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/guides/coral-edge-tpu-on-raspberry-pi.md

@@ -138,3 +138,87 @@ Find comprehensive information on the [Predict](../modes/predict.md) page for fu
     ```
 
     If you want a `tflite-runtime` wheel for `tensorflow` 2.15.0 download it from [here](https://github.com/feranick/TFlite-builds/releases) and install it using `pip` or your package manager of choice.
+
+## FAQ
+
+### What is a Coral Edge TPU and how does it enhance Raspberry Pi's performance with Ultralytics YOLOv8?
+
+The Coral Edge TPU is a compact device designed to add an Edge TPU coprocessor to your system. This coprocessor enables low-power, high-performance machine learning inference, particularly optimized for TensorFlow Lite models. When using a Raspberry Pi, the Edge TPU accelerates ML model inference, significantly boosting performance, especially for Ultralytics YOLOv8 models. You can read more about the Coral Edge TPU on their [home page](https://coral.ai/products/accelerator).
+
+### How do I install the Coral Edge TPU runtime on a Raspberry Pi?
+
+To install the Coral Edge TPU runtime on your Raspberry Pi, download the appropriate `.deb` package for your Raspberry Pi OS version from [this link](https://github.com/feranick/libedgetpu/releases). Once downloaded, use the following command to install it:
+
+```bash
+sudo dpkg -i path/to/package.deb
+```
+
+Make sure to uninstall any previous Coral Edge TPU runtime versions by following the steps outlined in the [Installation Walkthrough](#installation-walkthrough) section.
+
+### Can I export my Ultralytics YOLOv8 model to be compatible with Coral Edge TPU?
+
+Yes, you can export your Ultralytics YOLOv8 model to be compatible with the Coral Edge TPU. It is recommended to perform the export on Google Colab, an x86_64 Linux machine, or using the [Ultralytics Docker container](docker-quickstart.md). You can also use Ultralytics HUB for exporting. Here is how you can export your model using Python and CLI:
+
+!!! Exporting the model
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO("path/to/model.pt")  # Load an official model or custom model
+
+        # Export the model
+        model.export(format="edgetpu")
+        ```
+
+    === "CLI"
+
+        ```bash
+        yolo export model=path/to/model.pt format=edgetpu  # Export an official model or custom model
+        ```
+
+For more information, refer to the [Export Mode](../modes/export.md) documentation.
+
+### What should I do if TensorFlow is already installed on my Raspberry Pi but I want to use tflite-runtime instead?
+
+If you have TensorFlow installed on your Raspberry Pi and need to switch to `tflite-runtime`, you'll need to uninstall TensorFlow first using:
+
+```bash
+pip uninstall tensorflow tensorflow-aarch64
+```
+
+Then, install or update `tflite-runtime` with the following command:
+
+```bash
+pip install -U tflite-runtime
+```
+
+For a specific wheel, such as TensorFlow 2.15.0 `tflite-runtime`, you can download it from [this link](https://github.com/feranick/TFlite-builds/releases) and install it using `pip`. Detailed instructions are available in the section on running the model [Running the Model](#running-the-model).
+
+### How do I run inference with an exported YOLOv8 model on a Raspberry Pi using the Coral Edge TPU?
+
+After exporting your YOLOv8 model to an Edge TPU-compatible format, you can run inference using the following code snippets:
+
+!!! Running the model
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO("path/to/edgetpu_model.tflite")  # Load an official model or custom model
+
+        # Run Prediction
+        model.predict("path/to/source.png")
+        ```
+
+    === "CLI"
+
+        ```bash
+        yolo predict model=path/to/edgetpu_model.tflite source=path/to/source.png  # Load an official model or custom model
+        ```
+
+Comprehensive details on full prediction mode features can be found on the [Predict Page](../modes/predict.md).