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61 lines
2.4 KiB
Markdown
61 lines
2.4 KiB
Markdown
# Model Selection
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!!! Warning
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This page overlaps [Neural Network Types](../Neural-Network-Types). They should be merged to one page!
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In the [Graphical Configuration Page](Graphical-configuration), you can choose different models depending on your needs.
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This wiki page tries to help you on which model to select.
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For more technical/deeper explanations have a look on [Neural-Network-Types](https://github.com/jomjol/AI-on-the-edge-device/wiki/Neural-Network-Types).
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## Digit Models
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For digits on water meters, gas-meters or power meters you can select between two main types of models.
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### dig-class11
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This model can recognize full digits. All intermediate states shown a "N" for not a number. But in post process it uses older values to fill up the "N" values if possible.
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{: style="width:300px"}
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#### Main features
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* well suited for LCD digits
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* with the ExtendedResolution option is not supported. (Only in conjunction with ana-class100 / ana-cont)
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### dig-class100 / dig-cont
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These models are used to get a continuous reading with intermediate states. To see what the models are doing, you can go to the Recognition page.
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{: style="width:300px"}
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#### Main features
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* suitable for all digit displays.
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* Advantage over dig-class11 that results continue to be calculated in the transition between digits.
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* With the ExtendedResolution option, higher accuracy is possible by adding another digit.
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Look [here](https://jomjol.github.io/neural-network-digital-counter-readout) for a list of digit images used for the training
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#### dig-class100 vs. dig-cont
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The difference is in the internal processing. Take the one that gives you the best results.
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## Analog pointer models
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### ana-class100 / ana-cont
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For pointers on water meters use the analog models. You can only choose between ana-class100 and ana-cont. Both do mainly the same.
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{: style="width:250px"}
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#### Main features
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* for all analogue pointers, especially for water meters.
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* With the ExtendedResolution option, higher accuracy is possible by adding another digit.
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Look [here](https://jomjol.github.io/neural-network-analog-needle-readout/) for a list of pointer images used for the training
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#### ana-class100 vs. ana-cont
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The difference is in the internal processing. Take the one that gives you the best results. Both models learn from the same data. |