![]() ![]() Maximum validation sample size per class -sample.mv int Default value: 1000 ![]() If equal to -1, then the maximal size of the available training sample list per class will be equal to the surface area of the smallest class multiplied by the training sample ratio. Maximum size per class (in pixels) of the training sample list (default = 1000) (no limit = -1). Maximum training sample size per class -sample.mt int Default value: 1000 This group of parameters allows you to set training and validation sample lists parameters. Training and validation samples parameters ¶ If activated, the application will try to clean all temporary files it created Temporary files cleaning -cleanup bool Default value: true Output file containing the confusion matrix or contingency table (.csv format).The contingency table is output when we unsupervised algorithms is used otherwise the confusion matrix is output. Output confusion matrix or contingency table -io.confmatout filename Output file containing the model estimated (.txt format). XML file containing mean and variance of each feature. Input XML image statistics file -io.imstat filename Validation Vector Data List -io.valid vectorfile1 vectorfile2.Ī list of vector data to select the validation samples. MandatoryĪ list of vector data to select the training samples. ![]() Input Vector Data List -io.vd vectorfile1 vectorfile2. This group of parameters allows setting input and output data. The output of this application is a text model file, whose format corresponds to the ML model type chosen. This application is based on LibSVM, OpenCV Machine Learning, and Shark ML. In the header of the optional confusion matrix output file, the validation (reference) and predicted (produced) class labels are ordered according to the rows/columns of the confusion matrix. In the validation process, the confusion matrix is organized the following way: Two parameters manage the size of the training and validation sets per class and per image. One parameter controls the ratio between the number of samples in training and validation sets. Training and validation sample lists are built such that each class is equally represented in both lists. The name of this field can be set using the Class label field parameter. The training vector data must contain polygons with a positive integer field representing the class label. Samples are composed of pixel values in each band optionally centered and reduced using an XML statistics file produced by the ComputeImagesStatistics application. Train a classifier from multiple pairs of images and training vector data.
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