image_to_pixle_params_yoloSAM/ultralytics-main/examples/YOLO11-Triton-CPP

YOLO11 Triton Inference Server C++ Client

This example demonstrates how to perform object detection using Ultralytics YOLO11 models deployed on the NVIDIA Triton Inference Server. The implementation highlights efficient image preprocessing, FP16 (half-precision) data conversion, seamless communication with the Triton server via gRPC, and visualization of detection results with bounding boxes and confidence scores.

⚡ Features

• High-Performance Inference: Utilizes FP16 (half-precision) data format for optimized memory usage and accelerated inference.
• Non-Maximum Suppression (NMS): Removes duplicate detections to ensure precise object detection results.
• Seamless Triton Integration: Communicates with the NVIDIA Triton Inference Server via gRPC for efficient and scalable model serving.
• Detection Visualization: Annotates images with bounding boxes, class labels, and confidence scores for intuitive result interpretation.

🛠️ Dependencies

Ensure you have the following dependencies installed before proceeding:

Dependency	Version	Description
Triton Inference Server	22.06	Running with a deployed FP16 YOLO11 model
Triton Client libraries	2.23	Required for communication with Triton Server
C++ compiler	C++ 17+	For compiling the C++ client application
OpenCV library	3.4.15	For image processing and visualization
CMake	3.5+	For building the project

For more information on Triton, see the NVIDIA Triton Inference Server documentation and explore model deployment options with Ultralytics.

🏗️ Building the Project

1. Install the Triton Client libraries:

   wget https://github.com/triton-inference-server/server/releases/download/v2.23.0/v2.23.0_ubuntu2004.clients.tar.gz
   mkdir tritonclient
   tar -xvf v2.23.0_ubuntu2004.clients.tar.gz -C tritonclient
   rm -rf v2.23.0_ubuntu2004.clients.tar.gz
   

2. Clone the Ultralytics repository:

   git clone https://github.com/ultralytics/ultralytics.git
   cd ultralytics/examples/YOLO11-Triton-CPP
   

3. Configure and build the project using CMake:

   mkdir build
   cd build
   cmake .. -DTRITON_CLIENT_DIR=/path/to/tritonclient
   make
   

For additional guidance on integrating Ultralytics YOLO models with various platforms, check out the Ultralytics integrations documentation.

🚀 Usage

1. Deploy your FP16 (half-precision) YOLO11 model on a Triton Inference Server.
   Learn more about deploying models with Ultralytics YOLO.

2. Run the YOLO11-Triton-CPP application:

   ./YOLO11TritonCPP
   

By default, the application will:

• Connect to the Triton server at localhost:8001
• Use the model named yolov11 with version 1
• Process the image file test.jpg
• Save detection results to output.jpg

For more on object detection workflows, see Ultralytics object detection tasks.

⚙️ Configuration

You can modify the following parameters in main.cpp:

std::string triton_address = "localhost:8001";
std::string model_name = "yolov11";
std::string model_version = "1";
std::string image_path = "test.jpg";
std::string output_path = "output.jpg";
std::vector<std::string> object_class_list = {"class1", "class2"};

To learn more about configuring and customizing YOLO models, visit the Ultralytics configuration guide.

🌟 Contributors

Contributions are welcome! If you find any issues or have suggestions for improvements, please open an issue or submit a pull request on the main Ultralytics repository.

• Ahmet Selim Demirel
• Doğan Mehmet Başoğlu
• Enes Uzun
• Elif Cansu Ada
• Mevlüt Ardıç
• Serhat Karaca

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For more resources, explore the Ultralytics documentation, Ultralytics blog, and Ultralytics HUB.

We encourage your contributions to make this project even better! 🚀