YOLO V10 C++ TensorRT

🌐 Overview

The YOLOv10 C++ TensorRT Project is a high-performance object detection solution implemented in C++ and optimized using NVIDIA TensorRT. This project leverages the YOLOv10 model to deliver fast and accurate object detection, utilizing TensorRT to maximize inference efficiency and performance.

📢 Updates

Key Features:

• 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 image files.

By combining the advanced capabilities of YOLOv10 with TensorRT’s powerful optimization, this project provides a robust and scalable solution for real-time object detection tasks.

📑 Table of Contents

• Project Structure
• Dependencies
• Installation
• Usage
  • Convert ONNX Model to TensorRT Engine
  • Run Inference on Video
  • Run Inference on Image
• Configuration
• Troubleshooting

🏗️ Project Structure

YOLOv10-TensorRT/

│── include/

│ ├── YOLOv10.hpp

│── src/

│ ├── main.cpp

│ ├── YOLOv10.cpp

│── CMakeLists.txt

└── README.md

📦 Dependencies

• OpenCV V4.10.0: For image and video processing.
• CUDA V11.7: For GPU acceleration.
• TensorRT V8.6.1.6: For optimized inference with YOLOv10.
• cuDNN V9.2.1: For accelerating deep learning training and inference on NVIDIA GPUs.

💾 Installation

1. Install Dependencies

• OpenCV: Follow the instructions on the OpenCV official website to install OpenCV.
• CUDA & cuDNN: Install CUDA & cuDNN from the NVIDIA website.
• TensorRT: Download and install TensorRT from the NVIDIA Developer website.

2. Clone the Repository

git clone https://github.com/hamdiboukamcha/yolov10-tensorrt.git

cd yolov10-tensorrt/Yolov10-TensorRT

  mkdir build
  cd build
  cmake ..
  cmake --build .

🚀 Usage

Convert ONNX Model to TensorRT Engine

To convert an ONNX model to a TensorRT engine file, use the following command:

./YOLOv10Project 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.

Run Inference on Video

To run inference on a video, use the following command:

./YOLOv10Project 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 Video

Run Inference on Image To run inference on an image, use the following command:

./YOLOv10Project 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.

⚙️ Configuration

CMake Configuration

In the CMakeLists.txt, update the paths for TensorRT and OpenCV if they are installed in non-default locations:

Set the path to TensorRT installation

set(TENSORRT_PATH "path/to/TensorRT")  # Update this to the actual path

Ensure that the path points to the directory where TensorRT is installed.

Troubleshooting

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.

📞 Contact

For advanced inquiries, feel free to contact me on LinkedIn:

📜 Citation

If you use this code in your research, please cite the repository as follows:

    @misc{boukamcha2024yolov10,
        author = {Hamdi Boukamcha},
        title = {Yolo-V10-cpp-TensorRT},
        year = {2024},
        publisher = {GitHub},
        howpublished = {\url{https://github.com/hamdiboukamcha/Yolo-V10-cpp-TensorRT}},
    }