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Real-time Instance Segmentation with Open-source RF-DETR

Nicolai Nielsen16 June 2026Watch on YouTube

Description

Inside my school and program, I teach you my system to become an AI engineer or freelancer. Life-time access, personal help by me and I will show you exactly how I went from below average student to making $250/hr. Join the High Earner AI Career Program here 👉 https://www.nicolai-nielsen.com/aicareer (PRICES WILL INCREASE SOON) You will also get access to all the technical courses inside the program, also the ones I plan to make in the future! Check out the technical courses below 👇 _____________________________________________________________ In this video 📝 we are going to take a look at how we can set up the instance segmentation model from the RFD-ETR model. We are going to go through step by step how you can: 1. Set up a few lines of Python code 2. Connect a camera, webcam, or any video file 3. Run inference with that You can now run both object detection and instance segmentation with the RFD-ETR model. If you enjoyed this video, be sure to press the 👍 button so that I know what content you guys like to see. _____________________________________________________________ 🛠️ Freelance Work: https://www.nicolai-nielsen.com/nncode _____________________________________________________________ 💻💰🛠️ High Earner AI Career Program: https://www.nicolai-nielsen.com/aicareer 📈 Investment Course: https://www.nicos-school.com/p/investment-course ⚙️ Real-world AI Technical Courses: (https://www.nicos-school.com) 📗 OpenCV GPU in Python: https://www.nicos-school.com/p/opencv-gpu-course 📕 YOLOv7 Object Detection: https://www.nicos-school.com/p/yolov7-custom-object-detection-with-deployment 📒 Transformer & Segmentation: https://www.nicos-school.com/p/transformer-and-segmentation-course 📙 YOLOv8 Object Tracking: https://www.nicos-school.com/p/yolov8-object-tracking-course 📘 Research Paper Implementation: https://www.nicos-school.com/p/research-paper-implementation 📔 CustomGPT: https://www.nicos-school.com/p/customgpt-course _____________________________________________________________ 📞 Connect with Me: 🌳 https://linktr.ee/nicolainielsen 🌍 My Website: https://www.nicolai-nielsen.com/ 🤖 GitHub: https://github.com/niconielsen32 👉 LinkedIn: https://www.linkedin.com/in/nicolaiai 🐦 X/Twitter: https://twitter.com/NielsenCV_AI 🌆 Instagram: https://www.instagram.com/nicolaicodes/ _____________________________________________________________ 🇦🇪 Want to move to Dubai and get Visa? - Book a free consultation: https://www.genzoneconsulting.com/meetings/schedule-session/nny _____________________________________________________________ 📷 My camera calibration software, CharuCo Boards, and Checker boards Link to webshop: https://camera-calibrator.com _____________________________________________________________ Timestamps: Tags: #rfdetr #instancesegmentation #detection #computervision

What you'll learn

  • You learn how to set up the open-source RF-DETR model step by step for instance segmentation
  • You see how to write Python code to connect a camera, webcam or video file to the model
  • You discover how to run real-time inference with the RF-DETR model for both object detection and instance segmentation

Frequently asked questions

What is RF-DETR and what can it do?
RF-DETR is an open-source model that can perform both object detection and instance segmentation. With the model you can detect objects and determine their precise boundaries in images.
Can I use the tutorial with my own camera or video file?
Yes, the tutorial shows how to connect the model to a camera, webcam or any video file. You only need to set up a few lines of Python code.
What does this video teach me about real-time processing?
The video demonstrates how to perform real-time inference with RF-DETR so you can analyze live video and get immediate results.
Do I need extensive programming experience to follow this?
The video goes through the Python implementation step by step, suggesting it is accessible to people with basic knowledge of Python and computer vision.

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