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docs/en/integrations/tf-savedmodel.md
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@ -1,14 +1,14 @@
---
comments: true
description: Learn how to export Ultralytics YOLOv8 models to TensorFlow SavedModel format for easy deployment across various platforms and environments.
keywords: YOLOv8, TF SavedModel, Ultralytics, TensorFlow, model export, model deployment, machine learning, AI
description: Learn how to export Ultralytics YOLO11 models to TensorFlow SavedModel format for easy deployment across various platforms and environments.
keywords: YOLO11, TF SavedModel, Ultralytics, TensorFlow, model export, model deployment, machine learning, AI
---
# Understand How to Export to TF SavedModel Format From YOLOv8
# Understand How to Export to TF SavedModel Format From YOLO11
Deploying [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) models can be challenging. However, using an efficient and flexible model format can make your job easier. TF SavedModel is an open-source machine-learning framework used by TensorFlow to load machine-learning models in a consistent way. It is like a suitcase for TensorFlow models, making them easy to carry and use on different devices and systems.
Learning how to export to TF SavedModel from [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics) models can help you deploy models easily across different platforms and environments. In this guide, we'll walk through how to convert your models to the TF SavedModel format, simplifying the process of running inferences with your models on different devices.
Learning how to export to TF SavedModel from [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics) models can help you deploy models easily across different platforms and environments. In this guide, we'll walk through how to convert your models to the TF SavedModel format, simplifying the process of running inferences with your models on different devices.
## Why Should You Export to TF SavedModel?
@ -32,7 +32,7 @@ Here are the key features that make TF SavedModel a great option for AI develope
## Deployment Options with TF SavedModel
Before we dive into the process of exporting YOLOv8 models to the TF SavedModel 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 SavedModel format, let's explore some typical deployment scenarios where this format is used.
TF SavedModel provides a range of options to deploy your machine learning models:
@ -44,9 +44,9 @@ TF SavedModel provides a range of options to deploy your machine learning models
- **TensorFlow Runtime:** TensorFlow Runtime (`tfrt`) is a high-performance runtime for executing [TensorFlow](https://www.ultralytics.com/glossary/tensorflow) graphs. It provides lower-level APIs for loading and running TF SavedModels in C++ environments. TensorFlow Runtime offers better performance compared to the standard TensorFlow runtime. It is suitable for deployment scenarios that require low-latency inference and tight integration with existing C++ codebases.
## Exporting YOLOv8 Models to TF SavedModel
## Exporting YOLO11 Models to TF SavedModel
By exporting YOLOv8 models to the TF SavedModel format, you enhance their adaptability and ease of deployment across various platforms.
By exporting YOLO11 models to the TF SavedModel format, you enhance their adaptability and ease of deployment across various platforms.
### Installation
@ -57,15 +57,15 @@ 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"
@ -74,14 +74,14 @@ 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 SavedModel format
model.export(format="saved_model") # creates '/yolov8n_saved_model'
model.export(format="saved_model") # creates '/yolo11n_saved_model'
# Load the exported TF SavedModel model
tf_savedmodel_model = YOLO("./yolov8n_saved_model")
tf_savedmodel_model = YOLO("./yolo11n_saved_model")
# Run inference
results = tf_savedmodel_model("https://ultralytics.com/images/bus.jpg")
@ -90,18 +90,18 @@ Before diving into the usage instructions, it's important to note that while all
=== "CLI"
```bash
# Export a YOLOv8n PyTorch model to TF SavedModel format
yolo export model=yolov8n.pt format=saved_model # creates '/yolov8n_saved_model'
# Export a YOLO11n PyTorch model to TF SavedModel format
yolo export model=yolo11n.pt format=saved_model # creates '/yolo11n_saved_model'
# Run inference with the exported model
yolo predict model='./yolov8n_saved_model' source='https://ultralytics.com/images/bus.jpg'
yolo predict model='./yolo11n_saved_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 TF SavedModel Models
## Deploying Exported YOLO11 TF SavedModel Models
Now that you have exported your YOLOv8 model to the TF SavedModel format, the next step is to deploy it. The primary and recommended first step for running a TF GraphDef model is to use the YOLO("./yolov8n_saved_model") method, as previously shown in the usage code snippet.
Now that you have exported your YOLO11 model to the TF SavedModel format, the next step is to deploy it. The primary and recommended first step for running a TF GraphDef model is to use the YOLO("./yolo11n_saved_model") method, as previously shown in the usage code snippet.
However, for in-depth instructions on deploying your TF SavedModel models, take a look at the following resources:
@ -113,11 +113,11 @@ However, for in-depth instructions on deploying your TF SavedModel models, take
## Summary
In this guide, we explored how to export Ultralytics YOLOv8 models to the TF SavedModel format. By exporting to TF SavedModel, you gain the flexibility to optimize, deploy, and scale your YOLOv8 models on a wide range of platforms.
In this guide, we explored how to export Ultralytics YOLO11 models to the TF SavedModel format. By exporting to TF SavedModel, 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 [TF SavedModel official documentation](https://www.tensorflow.org/guide/saved_model).
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
@ -125,32 +125,32 @@ For more information on integrating Ultralytics YOLOv8 with other platforms and
Exporting an Ultralytics YOLO model to the TensorFlow SavedModel format is straightforward. You can use either Python or CLI to achieve this:
!!! example "Exporting YOLOv8 to TF SavedModel"
!!! example "Exporting YOLO11 to TF SavedModel"
=== "Python"
```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 SavedModel format
model.export(format="saved_model") # creates '/yolov8n_saved_model'
model.export(format="saved_model") # creates '/yolo11n_saved_model'
# Load the exported TF SavedModel for inference
tf_savedmodel_model = YOLO("./yolov8n_saved_model")
tf_savedmodel_model = YOLO("./yolo11n_saved_model")
results = tf_savedmodel_model("https://ultralytics.com/images/bus.jpg")
```
=== "CLI"
```bash
# Export the YOLOv8 model to TF SavedModel format
yolo export model=yolov8n.pt format=saved_model # creates '/yolov8n_saved_model'
# Export the YOLO11 model to TF SavedModel format
yolo export model=yolo11n.pt format=saved_model # creates '/yolo11n_saved_model'
# Run inference with the exported model
yolo predict model='./yolov8n_saved_model' source='https://ultralytics.com/images/bus.jpg'
yolo predict model='./yolo11n_saved_model' source='https://ultralytics.com/images/bus.jpg'
```
Refer to the [Ultralytics Export documentation](../modes/export.md) for more details.
@ -176,9 +176,9 @@ TF SavedModel can be deployed in various environments, including:
For detailed deployment options, visit the official guides on [deploying TensorFlow models](https://www.tensorflow.org/tfx/guide/serving).
### How can I install the necessary packages to export YOLOv8 models?
### How can I install the necessary packages to export YOLO11 models?
To export YOLOv8 models, you need to install the `ultralytics` package. Run the following command in your terminal:
To export YOLO11 models, you need to install the `ultralytics` package. Run the following command in your terminal:
```bash
pip install ultralytics