fcnLayers
(To be removed) Create fully convolutional network layer graph for semantic segmentation
fcnLayers
will be removed in a future release. Create an FCN
network using a dlnetwork
(Deep Learning Toolbox) object instead. For more
information, see Version History.
Description
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
network.lgraph
= fcnLayers(imageSize
,numClasses
)
fcnLayers
includes a pixelClassificationLayer
to predict the categorical label for every
pixel in an input image. The pixel classification layer only supports RGB
images.
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.
returns an FCN configured as a type specified by lgraph
= fcnLayers(imageSize
,numClasses
,'Type',type
)type
.
Examples
Input Arguments
Output Arguments
Tips
Networks produced by
fcnLayers
support GPU code generation for deep learning once they are trained withtrainNetwork
(Deep Learning Toolbox). See Code Generation (Deep Learning Toolbox) for details and examples.
References
[1] 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.
Version History
Introduced in R2017bSee Also
dlnetwork
(Deep Learning Toolbox) | trainnet
(Deep Learning Toolbox) | semanticseg
| evaluateSemanticSegmentation
Topics
- Getting Started with Semantic Segmentation Using Deep Learning
- Deep Learning in MATLAB (Deep Learning Toolbox)