.NET YOLO Detection Library

@ZohaibRoy

⭐⭐⭐⭐⭐ Create a .NET Library for Real-Time Object Detection with YOLO ❇️ Hi My Friend, I hope you are doing well. I've reviewed your project requirements and noticed you're looking for a .NET library for real-time object detection. Look no further; Zohaib is here to help you! My team has successfully completed 50+ similar projects in object detection. I will build the library from scratch to load ONNX weights, run inference on images, and return bounding boxes with class labels and confidence scores. ➡️ Why Me? I can easily create your .NET library as I have 5 years of experience in software development, focusing on real-time applications, object detection, and machine learning. My expertise includes .NET programming, ONNX integration, and image processing. Additionally, I have a strong grip on related technologies like YOLO and C# programming. ➡️ Let's have a quick chat to discuss your project in detail and let me show you samples of my previous work. Looking forward to discussing this with you! ➡️ Skills & Experience: ✅ .NET Development ✅ Object Detection ✅ YOLO ✅ ONNX Runtime ✅ C# Programming ✅ Image Processing ✅ Real-Time Applications ✅ API Development ✅ Software Testing ✅ Documentation ✅ Code Optimization ✅ Version Control Waiting for your response! Best Regards, Zohaib

@intelliresponse

My name is Steven, and I would love to offer my 13+ years of expertise in .NET and Python programming for your .NET YOLO Detection Library. Having worked extensively with Python for AI solutions and web automation, I have a deep understanding of the .NET framework, making me an excellent candidate for your project. My previous project experience includes developing tailor-made solutions, such as AI emergency caller using Vapi and Twilio, and OpenClaw AI bot installation on MacOS and Windows OS. In terms of machine learning (ML), I have a solid grasp on the concepts behind object detection, which is crucial for your real-time object detection using YOLO and ONNX runtime. Additionally, I am proficient in loading ONNX weights, running inference on images, and returning bounding boxes with class labels and confidence scores - all key components of your project. My commitment toward delivering high-quality work at every step aligns perfectly with your expectations mentioned in the acceptance criteria. Choosing me for this job would not only guarantee an efficiently built library for you but also provide you with a valuable resource who can contribute to any future enhancements or maintenance requirements. Let's collaborate today and create an outstanding .NET YOLO Detection Library together! Please do not hesitate to reach out to me should you need any further information.

@seefattechnologi

YES, it's aligned with my expertise ------- .NET YOLO Detection Library-----------I am highly proficient to work on this project . I am Passionate .NET /PYTHON /Full stack developer having rich experience with all the latest technologies with so many successful Tasks. I have some queries to give you accurate time and price Please ping me to get started and provide you great results. Thanks

@softsolution2000

I am confident in my ability to deliver a high-quality .NET YOLO Detection Library that meets all the specified requirements. With my expertise in Python, .NET, Machine Learning (ML), Tensorflow, and Predictive Analytics, I will ensure that the library can accurately detect objects in images, provide correct bounding boxes with class labels and confidence scores, and run inference in real-time using ONNX Runtime. I will also create a sample app for end-to-end testing and provide a comprehensive README for setup and usage instructions. My goal is to deliver a reliable and efficient solution that is free of errors and easy to use on a standard Windows dev machine. See the above links please. Please go through my profile its 15 years old see the work I did over the years. ---> No Win No Fee means that your satisfaction is my utmost priority. <---- Lets discuss the job details. Moreover, I am willing to start the job and perform tasks without even being hired; it is just to show my commitment to this project. Looking forward to hear from you. Regards Shah

@MSHOAIBK

Hello, I understand you need a lightweight .NET library built from scratch that uses YOLO with ONNX Runtime for real-time object detection, returning bounding boxes, class labels, and confidence scores. I can develop a clean, reusable C#/.NET library that loads ONNX models, performs image preprocessing and inference, applies post-processing (NMS), and returns structured detection results. I can also provide a sample application that demonstrates end-to-end detection and generates annotated output images. With experience in .NET development, image processing, API development, and AI/ML integrations, I am comfortable working with ONNX Runtime and building maintainable, production-ready solutions. Deliverables will include the .NET library, sample application, source code, README documentation, and a tested solution that builds successfully on a standard Windows development environment. Regards, Muhammad Shoaib

@techplusintl

Hi there, ★★★ .NET Expert ★★★ 7+ Years of Experience ★★★ I can create a .NET library for real-time object detection with ONNX Runtime, including loading weights and running inference. This will include: - Developing the library from scratch to detect objects in images. - Ensuring the sample app runs end-to-end and saves annotated images. - Providing a README for setup and usage instructions. I will follow a structured approach to ensure the library is efficient and meets the acceptance criteria. I’ll utilize ASP.NET and ONNX Runtime for seamless integration. Ready to start once you provide the ONNX weights and any specific requirements for the sample app. Thanks!

@ASSAMWEB

Greetings, I have read the project description I have been working on a similar project in recent time. I am interested in the work open a chat to discuss requirements in details.

@dfordesigners

Hi, I specialize in building custom .NET libraries for real-time object detection using YOLO and ONNX Runtime. I have extensive experience in creating similar projects from scratch and ensuring accurate results. My approach includes thorough testing to meet all acceptance criteria, including correct bounding boxes, annotated images, clear setup instructions, and error-free code. Let's discuss how I can deliver a high-quality solution for your .NET YOLO Detection Library. Thank you.

@mohjib092

Hi, I can build a clean .NET YOLO + ONNX Runtime object detection library from scratch, including ONNX model loading, image preprocessing, inference, post-processing with bounding boxes/class labels/confidence scores, and a sample app that saves an annotated output image. I’ll also provide a clear README so it builds and runs smoothly on a standard Windows development machine. Best regards, Muhammad Jibran Ahmed

@mogsten

Hi there, I reviewed your .NET YOLO and ONNX Runtime library requirements and can help build a clean real-time object detection library from scratch. My approach * Build a .NET library that loads YOLO ONNX weights, prepares input images, runs inference through ONNX Runtime, and returns bounding boxes, labels, and confidence scores. * Implement post-processing including confidence filtering, class mapping, box scaling, and non-maximum suppression. * Create a sample app that runs end to end on a test image and saves an annotated output image with detected objects. * Provide a clear README with setup steps, model placement, usage examples, Windows build instructions, and basic troubleshooting. I have experience with .NET, ONNX Runtime, computer vision, YOLO model integration, image preprocessing, inference pipelines, and sample app development. I’ll keep the code simple, well-structured, and easy to reuse in future applications. One question, do you already have the YOLO ONNX model file and class labels ready? Cheers, Alexander

@godesignpk

Hi, We went through your project description and it seems like our team is a great fit for this job. We are an expert team which have many years of experience on Python, .NET, Machine Learning (ML), Tensorflow, Predictive Analytics, Azure OpenAI, Convolutional Neural Network, Claude Code, Nvidia Jetson, Codeium Please come over chat and discuss your requirement in a detailed way. Thank You

@markupdudes

Hello, I have carefully reviewed your requirement for a .NET library focused on real-time object detection using YOLO and ONNX Runtime. With extensive experience in .NET development and cloud-based AI integration, I am confident in designing a solution that loads ONNX weights, performs inference on images, and accurately returns bounding boxes with class labels and confidence scores. I will ensure the library detects objects correctly in sample images, deliver a fully functional sample app that annotates images, and provide a clear README for easy setup and usage. This comprehensive approach guarantees smooth builds on standard Windows development machines. Looking forward to starting immediately and providing a robust solution within an agreed timeline. What specific YOLO version or ONNX model format do you prefer for this detection library? Thanks,

@techsty2014

Hello there, We will build your .NET library for YOLO object detection using ONNX Runtime: model loading, inference, and structured output with bounding boxes, labels, and confidence scores. For inference speed, we will pre-allocate input tensors and run sessions with a reusable buffer pool. This keeps memory stable during real-time processing loops. A couple of quick things to confirm: 1) Which YOLO version (v8, v11) and model size (nano, small, medium)? 2) Should the sample app support batch processing or single image only? The number quoted here is a starting estimate. The exact cost and timeline will be confirmed after we go through the full scope together. Looking forward to talking through the details. Faizan

@craigd43

With over 5 years of experience in web development, I know the value of reliable and efficient libraries like the .NET YOLO Detection Library you're seeking. Although my primary skillset is in Python, I have successfully developed libraries and APIs with ONNX Runtime integration similar to your project. This familiarity will ensure a smooth transition for me into building your .NET library from scratch. Considering my comprehensive grasp of both front-end and back-end development, I am adept at taking into account the various perspectives and user requirements that will come into play when building your sample app. My proven track record in delivering high-quality code on time demonstrates my ability to meet strict project acceptance criteria, including accurate object detection and proper annotation. Additionally, I believe in clear communication and collaboration at each step of the process. With me, you can expect prompt responses to your queries, regular update meetings, a comprehensive README outlining the setup and usage details as well as support post-delivery. Looking forward to leveraging my skills and experience to develop an efficient solution for you!

@hoangvanp

Hi, I can develop a custom .NET library for real-time object detection using YOLO and ONNX Runtime. The library will load ONNX weights, perform inference on images, and provide bounding boxes with class labels and confidence scores. I have experience with YOLO, ONNX, and real-time object detection projects. For this project, I would start by creating a modular architecture to seamlessly integrate YOLO and ONNX Runtime, ensuring efficient inference and accurate object detection. A key challenge is optimizing the library for real-time performance without compromising accuracy. I address this by implementing parallel processing techniques and optimizing model loading and inference steps. I will provide you with a fully functional .NET library, a sample application demonstrating end-to-end functionality, a comprehensive README for setup and usage, and error-free code that builds on a standard Windows development machine. Let's discuss the details via chat. Regards,

@johnedwardl

Hi, I will develop a .NET library for real-time object detection using YOLO and ONNX Runtime, ensuring it meets your specified criteria. My extensive experience with .NET and deep learning libraries positions me well for this task. I’ll implement a clean architecture that loads ONNX weights, executes image inference, and accurately returns bounding boxes along with class labels and confidence scores. The library will be engineered for optimal performance, with a focus on maintainability. I will also create a sample application that demonstrates the end-to-end functionality, including saving annotated images. A well-documented README will accompany the library, providing clear setup instructions and usage guidelines, and I will ensure the code builds seamlessly on a standard Windows development machine. Could you share any specific requirements for the types of objects you expect to detect? Additionally, are there any performance benchmarks you have in mind? Looking forward to collaborating on this project. Thank you.

@engruhulajom

Hello Dear! Good Day! Hope you are doing fine. This is Ruhul Ajom Sagor. I am an expert "Web Developer" with 10+ years of working experience in PHP, HTML5, CSS3, JavaScript, jQuery, Bootstrap, MySql and different Frameworks. I have completed my B.S.C Engineering in Computer Science and Engineering (CSE) from BUET. Hire me and you don't have to worry about your website problems again! I'll add value to your projects by creating astonishing designs and code with high impact and optimized user interaction that leads to bigger conversions. WHAT PROBLEMS CAN I HELP YOU SOLVE? • Custom Websites Using PHP and Frameworks • e-Commerce Websites (Woo-Commerce and Shopify) • Custom WordPress themes • On-Page and Off-Page SEO • WordPress themes Customization • Database Modeling/Development • WordPress migrations and upgrades • Responsive Coding (Make your website compatible with: smartphones, tablets, desktops) • Websites speed and loading time improvements • Cross-browser compatibility • PSD to HTML to WordPress conversion • HTML5/CSS3/jQuery websites based on Bootstrap I love challenges, talking to my clients, and meeting others’ standards as well as expectations. I will be discussing everything in detail, giving my full advice and delivering through best of my skills. You are cordially welcome to discuss your project. Thank You! Best Regards, Ruhul Ajom

@ihsan191

Hello there, I understand the requirements for developing a small .NET library for real-time object detection using YOLO and ONNX Runtime. With my expertise in developing custom libraries and working with machine learning models, I am confident in delivering a high-quality solution that meets your specifications. My experience includes working on similar projects where I have successfully implemented object detection algorithms using ONNX Runtime and YOLO. I am proficient in loading ONNX weights, running inference on images, and accurately returning bounding boxes with class labels and confidence scores. To approach this project, I will start by creating the library from scratch, ensuring it meets the acceptance criteria such as detecting objects in sample images, running an end-to-end sample app, providing a comprehensive README for setup and basic usage, and ensuring error-free code compilation on a standard Windows dev machine. I am excited about the opportunity to work on this project and showcase my skills in developing efficient and reliable solutions for real-time object detection. Please feel free to reach out if you have any questions or would like to discuss the project further. Ihsan Faridi

@SkillCrafts

I understand you need a robust .NET library for real-time object detection leveraging YOLO and ONNX Runtime, similar to the requirements for efficient inference pipelines I've built for [mention a similar project/tool if applicable, e.g., "real-time image analysis systems"]. My approach will focus on creating a clean, performant, and maintainable .NET Standard 2.0 library. The core of the solution will involve integrating ONNX Runtime directly, bypassing higher-level abstractions where unnecessary for direct control. I'll implement the YOLO model loading mechanism from ONNX weights, followed by pre-processing image data into the required tensor format and executing the inference session. Post-processing will involve parsing the raw output tensors to extract bounding boxes, class probabilities, and confidence scores, applying non-maximum suppression (NMS) for clean detection results. The sample application will demonstrate end-to-end functionality, including image loading, inference, and saving an annotated output image. How are you envisioning the library's integration into larger .NET applications – will it be primarily consumed via a simple API call, or are there specific event-driven patterns to consider? What are your target performance metrics for inference speed per image? I'm confident I can deliver a high-quality solution meeting your requirements. Let's schedule a brief call to discuss the specifics further.