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https://github.com/rlggyp/YOLOv10-OpenVINO-CPP-Inference
YOLOv10 C++ implementation using OpenVINO for efficient and accurate real-time object detection.
https://github.com/rlggyp/YOLOv10-OpenVINO-CPP-Inference
cpp object-detection opencv opencv-cpp openvino openvino-cpp real-time yolov10
Last synced: 3 days ago
JSON representation
YOLOv10 C++ implementation using OpenVINO for efficient and accurate real-time object detection.
- Host: GitHub
- URL: https://github.com/rlggyp/YOLOv10-OpenVINO-CPP-Inference
- Owner: rlggyp
- License: mit
- Created: 2024-05-28T14:55:44.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2024-07-11T08:49:37.000Z (4 months ago)
- Last Synced: 2024-08-02T01:20:03.608Z (3 months ago)
- Topics: cpp, object-detection, opencv, opencv-cpp, openvino, openvino-cpp, real-time, yolov10
- Language: C++
- Homepage:
- Size: 30.6 MB
- Stars: 47
- Watchers: 4
- Forks: 4
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-yolo-object-detection - rlggyp/YOLOv10-OpenVINO-CPP-Inference - OpenVINO-CPP-Inference?style=social"/> : YOLOv10 C++ implementation using OpenVINO for efficient and accurate real-time object detection. (Lighter and Deployment Frameworks)
- awesome-yolo-object-detection - rlggyp/YOLOv10-OpenVINO-CPP-Inference - OpenVINO-CPP-Inference?style=social"/> : YOLOv10 C++ implementation using OpenVINO for efficient and accurate real-time object detection. (Lighter and Deployment Frameworks)
README
# YOLOv10 OpenVINO C++ Inference
Implementing YOLOv10 object detection using OpenVINO for efficient and accurate real-time inference in C++.
## Features
- [x] Support for `ONNX` and `OpenVINO IR` model formats
- [x] Support for `FP32`, `FP16` and `INT8` precisions
- [x] 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
```bash
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](https://storage.openvinotoolkit.org/repositories/openvino/packages/2023.3/linux).
```bash
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:
```bash
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):
```bash
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](https://hub.docker.com/repository/docker/rlggyp/yolov10/general).
#### Running a Container
Grant the Docker container access to the X server by running the following command:
```bash
xhost +local:docker
````
To run a container from the image, use the following `docker run` command:
```bash
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:
```
## Build
```bash
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](https://github.com/rlggyp/YOLOv10-OpenVINO-CPP-Inference/raw/model/assets/yolov10n.onnx),
[OpenVINO IR FP32](https://github.com/rlggyp/YOLOv10-OpenVINO-CPP-Inference/raw/model/assets/yolov10n_fp32_openvino.zip),
[OpenVINO IR FP16](https://github.com/rlggyp/YOLOv10-OpenVINO-CPP-Inference/raw/model/assets/yolov10n_fp16_openvino.zip),
[OpenVINO IR INT8](https://github.com/rlggyp/YOLOv10-OpenVINO-CPP-Inference/raw/model/assets/yolov10n_int8_openvino.zip)
### Using an ONNX Model Format
```bash
# For video input:
./video
# For image input:
./detect
# For real-time inference with a camera:
./camera
```
### Using an OpenVINO IR Model Format
```bash
# For video input:
./video
# For image input:
./detect
# For real-time inference with a camera:
./camera
```
## References
- [How to export the YOLOv10 model](https://github.com/THU-MIG/yolov10?tab=readme-ov-file#export)
- [Convert and Optimize YOLOv10 with OpenVINO](https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/yolov10-optimization/yolov10-optimization.ipynb)
- [Exporting the model into OpenVINO format](https://docs.ultralytics.com/integrations/openvino/#usage-examples)
- [Model Export with Ultralytics YOLO](https://docs.ultralytics.com/modes/export/)
- [Supported models by OpenVINO](https://docs.openvino.ai/2023.3/openvino_docs_OV_UG_Integrate_OV_with_your_application.html#step-2-compile-the-model)
- [YOLOv10 exporter notebooks](notebooks/YOLOv10_exporter.ipynb)
## 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](LICENSE). See the LICENSE file for details.