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Learn-models-with-your-own-images/index.html

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 <p>The neural network is trained on base of a set of images, that have been collected over time. If your digits are included or at least very similar to included images, the chance is very high that the neural network is working fine for you as well.</p>
 <p>The neural network configuration is stored in the TensorFlow Lite format as <code>filename.tfl</code> or <code>filename.tflite</code> in the <code>/config</code> directory. It can be updated by uploading the new file and activating it on the configuration page or in the config file <code>/config/config.ini</code>.</p>
 <p>In order to incorporate new digits a training set of images is required. The training images needs to be collected in the final setup with the help of the <code>Digits</code> or <code>Analog</code> log settings (not to be confused with the <code>Data</code> or <code>Debug</code> log). Enable the logging of the images on the configuration page or in the config file <code>/config/config.ini</code>:</p>
-<p>[[/images/enable_log_image.jpg]]</p>
+<p><img alt="" src="../img/enable_log_image.jpg" /></p>
 <p>Now wait, until you have an image of each digit of every type on the SD card. Ideally remove the SD card from the camera and search for two to three images of each digit (<strong>not more! :-)</strong>). The format can be jpg.</p>
 <h2 id="collecting-images-for-dig-class100dig-contana-class100">Collecting images for dig-class100/dig-cont/ana-class100</h2>
 <p><a href="https://github.com/haverland/collectmeterdigits">Collectmeterdigits</a> and <a href="https://github.com/haverland/collectmeteranalog">collectmeteranalog</a> helps you to collect the images easily. Read the project readme for detailed instructions.</p>