diff --git a/tutorial.ipynb b/tutorial.ipynb index 8dcf6f8..4037b70 100644 --- a/tutorial.ipynb +++ b/tutorial.ipynb @@ -824,7 +824,7 @@ "source": [ "# 3. Train\n", "\n", - "Download [COCO128](https://www.kaggle.com/ultralytics/coco128), a small 128-image tutorial dataset, start tensorboard and train YOLOv5s from a pretrained checkpoint for 3 epochs (actual training is typically much longer, around **300-1000 epochs**, depending on your dataset)." + "Download [COCO128](https://www.kaggle.com/ultralytics/coco128), a small 128-image tutorial dataset, start tensorboard and train YOLOv5s from a pretrained checkpoint for 3 epochs (note actual training is typically much longer, around **300-1000 epochs**, depending on your dataset)." ] }, { @@ -885,9 +885,9 @@ "id": "_pOkGLv1dMqh" }, "source": [ - "Train a YOLOv5s model on [COCO128](https://www.kaggle.com/ultralytics/coco128) with dataset `--data coco128.yaml`, starting from pretrained `--weights yolov5s.pt`, or from randomly initialized `--weights '' --cfg yolov5s.yaml`. Models are downloaded automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases), and **COCO, COCO128, and VOC datasets are downloaded automatically** on first use.\n", + "Train a YOLOv5s model on [COCO128](https://www.kaggle.com/ultralytics/coco128) with `--data coco128.yaml`, starting from pretrained `--weights yolov5s.pt`, or from randomly initialized `--weights '' --cfg yolov5s.yaml`. Models are downloaded automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases), and **COCO, COCO128, and VOC datasets are downloaded automatically** on first use.\n", "\n", - "All training results are saved to `runs/train/exp0` for the first experiment, then `runs/exp1`, `runs/exp2` etc. for subsequent experiments.\n" + "All training results are saved to `runs/train/` with incrementing run directories, i.e. `runs/train/exp0`, `runs/train/exp1` etc.\n" ] }, { @@ -1012,7 +1012,7 @@ "source": [ "## Weights & Biases Logging (🚀 NEW)\n", "\n", - "[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 among team members. To enable W&B logging install `wandb`, and then train normally (you will be guided setup on first use).\n", + "[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", "```bash\n", "$ pip install wandb\n", "```\n", @@ -1030,7 +1030,7 @@ "source": [ "## Local Logging\n", "\n", - "All results are logged by default to the `runs/train/exp0` directory, with a new directory created for each new training as `runs/exp1`, `runs/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)." + "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)." ] }, { @@ -1053,7 +1053,7 @@ }, "source": [ "> \n", - "`train_batch0.jpg` train batch 0 mosaics and labels\n", + "`train_batch0.jpg` shows train batch 0 mosaics and labels\n", "\n", "> \n", "`test_batch0_labels.jpg` shows test batch 0 labels\n",