diff --git a/tutorial.ipynb b/tutorial.ipynb
index a70887e97..1c5d77813 100644
--- a/tutorial.ipynb
+++ b/tutorial.ipynb
@@ -439,7 +439,7 @@
         "id": "4JnkELT0cIJg"
       },
       "source": [
-        "# 1. Inference\n",
+        "# 1. Detect\n",
         "\n",
         "`detect.py` runs YOLOv5 inference on a variety of sources, downloading models automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases), and saving results to `runs/detect`. Example inference sources are:\n",
         "\n",
@@ -506,17 +506,7 @@
       },
       "source": [
         "# 2. Validate\n",
-        "Validate a model's accuracy on [COCO](https://cocodataset.org/#home) val or test-dev datasets. Models are downloaded automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases). To show results by class use the `--verbose` flag. Note that `pycocotools` metrics may be ~1% better than the equivalent repo metrics, as is visible below, due to slight differences in mAP computation."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "eyTZYGgRjnMc"
-      },
-      "source": [
-        "## COCO val\n",
-        "Download [COCO val 2017](https://github.com/ultralytics/yolov5/blob/74b34872fdf41941cddcf243951cdb090fbac17b/data/coco.yaml#L14) dataset (1GB - 5000 images), and test model accuracy."
+        "Validate a model's accuracy on the [COCO](https://cocodataset.org/#home) dataset's `val` or `test` splits. Models are downloaded automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases). To show results by class use the `--verbose` flag."
       ]
     },
     {
@@ -544,8 +534,8 @@
       },
       "source": [
         "# Download COCO val\n",
-        "torch.hub.download_url_to_file('https://ultralytics.com/assets/coco2017val.zip', 'tmp.zip')\n",
-        "!unzip -q tmp.zip -d ../datasets && rm tmp.zip"
+        "torch.hub.download_url_to_file('https://ultralytics.com/assets/coco2017val.zip', 'tmp.zip')  # download COCO val (1GB - 5000 images)\n",
+        "!unzip -q tmp.zip -d ../datasets && rm tmp.zip  # unzip"
       ],
       "execution_count": null,
       "outputs": [
@@ -575,7 +565,7 @@
         "outputId": "19a590ef-363e-424c-d9ce-78bbe0593cd5"
       },
       "source": [
-        "# Run YOLOv5x on COCO val\n",
+        "# Validate YOLOv5x on COCO val\n",
         "!python val.py --weights yolov5x.pt --data coco.yaml --img 640 --iou 0.65 --half"
       ],
       "execution_count": null,
@@ -631,40 +621,6 @@
         }
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "rc_KbFk0juX2"
-      },
-      "source": [
-        "## COCO test\n",
-        "Download [COCO test2017](https://github.com/ultralytics/yolov5/blob/74b34872fdf41941cddcf243951cdb090fbac17b/data/coco.yaml#L15) dataset (7GB - 40,000 images), to test model accuracy on test-dev set (**20,000 images, no labels**). Results are saved to a `*.json` file which should be **zipped** and submitted to the evaluation server at https://competitions.codalab.org/competitions/20794."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "V0AJnSeCIHyJ"
-      },
-      "source": [
-        "# Download COCO test-dev2017\n",
-        "!bash data/scripts/get_coco.sh --test"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "29GJXAP_lPrt"
-      },
-      "source": [
-        "# Run YOLOv5x on COCO test\n",
-        "!python val.py --weights yolov5x.pt --data coco.yaml --img 640 --iou 0.65 --half --task test"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -1136,6 +1092,19 @@
       "execution_count": null,
       "outputs": []
     },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Validate on COCO test. Zip results.json and submit to eval server at https://competitions.codalab.org/competitions/20794\n",
+        "!bash data/scripts/get_coco.sh --test  # download COCO test-dev2017 (7GB - 40,000 images, test 20,000)\n",
+        "!python val.py --weights yolov5x.pt --data coco.yaml --img 640 --iou 0.65 --half --task test"
+      ],
+      "metadata": {
+        "id": "aq4DPWGu0Bl1"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
     {
       "cell_type": "code",
       "metadata": {