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@ -1,14 +1,14 @@
---
comments: true
description: Learn how to export YOLOv8 models to TFLite Edge TPU format for high-speed, low-power inferencing on mobile and embedded devices.
keywords: YOLOv8, TFLite Edge TPU, TensorFlow Lite, model export, machine learning, edge computing, neural networks, Ultralytics
description: Learn how to export YOLO11 models to TFLite Edge TPU format for high-speed, low-power inferencing on mobile and embedded devices.
keywords: YOLO11, TFLite Edge TPU, TensorFlow Lite, model export, machine learning, edge computing, neural networks, Ultralytics
---
# Learn to Export to TFLite Edge TPU Format From YOLOv8 Model
# Learn to Export to TFLite Edge TPU Format From YOLO11 Model
Deploying computer vision models on devices with limited computational power, such as mobile or embedded systems, can be tricky. Using a model format that is optimized for faster performance simplifies the process. The [TensorFlow Lite](https://ai.google.dev/edge/litert) [Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) or TFLite Edge TPU model format is designed to use minimal power while delivering fast performance for neural networks.
The export to TFLite Edge TPU format feature allows you to optimize your [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics) models for high-speed and low-power inferencing. In this guide, we'll walk you through converting your models to the TFLite Edge TPU format, making it easier for your models to perform well on various mobile and embedded devices.
The export to TFLite Edge TPU format feature allows you to optimize your [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics) models for high-speed and low-power inferencing. In this guide, we'll walk you through converting your models to the TFLite Edge TPU format, making it easier for your models to perform well on various mobile and embedded devices.
## Why Should You Export to TFLite Edge TPU?
@ -32,7 +32,7 @@ Here are the key features that make TFLite Edge TPU a great model format choice
## Deployment Options with TFLite Edge TPU
Before we jump into how to export YOLOv8 models to the TFLite Edge TPU format, let's understand where TFLite Edge TPU models are usually used.
Before we jump into how to export YOLO11 models to the TFLite Edge TPU format, let's understand where TFLite Edge TPU models are usually used.
TFLite Edge TPU offers various deployment options for machine learning models, including:
@ -42,9 +42,9 @@ TFLite Edge TPU offers various deployment options for machine learning models, i
- **Hybrid Deployment**: A hybrid approach combines on-device and cloud deployment and offers a versatile and scalable solution for deploying machine learning models. Advantages include on-device processing for quick responses and [cloud computing](https://www.ultralytics.com/glossary/cloud-computing) for more complex computations.
## Exporting YOLOv8 Models to TFLite Edge TPU
## Exporting YOLO11 Models to TFLite Edge TPU
You can expand model compatibility and deployment flexibility by converting YOLOv8 models to TensorFlow Edge TPU.
You can expand model compatibility and deployment flexibility by converting YOLO11 models to TensorFlow Edge TPU.
### Installation
@ -55,15 +55,15 @@ To install the required package, run:
=== "CLI"
```bash
# Install the required package for YOLOv8
# Install the required package for YOLO11
pip install ultralytics
```
For detailed instructions and best practices related to the installation process, check our [Ultralytics Installation guide](../quickstart.md). While installing the required packages for YOLOv8, if you encounter any difficulties, consult our [Common Issues guide](../guides/yolo-common-issues.md) for solutions and tips.
For detailed instructions and best practices related to the installation process, check our [Ultralytics Installation guide](../quickstart.md). While installing the required packages for YOLO11, if you encounter any difficulties, consult our [Common Issues guide](../guides/yolo-common-issues.md) for solutions and tips.
### Usage
Before diving into the usage instructions, it's important to note that while all [Ultralytics YOLOv8 models](../models/index.md) are available for exporting, you can ensure that the model you select supports export functionality [here](../modes/export.md).
Before diving into the usage instructions, it's important to note that while all [Ultralytics YOLO11 models](../models/index.md) are available for exporting, you can ensure that the model you select supports export functionality [here](../modes/export.md).
!!! example "Usage"
@ -72,14 +72,14 @@ Before diving into the usage instructions, it's important to note that while all
```python
from ultralytics import YOLO
# Load the YOLOv8 model
model = YOLO("yolov8n.pt")
# Load the YOLO11 model
model = YOLO("yolo11n.pt")
# Export the model to TFLite Edge TPU format
model.export(format="edgetpu") # creates 'yolov8n_full_integer_quant_edgetpu.tflite'
model.export(format="edgetpu") # creates 'yolo11n_full_integer_quant_edgetpu.tflite'
# Load the exported TFLite Edge TPU model
edgetpu_model = YOLO("yolov8n_full_integer_quant_edgetpu.tflite")
edgetpu_model = YOLO("yolo11n_full_integer_quant_edgetpu.tflite")
# Run inference
results = edgetpu_model("https://ultralytics.com/images/bus.jpg")
@ -88,22 +88,22 @@ Before diving into the usage instructions, it's important to note that while all
=== "CLI"
```bash
# Export a YOLOv8n PyTorch model to TFLite Edge TPU format
yolo export model=yolov8n.pt format=edgetpu # creates 'yolov8n_full_integer_quant_edgetpu.tflite'
# Export a YOLO11n PyTorch model to TFLite Edge TPU format
yolo export model=yolo11n.pt format=edgetpu # creates 'yolo11n_full_integer_quant_edgetpu.tflite'
# Run inference with the exported model
yolo predict model=yolov8n_full_integer_quant_edgetpu.tflite source='https://ultralytics.com/images/bus.jpg'
yolo predict model=yolo11n_full_integer_quant_edgetpu.tflite source='https://ultralytics.com/images/bus.jpg'
```
For more details about supported export options, visit the [Ultralytics documentation page on deployment options](../guides/model-deployment-options.md).
## Deploying Exported YOLOv8 TFLite Edge TPU Models
## Deploying Exported YOLO11 TFLite Edge TPU Models
After successfully exporting your Ultralytics YOLOv8 models to TFLite Edge TPU format, you can now deploy them. The primary and recommended first step for running a TFLite Edge TPU model is to use the YOLO("model_edgetpu.tflite") method, as outlined in the previous usage code snippet.
After successfully exporting your Ultralytics YOLO11 models to TFLite Edge TPU format, you can now deploy them. The primary and recommended first step for running a TFLite Edge TPU model is to use the YOLO("model_edgetpu.tflite") method, as outlined in the previous usage code snippet.
However, for in-depth instructions on deploying your TFLite Edge TPU models, take a look at the following resources:
- **[Coral Edge TPU on a Raspberry Pi with Ultralytics YOLOv8](../guides/coral-edge-tpu-on-raspberry-pi.md)**: Discover how to integrate Coral Edge TPUs with Raspberry Pi for enhanced machine learning capabilities.
- **[Coral Edge TPU on a Raspberry Pi with Ultralytics YOLO11](../guides/coral-edge-tpu-on-raspberry-pi.md)**: Discover how to integrate Coral Edge TPUs with Raspberry Pi for enhanced machine learning capabilities.
- **[Code Examples](https://coral.ai/docs/edgetpu/compiler/)**: Access practical TensorFlow Edge TPU deployment examples to kickstart your projects.
@ -111,17 +111,17 @@ However, for in-depth instructions on deploying your TFLite Edge TPU models, tak
## Summary
In this guide, we've learned how to export Ultralytics YOLOv8 models to TFLite Edge TPU format. By following the steps mentioned above, you can increase the speed and power of your [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) applications.
In this guide, we've learned how to export Ultralytics YOLO11 models to TFLite Edge TPU format. By following the steps mentioned above, you can increase the speed and power of your [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) applications.
For further details on usage, visit the [Edge TPU official website](https://cloud.google.com/tpu).
Also, for more information on other Ultralytics YOLOv8 integrations, please visit our [integration guide page](index.md). There, you'll discover valuable resources and insights.
Also, for more information on other Ultralytics YOLO11 integrations, please visit our [integration guide page](index.md). There, you'll discover valuable resources and insights.
## FAQ
### How do I export a YOLOv8 model to TFLite Edge TPU format?
### How do I export a YOLO11 model to TFLite Edge TPU format?
To export a YOLOv8 model to TFLite Edge TPU format, you can follow these steps:
To export a YOLO11 model to TFLite Edge TPU format, you can follow these steps:
!!! example "Usage"
@ -130,14 +130,14 @@ To export a YOLOv8 model to TFLite Edge TPU format, you can follow these steps:
```python
from ultralytics import YOLO
# Load the YOLOv8 model
model = YOLO("yolov8n.pt")
# Load the YOLO11 model
model = YOLO("yolo11n.pt")
# Export the model to TFLite Edge TPU format
model.export(format="edgetpu") # creates 'yolov8n_full_integer_quant_edgetpu.tflite'
model.export(format="edgetpu") # creates 'yolo11n_full_integer_quant_edgetpu.tflite'
# Load the exported TFLite Edge TPU model
edgetpu_model = YOLO("yolov8n_full_integer_quant_edgetpu.tflite")
edgetpu_model = YOLO("yolo11n_full_integer_quant_edgetpu.tflite")
# Run inference
results = edgetpu_model("https://ultralytics.com/images/bus.jpg")
@ -146,18 +146,18 @@ To export a YOLOv8 model to TFLite Edge TPU format, you can follow these steps:
=== "CLI"
```bash
# Export a YOLOv8n PyTorch model to TFLite Edge TPU format
yolo export model=yolov8n.pt format=edgetpu # creates 'yolov8n_full_integer_quant_edgetpu.tflite'
# Export a YOLO11n PyTorch model to TFLite Edge TPU format
yolo export model=yolo11n.pt format=edgetpu # creates 'yolo11n_full_integer_quant_edgetpu.tflite'
# Run inference with the exported model
yolo predict model=yolov8n_full_integer_quant_edgetpu.tflite source='https://ultralytics.com/images/bus.jpg'
yolo predict model=yolo11n_full_integer_quant_edgetpu.tflite source='https://ultralytics.com/images/bus.jpg'
```
For complete details on exporting models to other formats, refer to our [export guide](../modes/export.md).
### What are the benefits of exporting YOLOv8 models to TFLite Edge TPU?
### What are the benefits of exporting YOLO11 models to TFLite Edge TPU?
Exporting YOLOv8 models to TFLite Edge TPU offers several benefits:
Exporting YOLO11 models to TFLite Edge TPU offers several benefits:
- **Optimized Performance**: Achieve high-speed neural network performance with minimal power consumption.
- **Reduced Latency**: Quick local data processing without the need for cloud dependency.