From e5991c986725d1229b6d1f5b1533e10f9b41c850 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Wed, 3 Aug 2022 00:57:40 +0200 Subject: [PATCH] Created using Colaboratory --- tutorial.ipynb | 30 +++++++++++++----------------- 1 file changed, 13 insertions(+), 17 deletions(-) diff --git a/tutorial.ipynb b/tutorial.ipynb index b5cb4964a..83be1039f 100644 --- a/tutorial.ipynb +++ b/tutorial.ipynb @@ -414,7 +414,7 @@ "import utils\n", "display = utils.notebook_init() # checks" ], - "execution_count": 1, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -466,7 +466,7 @@ "!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": 2, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -546,7 +546,7 @@ "torch.hub.download_url_to_file('https://ultralytics.com/assets/coco2017val.zip', 'tmp.zip')\n", "!unzip -q tmp.zip -d ../datasets && rm tmp.zip" ], - "execution_count": 3, + "execution_count": null, "outputs": [ { "output_type": "display_data", @@ -577,7 +577,7 @@ "# Run YOLOv5x on COCO val\n", "!python val.py --weights yolov5x.pt --data coco.yaml --img 640 --iou 0.65 --half" ], - "execution_count": 4, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -737,7 +737,7 @@ "# 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", @@ -917,13 +917,14 @@ "id": "DLI1JmHU7B0l" }, "source": [ - "## Weights & Biases Logging 🌟 NEW\n", + "## Weights & Biases Logging\n", "\n", - "[Weights & Biases](https://wandb.ai/site?utm_campaign=repo_yolo_notebook) (W&B) is now integrated with YOLOv5 for real-time visualization and cloud logging of training runs. This allows for better run comparison and introspection, as well improved visibility and collaboration for teams. To enable W&B `pip install wandb`, and then train normally (you will be guided through setup on first use). \n", + "[Weights & Biases](https://wandb.ai/site?utm_campaign=repo_yolo_notebook) (W&B) is integrated with YOLOv5 for real-time visualization and cloud logging of training runs. This allows for better run comparison and introspection, as well improved visibility and collaboration for teams. To enable W&B `pip install wandb`, and then train normally (you will be guided through setup on first use). \n", "\n", - "During training you will see live updates at [https://wandb.ai/home](https://wandb.ai/home?utm_campaign=repo_yolo_notebook), and you can create and share detailed [Reports](https://wandb.ai/glenn-jocher/yolov5_tutorial/reports/YOLOv5-COCO128-Tutorial-Results--VmlldzozMDI5OTY) of your results. For more information see the [YOLOv5 Weights & Biases Tutorial](https://github.com/ultralytics/yolov5/issues/1289). \n", + "During training you will see live updates at [https://wandb.ai/home](https://wandb.ai/home?utm_campaign=repo_yolo_notebook), and you can create and share detailed [Reports](https://wandb.ai/glenn-jocher/yolov5_tutorial/reports/YOLOv5-COCO128-Tutorial-Results--VmlldzozMDI5OTY) of your results. For more information see the [YOLOv5 Weights & Biases Tutorial](https://github.com/ultralytics/yolov5/issues/1289). \n", "\n", - "

\"Weights

" + "\n", + "\"Weights" ] }, { @@ -934,16 +935,11 @@ "source": [ "## Local Logging\n", "\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", + "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 statistics, mosaics, labels, predictions and augmentations, as well as metrics and charts including Precision-Recall curves and Confusion Matrices. \n", "\n", - "> \n", - "`train_batch0.jpg` shows train batch 0 mosaics and labels\n", + "A **Mosaic Dataloader** is used for training (shown in train*.jpg images), which combines 4 images into 1 mosaic during training.\n", "\n", - "> \n", - "`test_batch0_labels.jpg` shows val batch 0 labels\n", - "\n", - "> \n", - "`test_batch0_pred.jpg` shows val batch 0 _predictions_\n", + "\"Local\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", "\n",