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# YOLOv8 OnnxRuntime C++
This example demonstrates how to perform inference using YOLOv8 in C++ with ONNX Runtime and OpenCV's API.
We recommend using Visual Studio to build the project.
## Benefits
- Friendly for deployment in the industrial sector.
- Faster than OpenCV's DNN inference on both CPU and GPU.
- Supports CUDA acceleration.
- Easy to add FP16 inference (using template functions).
## Exporting YOLOv8 Models
To export YOLOv8 models, use the following Python script:
```python
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)
```
## Dependencies
| Dependency | Version |
| ----------------------- | -------- |
| Onnxruntime-win-x64-gpu | >=1.14.1 |
| OpenCV | >=4.0.0 |
| C++ | >=17 |
Note: The dependency on C++17 is due to the usage of the C++17 filesystem feature.
## Usage
```c++
// CPU inference
DCSP_INIT_PARAM params{ model_path, YOLO_ORIGIN_V8, {imgsz_w, imgsz_h}, class_num, 0.1, 0.5, false};
// GPU inference
DCSP_INIT_PARAM params{ model_path, YOLO_ORIGIN_V8, {imgsz_w, imgsz_h}, class_num, 0.1, 0.5, true};
// Load your image
cv::Mat img = cv::imread(img_path);
char* ret = p1->CreateSession(params);
ret = p->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.
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