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ultralytics 8.1.42 add YOLOv9 Segment models (#9296)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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docs/en/integrations/tf-graphdef.md
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@ -20,17 +20,17 @@ GraphDef models can use hardware accelerators such as GPUs, TPUs, and AI chips,
## Key Features of TF GraphDef Models
TF GraphDef offers distinct features for streamlining model deployment and optimization.
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.
- **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.
- **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.
- **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.
- **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
@ -44,7 +44,7 @@ Here's how you can deploy with TF GraphDef efficiently across various platforms.
- **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.
- **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