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Fix gitignore to format Docs datasets (#16071)

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Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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Glenn Jocher 2024-09-06 17:17:33 +02:00 • committed by GitHub
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7
docs/en/datasets/segment/package-seg.md
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@ -104,7 +104,7 @@ You can train an Ultralytics YOLOv8n model using both Python and CLI methods. Us
!!! Example "Train Example"
=== "Python"
```python
from ultralytics import YOLO
@ -116,7 +116,7 @@ You can train an Ultralytics YOLOv8n model using both Python and CLI methods. Us
```
=== "CLI"
```bash
# Start training from a pretrained *.pt model
yolo segment train data=package-seg.yaml model=yolov8n-seg.pt epochs=100 imgsz=640
@ -127,6 +127,7 @@ Refer to the model [Training](../../modes/train.md) page for more details.
### What are the components of the Package Segmentation Dataset, and how is it structured?
The dataset is structured into three main components:
- **Training set**: Contains 1920 images with annotations.
- **Testing set**: Comprises 89 images with corresponding annotations.
- **Validation set**: Includes 188 images with annotations.
@ -139,6 +140,6 @@ Ultralytics YOLOv8 provides state-of-the-art accuracy and speed for real-time ob
### How can I access and use the package-seg.yaml file for the Package Segmentation Dataset?
The `package-seg.yaml` file is hosted on Ultralytics' GitHub repository and contains essential information about the dataset's paths, classes, and configuration. You can download it from [here](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/package-seg.yaml). This file is crucial for configuring your models to utilize the dataset efficiently.
The `package-seg.yaml` file is hosted on Ultralytics' GitHub repository and contains essential information about the dataset's paths, classes, and configuration. You can download it from [here](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/package-seg.yaml). This file is crucial for configuring your models to utilize the dataset efficiently.
For more insights and practical examples, explore our [Usage](https://docs.ultralytics.com/usage/python/) section.