Training Overview - Supervised & Unsupervised (Deep-Learning)
In order to perform deep learning supervised training, firstly, end-users need to perform annotations on a set of images that is called Training Images, and the remaining will be called Test Images.
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Below you can find a Training Overview Fluxogram, which demonstrates the overall training steps. However, for detailed training information a specific module each module, please access the desired module’s page.
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Training Graph - Loss Function Chart
The Training Model begins after clicking on ‘Start Training’ button, from that point a dialog window will display a training chart only for the following modules:
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Please note the chart is not available for the Module /wiki/spaces/CONFLUENCE/pages/2982052, as the Fast Training function for this module uses by default, the mininum value for number of training cycles. As for Deep Training function, the end-user defines the number of training cycles.
Avoiding Overfitting
We would like to highlight that our algorithm is designed to prevent overfitting.
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Considering that, overfitting zones are likely to happen in machine learning, when simultaneously comparison between Traning 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 /wiki/spaces/CONFLUENCE/pages/7602237 & /wiki/spaces/CONFLUENCE/pages/7602198.
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Extend Model for Classifier Module
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Augmentation - Training Parameters
For detailed explaination regarding argumentation ( training parameters), visit the following article /wiki/spaces/CONFLUENCE/pages/35422286.
Model Validation
By default, each training module is inactive and a model must be selected to activate (validate) it.
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When a model is validated (selected), the statistics will then be based on the selected model, as shown on picture below:
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Fast Training Mode & Deep Learning Mode
At first, we recommend to run different models in Fast Traning mode, using different sizes of view-finder and training parameters (brighness 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).
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