Fix mkdocs.yml raw image URLs (#14213)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Burhan <62214284+Burhan-Q@users.noreply.github.com>

docs/en/integrations/dvc.md

@@ -169,3 +169,110 @@ This guide has led you through the process of integrating DVCLive with Ultralyti
 For further details on usage, visit [DVCLive's official documentation](https://dvc.org/doc/dvclive/ml-frameworks/yolo).
 
 Additionally, explore more integrations and capabilities of Ultralytics by visiting the [Ultralytics integration guide page](../integrations/index.md), which is a collection of great resources and insights.
+
+## FAQ
+
+### How do I integrate DVCLive with Ultralytics YOLOv8 for experiment tracking?
+
+Integrating DVCLive with Ultralytics YOLOv8 is straightforward. Start by installing the necessary packages:
+
+!!! Example "Installation"
+
+    === "CLI"
+
+    ```bash
+    pip install ultralytics dvclive
+    ```
+
+Next, initialize a Git repository and configure DVCLive in your project:
+
+!!! Example "Initial Environment Setup"
+
+    === "CLI"
+
+    ```bash
+    git init -q
+    git config --local user.email "you@example.com"
+    git config --local user.name "Your Name"
+    dvc init -q
+    git commit -m "DVC init"
+    ```
+
+Follow our [YOLOv8 Installation guide](../quickstart.md) for detailed setup instructions.
+
+### Why should I use DVCLive for tracking YOLOv8 experiments?
+
+Using DVCLive with YOLOv8 provides several advantages, such as:
+
+- **Automated Logging**: DVCLive automatically records key experiment details like model parameters and metrics.
+- **Easy Comparison**: Facilitates comparison of results across different runs.
+- **Visualization Tools**: Leverages DVCLive's robust data visualization capabilities for in-depth analysis.
+
+For further details, refer to our guide on [YOLOv8 Model Training](../modes/train.md) and [YOLO Performance Metrics](../guides/yolo-performance-metrics.md) to maximize your experiment tracking efficiency.
+
+### How can DVCLive improve my results analysis for YOLOv8 training sessions?
+
+After completing your YOLOv8 training sessions, DVCLive helps in visualizing and analyzing the results effectively. Example code for loading and displaying experiment data:
+
+```python
+import dvc.api
+import pandas as pd
+
+# Define columns of interest
+columns = ["Experiment", "epochs", "imgsz", "model", "metrics.mAP50-95(B)"]
+
+# Retrieve experiment data
+df = pd.DataFrame(dvc.api.exp_show(), columns=columns)
+
+# Clean data
+df.dropna(inplace=True)
+df.reset_index(drop=True, inplace=True)
+
+# Display DataFrame
+print(df)
+```
+
+To visualize results interactively, use Plotly's parallel coordinates plot:
+
+```python
+from plotly.express import parallel_coordinates
+
+fig = parallel_coordinates(df, columns, color="metrics.mAP50-95(B)")
+fig.show()
+```
+
+Refer to our guide on [YOLOv8 Training with DVCLive](#yolov8-training-with-dvclive) for more examples and best practices.
+
+### What are the steps to configure my environment for DVCLive and YOLOv8 integration?
+
+To configure your environment for a smooth integration of DVCLive and YOLOv8, follow these steps:
+
+1. **Install Required Packages**: Use `pip install ultralytics dvclive`.
+2. **Initialize Git Repository**: Run `git init -q`.
+3. **Setup DVCLive**: Execute `dvc init -q`.
+4. **Commit to Git**: Use `git commit -m "DVC init"`.
+
+These steps ensure proper version control and setup for experiment tracking. For in-depth configuration details, visit our [Configuration guide](../quickstart.md).
+
+### How do I visualize YOLOv8 experiment results using DVCLive?
+
+DVCLive offers powerful tools to visualize the results of YOLOv8 experiments. Here's how you can generate comparative plots:
+
+!!! Example "Generate Comparative Plots"
+
+    === "CLI"
+
+    ```bash
+    dvc plots diff $(dvc exp list --names-only)
+    ```
+
+To display these plots in a Jupyter Notebook, use:
+
+```python
+from IPython.display import HTML
+
+# Display plots as HTML
+HTML(filename="./dvc_plots/index.html")
+```
+
+These visualizations help identify trends and optimize model performance. Check our detailed guides on [YOLOv8 Experiment Analysis](#analyzing-results) for comprehensive steps and examples.