From 171630ad2ec689ad611d3b243cb52804b2fb64ff Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Sun, 29 Aug 2021 17:56:35 +0200 Subject: [PATCH] Created using Colaboratory --- tutorial.ipynb | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/tutorial.ipynb b/tutorial.ipynb index ba6d19113a93..d3388670f56c 100644 --- a/tutorial.ipynb +++ b/tutorial.ipynb @@ -415,7 +415,7 @@ "clear_output()\n", "print(f\"Setup complete. Using torch {torch.__version__} ({torch.cuda.get_device_properties(0).name if torch.cuda.is_available() else 'CPU'})\")" ], - "execution_count": 1, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -461,7 +461,7 @@ "!python detect.py --weights yolov5s.pt --img 640 --conf 0.25 --source data/images/\n", "#Image(filename='runs/detect/exp/zidane.jpg', width=600)" ], - "execution_count": 4, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -538,7 +538,7 @@ "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 ../datasets && rm tmp.zip" ], - "execution_count": 5, + "execution_count": null, "outputs": [ { "output_type": "display_data", @@ -571,7 +571,7 @@ "# Run YOLOv5x on COCO val2017\n", "!python val.py --weights yolov5x.pt --data coco.yaml --img 640 --iou 0.65 --half" ], - "execution_count": 6, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -734,7 +734,7 @@ "# 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": 8, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -853,13 +853,13 @@ "\n", "All results are logged by default to `runs/train`, with a new experiment directory created for each new training as `runs/train/exp2`, `runs/train/exp3`, etc. View train and val jpgs to see mosaics, labels, predictions and augmentation effects. Note an Ultralytics **Mosaic Dataloader** is used for training (shown below), which combines 4 images into 1 mosaic during training.\n", "\n", - "> \n", + "> \n", "`train_batch0.jpg` shows train batch 0 mosaics and labels\n", "\n", - "> \n", + "> \n", "`test_batch0_labels.jpg` shows val batch 0 labels\n", "\n", - "> \n", + "> \n", "`test_batch0_pred.jpg` shows val batch 0 _predictions_\n", "\n", "Training results are automatically logged to [Tensorboard](https://www.tensorflow.org/tensorboard) and [CSV](https://github.com/ultralytics/yolov5/pull/4148) as `results.csv`, which is plotted as `results.png` (below) after training completes. You can also plot any `results.csv` file manually:\n",