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The algorithm (machine learning) learns from the annotaded annotated data, which was used from the Traning set of images.
The set of Testing Images are then used to create a Prediction Model, which is finally used to provide a the first set of statistics. The statistics can be used to verify if the Bad Parts from Test Images are indeed indentified identified by the Prediction Model.
On the other hand, for the unsupervised model, unannotated (unlabeled) data is provided and the algorithm tries to make sense of by extracting features and patterns on its own.
PEKAT VISION software run runs training models using both Supervised and Unsupervised methods.
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The Training mode for PEKAT are available for the following Modules:
Below you can find a Training Overview Fluxogram, which demonstrates the overall training steps. However, for detailed training information on a specific module each module, please access the desired module’s page.
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The Training Model begins after clicking on ‘Start Training’ button, from that point a dialog window will display a training chart only for the following modules:
Surface Detection (Chart not available for option Type 2 - Experimental - The number of tranining cycles is defined by end-user)
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Preferably when the chart is not decreasing anymore (sideways graph)
Ideally, if the graph drops below at least one of the green lines
As shown below, the graph decreased over time and it had been already below one the green lines for a while, therefore, the end-user should click on 'Finish Training' button in order to evaulate evaluate the results.
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Please note the chart is not available for the Module Anomaly of Surface, since by default we have the following:
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Another reason for that is due to the fact that Machine Learning is applied initially to the Trainning Training Images set, therefore, while performing deep training, the Error Loss Function graph is displayed, in relation to the Traning Images set.
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For detailed explaination regarding argumentation (training parameters), visit the following article Augmentation Glossary.
Model Validation
By default, each training module is inactive and a model must be selected to activate (validate) it.
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