Update URLs to redirects (#16048)

docs/en/models/sam.md

@@ -23,7 +23,7 @@ SAM's advanced design allows it to adapt to new image distributions and tasks wi
 - **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.
 
-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).
+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).
 
 ## Available Models, Supported Tasks, and Operating Modes
 

docs/en/models/yolo-world.md

@@ -6,7 +6,7 @@ keywords: YOLO-World, Ultralytics, open-vocabulary detection, YOLOv8, real-time
 
 # YOLO-World Model
 
-The YOLO-World Model introduces an advanced, real-time [Ultralytics](https://ultralytics.com) [YOLOv8](yolov8.md)-based approach for Open-Vocabulary Detection tasks. This innovation enables the detection of any object within an image based on descriptive texts. By significantly lowering computational demands while preserving competitive performance, YOLO-World emerges as a versatile tool for numerous vision-based applications.
+The YOLO-World Model introduces an advanced, real-time [Ultralytics](https://www.ultralytics.com/) [YOLOv8](yolov8.md)-based approach for Open-Vocabulary Detection tasks. This innovation enables the detection of any object within an image based on descriptive texts. By significantly lowering computational demands while preserving competitive performance, YOLO-World emerges as a versatile tool for numerous vision-based applications.
 
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@@ -275,7 +275,7 @@ This approach provides a powerful means of customizing state-of-the-art object d
 | Dataset                                                           | Type      | Samples | Boxes | Annotation Files                                                                                                                           |
 | ----------------------------------------------------------------- | --------- | ------- | ----- | ------------------------------------------------------------------------------------------------------------------------------------------ |
 | [Objects365v1](https://opendatalab.com/OpenDataLab/Objects365_v1) | Detection | 609k    | 9621k | [objects365_train.json](https://opendatalab.com/OpenDataLab/Objects365_v1)                                                                 |
-| [GQA](https://nlp.stanford.edu/data/gqa/images.zip)               | Grounding | 621k    | 3681k | [final_mixed_train_no_coco.json](https://huggingface.co/GLIPModel/GLIP/blob/main/mdetr_annotations/final_mixed_train_no_coco.json)         |
+| [GQA](https://downloads.cs.stanford.edu/nlp/data/gqa/images.zip)  | Grounding | 621k    | 3681k | [final_mixed_train_no_coco.json](https://huggingface.co/GLIPModel/GLIP/blob/main/mdetr_annotations/final_mixed_train_no_coco.json)         |
 | [Flickr30k](https://shannon.cs.illinois.edu/DenotationGraph/)     | Grounding | 149k    | 641k  | [final_flickr_separateGT_train.json](https://huggingface.co/GLIPModel/GLIP/blob/main/mdetr_annotations/final_flickr_separateGT_train.json) |
 
 - Val data

docs/en/models/yolov10.md

@@ -6,7 +6,7 @@ keywords: YOLOv10, real-time object detection, NMS-free, deep learning, Tsinghua
 
 # YOLOv10: Real-Time End-to-End Object Detection
 
-YOLOv10, built on the [Ultralytics](https://ultralytics.com) [Python package](https://pypi.org/project/ultralytics/) by researchers at [Tsinghua University](https://www.tsinghua.edu.cn/en/), introduces a new approach to real-time object detection, addressing both the post-processing and model architecture deficiencies found in previous YOLO versions. By eliminating non-maximum suppression (NMS) and optimizing various model components, YOLOv10 achieves state-of-the-art performance with significantly reduced computational overhead. Extensive experiments demonstrate its superior accuracy-latency trade-offs across multiple model scales.
+YOLOv10, built on the [Ultralytics](https://www.ultralytics.com/) [Python package](https://pypi.org/project/ultralytics/) by researchers at [Tsinghua University](https://www.tsinghua.edu.cn/en/), introduces a new approach to real-time object detection, addressing both the post-processing and model architecture deficiencies found in previous YOLO versions. By eliminating non-maximum suppression (NMS) and optimizing various model components, YOLOv10 achieves state-of-the-art performance with significantly reduced computational overhead. Extensive experiments demonstrate its superior accuracy-latency trade-offs across multiple model scales.
 
 ![YOLOv10 consistent dual assignment for NMS-free training](https://github.com/ultralytics/docs/releases/download/0/yolov10-consistent-dual-assignment.avif)
 
@@ -223,7 +223,7 @@ YOLOv10 sets a new standard in real-time object detection by addressing the shor
 
 ## Citations and Acknowledgements
 
-We would like to acknowledge the YOLOv10 authors from [Tsinghua University](https://www.tsinghua.edu.cn/en/) for their extensive research and significant contributions to the [Ultralytics](https://ultralytics.com) framework:
+We would like to acknowledge the YOLOv10 authors from [Tsinghua University](https://www.tsinghua.edu.cn/en/) for their extensive research and significant contributions to the [Ultralytics](https://www.ultralytics.com/) framework:
 
 !!! Quote ""
 

docs/en/models/yolov5.md

@@ -111,7 +111,7 @@ If you use YOLOv5 or YOLOv5u in your research, please cite the Ultralytics YOLOv
         }
         ```
 
-Please note that YOLOv5 models are provided under [AGPL-3.0](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) and [Enterprise](https://ultralytics.com/license) licenses.
+Please note that YOLOv5 models are provided under [AGPL-3.0](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) and [Enterprise](https://www.ultralytics.com/license) licenses.
 
 ## FAQ
 

docs/en/models/yolov8.md

@@ -183,7 +183,7 @@ If you use the YOLOv8 model or any other software from this repository in your w
         }
         ```
 
-Please note that the DOI is pending and will be added to the citation once it is available. YOLOv8 models are provided under [AGPL-3.0](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) and [Enterprise](https://ultralytics.com/license) licenses.
+Please note that the DOI is pending and will be added to the citation once it is available. YOLOv8 models are provided under [AGPL-3.0](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) and [Enterprise](https://www.ultralytics.com/license) licenses.
 
 ## FAQ
 

docs/en/models/yolov9.md

@@ -6,7 +6,7 @@ keywords: YOLOv9, object detection, real-time, PGI, GELAN, deep learning, MS COC
 
 # YOLOv9: A Leap Forward in 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://ultralytics.com) [YOLOv5](yolov5.md), showcasing the collaborative spirit of the AI research community.
+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.
 
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