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examples/YOLOv8-ONNXRuntime-CPP/README.md

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+# YOLOv8 OnnxRuntime C++
+
+<img alt="C++" src="https://img.shields.io/badge/C++-17-blue.svg?style=flat&logo=c%2B%2B"> <img alt="Onnx-runtime" src="https://img.shields.io/badge/OnnxRuntime-717272.svg?logo=Onnx&logoColor=white">
+
+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 ☕
+
+1. Benefit for Ultralytics' latest release, a `Transpose` op is added to the YOLOv8 model, while make v8 and v5 has the same output shape. Therefore, you can run inference with YOLOv5/v7/v8 via this project.
+
+## 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
+```
+
+## Exporting YOLOv8 FP16 Models 📦
+
+```python
+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](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++ 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:
+
+    ```console
+    mkdir build && cd build
+    ```
+
+4. Run CMake to generate the build files:
+
+    ```console
+    cmake ..
+    ```
+
+5. Build the project:
+
+    ```console
+    make
+    ```
+
+6. The built executable should now be located in the `build` directory.
+
+## Usage 🚀
+
+```c++
+//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);
+```