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  1. tutorial.ipynb +5 -5
tutorial.ipynb CHANGED
@@ -1010,7 +1010,7 @@
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  "id": "DLI1JmHU7B0l"
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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",
@@ -1077,9 +1077,9 @@
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  "id": "MDznIqPF7nk3"
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  },
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  "source": [
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- "from utils.utils import plot_results \n",
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- "plot_results(save_dir='runs/train/exp') # plot results.txt as results.png\n",
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- "Image(filename='results.png', width=800) "
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  ],
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  "execution_count": null,
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  "outputs": []
@@ -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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  "id": "DLI1JmHU7B0l"
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  },
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  "source": [
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+ "## Weights & Biases Logging 🌟 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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  "id": "MDznIqPF7nk3"
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  },
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  "source": [
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+ "from utils.plots import plot_results \n",
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+ "plot_results(save_dir='runs/train/exp') # plot all results*.txt as results.png\n",
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+ "Image(filename='runs/train/exp/results.png', width=800)"
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  ],
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  "execution_count": null,
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  "outputs": []
 
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  "outputs": []
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  }
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  ]
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+ }