ultralytics 8.1.42 add YOLOv9 Segment models (#9296)

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>

docs/en/integrations/edge-tpu.md

@@ -24,7 +24,7 @@ The Edge TPU works with quantized models. Quantization makes models smaller and
 
 Here are the key features that make TFLite Edge TPU a great model format choice for developers:
 
-- **Optimized Performance on Edge Devices**: The TFLite Edge TPU achieves high-speed neural networking performance through quantization, model optimization, hardware acceleration, and compiler optimization. Its minimalistic architecture contributes to its smaller size and cost-efficiency. 
+- **Optimized Performance on Edge Devices**: The TFLite Edge TPU achieves high-speed neural networking performance through quantization, model optimization, hardware acceleration, and compiler optimization. Its minimalistic architecture contributes to its smaller size and cost-efficiency.
 
 - **High Computational Throughput**: TFLite Edge TPU combines specialized hardware acceleration and efficient runtime execution to achieve high computational throughput. It is well-suited for deploying machine learning models with stringent performance requirements on edge devices.
 
@@ -38,9 +38,9 @@ TFLite Edge TPU offers various deployment options for machine learning models, i
 
 - **On-Device Deployment**: TensorFlow Edge TPU models can be directly deployed on mobile and embedded devices. On-device deployment allows the models to execute directly on the hardware, eliminating the need for cloud connectivity.
 
-- **Edge Computing with Cloud TensorFlow TPUs**: In scenarios where edge devices have limited processing capabilities, TensorFlow Edge TPUs can offload inference tasks to cloud servers equipped with TPUs. 
+- **Edge Computing with Cloud TensorFlow TPUs**: In scenarios where edge devices have limited processing capabilities, TensorFlow Edge TPUs can offload inference tasks to cloud servers equipped with TPUs.
 
-- **Hybrid Deployment**: A hybrid approach combines on-device and cloud deployment and offers a versatile and scalable solution for deploying machine learning models. Advantages include on-device processing for quick responses and cloud computing for more complex computations. 
+- **Hybrid Deployment**: A hybrid approach combines on-device and cloud deployment and offers a versatile and scalable solution for deploying machine learning models. Advantages include on-device processing for quick responses and cloud computing for more complex computations.
 
 ## Exporting YOLOv8 Models to TFLite Edge TPU
 
@@ -99,7 +99,7 @@ For more details about supported export options, visit the [Ultralytics document
 
 ## Deploying Exported YOLOv8 TFLite Edge TPU Models
 
-After successfully exporting your Ultralytics YOLOv8 models to TFLite Edge TPU format, you can now deploy them. The primary and recommended first step for running a TFLite Edge TPU model is to use the YOLO("model_edgetpu.tflite") method, as outlined in the previous usage code snippet. 
+After successfully exporting your Ultralytics YOLOv8 models to TFLite Edge TPU format, you can now deploy them. The primary and recommended first step for running a TFLite Edge TPU model is to use the YOLO("model_edgetpu.tflite") method, as outlined in the previous usage code snippet.
 
 However, for in-depth instructions on deploying your TFLite Edge TPU models, take a look at the following resources:
 
@@ -111,7 +111,7 @@ However, for in-depth instructions on deploying your TFLite Edge TPU models, tak
 
 ## Summary
 
-In this guide, we’ve learned how to export Ultralytics YOLOv8 models to TFLite Edge TPU format. By following the steps mentioned above, you can increase the speed and power of your computer vision applications. 
+In this guide, we’ve learned how to export Ultralytics YOLOv8 models to TFLite Edge TPU format. By following the steps mentioned above, you can increase the speed and power of your computer vision applications.
 
 For further details on usage, visit the [Edge TPU official website](https://cloud.google.com/edge-tpu).
 

docs/en/integrations/index.md

@@ -64,7 +64,7 @@ Welcome to the Ultralytics Integrations page! This page provides an overview of
 - [CoreML](coreml.md): CoreML, developed by [Apple](https://www.apple.com/), is a framework designed for efficiently integrating machine learning models into applications across iOS, macOS, watchOS, and tvOS, using Apple's hardware for effective and secure model deployment.
 
 - [TF SavedModel](tf-savedmodel.md): Developed by [Google](https://www.google.com), TF SavedModel is a universal serialization format for TensorFlow models, enabling easy sharing and deployment across a wide range of platforms, from servers to edge devices.
-  
+
 - [TF GraphDef](tf-graphdef.md): Developed by [Google](https://www.google.com), GraphDef is TensorFlow's format for representing computation graphs, enabling optimized execution of machine learning models across diverse hardware.
 
 - [TFLite](tflite.md): Developed by [Google](https://www.google.com), TFLite is a lightweight framework for deploying machine learning models on mobile and edge devices, ensuring fast, efficient inference with minimal memory footprint.
@@ -72,7 +72,7 @@ Welcome to the Ultralytics Integrations page! This page provides an overview of
 - [TFLite Edge TPU](edge-tpu.md): Developed by [Google](https://www.google.com) for optimizing TensorFlow Lite models on Edge TPUs, this model format ensures high-speed, efficient edge computing.
 
 - [PaddlePaddle](paddlepaddle.md): An open-source deep learning platform by [Baidu](https://www.baidu.com/), PaddlePaddle enables the efficient deployment of AI models and focuses on the scalability of industrial applications.
-    
+
 - [NCNN](ncnn.md): Developed by [Tencent](http://www.tencent.com/), NCNN is an efficient neural network inference framework tailored for mobile devices. It enables direct deployment of AI models into apps, optimizing performance across various mobile platforms.
 
 ### Export Formats

docs/en/integrations/paddlepaddle.md

@@ -16,7 +16,7 @@ The ability to export to PaddlePaddle model format allows you to optimize your [
   <img width="75%" src="https://github.com/PaddlePaddle/Paddle/blob/develop/doc/imgs/logo.png?raw=true" alt="PaddlePaddle Logo">
 </p>
 
-Developed by Baidu, [PaddlePaddle](https://www.paddlepaddle.org.cn/en) (**PA**rallel **D**istributed **D**eep **LE**arning) is China's first open-source deep learning platform. Unlike some frameworks built mainly for research, PaddlePaddle prioritizes ease of use and smooth integration across industries.  
+Developed by Baidu, [PaddlePaddle](https://www.paddlepaddle.org.cn/en) (**PA**rallel **D**istributed **D**eep **LE**arning) is China's first open-source deep learning platform. Unlike some frameworks built mainly for research, PaddlePaddle prioritizes ease of use and smooth integration across industries.
 
 It offers tools and resources similar to popular frameworks like TensorFlow and PyTorch, making it accessible for developers of all experience levels. From farming and factories to service businesses, PaddlePaddle's large developer community of over 4.77 million is helping create and deploy AI applications.
 
@@ -26,11 +26,11 @@ By exporting your Ultralytics YOLOv8 models to PaddlePaddle format, you can tap
 
 PaddlePaddle models offer a range of key features that contribute to their flexibility, performance, and scalability across diverse deployment scenarios:
 
- - **Dynamic-to-Static Graph**: PaddlePaddle supports [dynamic-to-static compilation](https://www.paddlepaddle.org.cn/documentation/docs/en/guides/jit/index_en.html), where models can be translated into a static computational graph. This enables optimizations that reduce runtime overhead and boost inference performance.
+- **Dynamic-to-Static Graph**: PaddlePaddle supports [dynamic-to-static compilation](https://www.paddlepaddle.org.cn/documentation/docs/en/guides/jit/index_en.html), where models can be translated into a static computational graph. This enables optimizations that reduce runtime overhead and boost inference performance.
 
- - **Operator Fusion**: PaddlePaddle, like TensorRT, uses [operator fusion](https://developer.nvidia.com/gtc/2020/video/s21436-vid) to streamline computation and reduce overhead. The framework minimizes memory transfers and computational steps by merging compatible operations, resulting in faster inference.
+- **Operator Fusion**: PaddlePaddle, like TensorRT, uses [operator fusion](https://developer.nvidia.com/gtc/2020/video/s21436-vid) to streamline computation and reduce overhead. The framework minimizes memory transfers and computational steps by merging compatible operations, resulting in faster inference.
 
- - **Quantization**: PaddlePaddle supports [quantization techniques](https://www.paddlepaddle.org.cn/documentation/docs/en/api/paddle/quantization/PTQ_en.html), including post-training quantization and quantization-aware training. These techniques allow for the use of lower-precision data representations, effectively boosting performance and reducing model size.
+- **Quantization**: PaddlePaddle supports [quantization techniques](https://www.paddlepaddle.org.cn/documentation/docs/en/api/paddle/quantization/PTQ_en.html), including post-training quantization and quantization-aware training. These techniques allow for the use of lower-precision data representations, effectively boosting performance and reducing model size.
 
 ## Deployment Options in PaddlePaddle
 
@@ -103,7 +103,7 @@ For more details about supported export options, visit the [Ultralytics document
 
 ## Deploying Exported YOLOv8 PaddlePaddle Models
 
-After successfully exporting your Ultralytics YOLOv8 models to PaddlePaddle format, you can now deploy them. The primary and recommended first step for running a PaddlePaddle model is to use the YOLO("./model_paddle_model") method, as outlined in the previous usage code snippet. 
+After successfully exporting your Ultralytics YOLOv8 models to PaddlePaddle format, you can now deploy them. The primary and recommended first step for running a PaddlePaddle model is to use the YOLO("./model_paddle_model") method, as outlined in the previous usage code snippet.
 
 However, for in-depth instructions on deploying your PaddlePaddle models in various other settings, take a look at the following resources:
 

docs/en/integrations/tf-graphdef.md

@@ -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
 

docs/en/integrations/tf-savedmodel.md

@@ -32,7 +32,7 @@ Here are the key features that make TF SavedModel a great option for AI develope
 
 ## 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. 
+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:
 
@@ -63,7 +63,6 @@ To install the required package, run:
 
 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).