Add Docs glossary links (#16448)

Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>

docs/en/models/fast-sam.md

@@ -25,7 +25,7 @@ The Fast Segment Anything Model (FastSAM) is a novel, real-time CNN-based soluti
 
 ## Overview
 
-FastSAM is designed to address the limitations of the [Segment Anything Model (SAM)](sam.md), a heavy Transformer model with substantial computational resource requirements. The FastSAM decouples the segment anything task into two sequential stages: all-instance segmentation and prompt-guided selection. The first stage uses [YOLOv8-seg](../tasks/segment.md) to produce the segmentation masks of all instances in the image. In the second stage, it outputs the region-of-interest corresponding to the prompt.
+FastSAM is designed to address the limitations of the [Segment Anything Model (SAM)](sam.md), a heavy [Transformer](https://www.ultralytics.com/glossary/transformer) model with substantial computational resource requirements. The FastSAM decouples the segment anything task into two sequential stages: all-[instance segmentation](https://www.ultralytics.com/glossary/instance-segmentation) and prompt-guided selection. The first stage uses [YOLOv8-seg](../tasks/segment.md) to produce the segmentation masks of all instances in the image. In the second stage, it outputs the region-of-interest corresponding to the prompt.
 
 ## Key Features
 
@@ -58,7 +58,7 @@ The FastSAM models are easy to integrate into your Python applications. Ultralyt
 
 ### Predict Usage
 
-To perform object detection on an image, use the `predict` method as shown below:
+To perform [object detection](https://www.ultralytics.com/glossary/object-detection) on an image, use the `predict` method as shown below:
 
 !!! example
 
@@ -224,7 +224,7 @@ FastSAM is also available directly from the [https://github.com/CASIA-IVA-Lab/Fa
         python Inference.py --model_path ./weights/FastSAM.pt --img_path ./images/dogs.jpg --text_prompt "the yellow dog"
         ```
 
-    - Segment objects within a bounding box (provide box coordinates in xywh format):
+    - Segment objects within a [bounding box](https://www.ultralytics.com/glossary/bounding-box) (provide box coordinates in xywh format):
 
         ```shell
         python Inference.py --model_path ./weights/FastSAM.pt --img_path ./images/dogs.jpg --box_prompt "[570,200,230,400]"
@@ -262,7 +262,7 @@ The original FastSAM paper can be found on [arXiv](https://arxiv.org/abs/2306.12
 
 ### What is FastSAM and how does it differ from SAM?
 
-FastSAM, short for Fast Segment Anything Model, is a real-time convolutional neural network (CNN)-based solution designed to reduce computational demands while maintaining high performance in object segmentation tasks. Unlike the Segment Anything Model (SAM), which uses a heavier Transformer-based architecture, FastSAM leverages [Ultralytics YOLOv8-seg](../tasks/segment.md) for efficient instance segmentation in two stages: all-instance segmentation followed by prompt-guided selection.
+FastSAM, short for Fast Segment Anything Model, is a real-time [convolutional neural network](https://www.ultralytics.com/glossary/convolutional-neural-network-cnn) (CNN)-based solution designed to reduce computational demands while maintaining high performance in object segmentation tasks. Unlike the Segment Anything Model (SAM), which uses a heavier Transformer-based architecture, FastSAM leverages [Ultralytics YOLOv8-seg](../tasks/segment.md) for efficient instance segmentation in two stages: all-instance segmentation followed by prompt-guided selection.
 
 ### How does FastSAM achieve real-time segmentation performance?
 
@@ -270,7 +270,7 @@ FastSAM achieves real-time segmentation by decoupling the segmentation task into
 
 ### What are the practical applications of FastSAM?
 
-FastSAM is practical for a variety of computer vision tasks that require real-time segmentation performance. Applications include:
+FastSAM is practical for a variety of [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) tasks that require real-time segmentation performance. Applications include:
 
 - Industrial automation for quality control and assurance
 - Real-time video analysis for security and surveillance