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 N-by-K matrix, where N is the number of observations, and K is the number of classes.

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 SeriesNetwork or DAGNetwork object.

• Network from MATLAB function— Import a pretrained network from a MATLAB function. For example, by using the googlenet function.

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, use googlenet function to import the pretrained GoogLeNet model.

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'

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 ERT-based targets, the Support: variable-size signals parameter in the Code Generation> Interface pane must be enabled.

• 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