@@ -22,8 +22,7 @@
         "- DeepLabCut's GitHub: [github.com/DeepLabCut/DeepLabCut](https://github.com/DeepLabCut/DeepLabCut/tree/main)\n",
         "- DeepLabCut's Documentation: [deeplabcut.github.io/DeepLabCut](https://deeplabcut.github.io/DeepLabCut/README.html)\n",
         "\n",
-        "This notebook illustrates how to use the cloud to run pose estimation on humans using a pre-trained [RTMPose](https://arxiv.org/abs/2303.07399) model. 
-        **⚠️Note: It uses DeepLabCut's low-level interface, so may be suited for more experienced users. The top of the notebook is for images, the bottom for videos. ⚠️**\n",
+        "This notebook illustrates how to use the cloud to run pose estimation on humans using a pre-trained [RTMPose](https://arxiv.org/abs/2303.07399) model. **⚠️Note: It uses DeepLabCut's low-level interface, so may be suited for more experienced users.⚠️**\n",
         "\n",
         "RTMPose is a top-down pose estimation model, which means that bounding boxes must be obtained for individuals (which is usually done through an [object detection model](https://en.wikipedia.org/wiki/Object_detection)) before running pose estimation. We obtain bounding boxes using a pre-trained object detector provided by [`torchvision`](https://pytorch.org/vision/main/models.html#object-detection-instance-segmentation-and-person-keypoint-detection).\n",
         "\n",