🎥 New Video Series: YOLO11 Inference Step-by-Step 🚀

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Hi YOLO Community! :waving_hand:

About Me:
I’m a Deep Learning and Computer Vision enthusiast with a strong self-taught background in coding. I’m passionate about understanding how AI works under the hood and sharing that knowledge with others.

:rocket: I’m excited to share a new personal project I’ve been working on over the past few months: a 20-video technical walkthrough series on YOLO11.

I focused on explaining the code flow and model architecture in depth—from initialization all the way through inference and output. My goal was to go far beyond just “how to use it,” and instead shed light on what’s actually happening at each stage of the algorithm.

:brain: The series emphasizes the structure and logic of the neural network layers, including the Attention block, detection head, feature maps, and preprocessing steps like convolution/batch norm fusion and letterboxing. It also includes segments with a Colab notebook companion, so you can break down key code sections and follow along interactively.

:folded_hands: Huge thanks to Glenn Jocher and the Ultralytics team, as well as the broader open-source community, for pushing YOLO to the cutting edge. This series wouldn’t exist without your incredible contributions and documentation.

:movie_camera: If you’re curious to dive into YOLO11 at the code level—or want to understand how its architecture works—feel free to check it out. The first video is beginner-friendly, the second introduces the Colab notebook, and the rest dive deeper into the technical details.

:link: Playlist: https://www.youtube.com/playlist?list=PLTcDXKiPdqrHi4SNEpQEROMcnppVp9m8J
:link: Colab companion notebook: Google Colab

Hopefully, some of you will find it useful! I’d love to hear any feedback, questions, or suggestions from this amazing community. Thanks for all the support—and for being such a great place to learn and grow.

Happy learning! :tada:
Marc Tornero

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Hi Marc, thank you for creating and sharing this detailed YOLO11 video series and Colab notebook! It’s fantastic to see community members like yourself contributing such in-depth resources.

Explanations like these, going beyond basic usage to explore the underlying code and architecture, are incredibly valuable for everyone learning and working with YOLO models. We’re sure many in the community will appreciate this walkthrough and hopefully provide you with helpful feedback. Kudos to you for this effort!

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Thanks so much, @pderrenger! I really appreciate the encouragement and support—means a lot coming from the Ultralytics Team! Diving into YOLO11 has been an incredible learning experience, and it’s great to be able to give back to the community. Looking forward to keeping learning and growing with the vision community!

—Marc

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Thanks for the kind words, Marc! It’s wonderful to hear you had such a positive learning experience. The success of YOLO truly belongs to the vibrant community and the hard work of the Ultralytics team. We really appreciate you sharing your knowledge through the video series—it’s a fantastic contribution!