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/pose/coco8-pose.md

@@ -77,3 +77,55 @@ If you use the COCO dataset in your research or development work, please cite th
         ```
 
 We would like to acknowledge the COCO Consortium for creating and maintaining this valuable resource for the computer vision community. For more information about the COCO dataset and its creators, visit the [COCO dataset website](https://cocodataset.org/#home).
+
+## FAQ
+
+### What is the COCO8-Pose dataset, and how is it used with Ultralytics YOLOv8?
+
+The COCO8-Pose dataset is a small, versatile pose detection dataset that includes the first 8 images from the COCO train 2017 set, with 4 images for training and 4 for validation. It's designed for testing and debugging object detection models and experimenting with new detection approaches. This dataset is ideal for quick experiments with [Ultralytics YOLOv8](https://docs.ultralytics.com/models/yolov8/). For more details on dataset configuration, check out the dataset YAML file [here](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco8-pose.yaml).
+
+### How do I train a YOLOv8 model using the COCO8-Pose dataset in Ultralytics?
+
+To train a YOLOv8n-pose model on the COCO8-Pose dataset for 100 epochs with an image size of 640, follow these examples:
+
+!!! Example "Train Example"
+
+    === "Python"
+    
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO("yolov8n-pose.pt")
+
+        # Train the model
+        results = model.train(data="coco8-pose.yaml", epochs=100, imgsz=640)
+        ```
+
+    === "CLI"
+    
+        ```bash
+        yolo detect train data=coco8-pose.yaml model=yolov8n.pt epochs=100 imgsz=640
+        ```
+
+For a comprehensive list of training arguments, refer to the model [Training](../../modes/train.md) page.
+
+### What are the benefits of using the COCO8-Pose dataset?
+
+The COCO8-Pose dataset offers several benefits:
+
+- **Compact Size**: With only 8 images, it is easy to manage and perfect for quick experiments.
+- **Diverse Data**: Despite its small size, it includes a variety of scenes, useful for thorough pipeline testing.
+- **Error Debugging**: Ideal for identifying training errors and performing sanity checks before scaling up to larger datasets.
+
+For more about its features and usage, see the [Dataset Introduction](#introduction) section.
+
+### How does mosaicing benefit the YOLOv8 training process using the COCO8-Pose dataset?
+
+Mosaicing, demonstrated in the sample images of the COCO8-Pose dataset, combines multiple images into one, increasing the variety of objects and scenes within each training batch. This technique helps improve the model's ability to generalize across various object sizes, aspect ratios, and contexts, ultimately enhancing model performance. See the [Sample Images and Annotations](#sample-images-and-annotations) section for example images.
+
+### Where can I find the COCO8-Pose dataset YAML file and how do I use it?
+
+The COCO8-Pose dataset YAML file can be found [here](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco8-pose.yaml). This file defines the dataset configuration, including paths, classes, and other relevant information. Use this file with the YOLOv8 training scripts as mentioned in the [Train Example](#how-do-i-train-a-yolov8-model-using-the-coco8-pose-dataset-in-ultralytics) section.
+
+For more FAQs and detailed documentation, visit the [Ultralytics Documentation](https://docs.ultralytics.com/).