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

Install the Triton Client libraries:

bash

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 -f v2.23.0_ubuntu2004.clients.tar.gz

Clone the Ultralytics repository:

bash

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

Configure and build the project using CMake:

bash

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

Deploy your FP16 (half-precision) YOLO11 model on a Triton Inference Server. Learn more about deploying models with Ultralytics YOLO.
Run the YOLO11-Triton-CPP application:
bash
```
./YOLO11TritonCPP
```

By default, the application will:

Connect to the Triton server at localhost:8001
Use the model named yolo11 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:

cpp

std::string triton_address = "localhost:8001";
std::string model_name = "yolo11";
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

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

We encourage your contributions to help improve this project.