Add Docs glossary links (#16448)

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

docs/en/datasets/segment/carparts-seg.md

@@ -6,7 +6,7 @@ keywords: Carparts Segmentation Dataset, Roboflow, computer vision, automotive A
 
 # Roboflow Universe Carparts Segmentation Dataset
 
-The [Roboflow](https://roboflow.com/?ref=ultralytics) [Carparts Segmentation Dataset](https://universe.roboflow.com/gianmarco-russo-vt9xr/car-seg-un1pm?ref=ultralytics) is a curated collection of images and videos designed for computer vision applications, specifically focusing on segmentation tasks related to car parts. This dataset provides a diverse set of visuals captured from multiple perspectives, offering valuable annotated examples for training and testing segmentation models.
+The [Roboflow](https://roboflow.com/?ref=ultralytics) [Carparts Segmentation Dataset](https://universe.roboflow.com/gianmarco-russo-vt9xr/car-seg-un1pm?ref=ultralytics) is a curated collection of images and videos designed for [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) applications, specifically focusing on segmentation tasks related to car parts. This dataset provides a diverse set of visuals captured from multiple perspectives, offering valuable annotated examples for training and testing segmentation models.
 
 Whether you're working on automotive research, developing AI solutions for vehicle maintenance, or exploring computer vision applications, the Carparts Segmentation Dataset serves as a valuable resource for enhancing accuracy and efficiency in your projects.
 
@@ -18,7 +18,7 @@ Whether you're working on automotive research, developing AI solutions for vehic
     allowfullscreen>
   </iframe>
   <br>
-  <strong>Watch:</strong> Carparts Instance Segmentation Using Ultralytics HUB
+  <strong>Watch:</strong> Carparts [Instance Segmentation](https://www.ultralytics.com/glossary/instance-segmentation) Using Ultralytics HUB
 </p>
 
 ## Dataset Structure
@@ -45,7 +45,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
 
 ## Usage
 
-To train Ultralytics YOLOv8n model on the Carparts Segmentation dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+To train Ultralytics YOLOv8n model on the Carparts Segmentation dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
 
 !!! example "Train Example"
 
@@ -156,6 +156,6 @@ The dataset configuration file for the Carparts Segmentation dataset, `carparts-
 
 ### Why should I use the Carparts Segmentation Dataset?
 
-The Carparts Segmentation Dataset provides rich, annotated data essential for developing high-accuracy segmentation models in automotive computer vision. This dataset's diversity and detailed annotations improve model training, making it ideal for applications like vehicle maintenance automation, enhancing vehicle safety systems, and supporting autonomous driving technologies. Partnering with a robust dataset accelerates AI development and ensures better model performance.
+The Carparts Segmentation Dataset provides rich, annotated data essential for developing high-[accuracy](https://www.ultralytics.com/glossary/accuracy) segmentation models in automotive computer vision. This dataset's diversity and detailed annotations improve model training, making it ideal for applications like vehicle maintenance automation, enhancing vehicle safety systems, and supporting autonomous driving technologies. Partnering with a robust dataset accelerates AI development and ensures better model performance.
 
 For more details, visit the [CarParts Segmentation Dataset Page](https://universe.roboflow.com/gianmarco-russo-vt9xr/car-seg-un1pm?ref=ultralytics).

docs/en/datasets/segment/coco.md

@@ -6,7 +6,7 @@ keywords: COCO-Seg, dataset, YOLO models, instance segmentation, object detectio
 
 # COCO-Seg Dataset
 
-The [COCO-Seg](https://cocodataset.org/#home) dataset, an extension of the COCO (Common Objects in Context) dataset, is specially designed to aid research in object instance segmentation. It uses the same images as COCO but introduces more detailed segmentation annotations. This dataset is a crucial resource for researchers and developers working on instance segmentation tasks, especially for training YOLO models.
+The [COCO-Seg](https://cocodataset.org/#home) dataset, an extension of the COCO (Common Objects in Context) dataset, is specially designed to aid research in object [instance segmentation](https://www.ultralytics.com/glossary/instance-segmentation). It uses the same images as COCO but introduces more detailed segmentation annotations. This dataset is a crucial resource for researchers and developers working on instance segmentation tasks, especially for training YOLO models.
 
 ## COCO-Seg Pretrained Models
 
@@ -23,7 +23,7 @@ The [COCO-Seg](https://cocodataset.org/#home) dataset, an extension of the COCO
 - COCO-Seg retains the original 330K images from COCO.
 - The dataset consists of the same 80 object categories found in the original COCO dataset.
 - Annotations now include more detailed instance segmentation masks for each object in the images.
-- COCO-Seg provides standardized evaluation metrics like mean Average Precision (mAP) for object detection, and mean Average Recall (mAR) for instance segmentation tasks, enabling effective comparison of model performance.
+- COCO-Seg provides standardized evaluation metrics like [mean Average Precision](https://www.ultralytics.com/glossary/mean-average-precision-map) (mAP) for object detection, and mean Average [Recall](https://www.ultralytics.com/glossary/recall) (mAR) for instance segmentation tasks, enabling effective comparison of model performance.
 
 ## Dataset Structure
 
@@ -35,7 +35,7 @@ The COCO-Seg dataset is partitioned into three subsets:
 
 ## Applications
 
-COCO-Seg is widely used for training and evaluating deep learning models in instance segmentation, such as the YOLO models. The large number of annotated images, the diversity of object categories, and the standardized evaluation metrics make it an indispensable resource for computer vision researchers and practitioners.
+COCO-Seg is widely used for training and evaluating [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) models in instance segmentation, such as the YOLO models. The large number of annotated images, the diversity of object categories, and the standardized evaluation metrics make it an indispensable resource for computer vision researchers and practitioners.
 
 ## Dataset YAML
 
@@ -49,7 +49,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
 
 ## Usage
 
-To train a YOLOv8n-seg model on the COCO-Seg dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+To train a YOLOv8n-seg model on the COCO-Seg dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
 
 !!! example "Train Example"
 
@@ -101,7 +101,7 @@ If you use the COCO-Seg dataset in your research or development work, please cit
         }
         ```
 
-We extend our thanks to the COCO Consortium for creating and maintaining this invaluable resource for the computer vision community. For more information about the COCO dataset and its creators, visit the [COCO dataset website](https://cocodataset.org/#home).
+We extend our thanks to the COCO Consortium for creating and maintaining this invaluable resource for the [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) community. For more information about the COCO dataset and its creators, visit the [COCO dataset website](https://cocodataset.org/#home).
 
 ## FAQ
 
@@ -141,7 +141,7 @@ The COCO-Seg dataset includes several key features:
 - Retains the original 330K images from the COCO dataset.
 - Annotates the same 80 object categories found in the original COCO.
 - Provides more detailed instance segmentation masks for each object.
-- Uses standardized evaluation metrics such as mean Average Precision (mAP) for object detection and mean Average Recall (mAR) for instance segmentation tasks.
+- Uses standardized evaluation metrics such as mean Average [Precision](https://www.ultralytics.com/glossary/precision) (mAP) for [object detection](https://www.ultralytics.com/glossary/object-detection) and mean Average Recall (mAR) for instance segmentation tasks.
 
 ### What pretrained models are available for COCO-Seg, and what are their performance metrics?
 

docs/en/datasets/segment/coco8-seg.md

@@ -8,7 +8,7 @@ keywords: COCO8-Seg, Ultralytics, segmentation dataset, YOLOv8, COCO 2017, model
 
 ## Introduction
 
-[Ultralytics](https://www.ultralytics.com/) COCO8-Seg is a small, but versatile instance segmentation dataset composed of the first 8 images of the COCO train 2017 set, 4 for training and 4 for validation. This dataset is ideal for testing and debugging segmentation models, or for experimenting with new detection approaches. With 8 images, it is small enough to be easily manageable, yet diverse enough to test training pipelines for errors and act as a sanity check before training larger datasets.
+[Ultralytics](https://www.ultralytics.com/) COCO8-Seg is a small, but versatile [instance segmentation](https://www.ultralytics.com/glossary/instance-segmentation) dataset composed of the first 8 images of the COCO train 2017 set, 4 for training and 4 for validation. This dataset is ideal for testing and debugging segmentation models, or for experimenting with new detection approaches. With 8 images, it is small enough to be easily manageable, yet diverse enough to test training pipelines for errors and act as a sanity check before training larger datasets.
 
 This dataset is intended for use with Ultralytics [HUB](https://hub.ultralytics.com/) and [YOLOv8](https://github.com/ultralytics/ultralytics).
 
@@ -24,7 +24,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
 
 ## Usage
 
-To train a YOLOv8n-seg model on the COCO8-Seg dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+To train a YOLOv8n-seg model on the COCO8-Seg dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
 
 !!! example "Train Example"
 
@@ -76,7 +76,7 @@ If you use the COCO dataset in your research or development work, please cite th
         }
         ```
 
-We would like to acknowledge the COCO Consortium for creating and maintaining this valuable resource for the computer vision community. For more information about the COCO dataset and its creators, visit the [COCO dataset website](https://cocodataset.org/#home).
+We would like to acknowledge the COCO Consortium for creating and maintaining this valuable resource for the [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) community. For more information about the COCO dataset and its creators, visit the [COCO dataset website](https://cocodataset.org/#home).
 
 ## FAQ
 
@@ -121,4 +121,4 @@ The YAML configuration file for the **COCO8-Seg dataset** is available in the Ul
 
 ### What are some benefits of using mosaicing during training with the COCO8-Seg dataset?
 
-Using **mosaicing** during training helps increase the diversity and variety of objects and scenes in each training batch. This technique combines multiple images into a single composite image, enhancing the model's ability to generalize to different object sizes, aspect ratios, and contexts within the scene. Mosaicing is beneficial for improving a model's robustness and accuracy, especially when working with small datasets like COCO8-Seg. For an example of mosaiced images, see the [Sample Images and Annotations](#sample-images-and-annotations) section.
+Using **mosaicing** during training helps increase the diversity and variety of objects and scenes in each training batch. This technique combines multiple images into a single composite image, enhancing the model's ability to generalize to different object sizes, aspect ratios, and contexts within the scene. Mosaicing is beneficial for improving a model's robustness and [accuracy](https://www.ultralytics.com/glossary/accuracy), especially when working with small datasets like COCO8-Seg. For an example of mosaiced images, see the [Sample Images and Annotations](#sample-images-and-annotations) section.

docs/en/datasets/segment/crack-seg.md

@@ -6,9 +6,9 @@ keywords: Roboflow, Crack Segmentation Dataset, Ultralytics, transportation safe
 
 # Roboflow Universe Crack Segmentation Dataset
 
-The [Roboflow](https://roboflow.com/?ref=ultralytics) [Crack Segmentation Dataset](https://universe.roboflow.com/university-bswxt/crack-bphdr?ref=ultralytics) stands out as an extensive resource designed specifically for individuals involved in transportation and public safety studies. It is equally beneficial for those working on the development of self-driving car models or simply exploring computer vision applications for recreational purposes.
+The [Roboflow](https://roboflow.com/?ref=ultralytics) [Crack Segmentation Dataset](https://universe.roboflow.com/university-bswxt/crack-bphdr?ref=ultralytics) stands out as an extensive resource designed specifically for individuals involved in transportation and public safety studies. It is equally beneficial for those working on the development of self-driving car models or simply exploring [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) applications for recreational purposes.
 
-Comprising a total of 4029 static images captured from diverse road and wall scenarios, this dataset emerges as a valuable asset for tasks related to crack segmentation. Whether you are delving into the intricacies of transportation research or seeking to enhance the accuracy of your self-driving car models, this dataset provides a rich and varied collection of images to support your endeavors.
+Comprising a total of 4029 static images captured from diverse road and wall scenarios, this dataset emerges as a valuable asset for tasks related to crack segmentation. Whether you are delving into the intricacies of transportation research or seeking to enhance the [accuracy](https://www.ultralytics.com/glossary/accuracy) of your self-driving car models, this dataset provides a rich and varied collection of images to support your endeavors.
 
 ## Dataset Structure
 
@@ -34,7 +34,7 @@ A YAML (Yet Another Markup Language) file is employed to outline the configurati
 
 ## Usage
 
-To train Ultralytics YOLOv8n model on the Crack Segmentation dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+To train Ultralytics YOLOv8n model on the Crack Segmentation dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
 
 !!! example "Train Example"
 
@@ -129,7 +129,7 @@ The Crack Segmentation Dataset is exceptionally suited for self-driving car proj
 
 ### What unique features does Ultralytics YOLO offer for crack segmentation?
 
-Ultralytics YOLO offers advanced real-time object detection, segmentation, and classification capabilities that make it ideal for crack segmentation tasks. Its ability to handle large datasets and complex scenarios ensures high accuracy and efficiency. For example, the model [Training](../../modes/train.md), [Predict](../../modes/predict.md), and [Export](../../modes/export.md) modes cover comprehensive functionalities from training to deployment.
+Ultralytics YOLO offers advanced real-time [object detection](https://www.ultralytics.com/glossary/object-detection), segmentation, and classification capabilities that make it ideal for crack segmentation tasks. Its ability to handle large datasets and complex scenarios ensures high accuracy and efficiency. For example, the model [Training](../../modes/train.md), [Predict](../../modes/predict.md), and [Export](../../modes/export.md) modes cover comprehensive functionalities from training to deployment.
 
 ### How do I cite the Roboflow Crack Segmentation Dataset in my research paper?
 

docs/en/datasets/segment/index.md

@@ -91,9 +91,9 @@ The `train` and `val` fields specify the paths to the directories containing the
 
 ## Supported Datasets
 
-- [COCO](coco.md): A comprehensive dataset for object detection, segmentation, and captioning, featuring over 200K labeled images across a wide range of categories.
+- [COCO](coco.md): A comprehensive dataset for [object detection](https://www.ultralytics.com/glossary/object-detection), segmentation, and captioning, featuring over 200K labeled images across a wide range of categories.
 - [COCO8-seg](coco8-seg.md): A compact, 8-image subset of COCO designed for quick testing of segmentation model training, ideal for CI checks and workflow validation in the `ultralytics` repository.
-- [COCO128-seg](coco.md): A smaller dataset for instance segmentation tasks, containing a subset of 128 COCO images with segmentation annotations.
+- [COCO128-seg](coco.md): A smaller dataset for [instance segmentation](https://www.ultralytics.com/glossary/instance-segmentation) tasks, containing a subset of 128 COCO images with segmentation annotations.
 - [Carparts-seg](carparts-seg.md): A specialized dataset focused on the segmentation of car parts, ideal for automotive applications. It includes a variety of vehicles with detailed annotations of individual car components.
 - [Crack-seg](crack-seg.md): A dataset tailored for the segmentation of cracks in various surfaces. Essential for infrastructure maintenance and quality control, it provides detailed imagery for training models to identify structural weaknesses.
 - [Package-seg](package-seg.md): A dataset dedicated to the segmentation of different types of packaging materials and shapes. It's particularly useful for logistics and warehouse automation, aiding in the development of systems for package handling and sorting.

docs/en/datasets/segment/package-seg.md

@@ -6,7 +6,7 @@ keywords: Roboflow, Package Segmentation Dataset, computer vision, package ident
 
 # Roboflow Universe Package Segmentation Dataset
 
-The [Roboflow](https://roboflow.com/?ref=ultralytics) [Package Segmentation Dataset](https://universe.roboflow.com/factorypackage/factory_package?ref=ultralytics) is a curated collection of images specifically tailored for tasks related to package segmentation in the field of computer vision. This dataset is designed to assist researchers, developers, and enthusiasts working on projects related to package identification, sorting, and handling.
+The [Roboflow](https://roboflow.com/?ref=ultralytics) [Package Segmentation Dataset](https://universe.roboflow.com/factorypackage/factory_package?ref=ultralytics) is a curated collection of images specifically tailored for tasks related to package segmentation in the field of [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv). This dataset is designed to assist researchers, developers, and enthusiasts working on projects related to package identification, sorting, and handling.
 
 Containing a diverse set of images showcasing various packages in different contexts and environments, the dataset serves as a valuable resource for training and evaluating segmentation models. Whether you are engaged in logistics, warehouse automation, or any application requiring precise package analysis, the Package Segmentation Dataset provides a targeted and comprehensive set of images to enhance the performance of your computer vision algorithms.
 
@@ -34,7 +34,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
 
 ## Usage
 
-To train Ultralytics YOLOv8n model on the Package Segmentation dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+To train Ultralytics YOLOv8n model on the Package Segmentation dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
 
 !!! example "Train Example"
 
@@ -63,7 +63,7 @@ The Package Segmentation dataset comprises a varied collection of images and vid
 
 ![Dataset sample image](https://github.com/ultralytics/docs/releases/download/0/dataset-sample-image-1.avif)
 
-- This image displays an instance of image object detection, featuring annotated bounding boxes with masks outlining recognized objects. The dataset incorporates a diverse collection of images taken in different locations, environments, and densities. It serves as a comprehensive resource for developing models specific to this task.
+- This image displays an instance of image [object detection](https://www.ultralytics.com/glossary/object-detection), featuring annotated bounding boxes with masks outlining recognized objects. The dataset incorporates a diverse collection of images taken in different locations, environments, and densities. It serves as a comprehensive resource for developing models specific to this task.
 - The example emphasizes the diversity and complexity present in the VisDrone dataset, underscoring the significance of high-quality sensor data for computer vision tasks involving drones.
 
 ## Citations and Acknowledgments
@@ -136,7 +136,7 @@ This structure ensures a balanced dataset for thorough model training, validatio
 
 ### Why should I use Ultralytics YOLOv8 with the Package Segmentation Dataset?
 
-Ultralytics YOLOv8 provides state-of-the-art accuracy and speed for real-time object detection and segmentation tasks. Using it with the Package Segmentation Dataset allows you to leverage YOLOv8's capabilities for precise package segmentation. This combination is especially beneficial for industries like logistics and warehouse automation, where accurate package identification is critical. For more information, check out our [page on YOLOv8 segmentation](https://docs.ultralytics.com/models/yolov8/).
+Ultralytics YOLOv8 provides state-of-the-art [accuracy](https://www.ultralytics.com/glossary/accuracy) and speed for real-time object detection and segmentation tasks. Using it with the Package Segmentation Dataset allows you to leverage YOLOv8's capabilities for precise package segmentation. This combination is especially beneficial for industries like logistics and warehouse automation, where accurate package identification is critical. For more information, check out our [page on YOLOv8 segmentation](https://docs.ultralytics.com/models/yolov8/).
 
 ### How can I access and use the package-seg.yaml file for the Package Segmentation Dataset?