Replace Docs URLs with relative links (#11738)

docs/en/guides/index.md

@@ -52,9 +52,9 @@ Here's a compilation of in-depth guides to help you master different aspects of
 - [Objects Counting in Regions](region-counting.md) 🚀 NEW: Explore counting objects in specific regions with Ultralytics YOLOv8 for precise and efficient object detection in varied areas.
 - [Security Alarm System](security-alarm-system.md) 🚀 NEW: Discover the process of creating a security alarm system with Ultralytics YOLOv8. This system triggers alerts upon detecting new objects in the frame. Subsequently, you can customize the code to align with your specific use case.
 - [Heatmaps](heatmaps.md) 🚀 NEW: Elevate your understanding of data with our Detection Heatmaps! These intuitive visual tools use vibrant color gradients to vividly illustrate the intensity of data values across a matrix. Essential in computer vision, heatmaps are skillfully designed to highlight areas of interest, providing an immediate, impactful way to interpret spatial information.
-- [Instance Segmentation with Object Tracking](instance-segmentation-and-tracking.md) 🚀 NEW: Explore our feature on [Object Segmentation](https://docs.ultralytics.com/tasks/segment/) in Bounding Boxes Shape, providing a visual representation of precise object boundaries for enhanced understanding and analysis.
+- [Instance Segmentation with Object Tracking](instance-segmentation-and-tracking.md) 🚀 NEW: Explore our feature on [Object Segmentation](../tasks/segment.md) in Bounding Boxes Shape, providing a visual representation of precise object boundaries for enhanced understanding and analysis.
 - [VisionEye View Objects Mapping](vision-eye.md) 🚀 NEW: This feature aim computers to discern and focus on specific objects, much like the way the human eye observes details from a particular viewpoint.
-- [Speed Estimation](speed-estimation.md) 🚀 NEW: Speed estimation in computer vision relies on analyzing object motion through techniques like [object tracking](https://docs.ultralytics.com/modes/track/), crucial for applications like autonomous vehicles and traffic monitoring.
+- [Speed Estimation](speed-estimation.md) 🚀 NEW: Speed estimation in computer vision relies on analyzing object motion through techniques like [object tracking](../modes/track.md), crucial for applications like autonomous vehicles and traffic monitoring.
 - [Distance Calculation](distance-calculation.md) 🚀 NEW: Distance calculation, which involves measuring the separation between two objects within a defined space, is a crucial aspect. In the context of Ultralytics YOLOv8, the method employed for this involves using the bounding box centroid to determine the distance associated with user-highlighted bounding boxes.
 - [Queue Management](queue-management.md) 🚀 NEW: Queue management is the practice of efficiently controlling and directing the flow of people or tasks, often through strategic planning and technology implementation, to minimize wait times and improve overall productivity.
 - [Parking Management](parking-management.md) 🚀 NEW: Parking management involves efficiently organizing and directing the flow of vehicles in parking areas, often through strategic planning and technology integration, to optimize space utilization and enhance user experience.

docs/en/guides/nvidia-jetson.md

@@ -73,7 +73,7 @@ After this is done, skip to [Use TensorRT on NVIDIA Jetson section](#use-tensorr
 
 #### Install Ultralytics Package
 
-Here we will install ultralyics package on the Jetson with optional dependencies so that we can export the PyTorch models to other different formats. We will mainly focus on [NVIDIA TensorRT exports](https://docs.ultralytics.com/integrations/tensorrt) because TensoRT will make sure we can get the maximum performance out of the Jetson devices.
+Here we will install ultralyics package on the Jetson with optional dependencies so that we can export the PyTorch models to other different formats. We will mainly focus on [NVIDIA TensorRT exports](../integrations/tensorrt.md) because TensoRT will make sure we can get the maximum performance out of the Jetson devices.
 
 1. Update packages list, install pip and upgrade to latest
 
@@ -144,7 +144,7 @@ pip install onnxruntime_gpu-1.17.0-cp38-cp38-linux_aarch64.whl
 
 ## Use TensorRT on NVIDIA Jetson
 
-Out of all the model export formats supported by Ultralytics, TensorRT delivers the best inference performance when working with NVIDIA Jetson devices and our recommendation is to use TensorRT with Jetson. We also have a detailed document on TensorRT [here](https://docs.ultralytics.com/integrations/tensorrt).
+Out of all the model export formats supported by Ultralytics, TensorRT delivers the best inference performance when working with NVIDIA Jetson devices and our recommendation is to use TensorRT with Jetson. We also have a detailed document on TensorRT [here](../integrations/tensorrt.md).
 
 ## Convert Model to TensorRT and Run Inference
 
@@ -181,7 +181,7 @@ The YOLOv8n model in PyTorch format is converted to TensorRT to run inference wi
 
 !!! Note
 
-    Visit the [Export page](https://docs.ultralytics.com/modes/export/#arguments) to access additional arguments when exporting models to different model formats
+    Visit the [Export page](../modes/export.md#arguments) to access additional arguments when exporting models to different model formats
 
 ## NVIDIA Jetson Orin YOLOv8 Benchmarks
 

docs/en/guides/region-counting.md

@@ -8,7 +8,7 @@ keywords: Ultralytics, YOLOv8, Object Detection, Object Counting, Object Trackin
 
 ## What is Object Counting in Regions?
 
-[Object counting](https://docs.ultralytics.com/guides/object-counting/) in regions with [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics/) involves precisely determining the number of objects within specified areas using advanced computer vision. This approach is valuable for optimizing processes, enhancing security, and improving efficiency in various applications.
+[Object counting](../guides/object-counting.md) in regions with [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics/) involves precisely determining the number of objects within specified areas using advanced computer vision. This approach is valuable for optimizing processes, enhancing security, and improving efficiency in various applications.
 
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docs/en/guides/speed-estimation.md

@@ -8,7 +8,7 @@ keywords: Ultralytics, YOLOv8, Object Detection, Speed Estimation, Object Tracki
 
 ## What is Speed Estimation?
 
-Speed estimation is the process of calculating the rate of movement of an object within a given context, often employed in computer vision applications. Using [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics/) you can now calculate the speed of object using [object tracking](https://docs.ultralytics.com/modes/track/) alongside distance and time data, crucial for tasks like traffic and surveillance. The accuracy of speed estimation directly influences the efficiency and reliability of various applications, making it a key component in the advancement of intelligent systems and real-time decision-making processes.
+Speed estimation is the process of calculating the rate of movement of an object within a given context, often employed in computer vision applications. Using [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics/) you can now calculate the speed of object using [object tracking](../modes/track.md) alongside distance and time data, crucial for tasks like traffic and surveillance. The accuracy of speed estimation directly influences the efficiency and reliability of various applications, making it a key component in the advancement of intelligent systems and real-time decision-making processes.
 
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