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  1. tutorial.ipynb +3 -3
tutorial.ipynb CHANGED
@@ -1074,7 +1074,7 @@
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  "id": "7KN5ghjE6ZWh"
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  },
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  "source": [
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- "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)."
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  },
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  {
@@ -1084,7 +1084,7 @@
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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,
@@ -1096,7 +1096,7 @@
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  "id": "lfrEegCSW3fK"
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  },
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  "source": [
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- "<img src=\"https://user-images.githubusercontent.com/26833433/97808309-8182b180-1c66-11eb-8461-bffe1a79511d.png\" width=\"800\">\n"
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  ]
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  },
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  {
 
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  "id": "7KN5ghjE6ZWh"
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  },
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  "source": [
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+ "Training results are automatically logged to [Tensorboard](https://www.tensorflow.org/tensorboard) and `runs/train/exp/results.txt`, which is plotted as `results.png` (below) after training completes. You can also plot any `results.txt` file manually:"
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  ]
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  },
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  {
 
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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 files in 'runs/train/exp'\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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  "id": "lfrEegCSW3fK"
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  },
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  "source": [
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+ "<p align=\"left\"><img width=\"800\" alt=\"COCO128 Training Results\" src=\"https://github.com/ultralytics/yolov5/releases/download/v1.0/results_COCO128.png\"></p>"
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  ]
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  },
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  {