TrainImageContentDetector

TrainImageContentDetector[{img₁{bbox₁class₁,…},…}]

trains a ContentDetectorFunction[…] based on the examples given.

Details and Options

TrainImageContentDetector attempts to train an object detector using the provided training data.
Object detection is a computer vision technique used for recognizing subimages in an image.

TrainImageContentDetector returns a ContentDetectorFunction[…] that can then be applied to new input data.
The bounding boxes bbox_i should be given as Rectangle objects.
The classes class_i can be arbitrary expressions.
TrainImageContentDetector[…][image] can be used to detect objects in image and returns the result as a list of associations of the form <|"Image"img,"Class"class|>.
TrainImageContentDetector supports the following options:

PerformanceGoal	Automatic	favor settings with specific advantages
ProgressReporting	Automatic	how to report progress during training
RandomSeeding	1234	how to seed pseudorandom generators internally
TargetDevice	"CPU"	the target device on which to perform training
TimeGoal	Automatic	number of seconds to train for
ValidationSet	None	the set of data on which to evaluate the model during training

open allclose all

Train a basic object detector:

Apply the detector on a new image:

Highlight the detection on the input image:

Use PerformanceGoal"Quality" to emphasize the quality of the result:

Use PerformanceGoal"Speed" to emphasize the speed of computation:

By default, progress is reported in a dynamic panel:

Use ProgressReportingFalse to avoid displaying the progress panel:

Train a detector using the default system GPU, if available:

If a compatible GPU is not available, a message is issued:

The training time can be influenced by several factors, such as the number of examples and classes:

Use TimeGoal to specify a target time for the training:

By default, only cross-validation is performed on the detector:

Use ValidationSet to provide separate validation examples:

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