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Update YOLO11 Actions and Docs (#16596)

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docs/en/guides/coral-edge-tpu-on-raspberry-pi.md
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---
comments: true
description: Learn how to boost your Raspberry Pi's ML performance using Coral Edge TPU with Ultralytics YOLOv8. Follow our detailed setup and installation guide.
keywords: Coral Edge TPU, Raspberry Pi, YOLOv8, Ultralytics, TensorFlow Lite, ML inference, machine learning, AI, installation guide, setup tutorial
description: Learn how to boost your Raspberry Pi's ML performance using Coral Edge TPU with Ultralytics YOLO11. Follow our detailed setup and installation guide.
keywords: Coral Edge TPU, Raspberry Pi, YOLO11, Ultralytics, TensorFlow Lite, ML inference, machine learning, AI, installation guide, setup tutorial
---
# Coral Edge TPU on a Raspberry Pi with Ultralytics YOLOv8 🚀
# Coral Edge TPU on a Raspberry Pi with Ultralytics YOLO11 🚀
<p align="center">
<img width="800" src="https://github.com/ultralytics/docs/releases/download/0/edge-tpu-usb-accelerator-and-pi.avif" alt="Raspberry Pi single board computer with USB Edge TPU accelerator">
@ -152,9 +152,9 @@ Find comprehensive information on the [Predict](../modes/predict.md) page for fu
## FAQ
### What is a Coral Edge TPU and how does it enhance Raspberry Pi's performance with Ultralytics YOLOv8?
### What is a Coral Edge TPU and how does it enhance Raspberry Pi's performance with Ultralytics YOLO11?
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](https://www.ultralytics.com/glossary/machine-learning-ml) 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).
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](https://www.ultralytics.com/glossary/machine-learning-ml) 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 YOLO11 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?
@ -166,9 +166,9 @@ 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?
### Can I export my Ultralytics YOLO11 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:
Yes, you can export your Ultralytics YOLO11 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:
!!! note "Exporting the model"
@ -208,9 +208,9 @@ 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?
### How do I run inference with an exported YOLO11 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:
After exporting your YOLO11 model to an Edge TPU-compatible format, you can run inference using the following code snippets:
!!! note "Running the model"