Fix broken links (#161)

ultralytics · Jan 27, 2025 · cd97168 · cd97168
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diff --git a/docs/en/compare/yolo11-vs-damo-yolo.md b/docs/en/compare/yolo11-vs-damo-yolo.md
@@ -19,7 +19,7 @@ This page provides a detailed technical comparison between two state-of-the-art
 
 **Architecture and Key Features:**
 
-YOLO11's architecture focuses on optimizing the balance between model size and accuracy. Key improvements include enhanced feature extraction layers for more detailed feature capture and a streamlined network structure to reduce computational overhead. This results in models that are not only faster but also more parameter-efficient. The architecture is designed to be flexible, allowing for deployment across diverse platforms, from edge devices like [Raspberry Pi](https://docs.ultralytics.com/guides/raspberry-pi/) and [NVIDIA Jetson](docs.ultralytics.com/guides/nvidia-jetson/) to cloud servers. YOLO11 is also easily integrated with platforms like [Ultralytics HUB](https://www.ultralytics.com/hub) for streamlined training and deployment workflows.
+YOLO11's architecture focuses on optimizing the balance between model size and accuracy. Key improvements include enhanced feature extraction layers for more detailed feature capture and a streamlined network structure to reduce computational overhead. This results in models that are not only faster but also more parameter-efficient. The architecture is designed to be flexible, allowing for deployment across diverse platforms, from edge devices like [Raspberry Pi](https://docs.ultralytics.com/guides/raspberry-pi/) and [NVIDIA Jetson](https://docs.ultralytics.com/guides/nvidia-jetson/) to cloud servers. YOLO11 is also easily integrated with platforms like [Ultralytics HUB](https://www.ultralytics.com/hub) for streamlined training and deployment workflows.
 
 **Performance Metrics:**
 

diff --git a/docs/en/compare/yolov9-vs-damo-yolo.md b/docs/en/compare/yolov9-vs-damo-yolo.md
@@ -75,7 +75,7 @@ The following table provides a comparative overview of the performance metrics f
 **YOLOv9 Ideal Use Cases:**
 
 - **Real-time Object Detection**: Applications demanding high-speed processing, such as autonomous driving, robotics, and live video analytics ([AI in self-driving cars](https://www.ultralytics.com/solutions/ai-in-self-driving), [robotics](https://www.ultralytics.com/glossary/robotics)).
-- **Edge Devices**: Deployments on resource-constrained devices like [Raspberry Pi](https://docs.ultralytics.com/guides/raspberry-pi/) and [NVIDIA Jetson](docs.ultralytics.com/guides/nvidia-jetson/) where efficiency is paramount.
+- **Edge Devices**: Deployments on resource-constrained devices like [Raspberry Pi](https://docs.ultralytics.com/guides/raspberry-pi/) and [NVIDIA Jetson](https://docs.ultralytics.com/guides/nvidia-jetson/) where efficiency is paramount.
 - **General Object Detection Tasks**: Versatile for a wide range of object detection tasks due to its balance of speed and accuracy.
 
 **DAMO-YOLO Ideal Use Cases:**