YoloDotNet 1.0.0

There is a newer version of this package available.
See the version list below for details.
dotnet add package YoloDotNet --version 1.0.0                
NuGet\Install-Package YoloDotNet -Version 1.0.0                
This command is intended to be used within the Package Manager Console in Visual Studio, as it uses the NuGet module's version of Install-Package.
<PackageReference Include="YoloDotNet" Version="1.0.0" />                
For projects that support PackageReference, copy this XML node into the project file to reference the package.
paket add YoloDotNet --version 1.0.0                
#r "nuget: YoloDotNet, 1.0.0"                
#r directive can be used in F# Interactive and Polyglot Notebooks. Copy this into the interactive tool or source code of the script to reference the package.
// Install YoloDotNet as a Cake Addin
#addin nuget:?package=YoloDotNet&version=1.0.0

// Install YoloDotNet as a Cake Tool
#tool nuget:?package=YoloDotNet&version=1.0.0                

YoloDotNet

YoloDotNet is a C# .NET 7.0 implementation of Yolov8 and ONNX runtime with CUDA

Yolov8 is a real-time object detection tool by Ultralytics. YoloDotNet is a .NET 7 implementation of Yolov8 for detecting objects in images and videos using ML.NET and the ONNX runtime with GPU acceleration using CUDA.

result

<sup>image from pexels.com</sup>

Requirements

When using YoloDotNet with GPU-acceleration, you need CUDA and cuDNN.

ℹ️ Before you install CUDA and cuDNN, make sure to verify the ONNX runtime's current compatibility with specific versions.

ℹ️ For Video, you need FFmpeg and FFProbe

  • Download FFMPEG
  • Add FFmpeg and ffprobe to the Path-variable in your Environment Variables

Example - Image

using SixLabors.ImageSharp;
using YoloDotNet;
using YoloDotNet.Extensions;

// Instantiate a new Yolo object with your ONNX-model and CUDA
using var yolo = new Yolo(@"path\to\model.onnx");

// Load image
using var image = Image.Load<Rgb32>(@"path\to\image.jpg");

// Run inference
var results = yolo.RunInference(image);

// Draw boxes
image.DrawBoundingBoxes(results);

// Save image
image.Save(@"save\image.jpg");

Example - Video

using SixLabors.ImageSharp;
using YoloDotNet;
using YoloDotNet.Extensions;

// Instantiate a new Yolo object with your ONNX-model and CUDA
using var yolo = new Yolo(@"path\to\model.onnx");

// Run inference
yolo.RunInference(new VideoOptions
{
    VideoFile = @"path\to\video.mp4",
    OutputDir = @"path\to\outputfolder"
});

GPU

Object detection with GPU and GPU-Id = 0 is enabled by default

// Default setup. GPU with GPU-Id 0
using var yolo = new Yolo(@"path\to\model.onnx");

With a specific GPU-Id

// GPU with a user defined GPU-Id
using var yolo = new Yolo(@"path\to\model.onnx", true, 1);

CPU

YoloDotNet detection with CPU

// With CPU
using var yolo = new Yolo(@"path\to\model.onnx", false);

Access ONNX metadata and labels

The internal ONNX metadata such as input & output parameters, version, author, description, date along with the labels can be accessed via the yolo.OnnxModel property.

Example:

using var yolo = new Yolo(@"path\to\model.onnx");

// ONNX metadata and labels resides inside yolo.OnnxModel
Console.WriteLine(yolo.OnnxModel);

Example:

// Instantiate a new object
using var yolo = new Yolo(@"path\to\model.onnx");

// Display metadata
foreach (var property in yolo.OnnxModel.GetType().GetProperties())
{
    var value = property.GetValue(yolo.OnnxModel);
    Console.WriteLine($"{property.Name,-20}{value!}");
}

// Get ONNX labels
var labels = yolo.OnnxModel.Labels;

Console.WriteLine();
Console.WriteLine($"Labels ({labels.Length}):");
Console.WriteLine(new string('-', 58));

// Display
for (var i = 0; i < labels.Length; i++)
    Console.WriteLine($"index: {i,-8} label: {labels[i].Name,20} color: {labels[i].Color}");

// Output:

// InputName            = images
// OutputName           = output0
// Date                 = 2023-10-03 11:32:15
// Description          = Ultralytics YOLOv8m model trained on coco.yaml
// Author               = Ultralytics
// Task                 = detect
// License              = AGPL-3.0 https://ultralytics.com/license
// Version              = 8.0.181
// Stride               = 32
// BatchSize            = 1
// ImageSize            = Size[Width = 640, Height = 640]
// Input                = Input { BatchSize = 1, Channels = 3, Width = 640, Height = 640 }
// Output               = Output { BatchSize = 1, Dimensions = 84, Channels = 8400 }
//
// Labels (80):
// ---------------------------------------------------------
// index: 0        label: person              color: #5d8aa8
// index: 1        label: bicycle             color: #f0f8ff
// index: 2        label: car                 color: #e32636
// index: 3        label: motorcycle          color: #efdecd
// ...

References & Acknowledgements

https://github.com/ultralytics/ultralytics

https://github.com/sstainba/Yolov8.Net

https://github.com/mentalstack/yolov5-net

Product Compatible and additional computed target framework versions.
.NET net8.0 is compatible.  net8.0-android was computed.  net8.0-browser was computed.  net8.0-ios was computed.  net8.0-maccatalyst was computed.  net8.0-macos was computed.  net8.0-tvos was computed.  net8.0-windows was computed. 
Compatible target framework(s)
Included target framework(s) (in package)
Learn more about Target Frameworks and .NET Standard.

NuGet packages

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GitHub repositories

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Version Downloads Last updated
2.0.0 38 7/12/2024
1.7.0 659 5/2/2024
1.6.0 331 4/4/2024
1.5.0 209 3/14/2024
1.4.0 138 3/6/2024
1.3.0 239 2/25/2024
1.2.0 171 2/5/2024
1.1.0 166 1/17/2024
1.0.0 231 12/8/2023