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<li class="toctree-l3"><a class="reference internal" href="#dig-class11">dig-class11</a>
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<li class="toctree-l4"><a class="reference internal" href="#dig-class100-vs-dig-cont">dig-class100 vs. dig-cont</a>
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<li class="toctree-l4"><a class="reference internal" href="#main-features_2">Main features</a>
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<li class="toctree-l4"><a class="reference internal" href="#ana-class100-vs-ana-cont">ana-class100 vs. ana-cont</a>
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<li class="toctree-l1"><a class="reference internal" href="../Integration-Home-Assistant/">Integration into Home Assistant</a>
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<h1 id="model-selection">Model Selection</h1>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>This page overlaps <a href="../Neural-Network-Types">Neural Network Types</a>. They should be merged to one page!</p>
</div>
<p>In the <a href="../Graphical-configuration">Graphical Configuration Page</a>, you can choose different models depending on your needs.</p>
<p>This page tries to help you on which model to select.
For more technical/deeper explanations have a look on <a href="../Neural-Network-Types">Neural-Network-Types</a>.</p>
<h2 id="digit-models">Digit Models</h2>
<p>For digits on water meters, gas-meters or power meters you can select between two main types of models.</p>
<h3 id="dig-class11">dig-class11</h3>
<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>
<p><img alt="" src="../img/dig-class11.png" style="width:300px" /></p>
<h4 id="main-features">Main features</h4>
<ul>
<li>well suited for LCD digits</li>
<li>with the ExtendedResolution option is not supported. (Only in conjunction with ana-class100 / ana-cont)</li>
</ul>
<h3 id="dig-class100-dig-cont">dig-class100 / dig-cont</h3>
<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>
<p><img alt="" src="../img/dig-class100.png" style="width:300px" /></p>
<h4 id="main-features_1">Main features</h4>
<ul>
<li>suitable for all digit displays.</li>
<li>Advantage over dig-class11 that results continue to be calculated in the transition between digits.</li>
<li>With the ExtendedResolution option, higher accuracy is possible by adding another digit.</li>
</ul>
<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>
<h4 id="dig-class100-vs-dig-cont">dig-class100 vs. dig-cont</h4>
<p>The difference is in the internal processing. Take the one that gives you the best results.</p>
<h2 id="analog-pointer-models">Analog pointer models</h2>
<h3 id="ana-class100-ana-cont">ana-class100 / ana-cont</h3>
<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>
<p><img alt="" src="../img/ana-class100.png" style="width:250px" /></p>
<h4 id="main-features_2">Main features</h4>
<ul>
<li>for all analogue pointers, especially for water meters.</li>
<li>With the ExtendedResolution option, higher accuracy is possible by adding another digit.</li>
</ul>
<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>
<h4 id="ana-class100-vs-ana-cont">ana-class100 vs. ana-cont</h4>
<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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