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/modes/benchmark.md

@@ -104,3 +104,70 @@ Benchmarks will attempt to run automatically on all possible export formats belo
 | [NCNN](../integrations/ncnn.md)                   | `ncnn`            | `yolov8n_ncnn_model/`     | ✅       | `imgsz`, `half`, `batch`                                             |
 
 See full `export` details in the [Export](../modes/export.md) page.
+
+## FAQ
+
+### How do I benchmark my YOLOv8 model's performance using Ultralytics?
+
+Ultralytics YOLOv8 offers a Benchmark mode to assess your model's performance across different export formats. This mode provides insights into key metrics such as mean Average Precision (mAP50-95), accuracy, and inference time in milliseconds. To run benchmarks, you can use either Python or CLI commands. For example, to benchmark on a GPU:
+
+!!! Example
+
+    === "Python"
+
+    ```python
+    from ultralytics.utils.benchmarks import benchmark
+
+    # Benchmark on GPU
+    benchmark(model="yolov8n.pt", data="coco8.yaml", imgsz=640, half=False, device=0)
+    ```
+
+    === "CLI"
+
+    ```bash
+    yolo benchmark model=yolov8n.pt data='coco8.yaml' imgsz=640 half=False device=0
+    ```
+
+For more details on benchmark arguments, visit the [Arguments](#arguments) section.
+
+### What are the benefits of exporting YOLOv8 models to different formats?
+
+Exporting YOLOv8 models to different formats such as ONNX, TensorRT, and OpenVINO allows you to optimize performance based on your deployment environment. For instance:
+
+- **ONNX:** Provides up to 3x CPU speedup.
+- **TensorRT:** Offers up to 5x GPU speedup.
+- **OpenVINO:** Specifically optimized for Intel hardware.
+    These formats enhance both the speed and accuracy of your models, making them more efficient for various real-world applications. Visit the [Export](../modes/export.md) page for complete details.
+
+### Why is benchmarking crucial in evaluating YOLOv8 models?
+
+Benchmarking your YOLOv8 models is essential for several reasons:
+
+- **Informed Decisions:** Understand the trade-offs between speed and accuracy.
+- **Resource Allocation:** Gauge the performance across different hardware options.
+- **Optimization:** Determine which export format offers the best performance for specific use cases.
+- **Cost Efficiency:** Optimize hardware usage based on benchmark results.
+    Key metrics such as mAP50-95, Top-5 accuracy, and inference time help in making these evaluations. Refer to the [Key Metrics](#key-metrics-in-benchmark-mode) section for more information.
+
+### Which export formats are supported by YOLOv8, and what are their advantages?
+
+YOLOv8 supports a variety of export formats, each tailored for specific hardware and use cases:
+
+- **ONNX:** Best for CPU performance.
+- **TensorRT:** Ideal for GPU efficiency.
+- **OpenVINO:** Optimized for Intel hardware.
+- **CoreML & TensorFlow:** Useful for iOS and general ML applications.
+    For a complete list of supported formats and their respective advantages, check out the [Supported Export Formats](#supported-export-formats) section.
+
+### What arguments can I use to fine-tune my YOLOv8 benchmarks?
+
+When running benchmarks, several arguments can be customized to suit specific needs:
+
+- **model:** Path to the model file (e.g., "yolov8n.pt").
+- **data:** Path to a YAML file defining the dataset (e.g., "coco8.yaml").
+- **imgsz:** The input image size, either as a single integer or a tuple.
+- **half:** Enable FP16 inference for better performance.
+- **int8:** Activate INT8 quantization for edge devices.
+- **device:** Specify the computation device (e.g., "cpu", "cuda:0").
+- **verbose:** Control the level of logging detail.
+    For a full list of arguments, refer to the [Arguments](#arguments) section.