Neural network layer used to output fixed-size feature maps for rectangular ROIs
An ROI max pooling layer outputs fixed size feature maps for every rectangular ROI within the input feature map. Use this layer to create a Fast or Faster R-CNN object detection network.
Given an input feature map of size [H
W
C
N], where C is the number of channels and
N is the number of observations, the output feature map size is
[height
width
C
sum
(M)], where height and
width are the output size. M is a vector of length
N and M(i) is the number of ROIs
associated with the i-th input feature map.
There are two inputs to this layer:
'in'
— The input feature map that will be cropped
'roi'
— A list of ROIs to pool
Use the input names when connecting or disconnecting the ROI max pooling layer to other
layers using connectLayers
or
disconnectLayers
(requires Deep Learning
Toolbox™).
layer = roiMaxPooling2dLayer(outputSize)
creates a max pooling
layer for ROIs and sets the OutputSize
property.
connectLayers
| layerGraph
| maxPooling2dLayer
| removeLayers
| roiInputLayer
| trainFastRCNNObjectDetector
| trainFasterRCNNObjectDetector