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# YOLOv8 OnnxRuntime C++ |
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<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"> |
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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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## Note ☕ |
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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. |
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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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## Exporting YOLOv8 FP16 Models 📦 |
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```python |
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import onnx |
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from onnxconverter_common import float16 |
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model = onnx.load(R"YOUR_ONNX_PATH") |
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model_fp16 = float16.convert_float_to_float16(model) |
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onnx.save(model_fp16, R"YOUR_FP16_ONNX_PATH") |
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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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2. Navigate to the root directory of the repository. |
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3. 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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**Notice**: |
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If you encounter an error indicating that the `ONNXRUNTIME_ROOT` variable is not set correctly, you can resolve this by building the project using the appropriate command tailored to your system. |
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```console |
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# compiled in a win32 system |
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cmake -D WIN32=TRUE .. |
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# compiled in a linux system |
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cmake -D LINUX=TRUE .. |
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# compiled in an apple system |
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cmake -D APPLE=TRUE .. |
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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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//change your param as you like |
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//Pay attention to your device and the onnx model type(fp32 or fp16) |
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DL_INIT_PARAM params; |
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params.rectConfidenceThreshold = 0.1; |
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params.iouThreshold = 0.5; |
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params.modelPath = "yolov8n.onnx"; |
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params.imgSize = { 640, 640 }; |
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params.cudaEnable = true; |
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params.modelType = YOLO_DETECT_V8; |
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yoloDetector->CreateSession(params); |
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Detector(yoloDetector); |
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```
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