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docs/en/integrations/tfjs.md
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@ -1,14 +1,14 @@
---
comments: true
description: Convert your Ultralytics YOLOv8 models to TensorFlow.js for high-speed, local object detection. Learn how to optimize ML models for browser and Node.js apps.
keywords: YOLOv8, TensorFlow.js, TF.js, model export, machine learning, object detection, browser ML, Node.js, Ultralytics, YOLO, export models
description: Convert your Ultralytics YOLO11 models to TensorFlow.js for high-speed, local object detection. Learn how to optimize ML models for browser and Node.js apps.
keywords: YOLO11, TensorFlow.js, TF.js, model export, machine learning, object detection, browser ML, Node.js, Ultralytics, YOLO, export models
---
# Export to TF.js Model Format From a YOLOv8 Model Format
# Export to TF.js Model Format From a YOLO11 Model Format
Deploying [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) models directly in the browser or on Node.js can be tricky. You'll need to make sure your model format is optimized for faster performance so that the model can be used to run interactive applications locally on the user's device. The TensorFlow.js, or TF.js, model format is designed to use minimal power while delivering fast performance.
The 'export to TF.js model format' feature allows you to optimize your [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics) models for high-speed and locally-run [object detection](https://www.ultralytics.com/glossary/object-detection) inference. In this guide, we'll walk you through converting your models to the TF.js format, making it easier for your models to perform well on various local browsers and Node.js applications.
The 'export to TF.js model format' feature allows you to optimize your [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics) models for high-speed and locally-run [object detection](https://www.ultralytics.com/glossary/object-detection) inference. In this guide, we'll walk you through converting your models to the TF.js format, making it easier for your models to perform well on various local browsers and Node.js applications.
## Why Should You Export to TF.js?
@ -32,7 +32,7 @@ Here are the key features that make TF.js a powerful tool for developers:
## Deployment Options with TensorFlow.js
Before we dive into the process of exporting YOLOv8 models to the TF.js format, let's explore some typical deployment scenarios where this format is used.
Before we dive into the process of exporting YOLO11 models to the TF.js format, let's explore some typical deployment scenarios where this format is used.
TF.js provides a range of options to deploy your machine learning models:
@ -42,9 +42,9 @@ TF.js provides a range of options to deploy your machine learning models:
- **Chrome Extensions:** An interesting deployment scenario is the creation of Chrome extensions with TensorFlow.js. For instance, you can develop an extension that allows users to right-click on an image within any webpage to classify it using a pre-trained ML model. TensorFlow.js can be integrated into everyday web browsing experiences to provide immediate insights or augmentations based on machine learning.
## Exporting YOLOv8 Models to TensorFlow.js
## Exporting YOLO11 Models to TensorFlow.js
You can expand model compatibility and deployment flexibility by converting YOLOv8 models to TF.js.
You can expand model compatibility and deployment flexibility by converting YOLO11 models to TF.js.
### 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 TF.js format
model.export(format="tfjs") # creates '/yolov8n_web_model'
model.export(format="tfjs") # creates '/yolo11n_web_model'
# Load the exported TF.js model
tfjs_model = YOLO("./yolov8n_web_model")
tfjs_model = YOLO("./yolo11n_web_model")
# Run inference
results = tfjs_model("https://ultralytics.com/images/bus.jpg")
@ -88,18 +88,18 @@ Before diving into the usage instructions, it's important to note that while all
=== "CLI"
```bash
# Export a YOLOv8n PyTorch model to TF.js format
yolo export model=yolov8n.pt format=tfjs # creates '/yolov8n_web_model'
# Export a YOLO11n PyTorch model to TF.js format
yolo export model=yolo11n.pt format=tfjs # creates '/yolo11n_web_model'
# Run inference with the exported model
yolo predict model='./yolov8n_web_model' source='https://ultralytics.com/images/bus.jpg'
yolo predict model='./yolo11n_web_model' 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 TensorFlow.js Models
## Deploying Exported YOLO11 TensorFlow.js Models
Now that you have exported your YOLOv8 model to the TF.js format, the next step is to deploy it. The primary and recommended first step for running a TF.js is to use the YOLO("./yolov8n_web_model") method, as previously shown in the usage code snippet.
Now that you have exported your YOLO11 model to the TF.js format, the next step is to deploy it. The primary and recommended first step for running a TF.js is to use the `YOLO("./yolo11n_web_model")` method, as previously shown in the usage code snippet.
However, for in-depth instructions on deploying your TF.js models, take a look at the following resources:
@ -111,17 +111,17 @@ However, for in-depth instructions on deploying your TF.js models, take a look a
## Summary
In this guide, we learned how to export Ultralytics YOLOv8 models to the TensorFlow.js format. By exporting to TF.js, you gain the flexibility to optimize, deploy, and scale your YOLOv8 models on a wide range of platforms.
In this guide, we learned how to export Ultralytics YOLO11 models to the TensorFlow.js format. By exporting to TF.js, you gain the flexibility to optimize, deploy, and scale your YOLO11 models on a wide range of platforms.
For further details on usage, visit the [TensorFlow.js official documentation](https://www.tensorflow.org/js/guide).
For more information on integrating Ultralytics YOLOv8 with other platforms and frameworks, don't forget to check out our [integration guide page](index.md). It's packed with great resources to help you make the most of YOLOv8 in your projects.
For more information on integrating Ultralytics YOLO11 with other platforms and frameworks, don't forget to check out our [integration guide page](index.md). It's packed with great resources to help you make the most of YOLO11 in your projects.
## FAQ
### How do I export Ultralytics YOLOv8 models to TensorFlow.js format?
### How do I export Ultralytics YOLO11 models to TensorFlow.js format?
Exporting Ultralytics YOLOv8 models to TensorFlow.js (TF.js) format is straightforward. You can follow these steps:
Exporting Ultralytics YOLO11 models to TensorFlow.js (TF.js) format is straightforward. You can follow these steps:
!!! example "Usage"
@ -130,14 +130,14 @@ Exporting Ultralytics YOLOv8 models to TensorFlow.js (TF.js) format is straightf
```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 TF.js format
model.export(format="tfjs") # creates '/yolov8n_web_model'
model.export(format="tfjs") # creates '/yolo11n_web_model'
# Load the exported TF.js model
tfjs_model = YOLO("./yolov8n_web_model")
tfjs_model = YOLO("./yolo11n_web_model")
# Run inference
results = tfjs_model("https://ultralytics.com/images/bus.jpg")
@ -146,18 +146,18 @@ Exporting Ultralytics YOLOv8 models to TensorFlow.js (TF.js) format is straightf
=== "CLI"
```bash
# Export a YOLOv8n PyTorch model to TF.js format
yolo export model=yolov8n.pt format=tfjs # creates '/yolov8n_web_model'
# Export a YOLO11n PyTorch model to TF.js format
yolo export model=yolo11n.pt format=tfjs # creates '/yolo11n_web_model'
# Run inference with the exported model
yolo predict model='./yolov8n_web_model' source='https://ultralytics.com/images/bus.jpg'
yolo predict model='./yolo11n_web_model' 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).
### Why should I export my YOLOv8 models to TensorFlow.js?
### Why should I export my YOLO11 models to TensorFlow.js?
Exporting YOLOv8 models to TensorFlow.js offers several advantages, including:
Exporting YOLO11 models to TensorFlow.js offers several advantages, including:
1. **Local Execution:** Models can run directly in the browser or Node.js, reducing latency and enhancing user experience.
2. **Cross-Platform Support:** TF.js supports multiple environments, allowing flexibility in deployment.
@ -177,7 +177,7 @@ TensorFlow.js is specifically designed for efficient execution of ML models in b
Interested in learning more about TF.js? Check out the [official TensorFlow.js guide](https://www.tensorflow.org/js/guide).
### What are the key features of TensorFlow.js for deploying YOLOv8 models?
### What are the key features of TensorFlow.js for deploying YOLO11 models?
Key features of TensorFlow.js include:
@ -185,10 +185,10 @@ Key features of TensorFlow.js include:
- **Multiple Backends:** Supports CPU, WebGL for GPU acceleration, WebAssembly (WASM), and WebGPU for advanced operations.
- **Offline Capabilities:** Models can run directly in the browser without internet connectivity, making it ideal for developing responsive web applications.
For deployment scenarios and more in-depth information, see our section on [Deployment Options with TensorFlow.js](#deploying-exported-yolov8-tensorflowjs-models).
For deployment scenarios and more in-depth information, see our section on [Deployment Options with TensorFlow.js](#deploying-exported-yolo11-tensorflowjs-models).
### Can I deploy a YOLOv8 model on server-side Node.js applications using TensorFlow.js?
### Can I deploy a YOLO11 model on server-side Node.js applications using TensorFlow.js?
Yes, TensorFlow.js allows the deployment of YOLOv8 models on Node.js environments. This enables server-side machine learning applications that benefit from the processing power of a server and access to server-side data. Typical use cases include real-time data processing and machine learning pipelines on backend servers.
Yes, TensorFlow.js allows the deployment of YOLO11 models on Node.js environments. This enables server-side machine learning applications that benefit from the processing power of a server and access to server-side data. Typical use cases include real-time data processing and machine learning pipelines on backend servers.
To get started with Node.js deployment, refer to the [Run TensorFlow.js in Node.js](https://www.tensorflow.org/js/guide/nodejs) guide from TensorFlow.