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Alturos.yolo

Alturos.yolo is a C# wrapper around the Windows version of Darknet that runs YOLO object detection on image paths or byte arrays and returns detected object types, confidence scores and positions.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Alturos.yolo — OpenViST project profile in AI Developer Tools & SDKs.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when a C# application needs structured object-detection results from images and can meet the documented .NET and native runtime requirements.

Who it is for

  • C# developers adding object detection to desktop or server applications
  • Developers building image-based visual inspection tools

Documented uses

  • Submit images to a YOLO detector from C# code.
  • Retrieve object classes, confidence values and bounding-box coordinates for application processing.
  • Choose CPU processing or configure the documented NVIDIA GPU path.
Before you choose

Requirements & limits

  • The documented targets are .NET Framework 4.6.1 and .NET Standard 2.0.
  • The README lists the 64-bit Visual C++ Redistributable for Visual Studio 2015, 2017 and 2019 as a system requirement.
  • GPU processing is optional and requires the specified NVIDIA driver, CUDA 10.2 and cuDNN 7.6.5 setup.
  • The repository specifies the MIT license.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Add image detection to a C# application

Use the documented wrapper example to submit an image and access detected object data.

Before you start

  • .NET Framework 4.6.1 or .NET Standard 2.0
  • 64-bit Microsoft Visual C++ Redistributable for Visual Studio 2015, 2017 and 2019
  • Alturos.Yolo and a compatible pretrained dataset, installed through NuGet

Steps

  1. Install the Alturos.Yolo NuGet package and a pretrained dataset package, or prepare the model configuration, weights and class-name files.

  2. Create a ConfigurationDetector and use its detected configuration to construct a YoloWrapper, or construct the wrapper with the model files.

  3. Call Detect with an image path; the README also describes submitting a byte array.

  4. Read the returned object type, confidence and bounding-box coordinates.

Expected outcome

The application receives structured detection results for the submitted image.

Guide limitations

  • GPU processing requires the separately listed NVIDIA driver, CUDA Toolkit 10.2 and cuDNN 7.6.5 setup.
  • The README recommends reusing the wrapper instead of instantiating it for each image.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
16 October 2026
Original website title
AlturosDestinations/Alturos.Yolo · Official homepage title · retained in its source language
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