# 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:
```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)
```
Alternatively, you can use the following command for exporting the model in the terminal
```bash
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 ](https://raw.githubusercontent.com/ultralytics/ultralytics/main/ultralytics/cfg/datasets/coco.yaml )
## Dependencies
| Dependency | Version |
| -------------------------------- | ------------- |
| Onnxruntime(linux,windows,macos) | >=1.14.1 |
| OpenCV | >=4.0.0 |
| C++ | >=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.
## Usage
```c++
// 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.
