rpnSoftmaxLayer

(Not recommended) Softmax layer for region proposal network (RPN)

RPNSoftmaxLayer is not recommended. Instead, use a different type of object detector, such as a yoloxObjectDetector or yolov4ObjectDetector detector. For more information, see Version History.

Description

A region proposal network (RPN) softmax layer applies a softmax activation function to the input. Use this layer to create a Faster R-CNN object detection network.

Creation

Syntax

layer = rpnSoftmaxLayer

layer = rpnSoftmaxLayer('Name',Name)

Description

layer = rpnSoftmaxLayer creates a softmax layer for a Faster R-CNN object detection network.

layer = rpnSoftmaxLayer('Name',Name) creates a softmax layer and sets the optional Name property.

Properties

Name — Layer name

"" (default) | character vector | string scalar

Layer name, specified as a character vector or string scalar. For Layer array input, the trainnet (Deep Learning Toolbox) and dlnetwork (Deep Learning Toolbox) functions automatically assign names to layers with the name "".

The RPNSoftmaxLayer object stores this property as a character vector.

Data Types: char | string

NumInputs — Number of inputs

1 (default)

This property is read-only.

Number of inputs to the layer, returned as 1. This layer accepts a single input only.

Data Types: double

InputNames — Input names

{'in'} (default)

This property is read-only.

Input names, returned as {'in'}. This layer accepts a single input only.

Data Types: cell

NumOutputs — Number of outputs

1 (default)

This property is read-only.

Number of outputs from the layer, returned as 1. This layer has a single output only.

Data Types: double

OutputNames — Output names

{'out'} (default)

This property is read-only.

Output names, returned as {'out'}. This layer has a single output only.

Data Types: cell

Examples

Create Classification Branch of RPN

Create an RPN softmax layer with the name 'rpn_softmax'.

rpnSoftmax = rpnSoftmaxLayer('Name','rpn_softmax')

rpnSoftmax = 
  RPNSoftmaxLayer with properties:

    Name: 'rpn_softmax'

Create an RPN classification layer with the name 'rpn_cls'.

rpnClassification = rpnClassificationLayer('Name','rpn_cls')

rpnClassification = 
  RPNClassificationLayer with properties:

    Name: 'rpn_cls'

Add the RPN softmax and RPN classification layers to a Layer array, to form the classification branch of an RPN.

numAnchors = 3;
rpnClassLayers = [
    convolution2dLayer(1,numAnchors*2,'Name','conv1x1_box_cls')
    rpnSoftmax
    rpnClassification
    ]

rpnClassLayers = 
  3x1 Layer array with layers:

     1   'conv1x1_box_cls'   2-D Convolution             6 1x1 convolutions with stride [1  1] and padding [0  0  0  0]
     2   'rpn_softmax'       RPN Softmax                 rpn softmax
     3   'rpn_cls'           RPN Classification Output   cross-entropy loss with 'object' and 'background' classes

Version History

Introduced in R2018b

R2024b: Not Recommended

Starting in R2024b, R-CNN object detectors are no longer recommended. Instead, use a different type of object detector, such as a yoloxObjectDetector or yolov4ObjectDetector object. These object detectors are faster than R-CNN object detectors. For more information, see Choose an Object Detector.