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AI on the Edge Device
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Model Selection
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Getting Started
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Getting Started
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Welcome
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Installation
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Initial Setup
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Hardware Compatibility
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Best Practice
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Frequently Asked Questions
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Configuration
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Configuration
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<ul class="md-nav__list" data-md-scrollfix="">
<li class="md-nav__item">
<a class="md-nav__link" href="../Reference-Image/">
<span class="md-ellipsis">
Reference Image
</span>
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<li class="md-nav__item">
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<span class="md-ellipsis">
Alignment References
</span>
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<a class="md-nav__link" href="../ROI-Configuration/">
<span class="md-ellipsis">
ROIs (Regions of Interest)
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Graphical Configuration
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Model Selection
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Model Selection
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Table of contents
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<a class="md-nav__link" href="#digit-models">1.1
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Digit Models
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dig-class11
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<a class="md-nav__link" href="#dig-class100-and-dig-cont">1.1.2
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dig-class100 and dig-cont
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<li class="md-nav__item">
<a class="md-nav__link" href="#analog-pointer-models">1.2
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Analog pointer models
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<li class="md-nav__item">
<a class="md-nav__link" href="#different-types-of-models-normal-vs-quantized">1.3
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Different types of models (normal vs. quantized)
</span>
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</li>
<li class="md-nav__item">
<a class="md-nav__link" href="#model-naming-convention">1.4
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Model Naming Convention
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Version Number
</span>
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<a class="md-nav__link" href="#size-indicator">1.4.2
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Size Indicator
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<a class="md-nav__link" href="#quantization-indicator">1.4.3
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Quantization Indicator
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Over-The-Air (OTA) Update
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Advanced
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Advanced
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<li class="md-nav__item">
<a class="md-nav__link" href="../Parameters/">
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Parameters
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Integration into Home Assistant
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External LED
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Data Logging
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<a class="md-nav__link" href="../Password-Protection/">
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Password Protection
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<a class="md-nav__link" href="../unstable-installation/">
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Living on the Edge
</span>
</a>
</li>
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<a class="md-nav__link" href="../Watermeter-specific-analog---digit-transition/">
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Analog/Digit Transition on Water Meters
</span>
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</li>
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<a class="md-nav__link" href="../collect-new-images/">
<span class="md-ellipsis">
Collect images to improve the models
</span>
</a>
</li>
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<a class="md-nav__link" href="../Learn-models-with-your-own-images/">
<span class="md-ellipsis">
Learn a model with your own images
</span>
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</li>
<li class="md-nav__item">
<a class="md-nav__link" href="../Correction%20Algorithm/">
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Correction Algorithm
</span>
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</li>
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<a class="md-nav__link" href="../Neural-Network-Types/">
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Neural Network Types
</span>
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Additional Information
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<a class="md-nav__link" href="../New-Releases-Notification/">
<span class="md-ellipsis">
Notification about new Releases
</span>
</a>
</li>
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<a class="md-nav__link" href="../prometheus-openmetrics/">
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Prometheus/OpenMetrics
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Troubleshooting
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Troubleshooting
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<a class="md-nav__link" href="../Error-Codes/">
<span class="md-ellipsis">
Reduced webinterface (error codes)
</span>
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</li>
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<a class="md-nav__link" href="../StatusLED-BlinkCodes/">
<span class="md-ellipsis">
Board status LED (blink codes)
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<a class="md-nav__link" href="../WLAN-disconnect-reason/">
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WLAN disconnect reasons
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Often observed issues
</span>
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<a class="md-nav__link" href="../Frequent-Reboots/">
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Basic hardware/config issues
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Demo mode
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Quick Links
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Quick Links
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Releases
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<a class="md-nav__link" href="https://jomjol.github.io/AI-on-the-edge-device/index.html">
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Web Installer/Console
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<a class="md-nav__link" href="https://github.com/jomjol/AI-on-the-edge-device/issues">
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Create an Issue
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Join a Discussion
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API's
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<a class="md-nav__link" href="#digit-models">1.1
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Digit Models
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dig-class11
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<a class="md-nav__link" href="#dig-class100-and-dig-cont">1.1.2
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dig-class100 and dig-cont
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Analog pointer models
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Different types of models (normal vs. quantized)
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Size Indicator
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<h1 id="model-selection"><span class="enumerate-headings-plugin enumerate-heading-plugin">1.</span> Model Selection</h1>
<div class="admonition notes">
<p class="admonition-title">Notes</p>
<p>See <a href="../Neural-Network-Types/">Neural Network Types</a> for additional details.</p>
</div>
<p>In the <a href="../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"><span class="enumerate-headings-plugin enumerate-heading-plugin">1.1</span> Digit Models</h2>
<p>For digits on water meters, gas-meters or power meters you can select between two main types of models:</p>
<ul>
<li><code>dig-class11</code></li>
<li><code>dig-class100</code> and <code>dig-cont</code></li>
</ul>
<p><code>class</code> stands for <strong>classification</strong> and <code>cont</code> stands for <strong>continuous</strong>. The <code>11</code> means that there are 11 states (<code>0..9</code> and <code>N</code>). The <code>100</code> indicates that the model resolves into <code>x.1</code> steps by having 100 states (0.0, 0.1, 0.2, ... 9.7, 9.8, 9.9).</p>
<p><strong>continuous</strong> means, that there is no discrete model, that has discreate states, but there is a different mechanism, that provides a not discrete value in the interval between [0, 1[.</p>
<h3 id="dig-class11"><span class="enumerate-headings-plugin enumerate-heading-plugin">1.1.1</span> <code>dig-class11</code></h3>
<p>This model can recognize <strong>full digits</strong>. It was the first model version. All intermediate states shown a <code>N</code> for not-a-number (aka. <code>NaN</code>). But in post process it uses older values to fill up the <code>N</code> values if possible.</p>
<p><img alt="" src="../img/dig-class11.png" style="width:300px"/></p>
<p>It's possibly a good fallback, if <code>dig-cont</code> or <code>dig-class100</code> results are not good.</p>
<p>Main features:</p>
<ul>
<li>well suited for LCD digits</li>
<li>the ExtendedResolution option is not supported. (Only in conjunction with <code>ana-class100</code> or <code>ana-cont</code>)</li>
</ul>
<h3 id="dig-class100-and-dig-cont"><span class="enumerate-headings-plugin enumerate-heading-plugin">1.1.2</span> <code>dig-class100</code> and <code>dig-cont</code></h3>
<p>These models are used to get a <strong>continuous reading</strong> with intermediate states. To see what the models are doing, you can go to the Recognition page of your device.</p>
<p><img alt="" src="../img/dig-class100.png" style="width:300px"/></p>
<p>Main features:</p>
<ul>
<li>suitable for all digit displays.</li>
<li>Advantage over <code>dig-class11</code> 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>The difference between <code>dig-class100</code> and <code>dig-cont</code> is in the internal processing.
The <code>dig-class100</code> is a standard classification model. Each tenth step is an output.
<code>dig-cont</code> uses two outputs and arctangent to get the result.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Try both models on your device and take the one that gives you the best results.</p>
</div>
<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>
<h2 id="analog-pointer-models"><span class="enumerate-headings-plugin enumerate-heading-plugin">1.2</span> Analog pointer models</h2>
<p>For pointers on water meters use the analog models:</p>
<p><img alt="" src="../img/ana-class100.png" style="width:250px"/></p>
<p>You can choose between two models:</p>
<ul>
<li><code>ana-class100</code> </li>
<li><code>ana-cont</code></li>
</ul>
<p>Both do mainly the same.
<code>class</code> stands for <strong>classification</strong> and <code>cont</code> stands for <strong>continuous</strong>.</p>
<p>Main features:</p>
<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>Again, the difference between <code>ana-class100</code> and <code>ana-cont</code> is in the internal processing.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Take the one that gives you the best results. Both models learn from the same data.</p>
</div>
<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>
<h2 id="different-types-of-models-normal-vs-quantized"><span class="enumerate-headings-plugin enumerate-heading-plugin">1.3</span> Different types of models (normal vs. quantized)</h2>
<p>The normally trained network is calculating with internal floating point numbers. The saving of floating point numbers naturally takes more space than an integer type. Often the increased accuracy is not needed. Therefore there is the option, to "quantize" a neural network. In this case the internal values are rescaled to integer values, which is called "quantization". The stored tflite files are usually much smaller and runs faster on the edgeAI-device.
Usually the models are distrusted therefore in both versions. They can be distinguished by a <code>q</code> at the end of the logfile.</p>
<p>Example:</p>
<table>
<thead>
<tr>
<th style="text-align: left;">Type</th>
<th>Name</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align: left;">Normal</td>
<td><code>dig-cont_0610_s3.tflite</code></td>
</tr>
<tr>
<td style="text-align: left;">Quantized</td>
<td><code>dig-cont_0610_s3-q.tflite</code></td>
</tr>
</tbody>
</table>
<h2 id="model-naming-convention"><span class="enumerate-headings-plugin enumerate-heading-plugin">1.4</span> Model Naming Convention</h2>
<p>Model filenames follow a specific structure composed of several parts:</p>
<ol>
<li><strong>Model Type</strong>: Indicates the kind of model, such as <code>ana/dig</code>, <code>cont</code>, <code>class11</code>, or <code>class100</code>.</li>
<li><strong>Version Number</strong>: Denotes the version of the model.</li>
<li><strong>Size Indicator</strong>: Represents the size or complexity of the model.</li>
<li><strong>Quantization Indicator</strong> (optional): Specifies whether the model was quantized after training.</li>
<li><strong>File Type</strong>: Always <code>.tflite</code>.</li>
</ol>
<h3 id="version-number"><span class="enumerate-headings-plugin enumerate-heading-plugin">1.4.1</span> Version Number</h3>
<p>The version number consists of four digits:</p>
<ul>
<li>The <strong>first two digits</strong> represent the <em>main version</em>.</li>
<li>The <strong>last two digits</strong> represent the <em>sub-version</em>.</li>
</ul>
<p>Example:
<code>0610</code> → Main version: <code>06</code>, Sub-version: <code>10</code>
In general, higher numbers correspond to newer models.</p>
<h3 id="size-indicator"><span class="enumerate-headings-plugin enumerate-heading-plugin">1.4.2</span> Size Indicator</h3>
<ul>
<li>The size indicator typically starts with <code>s</code> followed by a number, e.g., <code>s3</code>.</li>
<li>This naming is not strictly standardized yet.</li>
<li>Training usually begins with a larger model (<code>s0</code>), and successive versions (<code>s1</code>, <code>s2</code>, ...) reduce the number of parameters to create smaller, faster networks.</li>
<li>Reducing model size can improve efficiency, but may eventually lead to a drop in recognition accuracy.</li>
<li>In newer model generations, only the best-performing sizes are retained and further trained.</li>
</ul>
<h3 id="quantization-indicator"><span class="enumerate-headings-plugin enumerate-heading-plugin">1.4.3</span> Quantization Indicator</h3>
<ul>
<li>If the model name ends with <code>-q</code>, it indicates that <strong>quantization</strong> was applied after training.</li>
<li>Quantization significantly reduces the model size, typically without a noticeable impact on recognition performance.</li>
</ul>
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