diff --git a/docs/en/datasets/segment/carparts-seg.md b/docs/en/datasets/segment/carparts-seg.md index a9031a43..e5ffda58 100644 --- a/docs/en/datasets/segment/carparts-seg.md +++ b/docs/en/datasets/segment/carparts-seg.md @@ -10,6 +10,17 @@ The [Roboflow](https://roboflow.com/?ref=ultralytics) [Carparts Segmentation Dat Whether you're working on automotive research, developing AI solutions for vehicle maintenance, or exploring computer vision applications, the Carparts Segmentation Dataset serves as a valuable resource for enhancing accuracy and efficiency in your projects. +

+
+ +
+ Watch: Carparts Instance Segmentation Using Ultralytics HUB +

+ ## Dataset Structure The data distribution within the Carparts Segmentation Dataset is organized as outlined below: diff --git a/docs/en/integrations/tf-graphdef.md b/docs/en/integrations/tf-graphdef.md index f7d3fdb6..68a6ab04 100644 --- a/docs/en/integrations/tf-graphdef.md +++ b/docs/en/integrations/tf-graphdef.md @@ -10,6 +10,10 @@ When you are deploying cutting-edge computer vision models, like YOLOv8, in diff 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.