Glenn Jocher
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11 months ago | |
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CMakeLists.txt | ||
README.md | 11 months ago | |
inference.cpp | 11 months ago | |
inference.h | 1 year ago | |
main.cpp | 1 year ago |
README.md
YOLOv8 OnnxRuntime C++
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 ☕
- 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:
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
yolo export model=yolov8n.pt opset=12 simplify=True dynamic=False format=onnx imgsz=640,640
Exporting YOLOv8 FP16 Models 📦
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
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 🛠️
-
Clone the repository to your local machine.
-
Navigate to the root directory of the repository.
-
Create a build directory and navigate to it:
mkdir build && cd build
-
Run CMake to generate the build files:
cmake ..
-
Build the project:
make
-
The built executable should now be located in the
build
directory.
Usage 🚀
//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);