roiInputLayer

ROI input layer for Fast R-CNN

Since R2018b

Description

An ROI input layer inputs images to a Fast R-CNN object detection network.

Creation

Syntax

layer = roiInputLayer

layer = roiInputLayer('Name',Name)

Description

layer = roiInputLayer creates an ROI input layer.

layer = roiInputLayer('Name',Name) creates an ROI input 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 trainNetwork (Deep Learning Toolbox), assembleNetwork (Deep Learning Toolbox), layerGraph (Deep Learning Toolbox), and dlnetwork (Deep Learning Toolbox) functions automatically assign names to layers with the name ''.

Data Types: char | string

NumOutputs — Number of outputs

1 (default)

This property is read-only.

Number of outputs of the layer. This layer has a single output only.

Data Types: double

OutputNames — Output names

{'out'} (default)

This property is read-only.

Output names of the layer. This layer has a single output only.

Data Types: cell

Examples

Connect ROI Input Layer to ROI Max Pooling Layer

Create an ROI input layer.

roiInput = roiInputLayer('Name','roi_input');

Create an ROI max pooling layer with output size [4 4].

outputSize = [4 4];
roiPool = roiMaxPooling2dLayer(outputSize,'Name','roi_pool');

Add the layers to a LayerGraph.

lgraph = layerGraph;
lgraph = addLayers(lgraph,roiInput);
lgraph = addLayers(lgraph,roiPool);

Specify that the output of the ROI input layer is the 'roi' input of the ROI max pooling layer.

lgraph = connectLayers(lgraph,'roi_input','roi_pool/roi');
plot(lgraph)

Version History

Introduced in R2018b