yolov8_test/examples/YOLOv8-ONNXRuntime-CPP/README.md

YOLOv8 OnnxRuntime C++

This example demonstrates how to perform inference using YOLOv8 in C++ with ONNX Runtime and OpenCV's API.

Benefits ✨

• Friendly for deployment in the industrial sector.
• Faster than OpenCV's DNN inference on both CPU and GPU.
• Supports FP32 and FP16 CUDA acceleration.

Exporting YOLOv8 Models 📦

To export YOLOv8 models, use the following Python script:

from ultralytics import YOLO

# Load a YOLOv8 model
model = YOLO("yolov8n.pt")

# Export the model
model.export(format="onnx", opset=12, simplify=True, dynamic=False, imgsz=640)

Alternatively, you can use the following command for exporting the model in the terminal

yolo export model=yolov8n.pt opset=12 simplify=True dynamic=False format=onnx imgsz=640,640

Download COCO.yaml file 📂

In order to run example, you also need to download coco.yaml. You can download the file manually from here

Dependencies ⚙️

Dependency	Version
Onnxruntime(linux,windows,macos)	>=1.14.1
OpenCV	>=4.0.0
C++ Standard	>=17
Cmake	>=3.5
Cuda (Optional)	>=11.4 <12.0
cuDNN (Cuda required)	=8

Note: The dependency on C++17 is due to the usage of the C++17 filesystem feature.

Note (2): Due to ONNX Runtime, we need to use CUDA 11 and cuDNN 8. Keep in mind that this requirement might change in the future.

Build 🛠️

1. Clone the repository to your local machine.
2. Navigate to the root directory of the repository.
3. Create a build directory and navigate to it:

mkdir build && cd build

4. Run CMake to generate the build files:

cmake ..

5. Build the project:

make

6. The built executable should now be located in the build directory.

Usage 🚀

// CPU inference
DCSP_INIT_PARAM params{ model_path, YOLO_ORIGIN_V8, {imgsz_w, imgsz_h}, 0.1, 0.5, false};
// GPU inference
DCSP_INIT_PARAM params{ model_path, YOLO_ORIGIN_V8, {imgsz_w, imgsz_h}, 0.1, 0.5, true};
// Load your image
cv::Mat img = cv::imread(img_path);
// Init Inference Session
char* ret = yoloDetector->CreateSession(params);

ret = yoloDetector->RunSession(img, res);

This repository should also work for YOLOv5, which needs a permute operator for the output of the YOLOv5 model, but this has not been implemented yet.