Fix gitignore to format Docs datasets (#16071)

Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>

docs/en/datasets/detect/visdrone.md

@@ -107,6 +107,7 @@ We would like to acknowledge the AISKYEYE team at the Lab of Machine Learning an
 ### What is the VisDrone Dataset and what are its key features?
 
 The [VisDrone Dataset](https://github.com/VisDrone/VisDrone-Dataset) is a large-scale benchmark created by the AISKYEYE team at Tianjin University, China. It is designed for various computer vision tasks related to drone-based image and video analysis. Key features include:
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 - **Composition**: 288 video clips with 261,908 frames and 10,209 static images.
 - **Annotations**: Over 2.6 million bounding boxes for objects like pedestrians, cars, bicycles, and tricycles.
 - **Diversity**: Collected across 14 cities, in urban and rural settings, under different weather and lighting conditions.
@@ -119,7 +120,7 @@ To train a YOLOv8 model on the VisDrone dataset for 100 epochs with an image siz
 !!! Example "Train Example"
 
     === "Python"
-    
+
         ```python
         from ultralytics import YOLO
 
@@ -131,7 +132,7 @@ To train a YOLOv8 model on the VisDrone dataset for 100 epochs with an image siz
         ```
 
     === "CLI"
-    
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         ```bash
         # Start training from a pretrained *.pt model
         yolo detect train data=VisDrone.yaml model=yolov8n.pt epochs=100 imgsz=640
@@ -142,6 +143,7 @@ For additional configuration options, please refer to the model [Training](../..
 ### What are the main subsets of the VisDrone dataset and their applications?
 
 The VisDrone dataset is divided into five main subsets, each tailored for a specific computer vision task:
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 1. **Task 1**: Object detection in images.
 2. **Task 2**: Object detection in videos.
 3. **Task 3**: Single-object tracking.