Update 301 redirects

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -81,6 +81,6 @@ body:
       label: Are you willing to submit a PR?
       description: >
         (Optional) We encourage you to submit a [Pull Request](https://github.com/ultralytics/docs/pulls) to help improve Ultralytics Docs, especially if you know how to fix the issue.
-        See the Ultralytics [Contributing Guide](https://docs.ultralytics.com/help/contributing/) to get started.
+        See the Ultralytics [Contributing Guide](https://docs.ultralytics.com/help/contributing) to get started.
       options:
         - label: Yes I'd like to help by submitting a PR!

.github/ISSUE_TEMPLATE/feature-request.yml

@@ -46,6 +46,6 @@ body:
       label: Are you willing to submit a PR?
       description: >
         (Optional) We encourage you to submit a [Pull Request](https://github.com/ultralytics/docs/pulls) to help improve Ultralytics Docs.
-        See the Ultralytics [Contributing Guide](https://docs.ultralytics.com/help/contributing/) to get started.
+        See the Ultralytics [Contributing Guide](https://docs.ultralytics.com/help/contributing) to get started.
       options:
         - label: Yes I'd like to help by submitting a PR!

README.md

@@ -2,7 +2,7 @@
 
 # 📚 Ultralytics Docs
 
-Welcome to Ultralytics Docs, your comprehensive resource for understanding and utilizing our state-of-the-art [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) tools and models, including [Ultralytics YOLO](https://docs.ultralytics.com/models/yolov8/). These documents are actively maintained and deployed to [https://docs.ultralytics.com](https://docs.ultralytics.com/) for easy access.
+Welcome to Ultralytics Docs, your comprehensive resource for understanding and utilizing our state-of-the-art [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) tools and models, including [Ultralytics YOLO](https://docs.ultralytics.com/models/yolov8). These documents are actively maintained and deployed to [https://docs.ultralytics.com](https://docs.ultralytics.com/) for easy access.
 
 [![Ultralytics Actions](https://github.com/ultralytics/docs/actions/workflows/format.yml/badge.svg)](https://github.com/ultralytics/docs/actions/workflows/format.yml)
 [![jsDelivr hits](https://data.jsdelivr.com/v1/package/gh/ultralytics/llm/badge?style=rounded)](https://www.jsdelivr.com/package/gh/ultralytics/llm)
@@ -121,7 +121,7 @@ To deploy your MkDocs documentation site, choose a hosting provider and configur
 
 ## 💡 Contribute
 
-We deeply value contributions from the open-source community to enhance Ultralytics projects. Your input helps drive innovation in [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) and [AI](https://www.ultralytics.com/glossary/artificial-intelligence-ai)! Please review our [Contributing Guide](https://docs.ultralytics.com/help/contributing/) for detailed information on how to get involved. You can also share your feedback and ideas through our quick [Survey](https://www.ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey). A heartfelt thank you 🙏 to all our contributors for their dedication and support!
+We deeply value contributions from the open-source community to enhance Ultralytics projects. Your input helps drive innovation in [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) and [AI](https://www.ultralytics.com/glossary/artificial-intelligence-ai)! Please review our [Contributing Guide](https://docs.ultralytics.com/help/contributing) for detailed information on how to get involved. You can also share your feedback and ideas through our quick [Survey](https://www.ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey). A heartfelt thank you 🙏 to all our contributors for their dedication and support!
 
 [![Ultralytics open-source contributors](https://raw.githubusercontent.com/ultralytics/assets/main/im/image-contributors.png)](https://github.com/ultralytics/ultralytics/graphs/contributors)
 
@@ -132,11 +132,11 @@ We look forward to your contributions!
 Ultralytics Docs are available under two licensing options to accommodate different usage scenarios:
 
 - **AGPL-3.0 License**: Ideal for students, researchers, and enthusiasts involved in academic pursuits and open collaboration. See the [LICENSE](https://github.com/ultralytics/docs/blob/main/LICENSE) file for full details. This license promotes sharing improvements back with the community, fostering an open and collaborative environment.
-- **Enterprise License**: Designed for commercial applications, this license allows seamless integration of Ultralytics software and [AI models](https://docs.ultralytics.com/models/) into commercial products and services without the open-source requirements of AGPL-3.0. Visit [Ultralytics Licensing](https://www.ultralytics.com/license) for more information on obtaining an Enterprise License.
+- **Enterprise License**: Designed for commercial applications, this license allows seamless integration of Ultralytics software and [AI models](https://docs.ultralytics.com/models) into commercial products and services without the open-source requirements of AGPL-3.0. Visit [Ultralytics Licensing](https://www.ultralytics.com/license) for more information on obtaining an Enterprise License.
 
 ## ✉️ Contact
 
-For bug reports, feature requests, and other issues related to the documentation, please use [GitHub Issues](https://github.com/ultralytics/docs/issues). For discussions, questions, and community support regarding Ultralytics software, the [Ultralytics Platform](https://docs.ultralytics.com/platform/quickstart/), and more, join the conversation with peers and the Ultralytics team on our [Discord server](https://discord.com/invite/ultralytics)!
+For bug reports, feature requests, and other issues related to the documentation, please use [GitHub Issues](https://github.com/ultralytics/docs/issues). For discussions, questions, and community support regarding Ultralytics software, the [Ultralytics Platform](https://docs.ultralytics.com/platform/quickstart), and more, join the conversation with peers and the Ultralytics team on our [Discord server](https://discord.com/invite/ultralytics)!
 
 <br>
 <div align="center">

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

@@ -91,31 +91,31 @@ EfficientDet is recommended for:
 
 ### When to Choose Ultralytics (YOLO26)
 
-For most new projects, [Ultralytics YOLO26](https://docs.ultralytics.com/models/yolo26/) offers the best combination of performance and developer experience:
+For most new projects, [Ultralytics YOLO26](https://docs.ultralytics.com/models/yolo26) offers the best combination of performance and developer experience:
 
 - **NMS-Free Edge Deployment:** Applications requiring consistent, low-latency inference without the complexity of Non-Maximum Suppression post-processing.
 - **CPU-Only Environments:** Devices without dedicated GPU acceleration, where YOLO26's up to 43% faster CPU inference provides a decisive advantage.
-- **Small Object Detection:** Challenging scenarios like [aerial drone imagery](https://docs.ultralytics.com/datasets/detect/visdrone/) or IoT sensor analysis where ProgLoss and STAL significantly boost accuracy on tiny objects.
+- **Small Object Detection:** Challenging scenarios like [aerial drone imagery](https://docs.ultralytics.com/datasets/detect/visdrone) or IoT sensor analysis where ProgLoss and STAL significantly boost accuracy on tiny objects.
 
 ## The Modern Alternative: Ultralytics YOLO26
 
-While both DAMO-YOLO and EfficientDet represent significant academic milestones, real-world deployment often requires a more balanced, feature-rich, and developer-friendly approach. This is where [Ultralytics YOLO26](https://docs.ultralytics.com/models/yolo26/) sets a new industry standard.
+While both DAMO-YOLO and EfficientDet represent significant academic milestones, real-world deployment often requires a more balanced, feature-rich, and developer-friendly approach. This is where [Ultralytics YOLO26](https://docs.ultralytics.com/models/yolo26) sets a new industry standard.
 
-Released in January 2026, YOLO26 builds upon the legacy of its predecessors, including [Ultralytics YOLO11](https://docs.ultralytics.com/models/yolo11/) and [YOLOv8](https://docs.ultralytics.com/models/yolov8/), delivering a paradigm shift in how we approach [object detection](https://docs.ultralytics.com/tasks/detect/).
+Released in January 2026, YOLO26 builds upon the legacy of its predecessors, including [Ultralytics YOLO11](https://docs.ultralytics.com/models/yolo11) and [YOLOv8](https://docs.ultralytics.com/models/yolov8), delivering a paradigm shift in how we approach [object detection](https://docs.ultralytics.com/tasks/detect).
 
 !!! tip "End-to-End Simplicity"
 
     YOLO26 features a native **End-to-End NMS-Free Design**. By eliminating Non-Maximum Suppression (NMS) during post-processing—a bottleneck that has plagued object detectors for years—YOLO26 offers a simpler, vastly faster deployment pipeline, especially on edge hardware.
 
 ### Unmatched Performance and Versatility
 
-YOLO26 does not just improve on speed; it redefines training stability and accuracy. It introduces the **MuSGD Optimizer**, a hybrid of SGD and Muon inspired by LLM training innovations, leading to dramatically faster convergence rates and superior training efficiency. Unlike heavy transformer-based alternatives like [RT-DETR](https://docs.ultralytics.com/models/rtdetr/), YOLO26 maintains incredibly low memory requirements, ensuring it can be trained on consumer-grade hardware.
+YOLO26 does not just improve on speed; it redefines training stability and accuracy. It introduces the **MuSGD Optimizer**, a hybrid of SGD and Muon inspired by LLM training innovations, leading to dramatically faster convergence rates and superior training efficiency. Unlike heavy transformer-based alternatives like [RT-DETR](https://docs.ultralytics.com/models/rtdetr), YOLO26 maintains incredibly low memory requirements, ensuring it can be trained on consumer-grade hardware.
 
 Furthermore, YOLO26 incorporates **ProgLoss + STAL**, heavily improving small-object recognition which is vital for use cases like [drone aerial imagery](https://www.ultralytics.com/blog/12-aerial-imagery-use-cases-powered-by-computer-vision) and robotics. To optimize for low-power devices, YOLO26 removed the Distribution Focal Loss (DFL), resulting in up to **43% faster CPU inference** compared to previous generations.
 
 ### Ecosystem and Ease of Use
 
-One of the largest hurdles with models like EfficientDet is the complex integration process. In contrast, the [Ultralytics Platform](https://platform.ultralytics.com) offers a well-maintained, end-to-end ecosystem. With a unified API, users can easily pivot between detection, [instance segmentation](https://docs.ultralytics.com/tasks/segment/), [pose estimation](https://docs.ultralytics.com/tasks/pose/), [image classification](https://docs.ultralytics.com/tasks/classify/), and [oriented bounding boxes (OBB)](https://docs.ultralytics.com/tasks/obb/).
+One of the largest hurdles with models like EfficientDet is the complex integration process. In contrast, the [Ultralytics Platform](https://platform.ultralytics.com) offers a well-maintained, end-to-end ecosystem. With a unified API, users can easily pivot between detection, [instance segmentation](https://docs.ultralytics.com/tasks/segment), [pose estimation](https://docs.ultralytics.com/tasks/pose), [image classification](https://docs.ultralytics.com/tasks/classify), and [oriented bounding boxes (OBB)](https://docs.ultralytics.com/tasks/obb).
 
 Here is how simple it is to train and run inference with YOLO26 using the Ultralytics Python package:
 
@@ -136,6 +136,6 @@ predictions = model.predict("image.jpg")
 
 ## Conclusion
 
-While exploring [DAMO-YOLO vs EfficientDet](https://docs.ultralytics.com/compare/damo-yolo-vs-efficientdet/) provides excellent insights into the trade-offs between Neural Architecture Search and compound scaling, modern developers require tools that bridge the gap between academic research and production reality.
+While exploring [DAMO-YOLO vs EfficientDet](https://docs.ultralytics.com/compare/damo-yolo-vs-efficientdet) provides excellent insights into the trade-offs between Neural Architecture Search and compound scaling, modern developers require tools that bridge the gap between academic research and production reality.
 
 For developers prioritizing ease of use, an active open-source community, and an uncompromised balance of speed and accuracy, **Ultralytics YOLO26** is the definitive choice. Its NMS-free architecture, low training overhead, and seamless integration with the comprehensive [Ultralytics ecosystem](https://www.ultralytics.com/) make it the ultimate framework for your next computer vision project.

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

@@ -53,7 +53,7 @@ The divergence in design philosophy between these two models heavily influences
 
 ### Feature Fusion and Backbones
 
-DAMO-YOLO's MAE-NAS generated backbones are highly tailored to edge devices, often providing a favorable speed-to-parameter ratio. However, these custom architectures can be rigid and complex to adapt for novel tasks like [instance segmentation](https://docs.ultralytics.com/tasks/segment/). The RepGFPN neck improves multi-scale feature fusion but adds complexity during the re-parameterization export phase.
+DAMO-YOLO's MAE-NAS generated backbones are highly tailored to edge devices, often providing a favorable speed-to-parameter ratio. However, these custom architectures can be rigid and complex to adapt for novel tasks like [instance segmentation](https://docs.ultralytics.com/tasks/segment). The RepGFPN neck improves multi-scale feature fusion but adds complexity during the re-parameterization export phase.
 
 PP-YOLOE+ relies on the more traditional, yet highly effective, CSPRepResNet. While this backbone requires a larger parameter footprint than DAMO-YOLO for similar accuracy, it is highly stable to train and easier to integrate into existing pipelines. Its ET-head efficiently handles classification and regression, but still requires post-processing steps like Non-Maximum Suppression (NMS).
 
@@ -84,7 +84,7 @@ As the table illustrates, DAMO-YOLO generally achieves lower latency on small (s
 
 DAMO-YOLO's reliance on distillation means you often need to train a much larger teacher model before training the smaller student model. This drastically increases the [CUDA memory requirements](https://docs.pytorch.org/docs/stable/notes/cuda.html) and overall computational budget. PP-YOLOE+ simplifies this with standard single-stage training but remains tightly coupled to the PaddlePaddle framework, which may limit flexibility for teams accustomed to PyTorch.
 
-By contrast, the modern [Ultralytics YOLO26](https://platform.ultralytics.com/ultralytics/yolo26) model resolves these bottlenecks. Utilizing the new **MuSGD Optimizer**—a hybrid of SGD and Muon inspired by LLM training innovations—YOLO26 achieves faster convergence and highly stable training without requiring convoluted distillation pipelines. Additionally, YOLO models typically require far less CUDA memory during training compared to transformer-based detectors like [RT-DETR](https://docs.ultralytics.com/models/rtdetr/).
+By contrast, the modern [Ultralytics YOLO26](https://platform.ultralytics.com/ultralytics/yolo26) model resolves these bottlenecks. Utilizing the new **MuSGD Optimizer**—a hybrid of SGD and Muon inspired by LLM training innovations—YOLO26 achieves faster convergence and highly stable training without requiring convoluted distillation pipelines. Additionally, YOLO models typically require far less CUDA memory during training compared to transformer-based detectors like [RT-DETR](https://docs.ultralytics.com/models/rtdetr).
 
 ## Real-World Applications and Ideal Use Cases
 
@@ -104,12 +104,12 @@ When upgrading your computer vision pipeline, **Ultralytics YOLO26** provides an
 
 - **Up to 43% Faster CPU Inference:** With the complete removal of Distribution Focal Loss (DFL), YOLO26 is remarkably fast on edge CPUs and low-power IoT devices.
 - **Improved Small Object Detection:** The integration of ProgLoss and STAL loss functions provides dramatic improvements in small-object recognition, vital for [aerial imagery](https://www.ultralytics.com/blog/12-aerial-imagery-use-cases-powered-by-computer-vision).
-- **Extensive Versatility:** Unlike PP-YOLOE+ which focuses strictly on detection, YOLO26 seamlessly handles [pose estimation](https://docs.ultralytics.com/tasks/pose/), [oriented bounding boxes (OBB)](https://docs.ultralytics.com/tasks/obb/), and semantic segmentation with task-specific architectural improvements.
+- **Extensive Versatility:** Unlike PP-YOLOE+ which focuses strictly on detection, YOLO26 seamlessly handles [pose estimation](https://docs.ultralytics.com/tasks/pose), [oriented bounding boxes (OBB)](https://docs.ultralytics.com/tasks/obb), and semantic segmentation with task-specific architectural improvements.
 
 ## Conclusion
 
 DAMO-YOLO and PP-YOLOE+ represent important milestones in the evolution of anchor-free object detection. DAMO-YOLO pushed the limits of neural architecture search for edge latency, while PP-YOLOE+ demonstrated the power of large-scale pre-training.
 
-However, for developers seeking the best balance of speed, accuracy, and deployment simplicity, the **Ultralytics YOLO26** model is the definitive choice. Its NMS-free architecture, robust Python API, and seamless integration with tools like [Weights & Biases](https://docs.ultralytics.com/integrations/weights-biases/) and [TensorRT](https://docs.ultralytics.com/integrations/tensorrt/) ensure your projects move smoothly from prototype to production.
+However, for developers seeking the best balance of speed, accuracy, and deployment simplicity, the **Ultralytics YOLO26** model is the definitive choice. Its NMS-free architecture, robust Python API, and seamless integration with tools like [Weights & Biases](https://docs.ultralytics.com/integrations/weights-biases) and [TensorRT](https://docs.ultralytics.com/integrations/tensorrt) ensure your projects move smoothly from prototype to production.
 
-Ready to get started? Explore the [Ultralytics Quickstart Guide](https://docs.ultralytics.com/quickstart/) or compare more models in our [YOLO11 vs DAMO-YOLO](https://docs.ultralytics.com/compare/yolo11-vs-damo-yolo/) overview.
+Ready to get started? Explore the [Ultralytics Quickstart Guide](https://docs.ultralytics.com/quickstart) or compare more models in our [YOLO11 vs DAMO-YOLO](https://docs.ultralytics.com/compare/yolo11-vs-damo-yolo) overview.