Image Classifier

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
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 NaN
s, 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 aSeriesNetwork
orDAGNetwork
object.Network from MATLAB function
— Import a pretrained network from a MATLAB function. For example, by using thegooglenet
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 toNone
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
See Also
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