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/obb/dota8.md

@@ -79,3 +79,46 @@ If you use the DOTA dataset in your research or development work, please cite th
         ```
 
 A special note of gratitude to the team behind the DOTA datasets for their commendable effort in curating this dataset. For an exhaustive understanding of the dataset and its nuances, please visit the [official DOTA website](https://captain-whu.github.io/DOTA/index.html).
+
+## FAQ
+
+### What is the DOTA8 dataset and how can it be used?
+
+The DOTA8 dataset is a small, versatile oriented object detection dataset made up of the first 8 images from the DOTAv1 split set, with 4 images designated for training and 4 for validation. It's ideal for testing and debugging object detection models like Ultralytics YOLOv8. Due to its manageable size and diversity, it helps in identifying pipeline errors and running sanity checks before deploying larger datasets. Learn more about object detection with [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics).
+
+### How do I train a YOLOv8 model using the DOTA8 dataset?
+
+To train a YOLOv8n-obb model on the DOTA8 dataset for 100 epochs with an image size of 640, you can use the following code snippets. For comprehensive argument options, refer to the model [Training](../../modes/train.md) page.
+
+!!! Example "Train Example"
+
+    === "Python"
+    
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO("yolov8n-obb.pt")  # load a pretrained model (recommended for training)
+
+        # Train the model
+        results = model.train(data="dota8.yaml", epochs=100, imgsz=640)
+        ```
+
+    === "CLI"
+    
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo obb train data=dota8.yaml model=yolov8n-obb.pt epochs=100 imgsz=640
+        ```
+
+### What are the key features of the DOTA dataset and where can I access the YAML file?
+
+The DOTA dataset is known for its large-scale benchmark and the challenges it presents for object detection in aerial images. The DOTA8 subset is a smaller, manageable dataset ideal for initial tests. You can access the `dota8.yaml` file, which contains paths, classes, and configuration details, at this [GitHub link](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/dota8.yaml).
+
+### How does mosaicing enhance model training with the DOTA8 dataset?
+
+Mosaicing combines multiple images into one during training, increasing the variety of objects and contexts within each batch. This improves a model's ability to generalize to different object sizes, aspect ratios, and scenes. This technique can be visually demonstrated through a training batch composed of mosaiced DOTA8 dataset images, helping in robust model development. Explore more about mosaicing and training techniques on our [Training](../../modes/train.md) page.
+
+### Why should I use Ultralytics YOLOv8 for object detection tasks?
+
+Ultralytics YOLOv8 provides state-of-the-art real-time object detection capabilities, including features like oriented bounding boxes (OBB), instance segmentation, and a highly versatile training pipeline. It's suitable for various applications and offers pretrained models for efficient fine-tuning. Explore further about the advantages and usage in the [Ultralytics YOLOv8 documentation](https://github.com/ultralytics/ultralytics).