The YOLOv11 C++ TensorRT Project is a high-performance object detection solution implemented in C++ and optimized using NVIDIA TensorRT. This project leverages the YOLOv11 model to deliver fast and accurate object detection, utilizing TensorRT to maximize inference efficiency and performance.
- Model Conversion: Convert ONNX models to TensorRT engine files to accelerate inference.
- Inference on Videos: Efficiently perform object detection on video files.
- Inference on Images: Execute object detection on individual images.
- High Efficiency: Optimized for real-time object detection using NVIDIA GPUs.
- Preprocessing with CUDA: CUDA-enabled preprocessing for faster input handling.
YOLOv11-TensorRT/
├── CMakeLists.txt # Build configuration for the project
├── include/ # Header files
├── src/
│ ├── main.cpp # Main entry point for the application
│ ├── yolov11.cpp # YOLOv11 implementation
│ └── preprocess.cu # CUDA preprocessing code
├── assets/ # Images and benchmarks for README
└── build/ # Compiled binaries
- CMake (version 3.18 or higher)
- TensorRT (V8.6.1.6: For optimized inference with YOLOv11.)
- CUDA Toolkit (V11.7: For GPU acceleration)
- OpenCV (V4.10.0: For image and video processing)
- NVIDIA GPU (with compute capability 7.5 or higher)
- Clone the repository:
git clone https://github.com/hamdiboukamcha/Yolo-V11-cpp-TensorRT.git cd YOLOv11-TensorRT - Update the TensorRT and OpenCV paths in CMakeLists.txt:
set(TENSORRT_PATH "F:/Program Files/TensorRT-8.6.1.6") # Adjust this to your path
- Build the project:
mkdir build cd build cmake .. make -j$(nproc)
from ultralytics import YOLO
Load the YOLO model
model = YOLO("yolo11s.pt")
#Export the model to ONNX format
export_path = model.export(format="onnx")
To convert an ONNX model to a TensorRT engine file, use the following command:
./YOLOv11TRT convert path_to_your_model.onnx path_to_your_engine.engine.
path_to_your_model.onnx: Path to the ONNX model file.
path_to_your_engine.engine: Path where the TensorRT engine file will be saved.
To run inference on a video, use the following command:
./YOLOv11TRT infer_video path_to_your_video.mp4 path_to_your_engine.engine
path_to_your_video.mp4: Path to the input video file.
path_to_your_engine.engine: Path to the TensorRT engine file.
Run Inference on Image To run inference on an image, use the following command:
./YOLOv11TRT infer_image path_to_your_image.jpg path_to_your_engine.engine
path_to_your_image.jpg: Path to the input image file.
path_to_your_engine.engine: Path to the TensorRT engine file.
In the CMakeLists.txt, update the paths for TensorRT and OpenCV if they are installed in non-default locations:
#Define the path to TensorRT installation
set(TENSORRT_PATH "F:/Program Files/TensorRT-8.6.1.6") # Update this to the actual path for TensorRT
Ensure that the path points to the directory where TensorRT is installed.
Cannot find nvinfer.lib: Ensure that TensorRT is correctly installed and that nvinfer.lib is in the specified path. Update CMakeLists.txt to include the correct path to TensorRT libraries.
Linker Errors: Verify that all dependencies (OpenCV, CUDA, TensorRT) are correctly installed and that their paths are correctly set in CMakeLists.txt.
Run-time Errors: Ensure that your system has the correct CUDA drivers and that TensorRT runtime libraries are accessible. Add TensorRT’s bin directory to your system PATH.
For advanced inquiries, feel free to contact me on LinkedIn: 
If you use this code in your research, please cite the repository as follows:
@misc{boukamcha2024yolov11,
author = {Hamdi Boukamcha},
title = {Yolo-V11-cpp-TensorRT},
year = {2024},
publisher = {GitHub},
howpublished = {\url{https://github.com/hamdiboukamcha/Yolo-V11-cpp-TensorRT/}},
}