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/detect/objects365.md

@@ -90,3 +90,51 @@ If you use the Objects365 dataset in your research or development work, please c
         ```
 
 We would like to acknowledge the team of researchers who created and maintain the Objects365 dataset as a valuable resource for the computer vision research community. For more information about the Objects365 dataset and its creators, visit the [Objects365 dataset website](https://www.objects365.org/).
+
+## FAQ
+
+### What is the Objects365 dataset used for?
+
+The [Objects365 dataset](https://www.objects365.org/) is designed for object detection tasks in machine learning and computer vision. It provides a large-scale, high-quality dataset with 2 million annotated images and 30 million bounding boxes across 365 categories. Leveraging such a diverse dataset helps improve the performance and generalization of object detection models, making it invaluable for research and development in the field.
+
+### How can I train a YOLOv8 model on the Objects365 dataset?
+
+To train a YOLOv8n model using the Objects365 dataset for 100 epochs with an image size of 640, follow these instructions:
+
+!!! Example "Train Example"
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
+
+        # Train the model
+        results = model.train(data="Objects365.yaml", epochs=100, imgsz=640)
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo detect train data=Objects365.yaml model=yolov8n.pt epochs=100 imgsz=640
+        ```
+
+Refer to the [Training](../../modes/train.md) page for a comprehensive list of available arguments.
+
+### Why should I use the Objects365 dataset for my object detection projects?
+
+The Objects365 dataset offers several advantages for object detection tasks:
+1. **Diversity**: It includes 2 million images with objects in diverse scenarios, covering 365 categories.
+2. **High-quality Annotations**: Over 30 million bounding boxes provide comprehensive ground truth data.
+3. **Performance**: Models pre-trained on Objects365 significantly outperform those trained on datasets like ImageNet, leading to better generalization.
+
+### Where can I find the YAML configuration file for the Objects365 dataset?
+
+The YAML configuration file for the Objects365 dataset is available at [Objects365.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/Objects365.yaml). This file contains essential information such as dataset paths and class labels, crucial for setting up your training environment.
+
+### How does the dataset structure of Objects365 enhance object detection modeling?
+
+The [Objects365 dataset](https://www.objects365.org/) is organized with 2 million high-resolution images and comprehensive annotations of over 30 million bounding boxes. This structure ensures a robust dataset for training deep learning models in object detection, offering a wide variety of objects and scenarios. Such diversity and volume help in developing models that are more accurate and capable of generalizing well to real-world applications. For more details on the dataset structure, refer to the [Dataset YAML](#dataset-yaml) section.