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/yolov4.md

@@ -71,41 +71,22 @@ The original YOLOv4 paper can be found on [arXiv](https://arxiv.org/abs/2004.109
 
 ## FAQ
 
-### What are the key features of the YOLOv4 model?
+### What is YOLOv4 and why should I use it for object detection?
 
-YOLOv4, which stands for "You Only Look Once version 4," is designed with several innovative features that optimize its performance. Key features include:
+YOLOv4, which stands for "You Only Look Once version 4," is a state-of-the-art real-time object detection model developed by Alexey Bochkovskiy in 2020. It achieves an optimal balance between speed and accuracy, making it highly suitable for real-time applications. YOLOv4's architecture incorporates several innovative features like Weighted-Residual-Connections (WRC), Cross-Stage-Partial-connections (CSP), and Self-adversarial-training (SAT), among others, to achieve state-of-the-art results. If you're looking for a high-performance model that operates efficiently on conventional GPUs, YOLOv4 is an excellent choice.
 
-- **Weighted-Residual-Connections (WRC)**
-- **Cross-Stage-Partial-connections (CSP)**
-- **Cross mini-Batch Normalization (CmBN)**
-- **Self-adversarial training (SAT)**
-- **Mish-activation**
-- **Mosaic data augmentation**
-- **DropBlock regularization**
-- **CIoU loss**
-    These features collectively enhance YOLOv4's speed and accuracy, making it ideal for real-time object detection tasks. For more details on its architecture, you can visit the [YOLOv4 section](https://docs.ultralytics.com/models/yolov4).
+### How does the architecture of YOLOv4 enhance its performance?
 
-### How does YOLOv4 compare to its predecessor, YOLOv3?
+The architecture of YOLOv4 includes several key components: the backbone, the neck, and the head. The backbone, which can be models like VGG, ResNet, or CSPDarknet53, is pre-trained to predict classes and bounding boxes. The neck, utilizing PANet, connects feature maps from different stages for comprehensive data extraction. Finally, the head, which uses configurations from YOLOv3, makes the final object detections. YOLOv4 also employs "bag of freebies" techniques like mosaic data augmentation and DropBlock regularization, further optimizing its speed and accuracy.
 
-YOLOv4 introduces several improvements over YOLOv3, including advanced features such as Weighted-Residual-Connections (WRC), Cross-Stage-Partial-connections (CSP), and Cross mini-Batch Normalization (CmBN). These enhancements contribute to better speed and accuracy in object detection:
+### What are "bag of freebies" in the context of YOLOv4?
 
-- **Higher Accuracy:** YOLOv4 achieves state-of-the-art results in object detection benchmarks.
-- **Improved Speed:** Despite its complex architecture, YOLOv4 maintains real-time performance.
-- **Better Backbone and Neck:** YOLOv4 utilizes CSPDarknet53 as the backbone and PANet as the neck, which are more advanced than YOLOv3's components.
-    For more information, compare the features in the [YOLOv3](yolov3.md) and YOLOv4 documentation.
+"Bag of freebies" refers to methods that improve the training accuracy of YOLOv4 without increasing the cost of inference. These techniques include various forms of data augmentation like photometric distortions (adjusting brightness, contrast, etc.) and geometric distortions (scaling, cropping, flipping, rotating). By increasing the variability of the input images, these augmentations help YOLOv4 generalize better to different types of images, thereby improving its robustness and accuracy without compromising its real-time performance.
 
-### Can YOLOv4 be used for training on a conventional GPU?
+### Why is YOLOv4 considered suitable for real-time object detection on conventional GPUs?
 
-Yes, YOLOv4 is designed to be efficient on conventional GPU hardware, making it accessible for various users. The model can be trained using a single GPU, which broadens its usability for researchers and developers without access to high-end hardware. The architecture balances efficiency and computational requirements, allowing real-time object detection even on affordable hardware. For specific training guidelines, refer to the instructions provided in the [YOLOv4 GitHub repository](https://github.com/AlexeyAB/darknet).
+YOLOv4 is designed to optimize both speed and accuracy, making it ideal for real-time object detection tasks that require quick and reliable performance. It operates efficiently on conventional GPUs, needing only one for both training and inference. This makes it accessible and practical for various applications ranging from recommendation systems to standalone process management, thereby reducing the need for extensive hardware setups and making it a cost-effective solution for real-time object detection.
 
-### What is the "bag of freebies" in YOLOv4?
+### How can I get started with YOLOv4 if Ultralytics does not currently support it?
 
-The "bag of freebies" in YOLOv4 refers to techniques that enhance model accuracy during training without increasing inference costs. These include:
-
-- **Photometric Distortions:** Adjusting brightness, contrast, hue, saturation, and noise.
-- **Geometric Distortions:** Applying random scaling, cropping, flipping, and rotating.
-    These techniques improve the model's robustness and ability to generalize across different image types. Learn more about these methods in the [YOLOv4 features and performance](#features-and-performance) section.
-
-### Is YOLOv4 supported by Ultralytics?
-
-As of the latest update, Ultralytics does not currently support YOLOv4 models. Users interested in utilizing YOLOv4 should refer to the original [YOLOv4 GitHub repository](https://github.com/AlexeyAB/darknet) for installation and usage instructions. Ultralytics intends to update their documentation and support once integration with YOLOv4 is implemented. For alternative models supported by Ultralytics, you can explore [Ultralytics YOLO models](https://docs.ultralytics.com/models/).
+To get started with YOLOv4, you should visit the official [YOLOv4 GitHub repository](https://github.com/AlexeyAB/darknet). Follow the installation instructions provided in the README file, which typically include cloning the repository, installing dependencies, and setting up environment variables. Once installed, you can train the model by preparing your dataset, configuring the model parameters, and following the usage instructions provided. Since Ultralytics does not currently support YOLOv4, it is recommended to refer directly to the YOLOv4 GitHub for the most up-to-date and detailed guidance.