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Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Muhammad Rizwan Munawar 2024-08-30 05:52:10 +05:00 • committed by GitHub
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docs/en/integrations/tf-savedmodel.md
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@ -15,7 +15,7 @@ Learning how to export to TF SavedModel from [Ultralytics YOLOv8](https://github
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.
<p align="center">
<img width="100%" src="https://sdtimes.com/wp-content/uploads/2019/10/0_C7GCWYlsMrhUYRYi.png" alt="TF SavedModel">
<img width="100%" src="https://github.com/ultralytics/docs/releases/download/0/tf-savedmodel-overview.avif" alt="TF SavedModel">
</p>
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.