Trains a custom raster object detection model based on the positive and negative samples.

Typical Uses

Trains a custom raster object detection model based on the positive and negative datasets. The resulting model file can be used to detect the desired object using RasterObjectDetector. For convenience, you may get the input datasets from RasterObjectDetectorSamplePreparer that takes multiple positive samples and a number of negative samples for preparation. Or you may generate artificial samples using RasterObjectDetectorSampleGenerator (note that artificially generated samples usually perform worse than hand-picked ones)

Note that the transformer calls an external (opencv_traincascade) process to perform the model training. At the moment, if translation is suspended or stopped, the opencv_traincascade process is going to remain running and will need to be killed manually.

Input Ports

Output Ports


Input Samples

Training Parameters



Boosted Classifier Parameters

Editing Transformer Parameters

Using a set of menu options, transformer parameters can be assigned by referencing other elements in the workspace. More advanced functions, such as an advanced editor and an arithmetic editor, are also available in some transformers. To access a menu of these options, click beside the applicable parameter. For more information, see Transformer Parameter Menu Options.

Defining Values

There are several ways to define a value for use in a Transformer. The simplest is to simply type in a value or string, which can include functions of various types such as attribute references, math and string functions, and workspace parameters. There are a number of tools and shortcuts that can assist in constructing values, generally available from the drop-down context menu adjacent to the value field.

Dialog Options - Tables

Transformers with table-style parameters have additional tools for populating and manipulating values.

FME Community

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