# YoloSharp

**Repository Path**: li-heng-dev/YoloSharp

## Basic Information

- **Project Name**: YoloSharp
- **Description**: YoloSharp库的学习与应用
- **Primary Language**: C#
- **License**: AGPL-3.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2026-01-04
- **Last Updated**: 2026-01-04

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# 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)

# 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/image");
```

## Example Images:

|         **Detect**         |        **Pose**        |
| :------------------------: | :--------------------: |
| ![detect](/img/detect.png) | ![pose](/img/pose.png) |
|        **Segment**         |        **Obb**         |
|    ![seg](/img/seg.png)    |  ![obb](/img/obb.png)  |


# 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](https://github.com/SixLabors/ImageSharp/blob/main/LICENSE)