Update YOLOv8-ONNXRuntime-CPP (#3455)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

examples/YOLOv8-ONNXRuntime-CPP/README.md

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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.