Add Docs glossary links (#16448)

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

docs/en/models/rtdetr.md

@@ -4,11 +4,11 @@ description: Explore Baidu's RT-DETR, a Vision Transformer-based real-time objec
 keywords: RT-DETR, Baidu, Vision Transformer, real-time object detection, PaddlePaddle, Ultralytics, pre-trained models, AI, machine learning, computer vision
 ---
 
-# Baidu's RT-DETR: A Vision Transformer-Based Real-Time Object Detector
+# Baidu's RT-DETR: A Vision [Transformer](https://www.ultralytics.com/glossary/transformer)-Based Real-Time Object Detector
 
 ## Overview
 
-Real-Time Detection Transformer (RT-DETR), developed by Baidu, is a cutting-edge end-to-end object detector that provides real-time performance while maintaining high accuracy. It is based on the idea of DETR (the NMS-free framework), meanwhile introducing conv-based backbone and an efficient hybrid encoder to gain real-time speed. RT-DETR efficiently processes multiscale features by decoupling intra-scale interaction and cross-scale fusion. The model is highly adaptable, supporting flexible adjustment of inference speed using different decoder layers without retraining. RT-DETR excels on accelerated backends like CUDA with TensorRT, outperforming many other real-time object detectors.
+Real-Time Detection Transformer (RT-DETR), developed by Baidu, is a cutting-edge end-to-end object detector that provides real-time performance while maintaining high [accuracy](https://www.ultralytics.com/glossary/accuracy). It is based on the idea of DETR (the NMS-free framework), meanwhile introducing conv-based backbone and an efficient hybrid encoder to gain real-time speed. RT-DETR efficiently processes multiscale features by decoupling intra-scale interaction and cross-scale fusion. The model is highly adaptable, supporting flexible adjustment of inference speed using different decoder layers without retraining. RT-DETR excels on accelerated backends like CUDA with TensorRT, outperforming many other real-time object detectors.
 
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@@ -25,7 +25,7 @@ Real-Time Detection Transformer (RT-DETR), developed by Baidu, is a cutting-edge
 
 ### Key Features
 
-- **Efficient Hybrid Encoder:** Baidu's RT-DETR uses an efficient hybrid encoder that processes multiscale features by decoupling intra-scale interaction and cross-scale fusion. This unique Vision Transformers-based design reduces computational costs and allows for real-time object detection.
+- **Efficient Hybrid Encoder:** Baidu's RT-DETR uses an efficient hybrid encoder that processes multiscale features by decoupling intra-scale interaction and cross-scale fusion. This unique Vision Transformers-based design reduces computational costs and allows for real-time [object detection](https://www.ultralytics.com/glossary/object-detection).
 - **IoU-aware Query Selection:** Baidu's RT-DETR improves object query initialization by utilizing IoU-aware query selection. This allows the model to focus on the most relevant objects in the scene, enhancing the detection accuracy.
 - **Adaptable Inference Speed:** Baidu's RT-DETR supports flexible adjustments of inference speed by using different decoder layers without the need for retraining. This adaptability facilitates practical application in various real-time object detection scenarios.
 
@@ -98,7 +98,7 @@ If you use Baidu's RT-DETR in your research or development work, please cite the
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-We would like to acknowledge Baidu and the [PaddlePaddle](https://github.com/PaddlePaddle/PaddleDetection) team for creating and maintaining this valuable resource for the computer vision community. Their contribution to the field with the development of the Vision Transformers-based real-time object detector, RT-DETR, is greatly appreciated.
+We would like to acknowledge Baidu and the [PaddlePaddle](https://github.com/PaddlePaddle/PaddleDetection) team for creating and maintaining this valuable resource for the [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) community. Their contribution to the field with the development of the Vision Transformers-based real-time object detector, RT-DETR, is greatly appreciated.
 
 ## FAQ
 
@@ -146,7 +146,7 @@ Baidu's RT-DETR stands out due to its efficient hybrid encoder and IoU-aware que
 
 ### How does RT-DETR support adaptable inference speed for different real-time applications?
 
-Baidu's RT-DETR allows flexible adjustments of inference speed by using different decoder layers without requiring retraining. This adaptability is crucial for scaling performance across various real-time object detection tasks. Whether you need faster processing for lower precision needs or slower, more accurate detections, RT-DETR can be tailored to meet your specific requirements.
+Baidu's RT-DETR allows flexible adjustments of inference speed by using different decoder layers without requiring retraining. This adaptability is crucial for scaling performance across various real-time object detection tasks. Whether you need faster processing for lower [precision](https://www.ultralytics.com/glossary/precision) needs or slower, more accurate detections, RT-DETR can be tailored to meet your specific requirements.
 
 ### Can I use RT-DETR models with other Ultralytics modes, such as training, validation, and export?