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tutorial.ipynb

@@ -713,6 +713,19 @@
       "execution_count": null,
       "outputs": []
     },
+    {
+      "cell_type": "code",
+      "source": [
+        "# ClearML  (optional)\n",
+        "%pip install -q clearml\n",
+        "!clearml-init"
+      ],
+      "metadata": {
+        "id": "DQhI6vvaRWjR"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
     {
       "cell_type": "code",
       "metadata": {
@@ -919,17 +932,14 @@
       "source": [
         "## ClearML Logging and Automation 🌟 NEW\n",
         "\n",
-        "[ClearML](https://cutt.ly/yolov5-notebook-clearml) is completely integrated into YOLOv5 to track your experimentation, manage dataset versions and even remotely execute training runs.\n",
+        "[ClearML](https://cutt.ly/yolov5-notebook-clearml) is completely integrated into YOLOv5 to track your experimentation, manage dataset versions and even remotely execute training runs. To enable ClearML (check cells above):\n",
         "\n",
-        "To enable ClearML (Check cells above):\n",
         "- `pip install clearml`\n",
-        "- run `clearml-init` to connect to a ClearML server (**deploy your own open-source server [here](https://github.com/allegroai/clearml-server)**, or use our free hosted server [here](https://cutt.ly/yolov5-notebook-clearml))\n",
+        "- run `clearml-init` to connect to a ClearML server (**deploy your own [open-source server](https://github.com/allegroai/clearml-server)**, or use our [free hosted server](https://cutt.ly/yolov5-notebook-clearml))\n",
         "\n",
         "You'll get all the great expected features from an experiment manager: live updates, model upload, experiment comparison etc. but ClearML also tracks uncommitted changes and installed packages for example. Thanks to that ClearML Tasks (which is what we call experiments) are also reproducible on different machines! With only 1 extra line, we can schedule a YOLOv5 training task on a queue to be executed by any number of ClearML Agents (workers).\n",
         "\n",
-        "You can use ClearML Data to version your dataset and then pass it to YOLOv5 simply using its unique ID. This will help you keep track of your data without adding extra hassle. \n",
-        "\n",
-        "Explore the [ClearML Tutorial](https://github.com/ultralytics/yolov5/tree/master/utils/loggers/clearml) for more info!\n",
+        "You can use ClearML Data to version your dataset and then pass it to YOLOv5 simply using its unique ID. This will help you keep track of your data without adding extra hassle. Explore the [ClearML Tutorial](https://github.com/ultralytics/yolov5/tree/master/utils/loggers/clearml) for details!\n",
         "\n",
         "<a href=\"https://cutt.ly/yolov5-notebook-clearml\">\n",
         "<img alt=\"ClearML Experiment Management UI\" src=\"https://github.com/thepycoder/clearml_screenshots/raw/main/scalars.jpg\" width=\"1280\"/></a>"
@@ -966,7 +976,7 @@
         "\n",
         "This directory contains train and val statistics, mosaics, labels, predictions and augmentated mosaics, as well as metrics and charts including precision-recall (PR) curves and confusion matrices. \n",
         "\n",
-        "<img alt=\"Local logging results\" src=\"https://user-images.githubusercontent.com/26833433/182486932-628e5bb0-cdea-4581-be1d-66f5e4ddada4.jpg\" width=\"1280\"/>\n"
+        "<img alt=\"Local logging results\" src=\"https://github.com/ultralytics/yolov5/releases/download/v1.0/image-local_logging.jpg\" width=\"1280\"/>\n"
       ]
     },
     {
@@ -1130,4 +1140,4 @@
       "outputs": []
     }
   ]
-}
+}