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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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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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After training, the new model is automatically selected and its preview is shown. A different model can be selected from the model list using the radio button and then clicking 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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