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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.
The Unsupervised module available is /wiki/spaces/CONFLUENCE/pages/2982052 Artificial Intelligence Modules,where the only required end-user input is to assign at least 1 image to as ‘OK’ class.
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The Training mode for PEKAT are available for the following Modules:
/wiki/spaces/CONFLUENCE/pages/2982052 /wiki/spaces/CONFLUENCE/pages/7602237 /wiki/spaces/CONFLUENCE/pages/7602198 /wiki/spaces/CONFLUENCE/pages/7634958Anomaly of Surface
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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Training Graph - Loss Function Chart
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:
/wiki/spaces/CONFLUENCE/pages/7602237
/wiki/spaces/CONFLUENCE/pages/7602198
/wiki/spaces/CONFLUENCE/pages/7634958 Classifier
Surface Detection (Chart not available in 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 /wiki/spaces/CONFLUENCE/pages/2982052, as the Fast Training function for this module uses by default, the mininum value for Anomaly of Surface, since by default we have the following:
Fast Training - Minimum amount of training cycle pre-determined by the software
Deep Training - End-user defines the number of training cycles.
As for Deep Training functiondescribed above, for both functions, the end-user defines the number of training cyclesepochs is determined before training starts, therefore loss function graph is not available.
Avoiding Overfitting
We would like to highlight that our algorithm is designed to prevent overfitting.
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.
If training is not interrupted when the graph is not decreasing anymore, the model will stop learning, meaning that you will not achieve better results. However, this will not cause weakness to the model, in other words, this will not increase the presence of false positives - It will just not provide better results.
Therefore, if the graph is not deacreasing anymore, it means the model stopped from improving.
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Extend model function is only available for the modules /wiki/spaces/CONFLUENCE/pages/7602237 & /wiki/spaces/CONFLUENCE/pages/7602198 Classifier& Detector.
This function allows you perform further training on an already trained model.
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For detailed explaination regarding argumentation (training parameters), visit the following article /wiki/spaces/CONFLUENCE/pages/35422286 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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