# YOLOv8 OnnxRuntime C++ C++ Onnx-runtime 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); ```