Add Docs glossary links (#16448)

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

docs/en/models/yolov9.md

@@ -4,7 +4,7 @@ description: Explore YOLOv9, the latest leap in real-time object detection, feat
 keywords: YOLOv9, object detection, real-time, PGI, GELAN, deep learning, MS COCO, AI, neural networks, model efficiency, accuracy, Ultralytics
 ---
 
-# YOLOv9: A Leap Forward in Object Detection Technology
+# YOLOv9: A Leap Forward in [Object Detection](https://www.ultralytics.com/glossary/object-detection) Technology
 
 YOLOv9 marks a significant advancement in real-time object detection, introducing groundbreaking techniques such as Programmable Gradient Information (PGI) and the Generalized Efficient Layer Aggregation Network (GELAN). This model demonstrates remarkable improvements in efficiency, accuracy, and adaptability, setting new benchmarks on the MS COCO dataset. The YOLOv9 project, while developed by a separate open-source team, builds upon the robust codebase provided by [Ultralytics](https://www.ultralytics.com/) [YOLOv5](yolov5.md), showcasing the collaborative spirit of the AI research community.
 
@@ -23,7 +23,7 @@ YOLOv9 marks a significant advancement in real-time object detection, introducin
 
 ## Introduction to YOLOv9
 
-In the quest for optimal real-time object detection, YOLOv9 stands out with its innovative approach to overcoming information loss challenges inherent in deep neural networks. By integrating PGI and the versatile GELAN architecture, YOLOv9 not only enhances the model's learning capacity but also ensures the retention of crucial information throughout the detection process, thereby achieving exceptional accuracy and performance.
+In the quest for optimal real-time object detection, YOLOv9 stands out with its innovative approach to overcoming information loss challenges inherent in deep [neural networks](https://www.ultralytics.com/glossary/neural-network-nn). By integrating PGI and the versatile GELAN architecture, YOLOv9 not only enhances the model's learning capacity but also ensures the retention of crucial information throughout the detection process, thereby achieving exceptional accuracy and performance.
 
 ## Core Innovations of YOLOv9
 
@@ -47,7 +47,7 @@ The concept of Reversible Functions is another cornerstone of YOLOv9's design. A
 X = v_zeta(r_psi(X))
 ```
 
-with `psi` and `zeta` as parameters for the reversible and its inverse function, respectively. This property is crucial for deep learning architectures, as it allows the network to retain a complete information flow, thereby enabling more accurate updates to the model's parameters. YOLOv9 incorporates reversible functions within its architecture to mitigate the risk of information degradation, especially in deeper layers, ensuring the preservation of critical data for object detection tasks.
+with `psi` and `zeta` as parameters for the reversible and its inverse function, respectively. This property is crucial for [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) architectures, as it allows the network to retain a complete information flow, thereby enabling more accurate updates to the model's parameters. YOLOv9 incorporates reversible functions within its architecture to mitigate the risk of information degradation, especially in deeper layers, ensuring the preservation of critical data for object detection tasks.
 
 ### Impact on Lightweight Models
 
@@ -86,7 +86,7 @@ By benchmarking, you can ensure that your model not only performs well in contro
 
 ## Performance on MS COCO Dataset
 
-The performance of YOLOv9 on the [COCO dataset](../datasets/detect/coco.md) exemplifies its significant advancements in real-time object detection, setting new benchmarks across various model sizes. Table 1 presents a comprehensive comparison of state-of-the-art real-time object detectors, illustrating YOLOv9's superior efficiency and accuracy.
+The performance of YOLOv9 on the [COCO dataset](../datasets/detect/coco.md) exemplifies its significant advancements in real-time object detection, setting new benchmarks across various model sizes. Table 1 presents a comprehensive comparison of state-of-the-art real-time object detectors, illustrating YOLOv9's superior efficiency and [accuracy](https://www.ultralytics.com/glossary/accuracy).
 
 **Table 1. Comparison of State-of-the-Art Real-Time Object Detectors**
 
@@ -109,7 +109,7 @@ The performance of YOLOv9 on the [COCO dataset](../datasets/detect/coco.md) exem
         | [YOLOv9c-seg](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov9c-seg.pt)  | 640                   | 52.4                 | 42.2                  | 27.9               | 159.4             |
         | [YOLOv9e-seg](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov9e-seg.pt)  | 640                   | 55.1                 | 44.3                  | 60.5               | 248.4             |
 
-YOLOv9's iterations, ranging from the tiny `t` variant to the extensive `e` model, demonstrate improvements not only in accuracy (mAP metrics) but also in efficiency with a reduced number of parameters and computational needs (FLOPs). This table underscores YOLOv9's ability to deliver high precision while maintaining or reducing the computational overhead compared to prior versions and competing models.
+YOLOv9's iterations, ranging from the tiny `t` variant to the extensive `e` model, demonstrate improvements not only in accuracy (mAP metrics) but also in efficiency with a reduced number of parameters and computational needs (FLOPs). This table underscores YOLOv9's ability to deliver high [precision](https://www.ultralytics.com/glossary/precision) while maintaining or reducing the computational overhead compared to prior versions and competing models.
 
 Comparatively, YOLOv9 exhibits remarkable gains:
 
@@ -118,7 +118,7 @@ Comparatively, YOLOv9 exhibits remarkable gains:
 
 The YOLOv9c model, in particular, highlights the effectiveness of the architecture's optimizations. It operates with 42% fewer parameters and 21% less computational demand than YOLOv7 AF, yet it achieves comparable accuracy, demonstrating YOLOv9's significant efficiency improvements. Furthermore, the YOLOv9e model sets a new standard for large models, with 15% fewer parameters and 25% less computational need than [YOLOv8x](yolov8.md), alongside an incremental 1.7% improvement in AP.
 
-These results showcase YOLOv9's strategic advancements in model design, emphasizing its enhanced efficiency without compromising on the precision essential for real-time object detection tasks. The model not only pushes the boundaries of performance metrics but also emphasizes the importance of computational efficiency, making it a pivotal development in the field of computer vision.
+These results showcase YOLOv9's strategic advancements in model design, emphasizing its enhanced efficiency without compromising on the precision essential for real-time object detection tasks. The model not only pushes the boundaries of performance metrics but also emphasizes the importance of computational efficiency, making it a pivotal development in the field of [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv).
 
 ## Conclusion
 
@@ -132,7 +132,7 @@ This example provides simple YOLOv9 training and inference examples. For full do
 
     === "Python"
 
-        PyTorch pretrained `*.pt` models as well as configuration `*.yaml` files can be passed to the `YOLO()` class to create a model instance in python:
+        [PyTorch](https://www.ultralytics.com/glossary/pytorch) pretrained `*.pt` models as well as configuration `*.yaml` files can be passed to the `YOLO()` class to create a model instance in python:
 
         ```python
         from ultralytics import YOLO
@@ -235,4 +235,4 @@ YOLOv9 is designed to mitigate information loss, which is particularly important
 
 ### What tasks and modes does YOLOv9 support?
 
-YOLOv9 supports various tasks including object detection and instance segmentation. It is compatible with multiple operational modes such as inference, validation, training, and export. This versatility makes YOLOv9 adaptable to diverse real-time computer vision applications. Refer to the [supported tasks and modes](#supported-tasks-and-modes) section for more information.
+YOLOv9 supports various tasks including object detection and [instance segmentation](https://www.ultralytics.com/glossary/instance-segmentation). It is compatible with multiple operational modes such as inference, validation, training, and export. This versatility makes YOLOv9 adaptable to diverse real-time computer vision applications. Refer to the [supported tasks and modes](#supported-tasks-and-modes) section for more information.