Use YOLOv8 in real-time for object detection, instance segmentation, pose estimation and image classification, via ONNX Runtime
The YoloV8
project is available in two nuget packages: YoloV8 and YoloV8.Gpu, if you use with CPU add the YoloV8 package reference to your project (contains reference to Microsoft.ML.OnnxRuntime package)
dotnet add package YoloV8 --version 1.6.0
If you use with GPU you need to add the YoloV8.Gpu package reference (contains reference to Microsoft.ML.OnnxRuntime.Gpu package)
dotnet add package YoloV8.Gpu --version 1.6.0
Run the following python code to export the model to ONNX format:
from ultralytics import YOLO
# Load a model
model = YOLO('path/to/best')
# export the model to ONNX format
model.export(format='onnx')
using Compunet.YoloV8;
using SixLabors.ImageSharp;
using var predictor = new YoloV8(model);
var result = predictor.Detect("path/to/image");
// or
var result = await predictor.DetectAsync("path/to/image");
Console.WriteLine(result);
You can to plot the input image for preview the model prediction results, this code demonstrates how to perform a prediction with the model and then plot the prediction results on the input image and save to file:
using Compunet.YoloV8;
using Compunet.YoloV8.Plotting;
using SixLabors.ImageSharp;
var imagePath = "path/to/image";
using var predictor = new YoloV8("path/to/model");
var result = await predictor.PoseAsync(imagePath);
using var image = Image.Load(imagePath);
using var ploted = await result.PlotImageAsync(image);
ploted.Save("./pose_demo.jpg")
MIT License