YOLO11 Architecture - Detailed Explanation

Hey YOLO enthusiasts! The latest YOLO11 version is here, and it’s bringing big changes to the world of computer vision. Actually, YOLO11 has image classification, instance segmentation, pose estimation, and object tracking. However, in this video, we’ll focus to dive deep into the YOLO11 object detection architecture, breaking down its main components: backbone, neck, and head, to help you truly understand how this model works.

We’ll walk you through the key YOLOv11 architecture parameters—depth_multiple, width_multiple, and max_channels—explaining how they determine different model variants. You’ll also get a clear overview of the essential building blocks, like Conv, C3k2 (new), SPPF, C2PSA (new), and Detect. We’ll guide you step-by-step using an intuitive numbering system, showing how YOLO11 detects small, medium, and large objects effortlessly.

By the end, you’ll have a solid grasp of YOLOv11 architecture and its configurations. Hit play and let’s keep pushing the boundaries of deep learning together!

Created by:
Priyanto Hidayatullah
Refdinal Tubagus

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References:
1. https://docs.ultralytics.com/
2. https://github.com/ultralytics/ultral...

#yolo11 #yolov11 #objectdetection #computervision #deeplearning #artificialintelligence