Add FAQ sections to Modes and Tasks (#14181)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Abirami Vina <abirami.vina@gmail.com>
Co-authored-by: RizwanMunawar <chr043416@gmail.com>
Co-authored-by: Muhammad Rizwan Munawar <muhammadrizwanmunawar123@gmail.com>

docs/en/tasks/index.md

@@ -55,3 +55,68 @@ Oriented object detection goes a step further than regular object detection with
 ## Conclusion
 
 YOLOv8 supports multiple tasks, including detection, segmentation, classification, oriented object detection and keypoints detection. Each of these tasks has different objectives and use cases. By understanding the differences between these tasks, you can choose the appropriate task for your computer vision application.
+
+## FAQ
+
+### What tasks can Ultralytics YOLOv8 perform?
+
+Ultralytics YOLOv8 is a versatile AI framework capable of performing various computer vision tasks with high accuracy and speed. These tasks include:
+
+- **[Detection](detect.md):** Identifying and localizing objects in images or video frames by drawing bounding boxes around them.
+- **[Segmentation](segment.md):** Segmenting images into different regions based on their content, useful for applications like medical imaging.
+- **[Classification](classify.md):** Categorizing entire images based on their content, leveraging variants of the EfficientNet architecture.
+- **[Pose estimation](pose.md):** Detecting specific keypoints in an image or video frame to track movements or poses.
+- **[Oriented Object Detection (OBB)](obb.md):** Detecting rotated objects with an added orientation angle for enhanced accuracy.
+
+### How do I use Ultralytics YOLOv8 for object detection?
+
+To use Ultralytics YOLOv8 for object detection, follow these steps:
+
+1. Prepare your dataset in the appropriate format.
+2. Train the YOLOv8 model using the detection task.
+3. Use the model to make predictions by feeding in new images or video frames.
+
+!!! Example
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        model = YOLO("yolov8n.pt")  # Load pre-trained model
+        results = model.predict(source="image.jpg")  # Perform object detection
+        results[0].show()
+        ```
+
+    === "CLI"
+
+        ```bash
+        yolo detect model=yolov8n.pt source='image.jpg'
+        ```
+
+For more detailed instructions, check out our [detection examples](detect.md).
+
+### What are the benefits of using YOLOv8 for segmentation tasks?
+
+Using YOLOv8 for segmentation tasks provides several advantages:
+
+1. **High Accuracy:** The segmentation task leverages a variant of the U-Net architecture to achieve precise segmentation.
+2. **Speed:** YOLOv8 is optimized for real-time applications, offering quick processing even for high-resolution images.
+3. **Multiple Applications:** It is ideal for medical imaging, autonomous driving, and other applications requiring detailed image segmentation.
+
+Learn more about the benefits and use cases of YOLOv8 for segmentation in the [segmentation section](segment.md).
+
+### Can Ultralytics YOLOv8 handle pose estimation and keypoint detection?
+
+Yes, Ultralytics YOLOv8 can effectively perform pose estimation and keypoint detection with high accuracy and speed. This feature is particularly useful for tracking movements in sports analytics, healthcare, and human-computer interaction applications. YOLOv8 detects keypoints in an image or video frame, allowing for precise pose estimation.
+
+For more details and implementation tips, visit our [pose estimation examples](pose.md).
+
+### Why should I choose Ultralytics YOLOv8 for oriented object detection (OBB)?
+
+Oriented Object Detection (OBB) with YOLOv8 provides enhanced precision by detecting objects with an additional angle parameter. This feature is beneficial for applications requiring accurate localization of rotated objects, such as aerial imagery analysis and warehouse automation.
+
+- **Increased Precision:** The angle component reduces false positives for rotated objects.
+- **Versatile Applications:** Useful for tasks in geospatial analysis, robotics, etc.
+
+Check out the [Oriented Object Detection section](obb.md) for more details and examples.