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tutorial.ipynb

         "clear_output()\n",
         "print('Setup complete. Using torch %s %s' % (torch.__version__, torch.cuda.get_device_properties(0) if torch.cuda.is_available() else 'CPU'))"
       ],
-      "execution_count": 1,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
         "gdrive_download('1Y6Kou6kEB0ZEMCCpJSKStCor4KAReE43','coco2017val.zip')  # val2017 dataset\n",
         "!mv ./coco ../  # move folder alongside /yolov5"
       ],
-      "execution_count": 10,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
         "# Run YOLOv5x on COCO val2017\n",
         "!python test.py --weights yolov5x.pt --data coco.yaml --img 672"
       ],
-      "execution_count": 15,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
         "gdrive_download('1n_oKgR81BJtqk75b00eAjdv03qVCQn2f','coco128.zip')  # coco128 dataset\n",
         "!mv ./coco128 ../  # move folder alongside /yolov5"
       ],
-      "execution_count": 16,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
         "# Train YOLOv5s on coco128 for 3 epochs\n",
         "!python train.py --img 640 --batch 16 --epochs 3 --data coco128.yaml --cfg yolov5s.yaml --weights yolov5s.pt --nosave --cache"
       ],
-      "execution_count": 24,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
         "colab_type": "text"
       },
       "source": [
-        "Training losses and performance metrics are saved to Tensorboard and also to a `runs/exp0/results.txt` logfile. `results.txt` is plotted as `results.png` after training completes. Partially completed `results.txt` files can be plotted with `from utils.utils import plot_results; plot_results()`. Here we show YOLOv5s trained on coco128 to 300 epochs, starting from scratch (orange), and from pretrained `yolov5s.pt` (blue)."
+        "Training losses and performance metrics are saved to Tensorboard and also to a `runs/exp0/results.txt` logfile. `results.txt` is plotted as `results.png` after training completes. Partially completed `results.txt` files can be plotted with `from utils.utils import plot_results; plot_results()`. Here we show YOLOv5s trained on coco128 to 300 epochs, starting from scratch (blue), and from pretrained `yolov5s.pt` (orange)."
       ]
     },
     {
       "source": [
         "from utils.utils import plot_results; plot_results()  # plot results.txt files as results.png"
       ],
-      "execution_count": 29,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "execute_result",
         "!rm -rf yolov5 && git clone https://github.com/ultralytics/yolov5\n",
         "%cd yolov5"
       ],
-      "execution_count": 9,
+      "execution_count": null,
       "outputs": []
     },
     {