...
In order to provide a better understanding, find below a top-level schematic on of how supervised training works in machine learning:
...
Based on the chart displayed, its graph gives you an indication for of the ideal moment to stop the training.
The graph should gradually decrease over the training duration , because as the graph decreases, the model is improving.
...
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.
...
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 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 on in this comparison between Training and Validation loss graphs.
...
If training is not interrupted when the graph is not decreasing anymore, the model will stop improving, meaning that you will not achieve better results. However, this will not cause weakness to in the model, in other words, this will not increase the presence of false positives/negatives - it will just not provide better results. Therefore, if the graph is not decreasing anymore, it means the model stopped from improving.
Extend Model
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.
...
Extend Model for Classifier Module
...
...
Augmentation
For a detailed explanation regarding augmentation, visit the article Augmentation Glossary.
...
Fast Training Mode & Deep Learning Mode
At first, we recommend to run running different models in Fast Training mode, using different sizes of view-finder and training parameters (brightness resistance, resistance to deviation, etc.) to find out what combination works best in your case. When you find suitable settings, but you would like to achieve even more precise results, you may try Deep Training mode, with more training cycles, however, using the same training parameters (settings).
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.
...