YoloDotNet v2.2

YoloDotNet is a blazing-fast C# .NET 8 implementation of Yolo and Yolo-World models for real-time object detection in images and videos. Powered by ONNX Runtime, and supercharged with GPU acceleration using CUDA, this app is all about detecting objects at lightning speed!

Supported Versions:

Yolov8 Yolov9 Yolov10 Yolov11 Yolo-World

Supported Tasks:

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

Batteries not included.

Classification	Object Detection	OBB Detection	Segmentation	Pose Estimation

_{image from pexels.com}	_{image from pexels.com}	_{image from pexels.com}	_{image from pexels.com}	_{image from pexels.com}

What's new in YoloDotNet v2.2?

Things are moving fast, YoloDotNet 2.2 is already here packed with exciting new features! Ever wanted to experiment with Yolo-World models in .NET? Now's your chance. With Open-Vocabulary detection and real-time, zero-shot object detection, the model can now find objects it hasn't even been trained on. It's a whole new level of object detection fun!

Yolo-World Support: Yep, you read that right – Open-Vocabulary detection is now in the mix! Real-time, zero-shot object detection is here, so the model can spot stuff it’s never been trained on. Mind-blowing, right? More about Yolo-World here. Pixel Confidence for Segmentation: Need more control? An optional pixel confidence parameter has been added for segmentation. Now it's possible to fine-tune those segmentation masks like a pro ;)

Nuget

> dotnet add package YoloDotNet

Install CUDA (optional)

YoloDotNet with GPU-acceleration requires CUDA Toolkit 12.x and cuDNN 9.x.

ONNX runtime's current compatibility with specific versions.

Install CUDA v12.x
Install cuDNN v9.x
Update your system PATH-variable

Open File Explorer and navigate to the folder where the cuDNN-dll's are installed. The typical path looks like:
C:\Program Files\NVIDIA\CUDNN\v9.x\bin\v12.x (where x is your version)
Once you are in this specific folder (which contains .dll files), copy the folder path from the address bar at the top of the window.
Add the cuDNN-Path to your System Variables:
- Type env in windows search
- Click on Edit the system environment variables
- Click on Environment Variables
- Under System Variables select the Path-variable and click Edit
- Click on New and paste in your cuDNN dll-folder path
- Click Ok a million times to save the changes
Super-duper-important! In order for Windows to pick up the changes in your Environment Variables, make sure to close all open programs before you continue with whatever you were doing ;)

Export Yolo models to ONNX

All models must be exported to ONNX format. How to export to ONNX format.
The ONNX-models included in this repo are from Ultralytics s-series (small). https://docs.ultralytics.com/models.

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 1 - Image inference

using YoloDotNet;
using YoloDotNet.Enums;
using YoloDotNet.Models;
using YoloDotNet.Extensions;
using SkiaSharp;

// Instantiate a new Yolo object
using var yolo = new Yolo(new YoloOptions
{
    OnnxModel = @"path\to\model.onnx",      // Your Yolo model in onnx format
    ModelType = ModelType.ObjectDetection,  // Set your model type
    Cuda = false,                           // Use CPU or CUDA for GPU accelerated inference. Default = true
    GpuId = 0                               // Select Gpu by id. Default = 0
    PrimeGpu = false,                       // Pre-allocate GPU before first inference. Default = false
});

// Load image
using var image = SKImage.FromEncodedData(@"path\to\image.jpg");

// Run inference and get the results
var results = yolo.RunObjectDetection(image, confidence: 0.25, iou: 0.7);

// Draw results
using var resultImage = image.Draw(results);

// Save to file
resultImage.Save(@"save\as\new_image.jpg", SKEncodedImageFormat.Jpeg, 80);

Example 2 - Inference on a batch of images

using System;
using System.IO;
using SkiaSharp;
using YoloDotNet;
using YoloDotNet.Enums;
using YoloDotNet.Models;
using YoloDotNet.Extensions;
using System.Threading.Tasks;

// Instantiate a new yolo-object
using var yolo = new Yolo(new YoloOptions()
{
    OnnxModel = @"path\to\model.onnx",
    Cuda = true,
    PrimeGpu = true,
    ModelType = ModelType.ObjectDetection
});

// Collect images
var images = Directory.GetFiles(@"path\to\image\folder");

// Process images using parallelism for faster processing
Parallel.ForEach(images, image =>
{
    // Load image
    using var img = SKImage.FromEncodedData(image);

    // Run inference
    var results = yolo.RunObjectDetection(img, 0.25, 0.5);

    // Draw results
    using var resultImg = img.Draw(results);

    // Save results
    resultImg.Save(Path.Combine(@"path\to\save\folder", Path.GetFileName(image)));

    // Do further processing if needed...

});

Example 3 - Video inference

Important

Processing video requires FFmpeg and FFProbe

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

using YoloDotNet;
using YoloDotNet.Enums;
using YoloDotNet.Models;

// Instantiate a new Yolo object
using var yolo = new Yolo(new YoloOptions
{
    OnnxModel = @"path\to\model.onnx",      // Your Yolov8 or Yolov10 model in onnx format
    ModelType = ModelType.ObjectDetection,  // Set your model type
    Cuda = false,                           // Use CPU or CUDA for GPU accelerated inference. Default = true
    GpuId = 0                               // Select Gpu by id. Default = 0
    PrimeGpu = false,                       // Pre-allocate GPU before first. Default = false
});

// Set video options
var options = new VideoOptions
{
    VideoFile = @"path\to\video.mp4",
    OutputDir = @"path\to\output\dir",
    //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 inference on video
var results = yolo.RunObjectDetection(options, 0.25, 0.7);

// Do further processing with 'results'...

Custom KeyPoint configuration for Pose Estimation

Example on how to configure Keypoints for a Pose Estimation model

// Pass in a KeyPoint options 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
// ...

Donate

https://paypal.me/nickswardh

References & Acknowledgements

https://github.com/ultralytics/ultralytics

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

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

Benchmarks

There are some benchmarks included in the project. To run them, you simply need to build the project and run the YoloDotNet.Benchmarks project. The solution must be set to Release mode to run the benchmarks.

There is a if DEBUG section in the benchmark project that will run the benchmarks in Debug mode, but it is not recommended as it will not give accurate results. This is however useful to debug and step through the code. Two examples have been left in place to show how to run the benchmarks in Debug mode, but have been commented out.

Because there is no persistant storage for benchmark results, the results below are in the form of starting point and ending point. If one makes changes to the benchmarks, you would move the ending point to the starting point and run the benchmarks again to see the improvements and those values would be the new ending point.

Benchmark results would be very much based on the hardware used. It is important to try run benchmarks on the same hardware for future comparisons. If different hardware is used, it is important to note the hardware used, as the results would be different, thus the starting point and ending point would need to be updated. Hopefully in future a single hardware configuration can be used for benchmarks, before updating documentation.

Simple Benchmarks

Simple benchmarks were modeled around the test project. The test project uses the same images and models as the benchmarks. The benchmarks are run on the same images and models as the test project. These benchmarks provide a good starting point to identify bottlenecks and areas for improvement.

The hardware these benchmarks used are detailed below, the graphics card used was a NVIDIA GeForce RTX 3060 12GB.

* Summary *

Starting Point, YoloDotNet v2.0

BenchmarkDotNet v0.13.12, Windows 10 (10.0.19045.4529/22H2/2022Update)
Intel Core i7-7700K CPU 4.20GHz (Kaby Lake), 1 CPU, 8 logical and 4 physical cores
.NET SDK 8.0.302
[Host]     : .NET 8.0.6 (8.0.624.26715), X64 RyuJIT AVX2
DefaultJob : .NET 8.0.6 (8.0.624.26715), X64 RyuJIT AVX2

Method	Mean	Error	StdDev	Gen0	Gen1	Gen2	Allocated
ClassificationCpu	5.734 ms	0.1100 ms	0.0859 ms	7.8125	-	-	59.98 KB
ClassificationGpu	2.255 ms	0.0054 ms	0.0059 ms	11.7188	-	-	59.98 KB
ObjectDetectionCpu	113.954 ms	1.3054 ms	1.0901 ms	-	-	-	67.5 KB
ObjectDetectionGpu	13.751 ms	0.2164 ms	0.1918 ms	15.6250	-	-	67.37 KB
SegmentationCpu	178.411 ms	2.7077 ms	2.5328 ms	1000.0000	333.3333	-	7453.61 KB
SegmentationGpu	37.857 ms	0.7501 ms	0.9212 ms	1214.2857	714.2857	214.2857	7418.45 KB
PoseEstimationCpu	116.557 ms	0.9387 ms	1.1528 ms	-	-	-	39.71 KB
PoseEstimationGpu	12.582 ms	0.1421 ms	0.1187 ms	-	-	-	39.57 KB
ObbDetectionCpu	346.193 ms	4.7002 ms	4.3965 ms	-	-	-	16.48 KB
ObbDetectionGpu	27.591 ms	0.2080 ms	0.1844 ms	-	-	-	15.78 KB

Ending Point, YoloDotNet v2.1

BenchmarkDotNet v0.13.12, Windows 11 (10.0.22631.4169/23H2/2023Update/SunValley3)
Intel Core i7-14700KF, 1 CPU, 28 logical and 20 physical cores
.NET SDK 8.0.400
[Host]     : .NET 8.0.8 (8.0.824.36612), X64 RyuJIT AVX2
DefaultJob : .NET 8.0.8 (8.0.824.36612), X64 RyuJIT AVX2

CLASSIFICATION (Input image size: 1280x844)

Method	Mean	Error	StdDev	Median	Gen0	Allocated	Model Used
ClassificationYolov8Cpu	3.027 ms	0.0603 ms	0.1176 ms	3.037 ms	-	40.17 KB	yolov8s-cls
ClassificationYolov8Gpu	1.451 ms	0.0290 ms	0.0310 ms	1.456 ms	1.9531	40.17 KB	yolov8s-cls
ClassificationYolov11Cpu	6.721 ms	0.1341 ms	0.2829 ms	6.760 ms	-	41.17 KB	yolov11s-cls
ClassificationYolov11Gpu	3.850 ms	0.1590 ms	0.4689 ms	3.610 ms	-	41.17 KB	yolov11s-cls

OBJECT DETECTION (input image size: 1280x851)

Method	Mean	Error	StdDev	Allocated	Model Used
ObjectDetectionYolov8Cpu	34.462 ms	0.6583 ms	0.8559 ms	34.67 KB	yolov8s
ObjectDetectionYolov8Gpu	8.089 ms	0.0795 ms	0.0705 ms	34.63 KB	yolov8s
ObjectDetectionYolov9Cpu	38.676 ms	0.7529 ms	0.7394 ms	29.65 KB	yolov9s
ObjectDetectionYolov9Gpu	9.730 ms	0.1243 ms	0.0971 ms	29.61 KB	yolov9s
ObjectDetectionYolov10Cpu	31.709 ms	0.6309 ms	0.5901 ms	24.67 KB	yolov10s
ObjectDetectionYolov10Gpu	7.062 ms	0.1392 ms	0.1368 ms	24.63 KB	yolov10s
ObjectDetectionYolov11Cpu	31.856 ms	0.6252 ms	0.7678 ms	32.79 KB	yolov11s
ObjectDetectionYolov11Gpu	7.321 ms	0.0445 ms	0.0825 ms	32.75 KB	yolov11s

ORIENTED OBJECT DETECTION (OBB) (input image size: 1280x720)

Method	Mean	Error	StdDev	Allocated	Model Used
ObbDetectionYolov8Cpu	91.81 ms	1.734 ms	1.622 ms	8.43 KB	yolov8s-obb
ObbDetectionYolov8Gpu	13.39 ms	0.041 ms	0.036 ms	8.37 KB	yolov8s-obb
ObbDetectionYolov11Cpu	81.91 ms	1.423 ms	1.331 ms	8.43 KB	yolov11s-obb
ObbDetectionYolov11Gpu	14.00 ms	0.027 ms	0.025 ms	8.37 KB	yolov11s-obb

POSE ESTIMATION (input image size: 1280x720)

Method	Mean	Error	StdDev	Median	Allocated	Model Used
PoseEstimationYolov8Cpu	35.275 ms	0.4895 ms	0.4579 ms	35.180 ms	24.14 KB	yolov8s-pose
PoseEstimationYolov8Gpu	7.445 ms	0.1474 ms	0.3415 ms	7.586 ms	24.11 KB	yolov8s-pose
PoseEstimationYolov11Cpu	32.237 ms	0.6384 ms	0.9938 ms	32.056 ms	22.15 KB	yolov11s-pose
PoseEstimationYolov11Gpu	7.190 ms	0.1401 ms	0.1721 ms	7.206 ms	22.13 KB	yolov11s-pose

SEGMENTATION (input image size: 1280x853)

Method	Mean	Error	StdDev	Gen0	Gen1	Gen2	Allocated	Model Used
SegmentationYolov8Cpu	56.79 ms	1.121 ms	1.246 ms	444.4444	333.3333	111.1111	7.31 MB	yolov8s-seg
SegmentationYolov8Gpu	31.50 ms	0.630 ms	1.198 ms	468.7500	437.5000	156.2500	7.28 MB	yolov8s-seg
SegmentationYolov11Cpu	96.84 ms	1.848 ms	2.270 ms	333.3333	166.6667	-	6.8 MB	yolov11s-seg
SegmentationYolov11Gpu	28.97 ms	0.293 ms	0.274 ms	406.2500	375.0000	125.0000	6.72 MB	yolov11s-seg

Name		Name	Last commit message	Last commit date
Latest commit History 303 Commits
ConsoleDemo		ConsoleDemo
YoloDotNet		YoloDotNet
test		test
.gitattributes		.gitattributes
.gitignore		.gitignore
LICENSE.txt		LICENSE.txt
README.md		README.md
YoloDotNet.sln		YoloDotNet.sln

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YoloDotNet v2.2

Supported Versions:

Supported Tasks:

What's new in YoloDotNet v2.2?

Nuget

Install CUDA (optional)

Export Yolo models to ONNX

Verify your model

Example 1 - Image inference

Example 2 - Inference on a batch of images

Example 3 - Video inference

Custom KeyPoint configuration for Pose Estimation

Access ONNX metadata and labels

Donate

References & Acknowledgements

Benchmarks

Simple Benchmarks

Starting Point, YoloDotNet v2.0

Ending Point, YoloDotNet v2.1

CLASSIFICATION (Input image size: 1280x844)

OBJECT DETECTION (input image size: 1280x851)

ORIENTED OBJECT DETECTION (OBB) (input image size: 1280x720)

POSE ESTIMATION (input image size: 1280x720)

SEGMENTATION (input image size: 1280x853)

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YoloDotNet v2.2

Supported Versions:

Supported Tasks:

What's new in YoloDotNet v2.2?

Nuget

Install CUDA (optional)

Export Yolo models to ONNX

Verify your model

Example 1 - Image inference

Example 2 - Inference on a batch of images

Example 3 - Video inference

Custom KeyPoint configuration for Pose Estimation

Access ONNX metadata and labels

Donate

References & Acknowledgements

Benchmarks

Simple Benchmarks

Starting Point, YoloDotNet v2.0

Ending Point, YoloDotNet v2.1

CLASSIFICATION (Input image size: 1280x844)

OBJECT DETECTION (input image size: 1280x851)

ORIENTED OBJECT DETECTION (OBB) (input image size: 1280x720)

POSE ESTIMATION (input image size: 1280x720)

SEGMENTATION (input image size: 1280x853)

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