Image Classifier
Classify data using a trained deep learning neural network
- Library:
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.
Note
The Image Classifier block does not support sequence networks and multiple input and multiple output networks (MIMO).
Ports
Input
image — Image 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. 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 | squeezenet
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 aSeriesNetworkor aDAGNetworkobject.Network from MATLAB function— Import a pretrained network from a MATLAB function. For example, by using thegooglenetfunction.
File path — MAT-file containing trained network
untitled.mat (default) | MAT-file 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.
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.
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.
Resize input — Resize the input dimensions
on (default) | off
Resize the data at the input port to the input size of the network.
Classification — Output predicted label with highest score
on (default) | off
Enable output port ypred that outputs the label with the
highest score.
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.
Extended Capabilities
C/C++ Code Generation
Generate C and C++ code using Simulink® Coder™.
Usage notes and limitations:
The Language parameter in the Configuration Parameters > Code Generation general category must be set to
C++.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 C++ 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 and Layers (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).