Skip to content

Understand How to Export to TF SavedModel Format From YOLOv8

Deploying machine learning 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 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?

The TensorFlow SavedModel format is a part of the TensorFlow ecosystem developed by Google as shown below. It is designed to save and serialize TensorFlow models seamlessly. It encapsulates the complete details of models like the architecture, weights, and even compilation information. This makes it straightforward to share, deploy, and continue training across different environments.

TF SavedModel

The TF SavedModel has a key advantage: its compatibility. It works well with TensorFlow Serving, TensorFlow Lite, and TensorFlow.js. This compatibility makes it easier to share and deploy models across various platforms, including web and mobile applications. The TF SavedModel format is useful both for research and production. It provides a unified way to manage your models, ensuring they are ready for any application.

Key Features of TF SavedModels

Here are the key features that make TF SavedModel a great option for AI developers:

  • Portability: TF SavedModel provides a language-neutral, recoverable, hermetic serialization format. They enable higher-level systems and tools to produce, consume, and transform TensorFlow models. SavedModels can be easily shared and deployed across different platforms and environments.

  • Ease of Deployment: TF SavedModel bundles the computational graph, trained parameters, and necessary metadata into a single package. They can be easily loaded and used for inference without requiring the original code that built the model. This makes the deployment of TensorFlow models straightforward and efficient in various production environments.

  • Asset Management: TF SavedModel supports the inclusion of external assets such as vocabularies, embeddings, or lookup tables. These assets are stored alongside the graph definition and variables, ensuring they are available when the model is loaded. This feature simplifies the management and distribution of models that rely on external resources.

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.

TF SavedModel provides a range of options to deploy your machine learning models:

  • TensorFlow Serving: TensorFlow Serving is a flexible, high-performance serving system designed for production environments. It natively supports TF SavedModels, making it easy to deploy and serve your models on cloud platforms, on-premises servers, or edge devices.

  • Cloud Platforms: Major cloud providers like Google Cloud Platform (GCP), Amazon Web Services (AWS), and Microsoft Azure offer services for deploying and running TensorFlow models, including TF SavedModels. These services provide scalable and managed infrastructure, allowing you to deploy and scale your models easily.

  • Mobile and Embedded Devices: TensorFlow Lite, a lightweight solution for running machine learning models on mobile, embedded, and IoT devices, supports converting TF SavedModels to the TensorFlow Lite format. This allows you to deploy your models on a wide range of devices, from smartphones and tablets to microcontrollers and edge devices.

  • TensorFlow Runtime: TensorFlow Runtime (tfrt) is a high-performance runtime for executing 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

By exporting YOLOv8 models to the TF SavedModel format, you enhance their adaptability and ease of deployment across various platforms.


To install the required package, run:


# Install the required package for YOLOv8
pip install ultralytics

For detailed instructions and best practices related to the installation process, check our Ultralytics Installation guide. While installing the required packages for YOLOv8, if you encounter any difficulties, consult our Common Issues guide for solutions and tips.


Before diving into the usage instructions, it's important to note that while all Ultralytics YOLOv8 models are available for exporting, you can ensure that the model you select supports export functionality here.


from ultralytics import YOLO

# Load the YOLOv8 model
model = YOLO('')

# Export the model to TF SavedModel format
model.export(format='saved_model')  # creates '/yolov8n_saved_model'

# Load the exported TF SavedModel model
tf_savedmodel_model = YOLO('./yolov8n_saved_model')

# Run inference
results = tf_savedmodel_model('')
# Export a YOLOv8n PyTorch model to TF SavedModel format
yolo export format=saved_model  # creates '/yolov8n_saved_model'

# Run inference with the exported model
yolo predict model='./yolov8n_saved_model' source=''

For more details about supported export options, visit the Ultralytics documentation page on deployment options.

Deploying Exported YOLOv8 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.

However, for in-depth instructions on deploying your TF SavedModel models, take a look at the following resources:

  • TensorFlow Serving: Here’s the developer documentation for how to deploy your TF SavedModel models using TensorFlow Serving.

  • Run a TensorFlow SavedModel in Node.js: A TensorFlow blog post on running a TensorFlow SavedModel in Node.js directly without conversion.

  • Deploying on Cloud: A TensorFlow blog post on deploying a TensorFlow SavedModel model on the Cloud AI Platform.


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.

For further details on usage, visit the TF SavedModel official documentation.

For more information on integrating Ultralytics YOLOv8 with other platforms and frameworks, don't forget to check out our integration guide page. It's packed with great resources to help you make the most of YOLOv8 in your projects.

Created 2024-03-23, Updated 2024-04-02
Authors: Burhan-Q (1), abirami-vina (1)