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/index.md

@@ -83,3 +83,62 @@ Transitioning labels from the DOTA dataset format to the YOLO OBB format can be
 This conversion mechanism is instrumental for datasets in the DOTA format, ensuring alignment with the Ultralytics YOLO OBB format.
 
 It's imperative to validate the compatibility of the dataset with your model and adhere to the necessary format conventions. Properly structured datasets are pivotal for training efficient object detection models with oriented bounding boxes.
+
+## FAQ
+
+### What are Oriented Bounding Boxes (OBB) and how are they used in Ultralytics YOLO models?
+
+Oriented Bounding Boxes (OBB) are a type of bounding box annotation where the box can be rotated to align more closely with the object being detected, rather than just being axis-aligned. This is particularly useful in aerial or satellite imagery where objects might not be aligned with the image axes. In Ultralytics YOLO models, OBBs are represented by their four corner points in the YOLO OBB format. This allows for more accurate object detection since the bounding boxes can rotate to fit the objects better.
+
+### How do I convert my existing DOTA dataset labels to YOLO OBB format for use with Ultralytics YOLOv8?
+
+You can convert DOTA dataset labels to YOLO OBB format using the `convert_dota_to_yolo_obb` function from Ultralytics. This conversion ensures compatibility with the Ultralytics YOLO models, enabling you to leverage the OBB capabilities for enhanced object detection. Here's a quick example:
+
+```python
+from ultralytics.data.converter import convert_dota_to_yolo_obb
+
+convert_dota_to_yolo_obb("path/to/DOTA")
+```
+
+This script will reformat your DOTA annotations into a YOLO-compatible format.
+
+### How do I train a YOLOv8 model with oriented bounding boxes (OBB) on my dataset?
+
+Training a YOLOv8 model with OBBs involves ensuring your dataset is in the YOLO OBB format and then using the Ultralytics API to train the model. Here's an example in both Python and CLI:
+
+!!! Example
+
+    === "Python"
+    
+        ```python
+        from ultralytics import YOLO
+
+        # Create a new YOLOv8n-OBB model from scratch
+        model = YOLO("yolov8n-obb.yaml")
+
+        # Train the model on the custom dataset
+        results = model.train(data="your_dataset.yaml", epochs=100, imgsz=640)
+        ```
+    
+
+    === "CLI"
+    
+        ```bash
+        # Train a new YOLOv8n-OBB model on the custom dataset
+        yolo detect train data=your_dataset.yaml model=yolov8n.pt epochs=100 imgsz=640
+        ```
+        
+This ensures your model leverages the detailed OBB annotations for improved detection accuracy.
+
+### What datasets are currently supported for OBB training in Ultralytics YOLO models?
+
+Currently, Ultralytics supports the following datasets for OBB training:
+
+- [DOTA-v2](dota-v2.md): This dataset includes 1.7 million instances with oriented bounding boxes and 11,268 images, primarily focusing on aerial object detection.
+- [DOTA8](dota8.md): A smaller, 8-image subset of the DOTA dataset used for testing and continuous integration (CI) checks.
+
+These datasets are tailored for scenarios where OBBs offer a significant advantage, such as aerial and satellite image analysis.
+
+### Can I use my own dataset with oriented bounding boxes for YOLOv8 training, and if so, how?
+
+Yes, you can use your own dataset with oriented bounding boxes for YOLOv8 training. Ensure your dataset annotations are converted to the YOLO OBB format, which involves defining bounding boxes by their four corner points. You can then create a YAML configuration file specifying the dataset paths, classes, and other necessary details. For more information on creating and configuring your datasets, refer to the [Supported Datasets](#supported-datasets) section.