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 a string scalar. For Layer array input, the trainnet (Deep Learning Toolbox) and dlnetwork (Deep Learning Toolbox) functions automatically assign names to unnamed layers.

The RPNSoftmaxLayer object stores this property as a character vector.

Data Types: char | string

NumInputs — Number of inputs

Read-only: 1 (default)

This property is read-only.

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

Data Types: double

InputNames — Input names

Read-only: {'in'} (default)

This property is read-only.

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

Data Types: cell

NumOutputs — Number of outputs

Read-only: 1 (default)

This property is read-only.

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

Data Types: double

OutputNames — Output names

Read-only: {'out'} (default)

This property is read-only.

Output names, stored 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 = 
  3×1 Layer array with layers:

     1   'conv1x1_box_cls'   2-D Convolution             6 1×1 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.