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C# Yolo Darknet Wrapper (real-time object detection)
| Date | Stars |
|---|---|
| 2026-07-24 | 431 |
| 2026-07-25 | 431 |
| 2026-07-28 | 431 |
| 2026-07-30 | 431 |
| 2026-08-06 | 431 |
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# Alturos.Yolo
A state of the art real-time object detection system for C# (Visual Studio). This project has CPU and GPU support, with GPU the detection works much faster. The primary goal of this project is an easy use of yolo, this package is available on nuget and you must only install two packages to start detection. In the background we are use the Windows Yolo version of [AlexeyAB/darknet](https://github.com/AlexeyAB/darknet). Send an image path or the byte array to [yolo](https://github.com/pjreddie/darknet) and receive the position of the detected objects. Our project is meant to return the object-type and -position as processable data. This library supports [YoloV3 and YoloV2 Pre-Trained Datasets](#pre-trained-dataset)
## NuGet
Quick install Alturos.Yolo over [NuGet](https://www.nuget.org/packages/Alturos.Yolo)
```
PM> install-package Alturos.Yolo (C# wrapper and C++ dlls 28MB)
PM> install-package Alturos.YoloV2TinyVocData (YOLOv2-tiny Pre-Trained Dataset 56MB)
```
## Object Detection
## Example code
### Detect the type and the position of an image (Automatic configuration)
```cs
var configurationDetector = new ConfigurationDetector();
var config = configurationDetector.Detect();
using (var yoloWrapper = new YoloWrapper(config))
{
var items = yoloWrapper.Detect(@"image.jpg");
//items[0].Type -> "Person, Car, ..."
//items[0].Confidence -> 0.0 (low) -> 1.0 (high)
//items[0].X -> bounding box
//items[0].Y -> bounding box
//items[0].Width -> bounding box
//items[0].Height -> bounding box
}
```
### Detect the type and the position of an image (Manual configuration)
```cs
using (var yoloWrapper = new YoloWrapper("yolov2-tiny-voc.cfg", "yolov2-tiny-voc.weights", "voc.names"))
{
var items = yoloWrapper.Detect(@"image.jpg");
//items[0].Type -> "Person, Car, ..."
//items[0].Confidence -> 0.0 (low) -> 1.0 (high)
//items[0].X -> bounding box
//items[0].Y -> bounding box
//items[0].Width -> bounding box
//items[0].Height -> bounding box
}
```
## Performance
It is important to use GPU mode for fast object detection. It is also important not to instantiate the wrapper over and over again. A further optimization is to transfer the images as byte stream instead of passing a file path. GPU detection is usually 10 times faster!
## System requirements
- .NET Framework 4.6.1 or .NET standard 2.0
- [Microsoft Visual C++ Redistributable for Visual Studio 2015, 2017 und 2019 x64](https://aka.ms/vs/16/release/vc_redist.x64.exe)
### GPU requirements (optional)
It is important to use the mentioned version `10.2`
1) Install the latest Nvidia driver for your graphic device
2) [Install Nvidia CUDA Toolkit 10.2](https://developer.nvidia.com/cuda-downloads) (must be installed add a hardware driver for cuda support)
3) [Download Nvidia cuDNN v7.6.5 for CUDA 10.2](https://developer.nvidia.com/rdp/cudnn-download)
4) Copy the `cudnn64_7.dll` from the output directory of point 2. into the project folder.
## Build requirements
- Visual Studio 2019
## Benchmark / Performance
Average processing speed of test images bird1.png, bird2.png, car1.png, motorbike1.png
### CPU
Processor | YOLOv2-tiny | YOLOv3 | yolo9000 |
--- | --- | --- | --- |
Intel i7 3770 | 260 ms | 2200 ms | - |
Intel Xeon E5-1620 v3 | 207 ms | 4327 ms | - |
Intel Xeon E3-1240 v6 | 182 ms | 3213 ms | - |
### GPU
Graphic card | Single precision | Memory | Slot | YOLOv2-tiny | YOLOv3 | yolo9000 |
--- | --- | --- | --- | --- | --- | --- |
NVIDIA Quadro K420 | 300 GFLOPS | 2 GB | Single | - | - | - |
NVIDIA Quadro K620 | 768 GFLOPS | 2 GB | Single | - | - | - |
NVIDIA Quadro K1200 | 1151 GFLOPS | 4 GB | Single | - | - | - |
NVIDIA Quadro P400 | 599 GFLOPS | 2 GB | Single | - | - | - |
NVIDIA Quadro P600 | 1117 GFLOPS | 2 GB | Single | - | - | - |
NVIDIA Quadro P620 | 1386 GFLOPS | 2 GB | Single | - | - | - |
NVIDIA Quadro P1000 | 1862 GFLOPS | 4 GB | Single | - | - |Excerpt of 8,305 characters
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matched fp:86c83a7b101f79b3, topic:computer-vision, topic:object-detection, topic:yolo