Image Classifier

Classify data using a trained deep learning neural network

Since R2020b

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Libraries:
Deep Learning Toolbox / Deep Neural Networks

Description

The Image Classifier block predicts class labels for the data at the input by using the trained network specified through the block parameter. This block allows loading of a pretrained network into the Simulink^® model from a MAT-file or from a MATLAB^® function.

Examples

Classify Images in Simulink Using GoogLeNet

Classify an image in Simulink® using the Image Classifier block. The example uses the pretrained deep convolutional neural network GoogLeNet to perform the classification.

• Open Script

Classify ECG Signals in Simulink Using Deep Learning

Use wavelet transforms and a deep learning network within a Simulink (R) model to classify ECG signals. This example uses the pretrained convolutional neural network from the Classify Time Series Using Wavelet Analysis and Deep Learning example of the Wavelet Toolbox™ to classify ECG signals based on images from the CWT of the time series data. For information on training, see Classify Time Series Using Wavelet Analysis and Deep Learning (Wavelet Toolbox).

• Open Script

Limitations

• The Image Classifier block does not support sequence networks and multiple input and multiple output networks (MIMO).

• The Image Classifier block does not support MAT-file logging.

Ports

Input

image — Image or feature data

numeric array

A h-by-w-by-c-by-N numeric array, where h, w, and c are the height, width, and number of channels of the images, respectively, and N is the number of images.

A N-by-numFeatures numeric array, where N is the number of observations and numFeatures is the number of features of the input data.

If the array contains NaNs, then they are propagated through the network.

Output

ypred — Predicted class labels

enumerated

Predicted class labels with the highest score, returned as a N-by-1 enumerated vector of labels, where N is the number of observations.

scores — Predicted class scores

matrix

Predicted scores, returned as a K-by-N matrix, where K is the number of classes, and N is the number of observations.

labels — Class labels for predicted scores

matrix

Labels associated with the predicted scores, returned as a N-by-K matrix, where N is the number of observations, and K is the number of classes.

Parameters

Network — Source for trained network

Network from MAT-file (default) | Network from MATLAB function

Specify the source for the trained network. Select one of the following:

• Network from MAT-file— Import a trained network from a MAT-file containing a dlnetwork object.

• Network from MATLAB function— Import a pretrained network from a MATLAB function. For example, to use a pretrained GoogLeNet, create a function pretrainedGoogLeNet in a MATLAB M-file, and then import this function.

  function net = pretrainedGoogLeNet
    net = imagePretrainedNetwork("googlenet");
  end

Programmatic Use

Block Parameter: Network

Type: character vector, string

Values: 'Network from MAT-file' | 'Network from MATLAB function'

Default: 'Network from MAT-file'

File path — MAT-file containing trained network

untitled.mat (default) | MAT-file path or name

This parameter specifies the name of the MAT-file that contains the trained deep learning network to load. If the file is not on the MATLAB path, use the Browse button to locate the file.

Dependencies

To enable this parameter, set the Network parameter to Network from MAT-file.

Programmatic Use

Block Parameter: NetworkFilePath

Type: character vector, string

Values: MAT-file path or name

Default: 'untitled.mat'

MATLAB function — MATLAB function name

squeezenet (default) | MATLAB function name

This parameter specifies the name of the MATLAB function for the pretrained deep learning network. For example, to use a pretrained GoogLeNet, create a function pretrainedGoogLeNet in a MATLAB M-file, and then import this function.

function net = pretrainedGoogLeNet
  net = imagePretrainedNetwork("googlenet");
end

Dependencies

To enable this parameter, set the Network parameter to Network from MATLAB function.

Programmatic Use

Block Parameter: NetworkFunction

Type: character vector, string

Values: MATLAB function name

Default: 'squeezenet'

Mini-batch size — Size of mini-batches

128 (default) | positive integer

Size of mini-batches to use for prediction, specified as a positive integer. Larger mini-batch sizes require more memory, but can lead to faster predictions.

Programmatic Use

Block Parameter: MiniBatchSize

Type: character vector, string

Values: positive integer

Default: '128'

Resize input — Resize input dimensions

on (default) | off

Resize the data at the input port to the input size of the network.

Programmatic Use

Block Parameter: ResizeInput

Type: character vector, string

Values: 'off' | 'on'

Default: 'on'

Classification — Output predicted label with highest score

on (default) | off

Enable output port ypred that outputs the label with the highest score.

Programmatic Use

Block Parameter: Classification

Type: character vector, string

Values: 'off' | 'on'

Default: 'on'

Predictions — Output all scores and associated labels

off (default) | on

Enable output ports scores and labels that output all predicted scores and associated class labels.

Programmatic Use

Block Parameter: Predictions

Type: character vector, string

Values: 'off' | 'on'

Default: 'off'

Class names workspace variable — Workspace variable containing class names of the network output

classNames (default) | categorical vector variable | string array variable | cell array of character vectors variable name

Variable containing class names, specified as a categorical vector, a string array, or a cell array of character vectors.

The output size of the network must match the number of classes.

Dependencies

To enable this parameter, set the Network parameter to Network from MAT-file to import a trained dlnetwork object from a MAT-file.

Programmatic Use

Block Parameter: classNames

Type:variable name of a categorical vector, a string array, or a cell array of character vectors.

Values: Name of a variable containing class names, specified as a categorical vector, a string array, or a cell array of character vectors.

Default: The workspace variable classNames.

Tips

• You can accelerate your simulations with code generation taking advantage of the Intel^® MKL-DNN library. For more details, see Acceleration for Simulink Deep Learning Models.

Extended Capabilities

C/C++ Code Generation

Generate C and C++ code using Simulink® Coder™.

Usage notes and limitations:

• To generate generic C code that does not depend on third-party libraries, in the Configuration Parameters > Code Generation general category, set the Language parameter to C.

• To generate C++ code, in the Configuration Parameters > Code Generation general category, set the Language parameter to C++. To specify the target library for code generation, in the Code Generation > Interface category, set the Target Library parameter. Setting this parameter to None generates generic C++ code that does not depend on third-party libraries.

• For a list of networks and layers supported for code generation, see Networks and Layers Supported for Code Generation (MATLAB Coder).

GPU Code Generation

Generate CUDA® code for NVIDIA® GPUs using GPU Coder™.

Usage notes and limitations:

• The Language parameter in the Configuration Parameters > Code Generation general category must be set to C++.

• For a list of networks and layers supported for CUDA^® code generation, see Supported Networks, Layers, and Classes (GPU Coder).

• To learn more about generating code for Simulink models containing the Image Classifier block, see Code Generation for a Deep Learning Simulink Model to Classify ECG Signals (GPU Coder).

Version History

Introduced in R2020b

R2024a: SeriesNetwork and DAGNetwork are not recommended

Starting in R2024a, the SeriesNetwork and DAGNetwork objects are not recommended. This recommendation means that SeriesNetwork and DAGNetwork inputs to the Image Classifier block are not recommended. Use the dlnetwork objects instead. dlnetwork objects have these advantages:

• dlnetwork objects are a unified data type that supports network building, prediction, built-in training, visualization, compression, verification, and custom training loops.

• dlnetwork objects support a wider range of network architectures that you can create or import from external platforms.

• The trainnet function supports dlnetwork objects, which enables you to easily specify loss functions. You can select from built-in loss functions or specify a custom loss function.

• Training and prediction with dlnetwork objects is typically faster than LayerGraph and trainNetwork workflows.

Simulink block models with dlnetwork objects behave differently. The predicted scores are returned as a K-by-N matrix, where K is the number of classes, and N is the number of observations. If you have an existing Simulink block model with a SeriesNetwork or DAGNetwork object, follow these steps to use a dlnetwork object instead:

1. Convert the SeriesNetwork or DAGNetwork object to a dlnetwork using the dag2dlnetwork function.

2. Define a workspace variable containing class names of the network output corresponding to the block parameter Class names workspace variable.

3. Transpose the predicted scores using a transpose block to an N-by-K array, where N is the number of observations, and K is the number of classes.