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tutorial.ipynb

@@ -534,7 +534,7 @@
       },
       "source": [
         "# Download COCO val\n",
-        "torch.hub.download_url_to_file('https://ultralytics.com/assets/coco2017val.zip', 'tmp.zip')  # download COCO val (1GB - 5000 images)\n",
+        "torch.hub.download_url_to_file('https://ultralytics.com/assets/coco2017val.zip', 'tmp.zip')  # download (780M - 5000 images)\n",
         "!unzip -q tmp.zip -d ../datasets && rm tmp.zip  # unzip"
       ],
       "execution_count": null,
@@ -657,7 +657,7 @@
     {
       "cell_type": "code",
       "source": [
-        "#@title Select YOLOv5 🚀 logger\n",
+        "#@title Select YOLOv5 🚀 logger {run: 'auto'}\n",
         "logger = 'TensorBoard' #@param ['TensorBoard', 'ClearML', 'W&B']\n",
         "\n",
         "if logger == 'TensorBoard':\n",
@@ -1077,7 +1077,7 @@
       "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",
+        "!bash data/scripts/get_coco.sh --test  # download COCO test-dev2017 (7G - 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": {