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https://github.com/hamdiboukamcha/Yolo-V10-cpp-TensorRT

The YOLOv10 C++ TensorRT Project in C++ and optimized using NVIDIA TensorRT
https://github.com/hamdiboukamcha/Yolo-V10-cpp-TensorRT

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The YOLOv10 C++ TensorRT Project in C++ and optimized using NVIDIA TensorRT

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README 

        
# YOLO V10  C++ TensorRT



![Inference Time of Yolo V10 Models](assets/Yolo_v10_cpp_tenosrrt.PNG)





    GitHub

  



  

    License

  



## 🌐 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. 



![Inference Time of Yolo V10 Models](assets/Bench_YOLO_V10.jpg)



## 📢 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](#project-structure)

- [Dependencies](#dependencies)

- [Installation](#installation)

- [Usage](#usage)

  - [Convert ONNX Model to TensorRT Engine](#convert-onnx-model-to-tensorrt-engine)

  - [Run Inference on Video](#run-inference-on-video)

  - [Run Inference on Image](#run-inference-on-image)

- [Configuration](#configuration)

- [Troubleshooting](#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](https://opencv.org/) to install OpenCV.

- **CUDA & cuDNN**: Install CUDA & cuDNN from the [NVIDIA website](https://developer.nvidia.com/cuda-toolkit).

- **TensorRT**: Download and install TensorRT from the [NVIDIA Developer website](https://developer.nvidia.com/tensorrt).



### 2. Clone the Repository



git clone [https://github.com/hamdiboukamcha/yolov10-tensorrt.git](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:  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}},

        }