YOLOv10 OpenVINO C++ Inference

Implementing YOLOv10 object detection using OpenVINO for efficient and accurate real-time inference in C++.

Features

Support for ONNX and OpenVINO IR model formats
Support for FP32, FP16 and INT8 precisions
Support for loading model with dynamic shape

Tested on Ubuntu 18.04, 20.04, 22.04.

Dependencies

Dependency	Version
OpenVINO	>=2023.3
OpenCV	>=3.2.0
C++	>=14
CMake	>=3.10.2

Installation Options

You have two options for setting up the environment: manually installing dependencies or using Docker.

Manual Installation

Install Dependencies

apt-get update
apt-get install -y \
    libtbb2 \
    cmake \
    make \
    git \
    libyaml-cpp-dev \
    wget \
    libopencv-dev \
    pkg-config \
    g++ \
    gcc \
    libc6-dev \
    make \
    build-essential \
    sudo \
    ocl-icd-libopencl1 \
    python3 \
    python3-venv \
    python3-pip \
    libpython3.8

Install OpenVINO

You can download OpenVINO from here.

wget -O openvino.tgz https://storage.openvinotoolkit.org/repositories/openvino/packages/2023.3/linux/l_openvino_toolkit_ubuntu20_2023.3.0.13775.ceeafaf64f3_x86_64.tgz && \
sudo mkdir /opt/intel
sudo mv openvino.tgz /opt/intel/
cd /opt/intel
sudo tar -xvf openvino.tgz
sudo rm openvino.tgz
sudo mv l_openvino* openvino

Using Docker

Building the Docker Image

To build the Docker image yourself, use the following command:

docker build . -t yolov10

Pulling the Docker Image

Alternatively, you can pull the pre-built Docker image from Docker Hub (available for Ubuntu 18.04, 20.04, and 22.04):

docker pull rlggyp/yolov10:18.04
docker pull rlggyp/yolov10:20.04
docker pull rlggyp/yolov10:22.04

For detailed usage information, please visit the Docker Hub repository page.

Running a Container

Grant the Docker container access to the X server by running the following command:

xhost +local:docker

To run a container from the image, use the following docker run command:

docker run -it --rm --mount type=bind,src=$(pwd),dst=/repo \
    --env DISPLAY=$DISPLAY \
    -v /tmp/.X11-unix:/tmp/.X11-unix \
    -v /dev:/dev \
    -w /repo \
    rlggyp/yolov10:<tag>

Build

git clone https://github.com/rlggyp/YOLOv10-OpenVINO-CPP-Inference.git
cd YOLOv10-OpenVINO-CPP-Inference/src

mkdir build
cd build
cmake ..
make

Usage

You can download the YOLOv10 model from here: ONNX, OpenVINO IR FP32, OpenVINO IR FP16, OpenVINO IR INT8

Using an ONNX Model Format

# For video input: 
./video <model_path.onnx> <video_path>
# For image input: 
./detect <model_path.onnx> <image_path>
# For real-time inference with a camera: 
./camera <model_path.onnx> <camera_index>

Using an OpenVINO IR Model Format

# For video input: 
./video <model_path.xml> <video_path>
# For image input: 
./detect <model_path.xml> <image_path>
# For real-time inference with a camera: 
./camera <model_path.xml> <camera_index>

References

Contributing

Contributions are welcome! If you have any suggestions, bug reports, or feature requests, please open an issue or submit a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for details.