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/classify/imagenet.md

@@ -91,3 +91,48 @@ If you use the ImageNet dataset in your research or development work, please cit
         ```
 
 We would like to acknowledge the ImageNet team, led by Olga Russakovsky, Jia Deng, and Li Fei-Fei, for creating and maintaining the ImageNet dataset as a valuable resource for the machine learning and computer vision research community. For more information about the ImageNet dataset and its creators, visit the [ImageNet website](https://www.image-net.org/).
+
+## FAQ
+
+### What is the ImageNet dataset and how is it used in computer vision?
+
+The [ImageNet dataset](https://www.image-net.org/) is a large-scale database consisting of over 14 million high-resolution images categorized using WordNet synsets. It is extensively used in visual object recognition research, including image classification and object detection. The dataset's annotations and sheer volume provide a rich resource for training deep learning models. Notably, models like AlexNet, VGG, and ResNet have been trained and benchmarked using ImageNet, showcasing its role in advancing computer vision.
+
+### How can I use a pretrained YOLO model for image classification on the ImageNet dataset?
+
+To use a pretrained Ultralytics YOLO model for image classification on the ImageNet dataset, follow these steps:
+
+!!! Example "Train 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="imagenet", epochs=100, imgsz=224)
+        ```
+
+    === "CLI"
+    
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo train data=imagenet model=yolov8n-cls.pt epochs=100 imgsz=224
+        ```
+
+For more in-depth training instruction, refer to our [Training page](../../modes/train.md).
+
+### Why should I use the Ultralytics YOLOv8 pretrained models for my ImageNet dataset projects?
+
+Ultralytics YOLOv8 pretrained models offer state-of-the-art performance in terms of speed and accuracy for various computer vision tasks. For example, the YOLOv8n-cls model, with a top-1 accuracy of 69.0% and a top-5 accuracy of 88.3%, is optimized for real-time applications. Pretrained models reduce the computational resources required for training from scratch and accelerate development cycles. Learn more about the performance metrics of YOLOv8 models in the [ImageNet Pretrained Models section](#imagenet-pretrained-models).
+
+### How is the ImageNet dataset structured, and why is it important?
+
+The ImageNet dataset is organized using the WordNet hierarchy, where each node in the hierarchy represents a category described by a synset (a collection of synonymous terms). This structure allows for detailed annotations, making it ideal for training models to recognize a wide variety of objects. The diversity and annotation richness of ImageNet make it a valuable dataset for developing robust and generalizable deep learning models. More about this organization can be found in the [Dataset Structure](#dataset-structure) section.
+
+### What role does the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) play in computer vision?
+
+The annual [ImageNet Large Scale Visual Recognition Challenge (ILSVRC)](https://image-net.org/challenges/LSVRC/) has been pivotal in driving advancements in computer vision by providing a competitive platform for evaluating algorithms on a large-scale, standardized dataset. It offers standardized evaluation metrics, fostering innovation and development in areas such as image classification, object detection, and image segmentation. The challenge has continuously pushed the boundaries of what is possible with deep learning and computer vision technologies.