helloworld20200908 / yolov8

Use YOLOv8 in real-time, for object detection, instance segmentation, pose estimation and image classification, via ONNX Runtime.

YOLOv8

Use YOLOv8 in real-time for object detection, instance segmentation, pose estimation and image classification, via ONNX Runtime

Install

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

Use

Export the model from PyTorch to ONNX format:

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')

Use in exported model with C#:

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);

Plotting

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")

Demo Images:

Detection:

Pose:

Segmentation:

License

MIT License