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The Unsupervised module available is Anomaly Detector https://pekatvision.atlassian.net/wiki/x/p4abKQ,where the only required end-user input is to assign at least 30% of the images to the OK class.
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The Training mode for PEKAT is available for the following AI Modules:Anomaly Detector
Below you can find a Training Overview Flowchart, 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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The Training begins after clicking on the 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 an indication of the ideal moment to stop the training.
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Please note the chart is not available for the Module Anomaly Detector, since by default we have the following:
Fast Training - Minimum amount of training cycles pre-determined by the software
Deep Training - End-user defines the number of training cycles
As described above, for both functions, the number of epochs is determined before training startsDetector,OCR modules. The training requirements are determined before training, therefore loss function graph is not available.For the OCR module, the number of epochs is also set before starting the training
These modules are trained either by pre-determined cycles or by user-specified iterations.
Avoiding Overfitting
Overfitting is a situation when the model becomes so specific to the training set that it starts to lose the ability to generalize and has bad results on different (testing) images. While performing training, the Loss Function graph is displayed, showing the error about the Training image set. In some cases, the Validation image set can be used to evaluate how well the model performs on unseen images already during training. Overfitting zones are likely to happen in machine learning when the loss function for the Validation set reaches its lowest peak as can be seen in this comparison between Training and Validation loss graphs.
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Extend model function is only available for the modules Classifier, Detector, and OCR.
This function allows you to perform further training on an already-trained model.
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Extend Model for Detector Module
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Extend Model for Classifier Module
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For a detailed explanation regarding augmentation, visit the article Augmentation Glossary https://pekatvision.atlassian.net/wiki/x/e4WbKQ.
Model Validation
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 Change button and then clicking on the desired model to check the model’s results, as shown in the pictures below. When a model is selected, the statistics will then be based on the selected model.
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Setup Evaluation to automatically see
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To delete the trained model click the trash icon on the right or use CTRL for selecting the model and then click the DELETE SELECTED button.
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results. To receive quantitative statistics about the models performance visit Report |
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Information on the training process and conditions used for a particular model are available by clicking on the info icon in the list of models, as shown in the picture below.
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To understand the Confusion Matrix evaluation have a look at Confusion Matrix
Model deletion
To delete an unwanted trained model click on the trash icon 🗑️ on the right.
To delete multiple models use CTRL for selecting the models and then click the DELETE SELECTED button.
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