--- comments: true description: A guide that walks you step-by-step through how to export Ultralytics YOLOv8 models to TF GraphDef format for smooth deployment and efficient model performance. keywords: Ultralytics, YOLOv8, TF GraphDef Export, Model Deployment, TensorFlow Ecosystem, Cross-Platform Compatibility, Performance Optimization --- # How to Export to TF GraphDef from YOLOv8 for Deployment When you are deploying cutting-edge computer vision models, like YOLOv8, in different environments, you might run into compatibility issues. Google's TensorFlow GraphDef, or TF GraphDef, offers a solution by providing a serialized, platform-independent representation of your model. Using the TF GraphDef model format, you can deploy your YOLOv8 model in environments where the complete TensorFlow ecosystem may not be available, such as mobile devices or specialized hardware. In this guide, we'll walk you step by step through how to export your [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics) models to the TF GraphDef model format. By converting your model, you can streamline deployment and use YOLOv8's computer vision capabilities in a broader range of applications and platforms.

TensorFlow GraphDef

## Why Should You Export to TF GraphDef? TF GraphDef is a powerful component of the TensorFlow ecosystem that was developed by Google. It can be used to optimize and deploy models like YOLOv8. Exporting to TF GraphDef lets us move models from research to real-world applications. It allows models to run in environments without the full TensorFlow framework. The GraphDef format represents the model as a serialized computation graph. This enables various optimization techniques like constant folding, quantization, and graph transformations. These optimizations ensure efficient execution, reduced memory usage, and faster inference speeds. GraphDef models can use hardware accelerators such as GPUs, TPUs, and AI chips, unlocking significant performance gains for the YOLOv8 inference pipeline. The TF GraphDef format creates a self-contained package with the model and its dependencies, simplifying deployment and integration into diverse systems. ## Key Features of TF GraphDef Models TF GraphDef offers distinct features for streamlining model deployment and optimization. Here's a look at its key characteristics: - **Model Serialization**: TF GraphDef provides a way to serialize and store TensorFlow models in a platform-independent format. This serialized representation allows you to load and execute your models without the original Python codebase, making deployment easier. - **Graph Optimization**: TF GraphDef enables the optimization of computational graphs. These optimizations can boost performance by streamlining execution flow, reducing redundancies, and tailoring operations to suit specific hardware. - **Deployment Flexibility**: Models exported to the GraphDef format can be used in various environments, including resource-constrained devices, web browsers, and systems with specialized hardware. This opens up possibilities for wider deployment of your TensorFlow models. - **Production Focus**: GraphDef is designed for production deployment. It supports efficient execution, serialization features, and optimizations that align with real-world use cases. ## Deployment Options with TF GraphDef Before we dive into the process of exporting YOLOv8 models to TF GraphDef, let's take a look at some typical deployment situations where this format is used. Here's how you can deploy with TF GraphDef efficiently across various platforms. - **TensorFlow Serving:** This framework is designed to deploy TensorFlow models in production environments. TensorFlow Serving offers model management, versioning, and the infrastructure for efficient model serving at scale. It's a seamless way to integrate your GraphDef-based models into production web services or APIs. - **Mobile and Embedded Devices:** With tools like TensorFlow Lite, you can convert TF GraphDef models into formats optimized for smartphones, tablets, and various embedded devices. Your models can then be used for on-device inference, where execution is done locally, often providing performance gains and offline capabilities. - **Web Browsers:** TensorFlow.js enables the deployment of TF GraphDef models directly within web browsers. It paves the way for real-time object detection applications running on the client side, using the capabilities of YOLOv8 through JavaScript. - **Specialized Hardware:** TF GraphDef's platform-agnostic nature allows it to target custom hardware, such as accelerators and TPUs (Tensor Processing Units). These devices can provide performance advantages for computationally intensive models. ## Exporting YOLOv8 Models to TF GraphDef You can convert your YOLOv8 object detection model to the TF GraphDef format, which is compatible with various systems, to improve its performance across platforms. ### Installation To install the required package, run: !!! Tip "Installation" === "CLI" ```bash # 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](../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. ### 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). !!! Example "Usage" === "Python" ```python from ultralytics import YOLO # Load the YOLOv8 model model = YOLO('yolov8n.pt') # Export the model to TF GraphDef format model.export(format='pb') # creates 'yolov8n.pb' # Load the exported TF GraphDef model tf_graphdef_model = YOLO('yolov8n.pb') # Run inference results = tf_graphdef_model('https://ultralytics.com/images/bus.jpg') ``` === "CLI" ```bash # Export a YOLOv8n PyTorch model to TF GraphDef format yolo export model=yolov8n.pt format=pb # creates 'yolov8n.pb' # Run inference with the exported model yolo predict model='yolov8n.pb' 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 GraphDef Models Once you’ve exported your YOLOv8 model to the TF GraphDef format, the next step is deployment. The primary and recommended first step for running a TF GraphDef model is to use the YOLO("model.pb") method, as previously shown in the usage code snippet. However, for more information on deploying your TF GraphDef models, take a look at the following resources: - **[TensorFlow Serving](https://www.tensorflow.org/tfx/guide/serving)**: A guide on TensorFlow Serving that teaches how to deploy and serve machine learning models efficiently in production environments. - **[TensorFlow Lite](https://www.tensorflow.org/api_docs/python/tf/lite/TFLiteConverter)**: This page describes how to convert machine learning models into a format optimized for on-device inference with TensorFlow Lite. - **[TensorFlow.js](https://www.tensorflow.org/js/guide/conversion)**: A guide on model conversion that teaches how to convert TensorFlow or Keras models into TensorFlow.js format for use in web applications. ## Summary In this guide, we explored how to export Ultralytics YOLOv8 models to the TF GraphDef format. By doing this, you can flexibly deploy your optimized YOLOv8 models in different environments. For further details on usage, visit the [TF GraphDef official documentation](https://www.tensorflow.org/api_docs/python/tf/Graph). 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 has great resources and insights to help you make the most of YOLOv8 in your projects.