Add FAQs to Docs Datasets and Help sections (#14211)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>

docs/en/datasets/detect/visdrone.md

@@ -90,3 +90,74 @@ If you use the VisDrone dataset in your research or development work, please cit
         ```
 
 We would like to acknowledge the AISKYEYE team at the Lab of Machine Learning and Data Mining, Tianjin University, China, for creating and maintaining the VisDrone dataset as a valuable resource for the drone-based computer vision research community. For more information about the VisDrone dataset and its creators, visit the [VisDrone Dataset GitHub repository](https://github.com/VisDrone/VisDrone-Dataset).
+
+## FAQ
+
+### 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:
+- **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.
+- **Tasks**: Split into five main tasks—object detection in images and videos, single-object and multi-object tracking, and crowd counting.
+
+### How can I use the VisDrone Dataset to train a YOLOv8 model with Ultralytics?
+
+To train a YOLOv8 model on the VisDrone dataset for 100 epochs with an image size of 640, you can follow these steps:
+
+!!! Example "Train Example"
+
+    === "Python"
+    
+        ```python
+        from ultralytics import YOLO
+
+        # Load a pretrained model
+        model = YOLO("yolov8n.pt")
+
+        # Train the model
+        results = model.train(data="VisDrone.yaml", epochs=100, imgsz=640)
+        ```
+
+    === "CLI"
+    
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo detect train data=VisDrone.yaml model=yolov8n.pt epochs=100 imgsz=640
+        ```
+
+For additional configuration options, please refer to the model [Training](../../modes/train.md) page.
+
+### 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:
+1. **Task 1**: Object detection in images.
+2. **Task 2**: Object detection in videos.
+3. **Task 3**: Single-object tracking.
+4. **Task 4**: Multi-object tracking.
+5. **Task 5**: Crowd counting.
+
+These subsets are widely used for training and evaluating deep learning models in drone-based applications such as surveillance, traffic monitoring, and public safety.
+
+### Where can I find the configuration file for the VisDrone dataset in Ultralytics?
+
+The configuration file for the VisDrone dataset, `VisDrone.yaml`, can be found in the Ultralytics repository at the following link:
+[VisDrone.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/VisDrone.yaml).
+
+### How can I cite the VisDrone dataset if I use it in my research?
+
+If you use the VisDrone dataset in your research or development work, please cite the following paper:
+
+!!! Quote "BibTeX"
+
+    ```bibtex
+    @ARTICLE{9573394,
+      author={Zhu, Pengfei and Wen, Longyin and Du, Dawei and Bian, Xiao and Fan, Heng and Hu, Qinghua and Ling, Haibin},
+      journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
+      title={Detection and Tracking Meet Drones Challenge},
+      year={2021},
+      volume={},
+      number={},
+      pages={1-1},
+      doi={10.1109/TPAMI.2021.3119563}}
+    ```