YoloV2 using AMD WinML Extension

YoloV2 using AMD WinML Extension#

This application shows how to run tiny yolov2 (20 classes) with MIVisionX RunTime:

  • A c/c++ implementation for region layer of yolov2

  • A sample for running yolov2 with MIVisionX

Pre-requisites#

  • Visual Studio extension for C++/WinRT

  • Set OpenCV_DIR environment variable to OpenCV/build folder

  • Add OpenCV_DIR\x64\vc14\bin or OpenCV_DIR\x64\vc15\bin to your PATH

Run#

Step 1. Get ONNX model#

Train your YoloV2 ONNX model or get it from onnx github. ONNX version 1.3 is recommended.

Step 2. Build the app using MIVisionX_winml_YoloV2.sln on Visual Studio.#

Step 3. Run tests#

Open up the command line prompt or Windows Powershell and use the following commands to run the tests.

Usage:#

  • Image

 .\MIVisionX_winml_YoloV2.exe --image [image - required] --modelLoc [modelLocation - required] --confidence [1-100 - optional, default: 20]

  • Camera Capture

 .\MIVisionX_winml_YoloV2.exe --capture [0 - required] --modelLoc [modelLocation - required] --confidence [1-100 - optional, default: 20]

  • Video

 .\MIVisionX_winml_YoloV2.exe --video [video - required] --modelLoc [modelLocation - required] --confidence [1-100 - optional, default: 20]

The confidence parameter is an optional parameter that sets the confidence level of detection. Lower the confidence level if the detection is not good enough.

Example#

.\MIVisionX_winml_YoloV2.exe --image image\cat.jpg --modelLoc model.onnx

Update parameters#

Please update parameters (biases, object names, etc) in /source/Region.cpp, and parameters (dim, blockwd, targetBlockwd, classes, etc) in /source/AnnieYoloDetect.cpp

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