YoloDotNet v1.4

YoloDotNet is a C# .NET 8 implementation of Yolov8 for detecting objects in images and videos using ML.NET and ONNX runtime with GPU acceleration using CUDA.

YoloDotNet supports the following:

  ✓   Classification   Categorize an image
  ✓   Object Detection   Detect multiple objects in a single image
  ✓   OBB Detection   OBB (Oriented Bounding Box), like Object Detection but with rotated bounding boxes
  ✓   Segmentation   Separate detected objects using pixel masks
  ✓   Pose Estimation   Identifying location of specific keypoints in an image

Classification	Object Detection	OOB Detection	Segmentation	Pose Estimation

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Nuget

> dotnet add package YoloDotNet

Install CUDA (optional)

YoloDotNet with GPU-acceleration requires CUDA and cuDNN.

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

Download and install CUDA
Download cuDNN and follow the installation instructions

Export Yolov8 model to ONNX

Yolov8 model exported to ONNX format

Verify your model

using YoloDotNet;

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

Console.WriteLine(yolo.OnnxModel.ModelType); // Output modeltype...

Example - Image

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

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

// Load image
using var image = Image.Load<Rgba32>(@"path\to\image.jpg");

// Run
var results = yolo.RunClassification(image, 5); // Top 5 classes
//var results = yolo.RunObjectDetection(image, 0.25);
//var results = yolo.RunObbDetection(options, 0.25);
//var results = yolo.RunSegmentation(image, 0.25);
//var results = yolo.RunPoseEstimation(image, 0.25);

image.Draw(results);
image.Save(@"path\to\save\image.jpg");

Example - Video

Important

Processing video requires FFmpeg and FFProbe

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

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

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

// Video options
var options = new VideoOptions
{
    VideoFile = @"path\to\video.mp4",
    OutputDir = @"path\to\output\folder",
    //GenerateVideo = true,
    //DrawLabels = true,
    //FPS = 30,
    //Width = 640, // Resize video...
    //Height = -2, // -2 automatically calculate dimensions to keep proportions
    //Quality = 28,
    //DrawConfidence = true,
    //KeepAudio = true,
    //KeepFrames = false,
    //DrawSegment = DrawSegment.Default,
    //PoseOptions = MyPoseMarkerConfiguration // Your own pose marker configuration...
};

// Run
var results = yolo.RunClassification(options, 5); // Top 5 classes
//var results = yolo.RunObjectDetection(options, 0.25);
//var results = yolo.RunObbDetection(options, 0.25);
//var results = yolo.RunSegmentation(options, 0.25);
//var results = yolo.RunPoseEstimation(options, 0.25);

// Do further processing with results if needed...

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);

Custom Pose-marker configuration

Example on how to configure PoseOptions for a Pose Estimation model

// Pass in a PoseOptions parameter to the Draw() extension method. Ex:
image.Draw(poseEstimationResults, poseOptions);

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!}");

    if (property.Name == nameof(yolo.OnnxModel.CustomMetaData))
        foreach (var data in (Dictionary<string, string>)value!)
            Console.WriteLine($"{"",-20}{data.Key,-20}{data.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:

// ModelType           ObjectDetection
// InputName           images
// OutputName          output0
// CustomMetaData      System.Collections.Generic.Dictionary`2[System.String,System.String]
//                     date                2023-11-07T13:33:33.565196
//                     description         Ultralytics YOLOv8n model trained on coco.yaml
//                     author              Ultralytics
//                     task                detect
//                     license             AGPL-3.0 https://ultralytics.com/license
//                     version             8.0.202
//                     stride              32
//                     batch               1
//                     imgsz               [640, 640]
//                     names               {0: 'person', 1: 'bicycle', 2: 'car' ... }
// ImageSize           Size [ Width=640, Height=640 ]
// Input               Input { BatchSize = 1, Channels = 3, Width = 640, Height = 640 }
// Output              ObjectDetectionShape { BatchSize = 1, Elements = 84, Channels = 8400 }
// Labels              YoloDotNet.Models.LabelModel[]
//
// 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
// ...