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<h3 id="dig-class11-models-digits"><span class="enumerate-headings-plugin enumerate-heading-plugin">1.0.1</span> dig-class11 models (digits)</h3>
<p>Fork and checkout <a href="https://github.com/jomjol/neural-network-digital-counter-readout">neural-network-digital-counter-readout</a>.</p>
<p>Install all requirements for running the notebooks.</p>
<pre><code class="language-shell">pip install -r requirements.txt
</code></pre>
<div class="language-shell highlight"><pre><span></span><code><span id="__span-0-1"><a href="#__codelineno-0-1" id="__codelineno-0-1" name="__codelineno-0-1"></a>pip<span class="w"> </span>install<span class="w"> </span>-r<span class="w"> </span>requirements.txt
</span></code></pre></div>
<p>Put your labeled images into <code>/ziffer_sortiert_raw</code> folder and run</p>
<ol>
<li><a href="https://github.com/jomjol/neural-network-digital-counter-readout/blob/master/Image_Preparation.ipynb">Image_Preparation.ipynb</a></li>
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<p>Fork and checkout <a href="https://github.com/jomjol/neural-network-digital-counter-readout">neural-network-digital-counter-readout</a>.</p>
<p>All labeled images you can find under <a href="https://github.com/haverland/Tenth-of-step-of-a-meter-digit/tree/master/images">Images</a></p>
<p>Install all requirements for running the notebooks.</p>
<pre><code class="language-shell">pip install -r requirements.txt
</code></pre>
<div class="language-shell highlight"><pre><span></span><code><span id="__span-1-1"><a href="#__codelineno-1-1" id="__codelineno-1-1" name="__codelineno-1-1"></a>pip<span class="w"> </span>install<span class="w"> </span>-r<span class="w"> </span>requirements.txt
</span></code></pre></div>
<p>Put your labeled images into <code>images/collected/&lt;typeofdevice&gt;/&lt;your_short&gt;/</code></p>
<p>Run <a href="https://github.com/haverland/Tenth-of-step-of-a-meter-digit/blob/master/dig-class100-s2.ipynb">dig-class100-s2.ipynb</a>. The model to upload to your device you can find under '/output'.</p>
<h3 id="ana-class100ana-cont-models-analog-pointers"><span class="enumerate-headings-plugin enumerate-heading-plugin">1.0.3</span> ana-class100/ana-cont models (analog pointers)</h3>
<p>Fork and checkout <a href="https://github.com/jomjol/neural-network-analog-needle-readout">neural-network-analog-needle-readout</a>.</p>
<p>All labeled images you can find under <a href="https://github.com/jomjol/neural-network-analog-needle-readout/tree/main/data_raw_all">data_raw_all</a></p>
<p>Install all requirements for running the notebooks.</p>
<pre><code class="language-shell">pip install -r requirements.txt
</code></pre>
<div class="language-shell highlight"><pre><span></span><code><span id="__span-2-1"><a href="#__codelineno-2-1" id="__codelineno-2-1" name="__codelineno-2-1"></a>pip<span class="w"> </span>install<span class="w"> </span>-r<span class="w"> </span>requirements.txt
</span></code></pre></div>
<p>Put your labeled images into <code>images/collected/&lt;typeofdevice&gt;/&lt;your_short&gt;/</code></p>
<p>After every adding of images you need to run <a href="https://github.com/jomjol/neural-network-analog-needle-readout/blob/main/Image_Preparation.ipynb">Image_Preparation.ipynb</a> before you train the models.</p>
<p>Run <a href="https://github.com/jomjol/neural-network-analog-needle-readout/blob/main/Train_CNN_Analog-Readout_100-Small1_Dropout.ipynb">Train_CNN_Analog-Readout_100-Small1_Dropout.ipynb</a> and/or <a href="https://github.com/jomjol/neural-network-analog-needle-readout/blob/main/Train_CNN_Analog-Readout_Version-Small2.ipynb">Train_CNN_Analog-Readout_Version-Small2.ipynb</a>. The model to upload to your device you can find in the project folder.</p>
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