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<li class="toctree-l2"><a class="reference internal" href="#digit-models">Digit Models</a>
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<li class="toctree-l1"><a class="reference internal" href="../Migrate-Old-Config-To-New-Config/">Migration from water-meter „old“ to water-meter “AI-on-the-edge-device”</a>
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<h1 id="which-model-should-i-use">Which model should I use?</h1>
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<p>In the <a href="Graphical-configuration">Graphical Configuration Page</a>, you can choose different models depending on your needs.</p>
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<p>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 <a href="https://github.com/jomjol/AI-on-the-edge-device/wiki/Neural-Network-Types">Neural-Network-Types</a>.</p>
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<h2 id="digit-models">Digit Models</h2>
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<p>For digits on water meters, gas-meters or power meters you can select between two main types of models.</p>
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<h3 id="dig-class11">dig-class11</h3>
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<p>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.</p>
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<p><img width="333" alt="image" src="https://user-images.githubusercontent.com/412645/190924459-e4023630-c6d0-4a8c-ab56-59e6c0e3ffd8.png"></p>
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<h4 id="main-features">Main features</h4>
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<ul>
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<li>well suited for LCD digits</li>
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<li>with the ExtendedResolution option is not supported. (Only in conjunction with ana-class100 / ana-cont)</li>
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</ul>
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<h3 id="dig-class100-dig-cont">dig-class100 / dig-cont</h3>
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<p>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.</p>
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<p><img width="323" alt="image" src="https://user-images.githubusercontent.com/412645/190924335-b8b75883-7b39-4fd6-a949-49c69834fee4.png"></p>
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<h4 id="main-features_1">Main features</h4>
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<ul>
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<li>suitable for all digit displays.</li>
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<li>Advantage over dig-class11 that results continue to be calculated in the transition between digits.</li>
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<li>With the ExtendedResolution option, higher accuracy is possible by adding another digit.</li>
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</ul>
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<p>Look <a href="https://jomjol.github.io/neural-network-digital-counter-readout">here</a> for a list of digit images used for the training </p>
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<h4 id="dig-class100-vs-dig-cont">dig-class100 vs. dig-cont</h4>
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<p>The difference is in the internal processing. Take the one that gives you the best results.</p>
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<h2 id="analog-pointer-models">Analog pointer models</h2>
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<h3 id="ana-class100-ana-cont">ana-class100 / ana-cont</h3>
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<p>For pointers on water meters use the analog models. You can only choose between ana-class100 and ana-cont. Both do mainly the same.</p>
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<p><img width="231" alt="image" src="https://user-images.githubusercontent.com/412645/190924487-18ed16e1-1c89-45f1-823e-305b7e78ac46.png"></p>
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<h4 id="main-features_2">Main features</h4>
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<ul>
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<li>for all analogue pointers, especially for water meters.</li>
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<li>With the ExtendedResolution option, higher accuracy is possible by adding another digit.</li>
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</ul>
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<p>Look <a href="https://jomjol.github.io/neural-network-analog-needle-readout/">here</a> for a list of pointer images used for the training</p>
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<h4 id="ana-class100-vs-ana-cont">ana-class100 vs. ana-cont</h4>
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<p>The difference is in the internal processing. Take the one that gives you the best results. Both models learn from the same data.</p>
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