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The set of Testing Images are then used to create a is then evaluated using the Prediction Model, which is finally used to provide a first set of statistics. The statistics can be used to 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 description of modules information can be found in part AI within the article Modules.
The Unsupervised module available is Anomaly of SurfaceDetector,where the only required end-user input is to assign at least 1 image 10 images to ‘OK’ class.
The remaining modules are supervised, where the end-user is required to perform detailed annotations, however, the results are extremely accurate.
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Note that the Training Model can be finished at any time, however, one of the following scenarios are the optimal moment :
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is when the chart is not decreasing anymore (sideways graph)
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As shown below, the graph decreased over time at first and then near the end it had been already below one the green lines 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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As described above, for both functions, the number of epochs 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 the fact that Machine Learning is applied initially to the Training Images set, therefore, while performing deep training, the Error 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 in relation to the Training Images set.image set. In some cases, 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 on this comparison between Training and Validation loss graphs.
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There are several methods that help to prevent this situation from happening and we would like to highlight that our algorithm is designed to prevent overfitting.
If training is not interrupted when the graph is not decreasing anymore, the model will stop learningimproving, 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/negatives - It it will just not provide better results. Therefore, if the graph is not decreasing anymore, it means the model stopped from improving.
Considering that, overfitting zones are likely to happen in machine learning, when simultaneously comparison between Training Images set & Testing Images set (Validation) is in place, and the Testing Set (Validation) reaches its lowest peak.
Extend Model
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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