer.
Verify Robustness of ONNX Network
This example shows how to verify the adversarial robustness of an imported ONNX™ deep neural network. (Since R2024a)

Python Coexecution

Classify Images Using TensorFlow Model Predict Block
Classify images using TensorFlow Model Predict block.
Classify Images Using ONNX Model Predict Block
Classify images using ONNX Model Predict block.
Classify Images Using PyTorch Model Predict Block
Classify images using PyTorch Model Predict block.
Predict Responses Using TensorFlow Model Predict Block
Predict Responses Using TensorFlow Model Predict block.
Predict Responses Using ONNX Model Predict Block
Predict Responses Using ONNX Model Predict block.
Predict Responses Using PyTorch Model Predict Block
Predict Responses Using PyTorch Model Predict block.
Predict Responses Using Custom Python Model in Simulink (Statistics and Machine Learning Toolbox)
This example shows how to use the Custom Python Model Predict (Statistics and Machine Learning Toolbox) block for prediction in Simulink®.

Custom Layers

Define Custom Deep Learning Layers
Learn how to define custom deep learning layers.

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