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tutorial.ipynb

@@ -564,7 +564,7 @@
         "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",
@@ -600,7 +600,7 @@
         "!python detect.py --weights yolov5s.pt --img 640 --conf 0.25 --source inference/images/\n",
         "Image(filename='inference/output/zidane.jpg', width=600)"
       ],
-      "execution_count": 38,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -641,7 +641,7 @@
         "id": "4qbaa3iEcrcE"
       },
       "source": [
-        "Available inference sources:\n",
+        "Results are saved to `inference/output`. A full list of available inference sources:\n",
         "<img src=\"https://user-images.githubusercontent.com/26833433/98274798-2b7a7a80-1f94-11eb-91a4-70c73593e26b.jpg\" width=\"900\"> "
       ]
     },
@@ -690,7 +690,7 @@
         "torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/releases/download/v1.0/coco2017val.zip', 'tmp.zip')\n",
         "!unzip -q tmp.zip -d ../ && rm tmp.zip"
       ],
-      "execution_count": 16,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "display_data",
@@ -730,7 +730,7 @@
         "# Run YOLOv5x on COCO val2017\n",
         "!python test.py --weights yolov5x.pt --data coco.yaml --img 640"
       ],
-      "execution_count": 17,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -797,9 +797,10 @@
       },
       "source": [
         "# Download COCO test-dev2017\n",
-        "gdrive_download('1cXZR_ckHki6nddOmcysCuuJFM--T-Q6L','coco2017labels.zip')  # annotations\n",
+        "torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/releases/download/v1.0/coco2017labels.zip', 'tmp.zip')\n",
+        "!unzip -q tmp.zip -d ../ && rm tmp.zip  # unzip labels\n",
         "!f=\"test2017.zip\" && curl http://images.cocodataset.org/zips/$f -o $f && unzip -q $f && rm $f  # 7GB,  41k images\n",
-        "!mv ./test2017 ./coco/images && mv ./coco ../  # move images into /coco and move /coco alongside /yolov5"
+        "%mv ./test2017 ./coco/images && mv ./coco ../  # move images to /coco and move /coco next to /yolov5"
       ],
       "execution_count": null,
       "outputs": []
@@ -852,7 +853,7 @@
         "torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128.zip', 'tmp.zip')\n",
         "!unzip -q tmp.zip -d ../ && rm tmp.zip"
       ],
-      "execution_count": 22,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "display_data",
@@ -916,7 +917,7 @@
         "# Train YOLOv5s on COCO128 for 3 epochs\n",
         "!python train.py --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --nosave --cache"
       ],
-      "execution_count": 23,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -1023,7 +1024,7 @@
       "source": [
         "## 4.2 Local Logging\n",
         "\n",
-        "All results are logged by default to the `runs/exp0` directory, with a new directory created for each new training as `runs/exp1`, `runs/exp2`, etc. View `train_batch*.jpg` to see training images, labels and augmentation effects. A **Mosaic Dataloader** is used for training (shown below), a new concept developed by Ultralytics and first featured in [YOLOv4](https://arxiv.org/abs/2004.10934)."
+        "All results are logged by default to the `runs/exp0` directory, with a new directory created for each new training as `runs/exp1`, `runs/exp2`, etc. View train and test jpgs to see mosaics, labels/predictions and augmentation effects. Note a **Mosaic Dataloader** is used for training (shown below), a new concept developed by Ultralytics and first featured in [YOLOv4](https://arxiv.org/abs/2004.10934)."
       ]
     },
     {
@@ -1046,7 +1047,7 @@
       },
       "source": [
         "> <img src=\"https://user-images.githubusercontent.com/26833433/83667642-90fcb200-a583-11ea-8fa3-338bbf7da194.jpeg\" width=\"750\">  \n",
-        "`test_batch0_gt.jpg` train batch 0 mosaics and labels\n",
+        "`train_batch0.jpg` train batch 0 mosaics and labels\n",
         "\n",
         "> <img src=\"https://user-images.githubusercontent.com/26833433/83667626-8c37fe00-a583-11ea-997b-0923fe59b29b.jpeg\" width=\"750\">  \n",
         "`test_batch0_gt.jpg` shows test batch 0 ground truth\n",
@@ -1061,7 +1062,7 @@
         "id": "7KN5ghjE6ZWh"
       },
       "source": [
-        "Training losses and performance metrics are also logged to Tensorboard and a custom `runs/exp0/results.txt` logfile. `results.txt` is plotted as `results.png` (below) after training completes. Here we show YOLOv5s trained on COCO128 to 300 epochs, starting from scratch (blue), and from pretrained `yolov5s.pt` (orange)."
+        "Training losses and performance metrics are also logged to [Tensorboard](https://www.tensorflow.org/tensorboard) and a custom `results.txt` logfile which is plotted as `results.png` (below) after training completes. Here we show YOLOv5s trained on COCO128 to 300 epochs, starting from scratch (blue), and from pretrained `--weights yolov5s.pt` (orange)."
       ]
     },
     {