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Update tutorial.ipynb

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  1. tutorial.ipynb +3 -3
tutorial.ipynb CHANGED
@@ -1012,7 +1012,7 @@
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  "source": [
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  "## Weights & Biases Logging (πŸš€ NEW)\n",
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  "\n",
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- "[Weights & Biases](https://www.wandb.com/) (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 logging install `wandb`, and then train normally (you will be guided setup on first use).\n",
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  "```bash\n",
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  "$ pip install wandb\n",
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  "```\n",
@@ -1030,7 +1030,7 @@
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  "source": [
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  "## Local Logging\n",
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  "\n",
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- "All results are logged by default to `runs/train`, with a new experiment directory created for each new training as `runs/train/exp1`, `runs/train/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)."
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  ]
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  },
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  {
@@ -1182,4 +1182,4 @@
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  "outputs": []
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  }
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  ]
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- }
 
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  "source": [
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  "## Weights & Biases Logging (πŸš€ NEW)\n",
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  "\n",
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+ "[Weights & Biases](https://www.wandb.com/) (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 logging install `wandb`, and then train normally (you will be guided through setup on first use).\n",
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  "```bash\n",
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  "$ pip install wandb\n",
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  "```\n",
 
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  "source": [
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  "## Local Logging\n",
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  "\n",
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+ "All results are logged by default to `runs/train`, with a new experiment directory created for each new training as `runs/train/exp0`, `runs/train/exp1`, 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)."
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  ]
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  },
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  {
 
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  "outputs": []
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  }
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  ]
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+ }