ultralytics/examples/YOLOv8-ONNXRuntime-CPP

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.

Note :coffee:

1.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. Benefit for ultralytics's latest release,a Transpose op is added to the Yolov8 model,while make v8 and v5 has the same output shape.Therefore,you can inference your yolov5/v7/v8 via this project.

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

Exporting YOLOv8 FP16 Models 📦

import onnx
from onnxconverter_common import float16

model = onnx.load(R'YOUR_ONNX_PATH')
model_fp16 = float16.convert_float_to_float16(model)
onnx.save(model_fp16, R'YOUR_FP16_ONNX_PATH')

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 🚀

//change your param as you like
//Pay attention to your device and the onnx model type(fp32 or fp16)
DL_INIT_PARAM params;
params.rectConfidenceThreshold = 0.1;
params.iouThreshold = 0.5;
params.modelPath = "yolov8n.onnx";
params.imgSize = { 640, 640 };
params.cudaEnable = true;
params.modelType = YOLO_DETECT_V8;
yoloDetector->CreateSession(params);
Detector(yoloDetector);