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<li class="toctree-l2"><a class="reference internal" href="#dig-class11-models-digits">dig-class11 models (digits)</a>
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<h1 id="learn-a-model-with-your-own-images">Learn a model with your own images</h1>
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<p>Once you have collected and selected your own images (see <a href="../collect-new-images/">Collect images to improve the models</a>), you can train your very own model with them.</p>
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<p><strong>This is an optional step and only suggested for advances users!</strong></p>
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<p>For training the model you will need a python and Jupyter installation.</p>
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<p>All current labeled images you can find under <a href="https://github.com/jomjol/neural-network-digital-counter-readout/tree/master/ziffer_sortiert_raw">ziffer_sortiert_raw</a></p>
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<h3 id="dig-class11-models-digits">dig-class11 models (digits)</h3>
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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>
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<p>Install all requirements for running the notebooks.</p>
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<pre><code class="language-shell">pip install -r requirements.txt
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</code></pre>
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<p>Put your labeled images into <code>/ziffer_sortiert_raw</code> folder and run</p>
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<ol>
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<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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<li><a href="https://github.com/jomjol/neural-network-digital-counter-readout/blob/master/Train_CNN_Digital-Readout-Small-v2.ipynb">Train_CNN_Digital-Readout-Small-v2.ipynb</a></li>
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</ol>
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<p>It creates a dig-class11_xxxx_s2.tflite model, you can upload to the <code>config</code> folder on your device and test it. </p>
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<h3 id="dig-class100-dig-cont-models-digits">dig-class100 / dig-cont models (digits)</h3>
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<p>Fork and checkout <a href="https://github.com/jomjol/neural-network-analog-needle-readout">neural-network-analog-needle-readout</a>.</p>
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<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>
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<p>Install all requirements for running the notebooks.</p>
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<pre><code class="language-shell">pip install -r requirements.txt
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</code></pre>
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<p>Put your labeled images into <code>images/collected/<typeofdevice>/<your_short>/</code></p>
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<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>
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<h3 id="ana-class100ana-cont-models-analog-pointers">ana-class100/ana-cont models (analog pointers)</h3>
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<p>Fork and checkout <a href="https://github.com/jomjol/neural-network-analog-needle-readout">neural-network-analog-needle-readout</a>.</p>
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<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>
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<p>Install all requirements for running the notebooks.</p>
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<pre><code class="language-shell">pip install -r requirements.txt
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</code></pre>
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<p>Put your labeled images into <code>images/collected/<typeofdevice>/<your_short>/</code></p>
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<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>
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<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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<h2 id="share-your-images">Share your images</h2>
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<p>If the results are good you can share the images as pull-request. Please images only!</p>
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<p>See <a href="../collect-new-images/#share-your-images">Share your images</a> for details.</p>
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