This example creates the SegNet network with weights initialized from the VGG-16 network. The dataset provides pixel-level labels for 32 semantic classes including car, pedestrian, and road. This dataset is a collection of images containing street-level views obtained while driving. This example uses the CamVid dataset from the University of Cambridge for training. The training procedure shown here can be applied to those networks too. Other types networks for semantic segmentation include fully convolutional networks (FCN) and U-Net. To illustrate the training procedure, this example trains SegNet, one type of convolutional neural network (CNN) designed for semantic image segmentation. To learn more, see Semantic Segmentation Basics. Applications for semantic segmentation include road segmentation for autonomous driving and cancer cell segmentation for medical diagnosis. Python | Difference between two dates (in minutes) using datetime.Today I want to show you a documentation example that shows how to train a semantic segmentation network using deep learning and the Computer Vision System Toolbox.Ī semantic segmentation network classifies every pixel in an image, resulting in an image that is segmented by class.Python program to convert a list to string.
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