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YOLOv5 C# Deployment Example

This directory provides infer.cs to finish the deployment of YOLOv5 on CPU/GPU.

Before deployment, two steps require confirmation

Please follow below instructions to compile and test in Windows. FastDeploy version 1.0.4 or above (x.x.x>=1.0.4) is required to support this model.

1. Download C# package management tool nuget client

https://dist.nuget.org/win-x86-commandline/v6.4.0/nuget.exe

Add nuget program into system variable PATH

2. Download model and image for test

https://bj.bcebos.com/paddlehub/fastdeploy/yolov5s.onnx https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg

3. Compile example code

Open x64 Native Tools Command Prompt for VS 2019 command tool on Windows, cd to the demo path of ppyoloe and execute commands

cd D:\Download\fastdeploy-win-x64-gpu-x.x.x\examples\vision\detection\yolov5\csharp

mkdir build && cd build
cmake .. -G "Visual Studio 16 2019" -A x64 -DFASTDEPLOY_INSTALL_DIR=D:\Download\fastdeploy-win-x64-gpu-x.x.x -DCUDA_DIRECTORY="C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v11.2"

nuget restore
msbuild infer_demo.sln /m:4 /p:Configuration=Release /p:Platform=x64

For more information about how to use FastDeploy SDK to compile a project with Visual Studio 2019. Please refer to

4. Execute compiled program

fastdeploy.dll and related dynamic libraries are required by the program. FastDeploy provide a script to copy all required dll to your program path.

cd D:\Download\fastdeploy-win-x64-gpu-x.x.x

fastdeploy_init.bat install %cd% D:\Download\fastdeploy-win-x64-gpu-x.x.x\examples\vision\detection\yolov5\csharp\build\Release

Then you can run your program and test the model with image

cd Release
infer_demo yolov5s.onnx 000000014439.jpg 0 # CPU
infer_demo yolov5s.onnx 000000014439.jpg 1  # GPU

YOLOv5 C# Interface

Model Class

fastdeploy.vision.detection.YOLOv5(
        string model_file,
        string params_file,
        fastdeploy.RuntimeOption runtime_option = null,
        fastdeploy.ModelFormat model_format = ModelFormat.ONNX)

YOLOv5 initialization.

Params

  • model_file(str): Model file path
  • params_file(str): Parameter file path，when model format is onnx，this can be empty string
  • runtime_option(RuntimeOption): Backend inference configuration. None by default, which is the default configuration
  • model_format(ModelFormat): Model format.

Predict Function

fastdeploy.DetectionResult Predict(OpenCvSharp.Mat im)

Model prediction interface. Input images and output results directly.

Params

  • im(Mat): Input images in HWC or BGR format

Return

  • result(DetectionResult): Detection result, including detection box and confidence of each box. Refer to Vision Model Prediction Result for DetectionResult