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/classify.md

@@ -179,3 +179,93 @@ Available YOLOv8-cls export formats are in the table below. You can export to an
 | [NCNN](../integrations/ncnn.md)                   | `ncnn`            | `yolov8n-cls_ncnn_model/`     | ✅       | `imgsz`, `half`, `batch`                                             |
 
 See full `export` details in the [Export](../modes/export.md) page.
+
+## FAQ
+
+### What is the purpose of YOLOv8 in image classification?
+
+YOLOv8 models, such as `yolov8n-cls.pt`, are designed for efficient image classification. They assign a single class label to an entire image along with a confidence score. This is particularly useful for applications where knowing the specific class of an image is sufficient, rather than identifying the location or shape of objects within the image.
+
+### How do I train a YOLOv8 model for image classification?
+
+To train a YOLOv8 model, you can use either Python or CLI commands. For example, to train a `yolov8n-cls` model on the MNIST160 dataset for 100 epochs at an image size of 64:
+
+!!! Example
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
+
+        # Train the model
+        results = model.train(data="mnist160", epochs=100, imgsz=64)
+        ```
+
+    === "CLI"
+
+        ```bash
+        yolo classify train data=mnist160 model=yolov8n-cls.pt epochs=100 imgsz=64
+        ```
+
+For more configuration options, visit the [Configuration](../usage/cfg.md) page.
+
+### Where can I find pretrained YOLOv8 classification models?
+
+Pretrained YOLOv8 classification models can be found in the [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models/v8) section. Models like `yolov8n-cls.pt`, `yolov8s-cls.pt`, `yolov8m-cls.pt`, etc., are pretrained on the [ImageNet](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/ImageNet.yaml) dataset and can be easily downloaded and used for various image classification tasks.
+
+### How can I export a trained YOLOv8 model to different formats?
+
+You can export a trained YOLOv8 model to various formats using Python or CLI commands. For instance, to export a model to ONNX format:
+
+!!! Example
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO("yolov8n-cls.pt")  # load the trained model
+
+        # Export the model to ONNX
+        model.export(format="onnx")
+        ```
+
+    === "CLI"
+
+        ```bash
+        yolo export model=yolov8n-cls.pt format=onnx  # export the trained model to ONNX format
+        ```
+
+For detailed export options, refer to the [Export](../modes/export.md) page.
+
+### How do I validate a trained YOLOv8 classification model?
+
+To validate a trained model's accuracy on a dataset like MNIST160, you can use the following Python or CLI commands:
+
+!!! Example
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO("yolov8n-cls.pt")  # load the trained model
+
+        # Validate the model
+        metrics = model.val()  # no arguments needed, uses the dataset and settings from training
+        metrics.top1  # top1 accuracy
+        metrics.top5  # top5 accuracy
+        ```
+
+    === "CLI"
+
+        ```bash
+        yolo classify val model=yolov8n-cls.pt  # validate the trained model
+        ```
+
+For more information, visit the [Validate](#val) section.