OpenVINO.NET demo for yolov8 classification model

This project is a minimal demo for my OpenVINO.NET project to infer yolov8 classification model.

NuGet packages requirements

• Sdcb.OpenVINO
• Sdcb.OpenVINO.runtime.win-x64
• OpenCvSharp4
• OpenCvSharp4.runtime.win

Brief Introduction to Model Inference

The yolov8n-cls model has 1000 classifications (the specific 1000 classifications can be found here).

This model has an input size of 1x3x224x224xF32 and an output size of 1x1000xF32.

The code will read the hen.jpg and try infer the most probabely classification, in this case, the output as follows:

class id=hen, score=0.59
preprocess time: 0.00ms
infer time: 1.65ms
postprocess time: 0.49ms
Total time: 2.14ms

Steps to convert from PyTorch yolov8 model into OpenVINO

• Downloaded from ultralytics official website, specifically, it's YOLOv8n-cls.pt(5.27MB).
• Install python, and install ultralytics: pip install ultralytics
• Convert YOLOv8n-cls.pt into OpenVINO xml model via command: yolo export model=yolov8n-cls.pt format=openvino
• After convert, you will get yolov8n-cls.xml(116KB) and yolov8n-cls.bin(10.3MB) in yolov8n-cls_openvino_model folder.