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🚀 A high performance real-time object detection solution using YOLO11 ⚡️ powered by ONNX-Runtime
| Date | Stars |
|---|---|
| 2026-07-24 | 560 |
| 2026-07-25 | 561 |
| 2026-07-28 | 561 |
| 2026-07-30 | 561 |
| 2026-08-06 | 563 |
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# YoloSharp
🚀 A high performance real-time object detection solution using [YOLO11](https://github.com/ultralytics/ultralytics) ⚡️ powered by ONNX-Runtime
# Features
- **YOLO Tasks** 🌟 Support for all YOLO vision tasks (
[Detect](https://docs.ultralytics.com/tasks/detect) |
[OBB](https://docs.ultralytics.com/tasks/obb) |
[Pose](https://docs.ultralytics.com/tasks/pose) |
[Segment](https://docs.ultralytics.com/tasks/segment) |
[Classify](https://docs.ultralytics.com/tasks/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](https://docs.ultralytics.com/models/yolov8)
[YOLOv10](https://docs.ultralytics.com/models/yolov10)
[YOLO11](https://docs.ultralytics.com/models/yolo11)
[YOLO12](https://docs.ultralytics.com/models/yolo12)
[YOLO26](https://docs.ultralytics.com/models/yolo26)
# Installation
The project provides the following NuGet packages:
| Package | Description | Dependencies |
| ------------------------------------------------------------------- | ----------------------------------------- | ---------------------------------------------------------------------------- |
| [**YoloSharp**](https://www.nuget.org/packages/YoloSharp) | CPU-based inference | Includes all runtime dependencies (all platforms) |
| [**YoloSharp.Gpu**](https://www.nuget.org/packages/YoloSharp.Gpu) | GPU-based inference | Includes all runtime dependencies (all platforms) |
| [**YoloSharp.Core**](https://www.nuget.org/packages/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:
```python
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:
```shell
dotnet add package YoloSharp
```
Use the following C# code to load the model and run basic prediction:
```csharp
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:
```csharp
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:
```csharp
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/Excerpt of 4,719 characters
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Muhammad Rizwan Munawar · @ultralytics
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:43dc9c54681eea7c, topic:object-detection, topic:yolo, topic:image-classification