9/3/2023 0 Comments Cmap matlab![]() However, the classes in CamVid are imbalanced, which is a common issue in automotive data-sets of street scenes. Ideally, all classes would have an equal number of observations. The imageDatastore enables you to efficiently load a large collection of images on disk. Use imageDatastore to load CamVid images. To use the file you downloaded from the web, change the outputFolder variable above to the location of the downloaded file. Alternatively, you can use your web browser to first download the dataset to your local disk. The commands used above block MATLAB until the download is complete. Note: Download time of the data depends on your Internet connection. Unzip(imagesZip, fullfile(outputFolder, 'images')) Unzip(labelsZip, fullfile(outputFolder, 'labels')) ĭisp( 'Downloading 557 MB CamVid dataset images.') ![]() If ~exist(labelsZip, 'file') || ~exist(imagesZip, 'file')ĭisp( 'Downloading 16 MB CamVid dataset labels.') ImagesZip = fullfile(outputFolder, 'images.zip') LabelsZip = fullfile(outputFolder, 'labels.zip') OutputFolder = fullfile(tempdir, 'CamVid') For information about the supported compute capabilities, see GPU Computing Requirements (Parallel Computing Toolbox). Use of a GPU requires Parallel Computing Toolbox™. The dataset provides pixel-level labels for 32 semantic classes including car, pedestrian, and road.Ī CUDA-capable NVIDIA™ GPU is highly recommended for running this example. This dataset is a collection of images containing street-level views obtained while driving. To illustrate the training procedure, this example uses the CamVid dataset from the University of Cambridge. The training procedure shown here can be applied to other types of semantic segmentation networks. Then, you can optionally download a dataset to train Deeplab v3 network using transfer learning. Other types of networks for semantic segmentation include fully convolutional networks (FCN), SegNet, and U-Net. This example first shows you how to segment an image using a pretrained Deeplab v3+ network, which is one type of convolutional neural network (CNN) designed for semantic image segmentation. To learn more, see Getting Started with Semantic Segmentation Using Deep Learning (Computer Vision Toolbox). Applications for semantic segmentation include road segmentation for autonomous driving and cancer cell segmentation for medical diagnosis.
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