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/detect.md

@@ -180,3 +180,111 @@ Available YOLOv8 export formats are in the table below. You can export to any fo
 | [NCNN](../integrations/ncnn.md)                   | `ncnn`            | `yolov8n_ncnn_model/`     | ✅       | `imgsz`, `half`, `batch`                                             |
 
 See full `export` details in the [Export](../modes/export.md) page.
+
+## FAQ
+
+### How do I train a YOLOv8 model on my custom dataset?
+
+Training a YOLOv8 model on a custom dataset involves a few steps:
+
+1. **Prepare the Dataset**: Ensure your dataset is in the YOLO format. For guidance, refer to our [Dataset Guide](../datasets/detect/index.md).
+2. **Load the Model**: Use the Ultralytics YOLO library to load a pre-trained model or create a new model from a YAML file.
+3. **Train the Model**: Execute the `train` method in Python or the `yolo detect train` command in CLI.
+
+!!! Example
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a pretrained model
+        model = YOLO("yolov8n.pt")
+
+        # Train the model on your custom dataset
+        model.train(data="my_custom_dataset.yaml", epochs=100, imgsz=640)
+        ```
+
+    === "CLI"
+
+        ```bash
+        yolo detect train data=my_custom_dataset.yaml model=yolov8n.pt epochs=100 imgsz=640
+        ```
+
+For detailed configuration options, visit the [Configuration](../usage/cfg.md) page.
+
+### What pretrained models are available in YOLOv8?
+
+Ultralytics YOLOv8 offers various pretrained models for object detection, segmentation, and pose estimation. These models are pretrained on the COCO dataset or ImageNet for classification tasks. Here are some of the available models:
+
+- [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n.pt)
+- [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s.pt)
+- [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m.pt)
+- [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l.pt)
+- [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x.pt)
+
+For a detailed list and performance metrics, refer to the [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models/v8) section.
+
+### How can I validate the accuracy of my trained YOLOv8 model?
+
+To validate the accuracy of your trained YOLOv8 model, you can use the `.val()` method in Python or the `yolo detect val` command in CLI. This will provide metrics like mAP50-95, mAP50, and more.
+
+!!! Example
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load the model
+        model = YOLO("path/to/best.pt")
+
+        # Validate the model
+        metrics = model.val()
+        print(metrics.box.map)  # mAP50-95
+        ```
+
+    === "CLI"
+
+        ```bash
+        yolo detect val model=path/to/best.pt
+        ```
+
+For more validation details, visit the [Val](../modes/val.md) page.
+
+### What formats can I export a YOLOv8 model to?
+
+Ultralytics YOLOv8 allows exporting models to various formats such as ONNX, TensorRT, CoreML, and more to ensure compatibility across different platforms and devices.
+
+!!! Example
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load the model
+        model = YOLO("yolov8n.pt")
+
+        # Export the model to ONNX format
+        model.export(format="onnx")
+        ```
+
+    === "CLI"
+
+        ```bash
+        yolo export model=yolov8n.pt format=onnx
+        ```
+
+Check the full list of supported formats and instructions on the [Export](../modes/export.md) page.
+
+### Why should I use Ultralytics YOLOv8 for object detection?
+
+Ultralytics YOLOv8 is designed to offer state-of-the-art performance for object detection, segmentation, and pose estimation. Here are some key advantages:
+
+1. **Pretrained Models**: Utilize models pretrained on popular datasets like COCO and ImageNet for faster development.
+2. **High Accuracy**: Achieves impressive mAP scores, ensuring reliable object detection.
+3. **Speed**: Optimized for real-time inference, making it ideal for applications requiring swift processing.
+4. **Flexibility**: Export models to various formats like ONNX and TensorRT for deployment across multiple platforms.
+
+Explore our [Blog](https://www.ultralytics.com/blog) for use cases and success stories showcasing YOLOv8 in action.