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The algorithm (machine learning) learns from the annotated data , which was used from the Training set of images, producing a Prediction Model.
The set of Testing Images is then evaluated using the Prediction Model, which is finally can be used to provide a first set of statistics. The statistics can be used to help us verify if the Bad Parts from Test Images are indeed identified by the Prediction Model.
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PEKAT VISION software offers training of models using both Supervised and Unsupervised methods. Detailed modules information can be found within the article Modules.
The Unsupervised module available is Anomaly Detector,where the only required end-user input is to assign at least 10 images to OK class.
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The Training mode for PEKAT is available for the following AI Modules:
Below you can find a Training Overview FluxogramFlowchart, which demonstrates the overall training steps. However, for detailed training information about a specific module, please access the desired module’s page.
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Training Graph - Loss Function Chart
The Training begins after clicking on Start Training button, from that point a dialog window will display a training chart only for the following modules:
Based on the chart displayed, its graph gives you indication for the ideal moment to stop the training model from running.
The graph should gradually decrease over the training duration, because as the graph decreases, the model is improving.
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As shown below, the graph decreased over time at first and then near the end it had not been decreasing for a while, therefore, the end-user should click on Finish Training button in order to evaluate the results.
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Please note the chart is not available for the Module Anomaly Detector, since by default we have the following:
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As described above, for both functions, the number of epochs is determined before training starts, therefore loss function graph is not available.
For OCR module the number of epochs is also set before starting the training.
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Extend model function is only available for the modules Classifier, Detector and OCR.
This function allows you perform further training on an already trained model.
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For detailed explanation regarding augmentation, visit the article Augmentation Glossary.
Model Validation
By default, each training module is inactive and a model must be selected to activate (validate) it.
The model can be selected by using the radio button and then you can click the Preview button to check the model’s results, as shown on in the picture below. When a model is selected, the statistics will then be based on the selected model.
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Information on training process and conditions used for a particular model are available by clicking on the info icon in the list of models, as shown on in the picture below.
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