Add Docs glossary links (#16448)

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

docs/en/models/sam.md

@@ -6,7 +6,7 @@ keywords: Segment Anything, SAM, image segmentation, promptable segmentation, ze
 
 # Segment Anything Model (SAM)
 
-Welcome to the frontier of image segmentation with the Segment Anything Model, or SAM. This revolutionary model has changed the game by introducing promptable image segmentation with real-time performance, setting new standards in the field.
+Welcome to the frontier of [image segmentation](https://www.ultralytics.com/glossary/image-segmentation) with the Segment Anything Model, or SAM. This revolutionary model has changed the game by introducing promptable image segmentation with real-time performance, setting new standards in the field.
 
 ## Introduction to SAM: The Segment Anything Model
 
@@ -21,7 +21,7 @@ SAM's advanced design allows it to adapt to new image distributions and tasks wi
 - **Promptable Segmentation Task:** SAM was designed with a promptable segmentation task in mind, allowing it to generate valid segmentation masks from any given prompt, such as spatial or text clues identifying an object.
 - **Advanced Architecture:** The Segment Anything Model employs a powerful image encoder, a prompt encoder, and a lightweight mask decoder. This unique architecture enables flexible prompting, real-time mask computation, and ambiguity awareness in segmentation tasks.
 - **The SA-1B Dataset:** Introduced by the Segment Anything project, the SA-1B dataset features over 1 billion masks on 11 million images. As the largest segmentation dataset to date, it provides SAM with a diverse and large-scale training data source.
-- **Zero-Shot Performance:** SAM displays outstanding zero-shot performance across various segmentation tasks, making it a ready-to-use tool for diverse applications with minimal need for prompt engineering.
+- **Zero-Shot Performance:** SAM displays outstanding zero-shot performance across various segmentation tasks, making it a ready-to-use tool for diverse applications with minimal need for [prompt engineering](https://www.ultralytics.com/glossary/prompt-engineering).
 
 For an in-depth look at the Segment Anything Model and the SA-1B dataset, please visit the [Segment Anything website](https://segment-anything.com/) and check out the research paper [Segment Anything](https://arxiv.org/abs/2304.02643).
 
@@ -36,7 +36,7 @@ This table presents the available models with their specific pre-trained weights
 
 ## How to Use SAM: Versatility and Power in Image Segmentation
 
-The Segment Anything Model can be employed for a multitude of downstream tasks that go beyond its training data. This includes edge detection, object proposal generation, instance segmentation, and preliminary text-to-mask prediction. With prompt engineering, SAM can swiftly adapt to new tasks and data distributions in a zero-shot manner, establishing it as a versatile and potent tool for all your image segmentation needs.
+The Segment Anything Model can be employed for a multitude of downstream tasks that go beyond its training data. This includes edge detection, object proposal generation, [instance segmentation](https://www.ultralytics.com/glossary/instance-segmentation), and preliminary text-to-mask prediction. With prompt engineering, SAM can swiftly adapt to new tasks and data distributions in a zero-shot manner, establishing it as a versatile and potent tool for all your image segmentation needs.
 
 ### SAM prediction example
 
@@ -222,7 +222,7 @@ If you find SAM useful in your research or development work, please consider cit
         }
         ```
 
-We would like to express our gratitude to Meta AI for creating and maintaining this valuable resource for the computer vision community.
+We would like to express our gratitude to Meta AI for creating and maintaining this valuable resource for the [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) community.
 
 ## FAQ
 
@@ -273,4 +273,4 @@ This function takes the path to your images and optional arguments for pre-train
 
 ### What datasets are used to train the Segment Anything Model (SAM)?
 
-SAM is trained on the extensive [SA-1B dataset](https://ai.facebook.com/datasets/segment-anything/) which comprises over 1 billion masks across 11 million images. SA-1B is the largest segmentation dataset to date, providing high-quality and diverse training data, ensuring impressive zero-shot performance in varied segmentation tasks. For more details, visit the [Dataset section](#key-features-of-the-segment-anything-model-sam).
+SAM is trained on the extensive [SA-1B dataset](https://ai.facebook.com/datasets/segment-anything/) which comprises over 1 billion masks across 11 million images. SA-1B is the largest segmentation dataset to date, providing high-quality and diverse [training data](https://www.ultralytics.com/glossary/training-data), ensuring impressive zero-shot performance in varied segmentation tasks. For more details, visit the [Dataset section](#key-features-of-the-segment-anything-model-sam).