Created using Colaboratory

tutorial.ipynb

@@ -661,35 +661,32 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "VUOiNLtMP5aG"
+        "id": "ZY2VXXXu74w5"
       },
       "source": [
         "# 3. Train\n",
-        "[<img src=\"https://uploads-ssl.webflow.com/5f6bc60e665f54545a1e52a5/615338ba77195c71bd2c5ab1_computer-vision-flow.png\">](https://roboflow.com/?ref=ultralytics)\n",
-        "*Close the active learning loop by sampling images from your inference conditions with the `roboflow` pip package*\n",
         "\n",
-        "<br>\n",
+        "<p align=\"\"><a href=\"https://roboflow.com/?ref=ultralytics\"><img width=\"1000\" src=\"https://uploads-ssl.webflow.com/5f6bc60e665f54545a1e52a5/615338ba77195c71bd2c5ab1_computer-vision-flow.png\"/></a></p>\n",
+        "Close the active learning loop by sampling images from your inference conditions with the `roboflow` pip package\n",
+        "<br><br>\n",
         "\n",
         "Train a YOLOv5s model on the [COCO128](https://www.kaggle.com/ultralytics/coco128) dataset with `--data coco128.yaml`, starting from pretrained `--weights yolov5s.pt`, or from randomly initialized `--weights '' --cfg yolov5s.yaml`.\n",
         "\n",
         "- **Pretrained [Models](https://github.com/ultralytics/yolov5/tree/master/models)** are downloaded\n",
         "automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases)\n",
         "- **[Datasets](https://github.com/ultralytics/yolov5/tree/master/data)** available for autodownload include: [COCO](https://github.com/ultralytics/yolov5/blob/master/data/coco.yaml), [COCO128](https://github.com/ultralytics/yolov5/blob/master/data/coco128.yaml), [VOC](https://github.com/ultralytics/yolov5/blob/master/data/VOC.yaml), [Argoverse](https://github.com/ultralytics/yolov5/blob/master/data/Argoverse.yaml), [VisDrone](https://github.com/ultralytics/yolov5/blob/master/data/VisDrone.yaml), [GlobalWheat](https://github.com/ultralytics/yolov5/blob/master/data/GlobalWheat2020.yaml), [xView](https://github.com/ultralytics/yolov5/blob/master/data/xView.yaml), [Objects365](https://github.com/ultralytics/yolov5/blob/master/data/Objects365.yaml), [SKU-110K](https://github.com/ultralytics/yolov5/blob/master/data/SKU-110K.yaml).\n",
         "- **Training Results** are saved to `runs/train/` with incrementing run directories, i.e. `runs/train/exp2`, `runs/train/exp3` etc.\n",
-        "<br>\n",
+        "<br><br>\n",
         "\n",
         "## Train on Custom Data with Roboflow 🌟 NEW\n",
         "\n",
         "[Roboflow](https://roboflow.com/?ref=ultralytics) enables you to easily **organize, label, and prepare** a high quality dataset with your own custom data. Roboflow also makes it easy to establish an active learning pipeline, collaborate with your team on dataset improvement, and integrate directly into your model building workflow with the `roboflow` pip package.\n",
         "\n",
         "- Custom Training Example: [https://blog.roboflow.com/how-to-train-yolov5-on-a-custom-dataset/](https://blog.roboflow.com/how-to-train-yolov5-on-a-custom-dataset/?ref=ultralytics)\n",
         "- Custom Training Notebook: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/roboflow-ai/yolov5-custom-training-tutorial/blob/main/yolov5-custom-training.ipynb)\n",
-        "\n",
         "<br>\n",
         "\n",
-        "[<img src=\"https://uploads-ssl.webflow.com/5f6bc60e665f54545a1e52a5/6152a275ad4b4ac20cd2e21a_roboflow-annotate.gif\">](https://roboflow.com/?ref=ultralytics)\n",
-        "\n",
-        "*Label images lightning fast (including with model-assisted labeling)*"
+        "<p align=\"\"><a href=\"https://roboflow.com/?ref=ultralytics\"><img width=\"480\" src=\"https://uploads-ssl.webflow.com/5f6bc60e665f54545a1e52a5/6152a275ad4b4ac20cd2e21a_roboflow-annotate.gif\"/></a></p>Label images lightning fast (including with model-assisted labeling)"
       ]
     },
     {