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/yolov9.md

@@ -95,7 +95,7 @@ Comparatively, YOLOv9 exhibits remarkable gains:
 - **Lightweight Models**: YOLOv9s surpasses the YOLO MS-S in parameter efficiency and computational load while achieving an improvement of 0.4∼0.6% in AP.
 - **Medium to Large Models**: YOLOv9m and YOLOv9e show notable advancements in balancing the trade-off between model complexity and detection performance, offering significant reductions in parameters and computations against the backdrop of improved accuracy.
 
-The YOLOv9c model, in particular, highlights the effectiveness of the architecture's optimizations. It operates with 42% fewer parameters and 21% less computational demand than YOLOv7 AF, yet it achieves comparable accuracy, demonstrating YOLOv9's significant efficiency improvements. Furthermore, the YOLOv9e model sets a new standard for large models, with 15% fewer parameters and 25% less computational need than [YOLOv8x](yolov8.md), alongside a incremental 1.7% improvement in AP.
+The YOLOv9c model, in particular, highlights the effectiveness of the architecture's optimizations. It operates with 42% fewer parameters and 21% less computational demand than YOLOv7 AF, yet it achieves comparable accuracy, demonstrating YOLOv9's significant efficiency improvements. Furthermore, the YOLOv9e model sets a new standard for large models, with 15% fewer parameters and 25% less computational need than [YOLOv8x](yolov8.md), alongside an incremental 1.7% improvement in AP.
 
 These results showcase YOLOv9's strategic advancements in model design, emphasizing its enhanced efficiency without compromising on the precision essential for real-time object detection tasks. The model not only pushes the boundaries of performance metrics but also emphasizes the importance of computational efficiency, making it a pivotal development in the field of computer vision.
 
@@ -180,32 +180,38 @@ The original YOLOv9 paper can be found on [arXiv](https://arxiv.org/pdf/2402.136
 
 ## FAQ
 
-### What is YOLOv9 and why should I use it for real-time object detection?
+### What innovations does YOLOv9 introduce for real-time object detection?
 
-YOLOv9 is the latest iteration of the YOLO (You Only Look Once) object detection family, featuring groundbreaking techniques like Programmable Gradient Information (PGI) and Generalized Efficient Layer Aggregation Network (GELAN). This model demonstrates significant improvements in efficiency, accuracy, and adaptability, setting new benchmarks on the [MS COCO](../datasets/detect/coco.md) dataset. The integration of PGI and GELAN ensures retention of crucial data throughout the detection process, making YOLOv9 an exceptional choice for real-time object detection.
+YOLOv9 introduces groundbreaking techniques such as Programmable Gradient Information (PGI) and the Generalized Efficient Layer Aggregation Network (GELAN). These innovations address information loss challenges in deep neural networks, ensuring high efficiency, accuracy, and adaptability. PGI preserves essential data across network layers, while GELAN optimizes parameter utilization and computational efficiency. Learn more about [YOLOv9's core innovations](#core-innovations-of-yolov9) that set new benchmarks on the MS COCO dataset.
 
-### How do Programmable Gradient Information (PGI) and GELAN improve YOLOv9's performance?
+### How does YOLOv9 perform on the MS COCO dataset compared to other models?
 
-Programmable Gradient Information (PGI) helps counteract information loss in deep neural networks by ensuring the preservation of essential data across network layers. This leads to more reliable gradient generation and better model convergence. The Generalized Efficient Layer Aggregation Network (GELAN) enhances parameter utilization and computational efficiency by allowing flexible integration of various computational blocks. These innovations collectively improve the accuracy and efficiency of YOLOv9. For more details, see the [Innovations section](#programmable-gradient-information-pgi).
+YOLOv9 outperforms state-of-the-art real-time object detectors by achieving higher accuracy and efficiency. On the [COCO dataset](../datasets/detect/coco.md), YOLOv9 models exhibit superior mAP scores across various sizes while maintaining or reducing computational overhead. For instance, YOLOv9c achieves comparable accuracy with 42% fewer parameters and 21% less computational demand than YOLOv7 AF. Explore [performance comparisons](#performance-on-ms-coco-dataset) for detailed metrics.
 
-### What makes YOLOv9 more efficient than previous YOLO versions?
+### How can I train a YOLOv9 model using Python and CLI?
 
-YOLOv9 incorporates several innovations like PGI and GELAN that effectively address the information bottleneck and improve layer aggregation, respectively. This results in high parameter efficiency and reduced computational load. Models such as YOLOv9s and YOLOv9e demonstrate significant gains in performance compared to previous versions. For a detailed performance comparison, refer to the [Performance section on MS COCO](#performance-on-ms-coco-dataset).
-
-### Can I train YOLOv9 on my custom dataset using Ultralytics?
-
-Yes, you can easily train YOLOv9 on your custom dataset using Ultralytics. The Ultralytics YOLO framework supports various modes including [Train](../modes/train.md), [Predict](../modes/predict.md), and [Val](../modes/val.md). For example, you can start training a YOLOv9c model with the following Python code snippet:
+You can train a YOLOv9 model using both Python and CLI commands. For Python, instantiate a model using the `YOLO` class and call the `train` method:
 
 ```python
 from ultralytics import YOLO
 
-# Load the YOLOv9c model configuration
-model = YOLO("yolov9c.yaml")
-
-# Train the model on your custom dataset
-results = model.train(data="custom_dataset.yaml", epochs=100, imgsz=640)
+# Build a YOLOv9c model from pretrained weights and train
+model = YOLO("yolov9c.pt")
+results = model.train(data="coco8.yaml", epochs=100, imgsz=640)
 ```
 
-### How does YOLOv9 compare to other state-of-the-art real-time object detectors?
+For CLI training, execute:
 
-YOLOv9 shows superior performance across different model sizes on the [COCO dataset](../datasets/detect/coco.md). It achieves higher mean Average Precision (mAP) with fewer parameters and computational resources compared to competitors. For instance, the YOLOv9c model operates with 42% fewer parameters and 21% less computational demand than YOLOv7 AF, while matching its accuracy. Detailed performance metrics can be found in the [Performance Comparison table](#performance-on-ms-coco-dataset).
+```bash
+yolo train model=yolov9c.yaml data=coco8.yaml epochs=100 imgsz=640
+```
+
+Learn more about [usage examples](#usage-examples) for training and inference.
+
+### What are the advantages of using Ultralytics YOLOv9 for lightweight models?
+
+YOLOv9 is designed to mitigate information loss, which is particularly important for lightweight models often prone to losing significant information. By integrating Programmable Gradient Information (PGI) and reversible functions, YOLOv9 ensures essential data retention, enhancing the model's accuracy and efficiency. This makes it highly suitable for applications requiring compact models with high performance. For more details, explore the section on [YOLOv9's impact on lightweight models](#impact-on-lightweight-models).
+
+### What tasks and modes does YOLOv9 support?
+
+YOLOv9 supports various tasks including object detection and instance segmentation. It is compatible with multiple operational modes such as inference, validation, training, and export. This versatility makes YOLOv9 adaptable to diverse real-time computer vision applications. Refer to the [supported tasks and modes](#supported-tasks-and-modes) section for more information.