YoloSharp

🚀 A high performance real-time object detection solution using YOLO11 ⚡️ powered by ONNX-Runtime

Features

• YOLO Tasks 🌟 Support for all YOLO vision tasks ( Detect | OBB | Pose | Segment | Classify)
• 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 ✏️ Draw the predictions on the target image to preview the model results
• YOLO Versions 🔧 Includes support for: YOLOv8 YOLOv10 YOLO11 YOLO12 YOLO26

Installation

The project provides the following NuGet packages:

| Package | Description | Dependencies | | ------------------------------------------------------------------- | ----------------------------------------- | ---------------------------------------------------------------------------- | | YoloSharp | CPU-based inference | Includes all runtime dependencies (all platforms) | | YoloSharp.Gpu | GPU-based inference | Includes all runtime dependencies (all platforms) | | YoloSharp.Core | Core library without runtime dependencies | None – suitable for lightweight production or for using alternative runtimes |

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 YoloSharp (or YoloSharp.Gpu) package to your project:

dotnet add package YoloSharp

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

using Compunet.YoloSharp;

// Load the YOLO 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.YoloSharp;
using Compunet.YoloSharp.Plotting;
using SixLabors.ImageSharp;

// Load the YOLO 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.YoloSharp;
using Compunet.YoloSharp.Plotting;
using SixLabors.ImageSharp;

// Load the YOLO 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:

| Detect | Pose | | :------------------------: | :--------------------: | | | | | Segment | Obb | | | |

Not Supported:

The following features are not currently supported, they may be added later

• Batch Processing: You have to predict them one by one
• Dynamic Size: The image resized according to imgsz

License

AGPL-3.0 License

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