deploy: 239913e

_sources/johannes_mueller/yolo_from_omero/train_yolo.ipynb

@@ -19,8 +19,8 @@
     "\n",
     "This already brings forth one of the key advantages of using YOLO for bio-medical image segmentation, *especially* in instance segmentation problems: Pixel classification, without an additional post-processing step is unable to split pixels into different objects - YOLO does this very natively. A typical result of a YOLO model could look like this:\n",
     "\n",
-    "<img src=\"./imgs/image1.png\" alt=\"YOLO result\" style=\"width:50%;\">\n",
-    "<p style=\"text-align: left;\">Images &copy; 2022 Johannes Soltwedel. All rights reserved.</p>\n",
+    "![YOLO result](./imgs/image1.png)\n",
+    "<p style=\"text-align: left;\">Image &copy; 2022 Johannes Soltwedel. All rights reserved.</p>\n",
     "\n",
     "## Getting started\n",
     "\n",

johannes_mueller/yolo_from_omero/train_yolo.html

@@ -448,8 +448,8 @@ <h2>OMERO<a class="headerlink" href="#omero" title="Permalink to this heading">#
 <h2>YOLO<a class="headerlink" href="#yolo" title="Permalink to this heading">#</a></h2>
 <p><a class="reference external" href="https://docs.ultralytics.com/models/yolov10/">YOLO</a> - short for <em>you only look once</em> is a class of continuously evolving object detection algorithms. YOLO is known for its speed and accuracy and is widely used in computer vision applications. YOLO is open-source and has been implemented in many programming languages and frameworks. It can be used for a variety of applications such as object detection, object classification, tracking or pose estimation - but I think one of the most bread-and-butter applications is still object detection. Contrary to many common segmentation algorithms in bio-image analysis, it does not seek to classify every pixel, but rather to predict <em>bounding boxes</em> around objects of interest.</p>
 <p>This already brings forth one of the key advantages of using YOLO for bio-medical image segmentation, <em>especially</em> in instance segmentation problems: Pixel classification, without an additional post-processing step is unable to split pixels into different objects - YOLO does this very natively. A typical result of a YOLO model could look like this:</p>
-<img src="./imgs/image1.png" alt="YOLO result" style="width:50%;">
-<p style="text-align: left;">Images &copy; 2022 Johannes Soltwedel. All rights reserved.</p>
+<p><img alt="YOLO result" src="../../_images/image1.png" /></p>
+<p style="text-align: left;">Image &copy; 2022 Johannes Soltwedel. All rights reserved.</p>
 </section>
 <section id="getting-started">
 <h2>Getting started<a class="headerlink" href="#getting-started" title="Permalink to this heading">#</a></h2>