Create fully convolutional network layers for semantic segmentation
returns a fully convolutional network (FCN), configured as FCN 8s, for semantic
segmentation. The FCN is preinitialized using layers and weights from the VGG-16
lgraph = fcnLayers(
fcnLayers includes a
pixelClassificationLayer to predict the categorical label for every
pixel in an input image. The pixel classification layer only supports RGB
This function requires the Deep Learning Toolbox™
Model for VGG-16 Network support package. If this support
package is not installed, then the
vgg16 (Deep Learning Toolbox) function provides a download link.
Define the image size and number of classes, then create the network.
imageSize = [480 640]; numClasses = 5; lgraph = fcnLayers(imageSize,numClasses)
Display the network.
Create a FCN 16s.
imageSize = [480 640]; numClasses = 5; lgraph = fcnLayers(imageSize,numClasses,'Type','16s')
Display the network.
imageSize— Network input image size
Network input image size, specified as a 2-element vector in the format [height, width]. The minimum image size is [224 224] because an FCN is based on the VGG-16 network.
numClasses— Number of classes
Number of classes in the semantic segmentation, specified as an integer greater than 1.
type— Type of FCN model
Type of FCN model, specified as one of the following:
Upsamples the final feature map by a factor of 32. This option provides coarse segmentation with a lower computational cost.
Upsamples the final feature map by a factor of 16 after fusing the feature map from the fourth pooling layer. This additional information from earlier layers provides medium-grain segmentation at the cost of additional computation.
Upsamples the final feature map by a factor of 8 after fusing feature maps from the third and fourth max pooling layers. This additional information from earlier layers provides finer-grain segmentation at the cost of additional computation.
Layers that represent the FCN network architecture, returned as a
layerGraph (Deep Learning Toolbox) object.
All transposed convolution layers are initialized using bilinear interpolation weights. All transposed convolution layer bias terms are fixed to zero.
 Long, J., E. Shelhamer, and T. Darrell. "Fully Convolutional Networks for Semantic Segmentation." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 3431–3440.
trainNetwork(Deep Learning Toolbox) |