From c61cc0dbb30067011a3ce47f559ab94313615158 Mon Sep 17 00:00:00 2001 From: Murat Raimbekov Date: Wed, 21 Oct 2026 15:27:45 +0600 Subject: [PATCH] fix broken jetson nano link on yolo26-vs-yolov9 compare page --- docs/en/compare/yolo26-vs-yolov9.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/en/compare/yolo26-vs-yolov9.md b/docs/en/compare/yolo26-vs-yolov9.md index f3be995bb1e..84a296d114c 100644 --- a/docs/en/compare/yolo26-vs-yolov9.md +++ b/docs/en/compare/yolo26-vs-yolov9.md @@ -31,7 +31,7 @@ Additionally, the removal of Distribution Focal Loss (DFL) streamlines the expor !!! tip "Edge Device Inference" - Thanks to architectural simplifications and the removal of DFL, YOLO26 achieves up to **43% faster CPU inference**, making it the ideal choice for resource-constrained edge devices like the [Raspberry Pi](https://www.raspberrypi.org/) or [NVIDIA Jetson Nano](https://developer.nvidia.com/embedded/jetson-nano-developer-kit). + Thanks to architectural simplifications and the removal of DFL, YOLO26 achieves up to **43% faster CPU inference**, making it the ideal choice for resource-constrained edge devices like the [Raspberry Pi](https://www.raspberrypi.org/) or [NVIDIA Jetson Nano](https://docs.ultralytics.com/guides/nvidia-jetson). For detecting highly challenging items in scenes like [drone aerial imagery](https://docs.ultralytics.com/datasets/detect/visdrone), YOLO26 utilizes the updated **ProgLoss + STAL** loss functions. These provide notable improvements in small-object recognition recall. Furthermore, it boasts task-specific enhancements, including multi-scale proto for [instance segmentation](https://docs.ultralytics.com/tasks/segment), Residual Log-Likelihood Estimation (RLE) for [pose estimation](https://docs.ultralytics.com/tasks/pose), and specialized angle loss for detecting [Oriented Bounding Boxes (OBB)](https://docs.ultralytics.com/tasks/obb).