diff --git a/docs/johannes_mueller/yolo_from_omero/train_yolo.ipynb b/docs/johannes_mueller/yolo_from_omero/train_yolo.ipynb
index 6931878..6b58e61 100644
--- a/docs/johannes_mueller/yolo_from_omero/train_yolo.ipynb
+++ b/docs/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",