Created using Colaboratory

tutorial.ipynb

@@ -65,7 +65,7 @@
         "import utils\n",
         "display = utils.notebook_init()  # checks"
       ],
-      "execution_count": 1,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -95,7 +95,7 @@
         "\n",
         "```shell\n",
         "python detect.py --source 0  # webcam\n",
-        "                          img.jpg  # image \n",
+        "                          img.jpg  # image\n",
         "                          vid.mp4  # video\n",
         "                          screen  # screenshot\n",
         "                          path/  # directory\n",
@@ -118,7 +118,7 @@
         "!python detect.py --weights yolov5s.pt --img 640 --conf 0.25 --source data/images\n",
         "# display.Image(filename='runs/detect/exp/zidane.jpg', width=600)"
       ],
-      "execution_count": 13,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -174,7 +174,7 @@
         "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": 3,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -198,7 +198,7 @@
         "# Validate YOLOv5s on COCO val\n",
         "!python val.py --weights yolov5s.pt --data coco.yaml --img 640 --half"
       ],
-      "execution_count": 4,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -308,7 +308,7 @@
         "# Train YOLOv5s on COCO128 for 3 epochs\n",
         "!python train.py --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --cache"
       ],
-      "execution_count": 5,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -539,7 +539,7 @@
         "\n",
         "Training results are automatically logged with [Tensorboard](https://www.tensorflow.org/tensorboard) and [CSV](https://github.com/ultralytics/yolov5/pull/4148) loggers to `runs/train`, with a new experiment directory created for each new training as `runs/train/exp2`, `runs/train/exp3`, etc.\n",
         "\n",
-        "This directory contains train and val statistics, mosaics, labels, predictions and augmentated mosaics, as well as metrics and charts including precision-recall (PR) curves and confusion matrices. \n",
+        "This directory contains train and val statistics, mosaics, labels, predictions and augmentated mosaics, as well as metrics and charts including precision-recall (PR) curves and confusion matrices.\n",
         "\n",
         "<img alt=\"Local logging results\" src=\"https://user-images.githubusercontent.com/26833433/183222430-e1abd1b7-782c-4cde-b04d-ad52926bf818.jpg\" width=\"1280\"/>\n"
       ]
@@ -593,7 +593,7 @@
         "# YOLOv5 PyTorch HUB Inference (DetectionModels only)\n",
         "import torch\n",
         "\n",
-        "model = torch.hub.load('ultralytics/yolov5', 'yolov5s', force_reload=True)  # yolov5n - yolov5x6 or custom\n",
+        "model = torch.hub.load('ultralytics/yolov5', 'yolov5s', force_reload=True, trust_repo=True)  # or yolov5n - yolov5x6 or custom\n",
         "im = 'https://ultralytics.com/images/zidane.jpg'  # file, Path, PIL.Image, OpenCV, nparray, list\n",
         "results = model(im)  # inference\n",
         "results.print()  # or .show(), .save(), .crop(), .pandas(), etc."
@@ -602,4 +602,4 @@
       "outputs": []
     }
   ]
-}
+}