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docs/en/compare/damo-yolo-vs-efficientdet.md

@@ -104,4 +104,4 @@ EfficientDet is well-suited for:
 
 Choosing between DAMO-YOLO and EfficientDet depends heavily on the specific application requirements. If real-time performance and speed are paramount, DAMO-YOLO is a strong contender. For applications prioritizing higher accuracy and efficiency in parameter usage, EfficientDet offers a robust solution.
 
-Users interested in other high-performance object detection models from Ultralytics may also consider exploring [YOLOv8](https://docs.ultralytics.com/models/yolov8/) and the latest [YOLOv11](https://docs.ultralytics.com/models/yolo11/), which offer state-of-the-art performance and a range of features for various computer vision tasks. Furthermore, for real-time applications, models like [RT-DETR](https://docs.ultralytics.com/models/rtdetr/) and [YOLO-NAS](https://docs.ultralytics.com/models/yolo-nas/) provide alternative architectures optimized for speed and efficiency.
+Users interested in other high-performance object detection models from Ultralytics may also consider exploring [YOLOv8](https://docs.ultralytics.com/models/yolov8/) and the latest [YOLOv11](https://docs.ultralytics.com/models/yolo11/), which offer state-of-the-art performance and a range of features for various computer vision tasks. Furthermore, for real-time applications, models like [RT-DETR](https://docs.ultralytics.com/models/rtdetr/) and [YOLO-NAS](https://docs.ultralytics.com/models/yolo-nas/) provide alternative architectures optimized for speed and efficiency.

docs/en/compare/damo-yolo-vs-pp-yoloe.md

@@ -83,4 +83,4 @@ PP-YOLOE+ is part of the PaddlePaddle YOLO series, emphasizing high accuracy and
 
 Consider exploring other models in the Ultralytics YOLO family such as [YOLOv8](https://docs.ultralytics.com/models/yolov8/), [YOLOv9](https://docs.ultralytics.com/models/yolov9/), and [YOLOv10](https://docs.ultralytics.com/models/yolov10/) for a broader range of options tailored to different needs. You might also be interested in models like [YOLO-NAS](https://docs.ultralytics.com/models/yolo-nas/), [RT-DETR](https://docs.ultralytics.com/models/rtdetr/), [FastSAM](https://docs.ultralytics.com/models/fast-sam/), [MobileSAM](https://docs.ultralytics.com/models/mobile-sam/), [SAM](https://docs.ultralytics.com/models/sam/), [SAM 2](https://docs.ultralytics.com/models/sam-2/) and [YOLO-World](https://docs.ultralytics.com/models/yolo-world/) depending on your specific requirements for speed, accuracy, and task.
 
-Ultimately, the best choice depends on the specific trade-offs you are willing to make between speed, accuracy, and resource utilization for your particular use case.
+Ultimately, the best choice depends on the specific trade-offs you are willing to make between speed, accuracy, and resource utilization for your particular use case.

docs/en/compare/damo-yolo-vs-rtdetr.md

@@ -97,4 +97,4 @@ Users interested in DAMO-YOLO and RTDETRv2 might also find other Ultralytics mod
 - [YOLOv10](https://docs.ultralytics.com/models/yolov10/): The latest iteration in the YOLO series, focusing on efficiency and real-time performance.
 - [YOLO-NAS](https://docs.ultralytics.com/models/yolo-nas/): A model designed through Neural Architecture Search (NAS) to optimize performance.
 
-Choosing between DAMO-YOLO and RTDETRv2, or other models, depends heavily on the specific requirements of your project. Consider the trade-offs between speed, accuracy, and computational resources to select the most appropriate model for your needs.
+Choosing between DAMO-YOLO and RTDETRv2, or other models, depends heavily on the specific requirements of your project. Consider the trade-offs between speed, accuracy, and computational resources to select the most appropriate model for your needs.

docs/en/compare/damo-yolo-vs-yolo11.md

@@ -108,4 +108,4 @@ By considering these factors and exploring the performance metrics, users can ch
 | YOLO11s    | 640                   | 47.0                 | 90.0                           | 2.5                                 | 9.4                | 21.5              |
 | YOLO11m    | 640                   | 51.5                 | 183.2                          | 4.7                                 | 20.1               | 68.0              |
 | YOLO11l    | 640                   | 53.4                 | 238.6                          | 6.2                                 | 25.3               | 86.9              |
-| YOLO11x    | 640                   | 54.7                 | 462.8                          | 11.3                                | 56.9               | 194.9             |
+| YOLO11x    | 640                   | 54.7                 | 462.8                          | 11.3                                | 56.9               | 194.9             |

docs/en/compare/damo-yolo-vs-yolov10.md

@@ -87,4 +87,4 @@ This table illustrates a trade-off: YOLOv10 models generally offer faster infere
 
 Choosing between DAMO-YOLO and YOLOv10 depends heavily on the specific application needs. If accuracy is paramount and computational resources are not strictly limited, DAMO-YOLO presents a robust option. Conversely, for real-time, efficient applications, YOLOv10 offers a compelling balance of speed and accuracy, making it an excellent choice for a wide range of practical deployments.
 
-Users interested in exploring other models might also consider [YOLO-NAS](https://docs.ultralytics.com/models/yolo-nas/) or [RT-DETR](https://docs.ultralytics.com/models/rtdetr/), each offering unique strengths in different aspects of object detection. For further exploration of Ultralytics models and capabilities, refer to the [Ultralytics documentation](https://docs.ultralytics.com/models/) and [guides](https://docs.ultralytics.com/guides/).
+Users interested in exploring other models might also consider [YOLO-NAS](https://docs.ultralytics.com/models/yolo-nas/) or [RT-DETR](https://docs.ultralytics.com/models/rtdetr/), each offering unique strengths in different aspects of object detection. For further exploration of Ultralytics models and capabilities, refer to the [Ultralytics documentation](https://docs.ultralytics.com/models/) and [guides](https://docs.ultralytics.com/guides/).

docs/en/compare/damo-yolo-vs-yolov5.md

@@ -82,4 +82,4 @@ Both DAMO-YOLO and YOLOv5 are powerful object detection models, each with unique
 - **Choose DAMO-YOLO if**: Your primary requirement is maximizing detection accuracy with good speed, and you are working in environments where customization is less critical than top-tier performance.
 - **Choose YOLOv5 if**: You need a versatile, easy-to-use model with strong community support, adaptable to various hardware constraints and application types, and where rapid development and deployment are key.
 
-Consider exploring other models within the Ultralytics ecosystem, such as Ultralytics YOLOv8 and Ultralytics YOLO11, for potentially different performance characteristics and features tailored to specific needs. For instance, YOLOv8 represents a significant advancement in the YOLO series, offering improvements in speed and accuracy, while YOLO11 pushes the boundaries further with innovative architectural changes and enhanced performance metrics, as highlighted in the [Ultralytics YOLO11 Has Arrived! Redefine What's Possible in AI!](https://www.ultralytics.com/blog/ultralytics-yolo11-has-arrived-redefine-whats-possible-in-ai) blog post. You can also find more information on model selection in the [Ultralytics YOLO Docs](https://docs.ultralytics.com/guides/).
+Consider exploring other models within the Ultralytics ecosystem, such as Ultralytics YOLOv8 and Ultralytics YOLO11, for potentially different performance characteristics and features tailored to specific needs. For instance, YOLOv8 represents a significant advancement in the YOLO series, offering improvements in speed and accuracy, while YOLO11 pushes the boundaries further with innovative architectural changes and enhanced performance metrics, as highlighted in the [Ultralytics YOLO11 Has Arrived! Redefine What's Possible in AI!](https://www.ultralytics.com/blog/ultralytics-yolo11-has-arrived-redefine-whats-possible-in-ai) blog post. You can also find more information on model selection in the [Ultralytics YOLO Docs](https://docs.ultralytics.com/guides/).

docs/en/compare/damo-yolo-vs-yolov6.md

@@ -79,4 +79,4 @@ YOLOv6 is developed by Meituan and is engineered for industrial applications, em
 
 Both DAMO-YOLO and YOLOv6-3.0 are powerful object detection models, each with unique strengths. DAMO-YOLO excels in accuracy and scalability, while YOLOv6-3.0 prioritizes industrial applicability and balanced performance. Your choice between these models should depend on the specific requirements of your project, considering factors like desired accuracy, speed constraints, and deployment environment.
 
-For users within the Ultralytics ecosystem, exploring models like [YOLOv8](https://docs.ultralytics.com/models/yolov8/) and the latest [YOLOv11](https://docs.ultralytics.com/models/yolo11/) could also be beneficial, as they offer state-of-the-art performance and extensive documentation and community support within Ultralytics [Guides](https://docs.ultralytics.com/guides/). Consider also exploring [YOLO-NAS](https://docs.ultralytics.com/models/yolo-nas/) and [RT-DETR](https://docs.ultralytics.com/models/rtdetr/) for other architectural approaches to object detection.
+For users within the Ultralytics ecosystem, exploring models like [YOLOv8](https://docs.ultralytics.com/models/yolov8/) and the latest [YOLOv11](https://docs.ultralytics.com/models/yolo11/) could also be beneficial, as they offer state-of-the-art performance and extensive documentation and community support within Ultralytics [Guides](https://docs.ultralytics.com/guides/). Consider also exploring [YOLO-NAS](https://docs.ultralytics.com/models/yolo-nas/) and [RT-DETR](https://docs.ultralytics.com/models/rtdetr/) for other architectural approaches to object detection.

docs/en/compare/damo-yolo-vs-yolov7.md

@@ -113,4 +113,4 @@ Both DAMO-YOLO and YOLOv7 are powerful object detection models, each with unique
 
 For users prioritizing high accuracy in detecting small objects and intricate details, especially in high-resolution images, DAMO-YOLO is a compelling choice. For those needing a robust, real-time object detector with a proven track record and extensive resources, YOLOv7 remains an excellent option.
 
-Consider exploring other models in the Ultralytics YOLO family such as [YOLOv8](https://www.ultralytics.com/yolo), [YOLOv9](https://docs.ultralytics.com/models/yolov9/), [YOLOv10](https://docs.ultralytics.com/models/yolov10/), [YOLO-NAS](https://docs.ultralytics.com/models/yolo-nas/), [YOLO-World](https://docs.ultralytics.com/models/yolo-world/) and the latest [YOLO11](https://docs.ultralytics.com/models/yolo11/) for potentially better or different performance characteristics depending on your specific use case.
+Consider exploring other models in the Ultralytics YOLO family such as [YOLOv8](https://www.ultralytics.com/yolo), [YOLOv9](https://docs.ultralytics.com/models/yolov9/), [YOLOv10](https://docs.ultralytics.com/models/yolov10/), [YOLO-NAS](https://docs.ultralytics.com/models/yolo-nas/), [YOLO-World](https://docs.ultralytics.com/models/yolo-world/) and the latest [YOLO11](https://docs.ultralytics.com/models/yolo11/) for potentially better or different performance characteristics depending on your specific use case.

docs/en/compare/damo-yolo-vs-yolov8.md

@@ -81,4 +81,4 @@ Users interested in exploring other models within the Ultralytics ecosystem migh
 - **YOLO-NAS**: A model from Deci AI, known for its Neural Architecture Search optimization and quantization support. [YOLO-NAS docs](https://docs.ultralytics.com/models/yolo-nas/).
 - **RT-DETR**: A real-time object detector based on Vision Transformers, offering an alternative architecture. [RT-DETR docs](https://docs.ultralytics.com/models/rtdetr/).
 
-Explore the full range of [Ultralytics models](https://docs.ultralytics.com/models/) to find the best fit for your computer vision needs.
+Explore the full range of [Ultralytics models](https://docs.ultralytics.com/models/) to find the best fit for your computer vision needs.

docs/en/compare/damo-yolo-vs-yolov9.md

@@ -84,4 +84,4 @@ Users interested in DAMO-YOLO and YOLOv9 might also find other Ultralytics YOLO
 - **RT-DETR**: For real-time performance with a transformer-based architecture, consider [RT-DETR models](https://docs.ultralytics.com/models/rtdetr/).
 - **YOLO-NAS**: If you require models optimized through Neural Architecture Search, [YOLO-NAS](https://docs.ultralytics.com/models/yolo-nas/) is a strong option.
 
-Choosing between DAMO-YOLO and YOLOv9 (or other YOLO models) depends on the specific requirements of your application. If raw speed and efficiency are paramount, DAMO-YOLO could be an excellent choice. For projects where top-tier accuracy is the priority, YOLOv9's advanced architecture and performance metrics make it a leading contender. Always consider testing models on your specific data and hardware to determine the optimal solution.
+Choosing between DAMO-YOLO and YOLOv9 (or other YOLO models) depends on the specific requirements of your application. If raw speed and efficiency are paramount, DAMO-YOLO could be an excellent choice. For projects where top-tier accuracy is the priority, YOLOv9's advanced architecture and performance metrics make it a leading contender. Always consider testing models on your specific data and hardware to determine the optimal solution.

docs/en/compare/damo-yolo-vs-yolox.md

@@ -87,4 +87,4 @@ The table below summarizes the performance metrics for various sizes of DAMO-YOL
 
 Both DAMO-YOLO and YOLOX are powerful object detection models, each with its strengths. DAMO-YOLO excels in achieving high accuracy and efficient GPU inference, making it ideal for demanding applications where computational resources are available. YOLOX, with its anchor-free design and wide range of model sizes, offers versatility and adaptability, particularly for edge deployment and applications requiring a balance of speed and accuracy across diverse platforms.
 
-For users seeking alternative models, Ultralytics also offers a range of YOLO models including [YOLOv8](https://docs.ultralytics.com/models/yolov8/), [YOLOv10](https://docs.ultralytics.com/models/yolov10/), [YOLO-NAS](https://docs.ultralytics.com/models/yolo-nas/), and [RT-DETR](https://docs.ultralytics.com/models/rtdetr/), each with unique architectural and performance characteristics catering to different computer vision needs. You can explore more models in the [Ultralytics documentation](https://docs.ultralytics.com/models/).
+For users seeking alternative models, Ultralytics also offers a range of YOLO models including [YOLOv8](https://docs.ultralytics.com/models/yolov8/), [YOLOv10](https://docs.ultralytics.com/models/yolov10/), [YOLO-NAS](https://docs.ultralytics.com/models/yolo-nas/), and [RT-DETR](https://docs.ultralytics.com/models/rtdetr/), each with unique architectural and performance characteristics catering to different computer vision needs. You can explore more models in the [Ultralytics documentation](https://docs.ultralytics.com/models/).

docs/en/compare/efficientdet-vs-damo-yolo.md

@@ -91,4 +91,4 @@ For users interested in exploring other high-performance object detection models
 
 [Learn more about EfficientDet](https://github.com/google/automl/tree/master/efficientdet){ .md-button }
 
-[Learn more about DAMO-YOLO](https://github.com/tinyvision/DAMO-YOLO){ .md-button }
+[Learn more about DAMO-YOLO](https://github.com/tinyvision/DAMO-YOLO){ .md-button }

docs/en/compare/efficientdet-vs-pp-yoloe.md

@@ -83,4 +83,4 @@ PP-YOLOE+ (Pretty and Powerful You Only Look Once Enhanced Plus) is part of the
 
 Choosing between EfficientDet and PP-YOLOE+ depends largely on the specific requirements of your application. If efficiency and scalability for deployment on less powerful hardware are key, EfficientDet is a strong contender. Its range of model sizes allows for fine-tuning the balance between accuracy and resource usage. On the other hand, if top accuracy and speed are paramount and computational resources are less of a constraint, PP-YOLOE+ offers state-of-the-art performance, especially with its larger variants.
 
-For users interested in other high-performance object detection models, Ultralytics offers a range of [YOLO models](https://docs.ultralytics.com/models/) including [YOLOv8](https://docs.ultralytics.com/models/yolov8/) and the latest [YOLOv11](https://docs.ultralytics.com/models/yolo11/). These models are designed for speed and accuracy, and can be easily trained and deployed using the [Ultralytics HUB](https://www.ultralytics.com/hub). You may also find models like [YOLO-NAS](https://docs.ultralytics.com/models/yolo-nas/) and [RT-DETR](https://docs.ultralytics.com/models/rtdetr/) relevant depending on your needs. Understanding the nuances of each model's architecture and performance metrics like [mAP](https://www.ultralytics.com/glossary/mean-average-precision-map) and [inference speed](https://www.ultralytics.com/glossary/inference-latency) is crucial for making the optimal choice for your [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) project.
+For users interested in other high-performance object detection models, Ultralytics offers a range of [YOLO models](https://docs.ultralytics.com/models/) including [YOLOv8](https://docs.ultralytics.com/models/yolov8/) and the latest [YOLOv11](https://docs.ultralytics.com/models/yolo11/). These models are designed for speed and accuracy, and can be easily trained and deployed using the [Ultralytics HUB](https://www.ultralytics.com/hub). You may also find models like [YOLO-NAS](https://docs.ultralytics.com/models/yolo-nas/) and [RT-DETR](https://docs.ultralytics.com/models/rtdetr/) relevant depending on your needs. Understanding the nuances of each model's architecture and performance metrics like [mAP](https://www.ultralytics.com/glossary/mean-average-precision-map) and [inference speed](https://www.ultralytics.com/glossary/inference-latency) is crucial for making the optimal choice for your [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) project.

docs/en/compare/efficientdet-vs-rtdetr.md

@@ -76,4 +76,4 @@ This table summarizes the performance metrics of different EfficientDet and RTDE
 - **EfficientDet:** Ideal for applications requiring efficient object detection on devices with limited computational resources such as mobile applications, drones, and embedded systems. It's also suitable for scenarios where a balance of accuracy and speed is needed without demanding top-tier performance.
 - **RTDETRv2:** Best suited for real-time object detection tasks where low latency and high accuracy are critical, such as autonomous driving, high-speed video analysis, and advanced surveillance systems. Its transformer-based architecture makes it effective in complex scenarios needing global context understanding.
 
-For users within the Ultralytics ecosystem, exploring [YOLOv8](https://docs.ultralytics.com/models/yolov8/) or the latest [YOLO11](https://docs.ultralytics.com/models/yolo11/) models might offer a balance of performance and ease of integration, with comprehensive [documentation](https://docs.ultralytics.com/guides/) and support available. Consider also exploring other models like [YOLOv7](https://docs.ultralytics.com/models/yolov7/) and [YOLOv9](https://docs.ultralytics.com/models/yolov9/) for different performance characteristics.
+For users within the Ultralytics ecosystem, exploring [YOLOv8](https://docs.ultralytics.com/models/yolov8/) or the latest [YOLO11](https://docs.ultralytics.com/models/yolo11/) models might offer a balance of performance and ease of integration, with comprehensive [documentation](https://docs.ultralytics.com/guides/) and support available. Consider also exploring other models like [YOLOv7](https://docs.ultralytics.com/models/yolov7/) and [YOLOv9](https://docs.ultralytics.com/models/yolov9/) for different performance characteristics.