YoloV8

Integrate YOLOv8 into your C# project for a variety of real-time tasks including object detection, instance segmentation, pose estimation and more, using ONNX Runtime.

Features

YOLOv8 Tasks 🌟 Support for all YOLOv8 tasks (Detect, Segment, Classify, Pose and OBB)
High Performance 🚀 Various techniques and use of .NET features to maximize performance
Reduced Memory Usage 🧠 By reusing memory blocks and reducing the pressure on the GC
Plotting Options ✏️ Plotting operations for preview of model results on the target image.
YOLOv10 Support 🔧 Includes additional support for YOLOv10

Installation

This project provides two NuGet packages:

For CPU inference, use the package: YoloV8 (includes the Microsoft.ML.OnnxRuntime package)
For GPU inference, use the package: YoloV8.Gpu (includes the Microsoft.ML.OnnxRuntime.Gpu package)

Usage

1. Export model to ONNX format:

For convert the pre-trained PyTorch model to ONNX format, run the following Python code:

from ultralytics import YOLO

# Load a model
model = YOLO('path/to/best.pt')

# Export the model to ONNX format
model.export(format='onnx')

2. Load the ONNX model with C#:

Add the YoloV8 (or YoloV8.Gpu) package to your project:

dotnet add package YoloV8

Use the following C# code to load the model and run basic prediction:

using Compunet.YoloV8;

// Load the YOLOv8 predictor
using var predictor = new YoloPredictor("path/to/model.onnx");

// Run model
var result = predictor.Detect("path/to/image.jpg");
// or
var result = await predictor.DetectAsync("path/to/image.jpg");

// Write result summary to terminal
Console.WriteLine(result);

Plotting

You can to plot the target image for preview the model results, this code demonstrates how to run a inference, plot the results on image and save to file:

using Compunet.YoloV8;
using Compunet.YoloV8.Plotting;
using SixLabors.ImageSharp;

// Load the YOLOv8 predictor
using var predictor = new YoloPredictor("path/to/model.onnx");

// Load the target image
using var image = Image.Load("path/to/image");

// Run model
var result = await predictor.PoseAsync(image);

// Create plotted image from model results
using var plotted = await result.PlotImageAsync(image);

// Write the plotted image to file
plotted.Save("./pose_demo.jpg");

You can also predict and save to file in one operation:

using Compunet.YoloV8;
using Compunet.YoloV8.Plotting;
using SixLabors.ImageSharp;

// Load the YOLOv8 predictor
using var predictor = new YoloPredictor("path/to/model.onnx");

// Run model, plot predictions and write to file
predictor.PredictAndSaveAsync("path/to/image");

Example Images:

Detection:

Pose:

Segmentation:

License

AGPL-3.0 License

Important Note: This project depends on ImageSharp, you should check the license details here

dme-compunet/YoloV8