diff --git a/tutorial.ipynb b/tutorial.ipynb
index f316dc5f550a..88adc08c0ef1 100644
--- a/tutorial.ipynb
+++ b/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",
"> \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",
">
\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": [
- "