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/models/yolov3.md

@@ -99,52 +99,87 @@ Thank you to Joseph Redmon and Ali Farhadi for developing the original YOLOv3.
 
 ## FAQ
 
-### What is YOLOv3, and how does it improve object detection?
+### What are the differences between YOLOv3, YOLOv3-Ultralytics, and YOLOv3u?
 
-YOLOv3 is the third iteration of the _You Only Look Once (YOLO)_ object detection algorithm. It enhances object detection accuracy by utilizing three different sizes of detection kernels: 13x13, 26x26, and 52x52. This allows the model to detect objects at multiple scales, improving accuracy for objects of varying sizes. YOLOv3 also supports multi-label predictions for bounding boxes and includes a superior feature extractor network.
-
-### Why should I use Ultralytics' implementation of YOLOv3?
-
-Ultralytics' implementation of YOLOv3, known as YOLOv3-Ultralytics, retains the original model's architecture but adds significant enhancements. It offers more pre-trained models, additional training methods, and customization options, making it user-friendly and versatile for practical applications. This implementation enhances the usability and flexibility of YOLOv3 in real-world object detection tasks.
-
-### How does YOLOv3u differ from YOLOv3 and YOLOv3-Ultralytics?
-
-YOLOv3u is an updated version of YOLOv3-Ultralytics that incorporates the anchor-free, objectness-free split head used in YOLOv8 models. This update eliminates the need for pre-defined anchor boxes and objectness scores, making YOLOv3u more robust and accurate in detecting objects of varying sizes and shapes, without altering the backbone and neck architecture of YOLOv3.
-
-### Can I use YOLOv3 models for multiple prediction tasks?
-
-Yes, the YOLOv3 series, including YOLOv3, YOLOv3-Ultralytics, and YOLOv3u, are designed for object detection tasks. They support several modes such as [Inference](../modes/predict.md), [Validation](../modes/val.md), [Training](../modes/train.md), and [Export](../modes/export.md). This versatility ensures they can be used effectively across different stages of model deployment and development in various applications.
+YOLOv3 is the third iteration of the YOLO (You Only Look Once) object detection algorithm developed by Joseph Redmon, known for its balance of accuracy and speed, utilizing three different scales (13x13, 26x26, and 52x52) for detections. YOLOv3-Ultralytics is Ultralytics' adaptation of YOLOv3 that adds support for more pre-trained models and facilitates easier model customization. YOLOv3u is an upgraded variant of YOLOv3-Ultralytics, integrating the anchor-free, objectness-free split head from YOLOv8, improving detection robustness and accuracy for various object sizes. For more details on the variants, refer to the [YOLOv3 series](https://github.com/ultralytics/yolov3).
 
 ### How can I train a YOLOv3 model using Ultralytics?
 
-You can train a YOLOv3 model using Ultralytics by leveraging the Python code or CLI commands:
+Training a YOLOv3 model with Ultralytics is straightforward. You can train the model using either Python or CLI:
 
-**Using Python:**
+!!! Example
 
-```python
-from ultralytics import YOLO
+    === "Python"
 
-# Load a COCO-pretrained YOLOv3n model
-model = YOLO("yolov3n.pt")
+        ```python
+        from ultralytics import YOLO
 
-# Display model information (optional)
-model.info()
+        # Load a COCO-pretrained YOLOv3n model
+        model = YOLO("yolov3n.pt")
 
-# Train the model on the COCO8 example dataset for 100 epochs
-results = model.train(data="coco8.yaml", epochs=100, imgsz=640)
+        # Train the model on the COCO8 example dataset for 100 epochs
+        results = model.train(data="coco8.yaml", epochs=100, imgsz=640)
+        ```
 
-# Run inference with the YOLOv3n model on the 'bus.jpg' image
-results = model("path/to/bus.jpg")
-```
+    === "CLI"
 
-**Using CLI:**
+        ```bash
+        # Load a COCO-pretrained YOLOv3n model and train it on the COCO8 example dataset for 100 epochs
+        yolo train model=yolov3n.pt data=coco8.yaml epochs=100 imgsz=640
+        ```
 
-```bash
-# Load a COCO-pretrained YOLOv3n model and train it on the COCO8 example dataset for 100 epochs
-yolo train model=yolov3n.pt data=coco8.yaml epochs=100 imgsz=640
+For more comprehensive training options and guidelines, visit our [Train mode documentation](../modes/train.md).
 
-# Load a COCO-pretrained YOLOv3n model and run inference on the 'bus.jpg' image
-yolo predict model=yolov3n.pt source=path/to/bus.jpg
-```
+### What makes YOLOv3u more accurate for object detection tasks?
 
-For more details, visit the [Train](../modes/train.md) and [Predict](../modes/predict.md) documentation pages.
+YOLOv3u improves upon YOLOv3 and YOLOv3-Ultralytics by incorporating the anchor-free, objectness-free split head used in YOLOv8 models. This upgrade eliminates the need for pre-defined anchor boxes and objectness scores, enhancing its capability to detect objects of varying sizes and shapes more precisely. This makes YOLOv3u a better choice for complex and diverse object detection tasks. For more information, refer to the [Why YOLOv3u](#overview) section.
+
+### How can I use YOLOv3 models for inference?
+
+You can perform inference using YOLOv3 models by either Python scripts or CLI commands:
+
+!!! Example
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a COCO-pretrained YOLOv3n model
+        model = YOLO("yolov3n.pt")
+
+        # Run inference with the YOLOv3n model on the 'bus.jpg' image
+        results = model("path/to/bus.jpg")
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Load a COCO-pretrained YOLOv3n model and run inference on the 'bus.jpg' image
+        yolo predict model=yolov3n.pt source=path/to/bus.jpg
+        ```
+
+Refer to the [Inference mode documentation](../modes/predict.md) for more details on running YOLO models.
+
+### What tasks are supported by YOLOv3 and its variants?
+
+YOLOv3, YOLOv3-Ultralytics, and YOLOv3u primarily support object detection tasks. These models can be used for various stages of model deployment and development, such as Inference, Validation, Training, and Export. For a comprehensive set of tasks supported and more in-depth details, visit our [Object Detection tasks documentation](../tasks/detect.md).
+
+### Where can I find resources to cite YOLOv3 in my research?
+
+If you use YOLOv3 in your research, please cite the original YOLO papers and the Ultralytics YOLOv3 repository. Example BibTeX citation:
+
+!!! Quote ""
+
+    === "BibTeX"
+
+        ```bibtex
+        @article{redmon2018yolov3,
+          title={YOLOv3: An Incremental Improvement},
+          author={Redmon, Joseph and Farhadi, Ali},
+          journal={arXiv preprint arXiv:1804.02767},
+          year={2018}
+        }
+        ```
+
+For more citation details, refer to the [Citations and Acknowledgements](#citations-and-acknowledgements) section.