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<h1 align="center">YOLOv8 OnnxRuntime C++</h1> |
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<p align="center"> |
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<img alt="C++" src="https://img.shields.io/badge/C++-17-blue.svg?style=flat&logo=c%2B%2B"> |
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<img alt="Onnx-runtime" src="https://img.shields.io/badge/OnnxRuntime-717272.svg?logo=Onnx&logoColor=white"></img> |
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</p> |
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This example demonstrates how to perform inference using YOLOv8 in C++ with ONNX Runtime and OpenCV's API. |
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## Benefits ✨ |
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- Friendly for deployment in the industrial sector. |
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- Faster than OpenCV's DNN inference on both CPU and GPU. |
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- Supports FP32 and FP16 CUDA acceleration. |
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## Exporting YOLOv8 Models 📦 |
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To export YOLOv8 models, use the following Python script: |
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```python |
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from ultralytics import YOLO |
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# Load a YOLOv8 model |
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model = YOLO("yolov8n.pt") |
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# Export the model |
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model.export(format="onnx", opset=12, simplify=True, dynamic=False, imgsz=640) |
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``` |
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Alternatively, you can use the following command for exporting the model in the terminal |
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```bash |
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yolo export model=yolov8n.pt opset=12 simplify=True dynamic=False format=onnx imgsz=640,640 |
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``` |
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## Download COCO.yaml file 📂 |
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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) |
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## Dependencies ⚙️ |
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| Dependency | Version | |
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| -------------------------------- | -------------- | |
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| Onnxruntime(linux,windows,macos) | >=1.14.1 | |
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| OpenCV | >=4.0.0 | |
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| C++ Standard | >=17 | |
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| Cmake | >=3.5 | |
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| Cuda (Optional) | >=11.4 \<12.0 | |
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| cuDNN (Cuda required) | =8 | |
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Note: The dependency on C++17 is due to the usage of the C++17 filesystem feature. |
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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. |
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## Build 🛠️ |
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1. Clone the repository to your local machine. |
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1. Navigate to the root directory of the repository. |
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1. Create a build directory and navigate to it: |
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```console |
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mkdir build && cd build |
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``` |
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4. Run CMake to generate the build files: |
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```console |
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cmake .. |
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``` |
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5. Build the project: |
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```console |
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make |
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``` |
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6. The built executable should now be located in the `build` directory. |
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## Usage 🚀 |
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```c++ |
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// CPU inference |
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DCSP_INIT_PARAM params{ model_path, YOLO_ORIGIN_V8, {imgsz_w, imgsz_h}, 0.1, 0.5, false}; |
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// GPU inference |
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DCSP_INIT_PARAM params{ model_path, YOLO_ORIGIN_V8, {imgsz_w, imgsz_h}, 0.1, 0.5, true}; |
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// Load your image |
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cv::Mat img = cv::imread(img_path); |
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// Init Inference Session |
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char* ret = yoloDetector->CreateSession(params); |
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ret = yoloDetector->RunSession(img, res); |
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``` |
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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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