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tutorial.ipynb

@@ -1036,28 +1036,8 @@
       "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 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)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "riPdhraOTCO0"
-      },
-      "source": [
-        "Image(filename='runs/train/exp/train_batch0.jpg', width=800)  # train batch 0 mosaics and labels\n",
-        "Image(filename='runs/train/exp/test_batch0_labels.jpg', width=800)  # val batch 0 labels\n",
-        "Image(filename='runs/train/exp/test_batch0_pred.jpg', width=800)  # val batch 0 predictions"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "OYG4WFEnTVrI"
-      },
-      "source": [
+        "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 combined each original image with 3 additional random training images.\n",
+        "\n",
         "> <img src=\"https://user-images.githubusercontent.com/26833433/124931219-48bf8700-e002-11eb-84f0-e05d95b118dd.jpg\" width=\"700\">  \n",
         "`train_batch0.jpg` shows train batch 0 mosaics and labels\n",
         "\n",
@@ -1065,38 +1045,16 @@
         "`test_batch0_labels.jpg` shows val batch 0 labels\n",
         "\n",
         "> <img src=\"https://user-images.githubusercontent.com/26833433/124931209-46f5c380-e002-11eb-9bd5-7a3de2be9851.jpg\" width=\"700\">  \n",
-        "`test_batch0_pred.jpg` shows val batch 0 _predictions_"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "7KN5ghjE6ZWh"
-      },
-      "source": [
-        "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:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "MDznIqPF7nk3"
-      },
-      "source": [
+        "`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",
+        "\n",
+        "```python\n",
         "from utils.plots import plot_results \n",
-        "plot_results(save_dir='runs/train/exp')  # plot all results*.txt files in 'runs/train/exp'\n",
-        "Image(filename='runs/train/exp/results.png', width=800)"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "lfrEegCSW3fK"
-      },
-      "source": [
-        "<p align=\"left\"><img width=\"800\" alt=\"COCO128 Training Results\" src=\"https://user-images.githubusercontent.com/26833433/125273596-6300aa00-e30d-11eb-8dc4-70a960c53013.png\"></p>"
+        "plot_results('path/to/results.csv')  # plot 'results.csv' as 'results.png'\n",
+        "```\n",
+        "\n",
+        "<p align=\"left\"><img width=\"800\" alt=\"COCO128 Training Results\" src=\"https://user-images.githubusercontent.com/26833433/126906780-8c5e2990-6116-4de6-b78a-367244a33ccf.png\"></p>"
       ]
     },
     {