Created using Colaboratory

tutorial.ipynb

@@ -1031,9 +1031,9 @@
       "source": [
         "## Weights & Biases Logging 🌟 NEW\n",
         "\n",
-        "[Weights & Biases](https://www.wandb.com/) (W&B) is now integrated with YOLOv5 for real-time visualization and cloud logging of training runs. This allows for better run comparison and introspection, as well improved visibility and collaboration for teams. To enable W&B `pip install wandb`, and then train normally (you will be guided through setup on first use). \n",
+        "[Weights & Biases](https://wandb.ai/site?utm_campaign=repo_yolo_notebook) (W&B) is now integrated with YOLOv5 for real-time visualization and cloud logging of training runs. This allows for better run comparison and introspection, as well improved visibility and collaboration for teams. To enable W&B `pip install wandb`, and then train normally (you will be guided through setup on first use). \n",
         "\n",
-        "During training you will see live updates at [https://wandb.ai/home](https://wandb.ai/home), and you can create and share detailed [Reports](https://wandb.ai/glenn-jocher/yolov5_tutorial/reports/YOLOv5-COCO128-Tutorial-Results--VmlldzozMDI5OTY) of your results. For more information see the [YOLOv5 Weights & Biases Tutorial](https://github.com/ultralytics/yolov5/issues/1289). \n",
+        "During training you will see live updates at [https://wandb.ai/home](https://wandb.ai/home?utm_campaign=repo_yolo_notebook), and you can create and share detailed [Reports](https://wandb.ai/glenn-jocher/yolov5_tutorial/reports/YOLOv5-COCO128-Tutorial-Results--VmlldzozMDI5OTY) of your results. For more information see the [YOLOv5 Weights & Biases Tutorial](https://github.com/ultralytics/yolov5/issues/1289). \n",
         "\n",
         "<img src=\"https://user-images.githubusercontent.com/26833433/98184457-bd3da580-1f0a-11eb-8461-95d908a71893.jpg\" width=\"800\">"
       ]
@@ -1177,6 +1177,29 @@
       "execution_count": null,
       "outputs": []
     },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "GMusP4OAxFu6"
+      },
+      "source": [
+        "# PyTorch Hub\n",
+        "import torch\n",
+        "\n",
+        "# Model\n",
+        "model = torch.hub.load('ultralytics/yolov5', 'yolov5s')\n",
+        "\n",
+        "# Images\n",
+        "dir = 'https://github.com/ultralytics/yolov5/raw/master/data/images/'\n",
+        "imgs = [dir + f for f in ('zidane.jpg', 'bus.jpg')]  # batch of images\n",
+        "\n",
+        "# Inference\n",
+        "results = model(imgs)\n",
+        "results.print()  # or .show(), .save()"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
     {
       "cell_type": "code",
       "metadata": {