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/tiger-pose.md

@@ -96,3 +96,69 @@ The example showcases the variety and complexity of the images in the Tiger-Pose
 ## Citations and Acknowledgments
 
 The dataset has been released available under the [AGPL-3.0 License](https://github.com/ultralytics/ultralytics/blob/main/LICENSE).
+
+## FAQ
+
+### What is the Ultralytics Tiger-Pose dataset used for?
+
+The Ultralytics Tiger-Pose dataset is designed for pose estimation tasks, consisting of 263 images sourced from a [YouTube video](https://www.youtube.com/watch?v=MIBAT6BGE6U&pp=ygUbVGlnZXIgd2Fsa2luZyByZWZlcmVuY2UubXA0). The dataset is divided into 210 training images and 53 validation images. It is particularly useful for testing, training, and refining pose estimation algorithms using [Ultralytics HUB](https://hub.ultralytics.com) and [YOLOv8](https://github.com/ultralytics/ultralytics).
+
+### How do I train a YOLOv8 model on the Tiger-Pose dataset?
+
+To train a YOLOv8n-pose model on the Tiger-Pose dataset for 100 epochs with an image size of 640, use the following code snippets. For more details, visit the [Training](../../modes/train.md) page:
+
+!!! Example "Train Example"
+
+    === "Python"
+    
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO("yolov8n-pose.pt")  # load a pretrained model (recommended for training)
+
+        # Train the model
+        results = model.train(data="tiger-pose.yaml", epochs=100, imgsz=640)
+        ```
+    
+
+    === "CLI"
+    
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo task=pose mode=train data=tiger-pose.yaml model=yolov8n.pt epochs=100 imgsz=640
+        ```
+
+### What configurations does the `tiger-pose.yaml` file include?
+
+The `tiger-pose.yaml` file is used to specify the configuration details of the Tiger-Pose dataset. It includes crucial data such as file paths and class definitions. To see the exact configuration, you can check out the [Ultralytics Tiger-Pose Dataset Configuration File](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/tiger-pose.yaml).
+
+### How can I run inference using a YOLOv8 model trained on the Tiger-Pose dataset?
+
+To perform inference using a YOLOv8 model trained on the Tiger-Pose dataset, you can use the following code snippets. For a detailed guide, visit the [Prediction](../../modes/predict.md) page:
+
+!!! Example "Inference Example"
+
+    === "Python"
+    
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO("path/to/best.pt")  # load a tiger-pose trained model
+
+        # Run inference
+        results = model.predict(source="https://youtu.be/MIBAT6BGE6U", show=True)
+        ```
+    
+
+    === "CLI"
+    
+        ```bash
+        # Run inference using a tiger-pose trained model
+        yolo task=pose mode=predict source="https://youtu.be/MIBAT6BGE6U" show=True model="path/to/best.pt"
+        ```
+
+### What are the benefits of using the Tiger-Pose dataset for pose estimation?
+
+The Tiger-Pose dataset, despite its manageable size of 210 images for training, provides a diverse collection of images that are ideal for testing pose estimation pipelines. The dataset helps identify potential errors and acts as a preliminary step before working with larger datasets. Additionally, the dataset supports the training and refinement of pose estimation algorithms using advanced tools like [Ultralytics HUB](https://hub.ultralytics.com) and [YOLOv8](https://github.com/ultralytics/ultralytics), enhancing model performance and accuracy.