Update README.md with YOLO11 section (#2296)

* Update README.md

* Remove Hindi links

* Auto-format by https://ultralytics.com/actions

* Update YOLO11

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Co-authored-by: UltralyticsAssistant <[email protected]>

README.md

@@ -42,11 +42,11 @@ To request an Enterprise License please complete the form at [Ultralytics Licens
 </div>
 <br>
 
-## <div align="center">YOLOv8 🚀 NEW</div>
+## <div align="center">YOLO11 🚀 NEW</div>
 
-We are thrilled to announce the launch of Ultralytics YOLOv8 🚀, our NEW cutting-edge, state-of-the-art (SOTA) model released at **[https://github.com/ultralytics/ultralytics](https://github.com/ultralytics/ultralytics)**. YOLOv8 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of object detection, image segmentation and image classification tasks.
+We are excited to unveil the launch of Ultralytics YOLO11 🚀, the latest advancement in our state-of-the-art (SOTA) vision models! Available now at **[GitHub](https://github.com/ultralytics/ultralytics)**, YOLO11 builds on our legacy of speed, precision, and ease of use. Whether you're tackling object detection, image segmentation, or image classification, YOLO11 delivers the performance and versatility needed to excel in diverse applications.
 
-See the [YOLOv8 Docs](https://docs.ultralytics.com/) for details and get started with:
+Get started today and unlock the full potential of YOLO11! Visit the [Ultralytics Docs](https://docs.ultralytics.com/) for comprehensive guides and resources:
 
 [![PyPI version](https://badge.fury.io/py/ultralytics.svg)](https://badge.fury.io/py/ultralytics) [![Downloads](https://static.pepy.tech/badge/ultralytics)](https://pepy.tech/project/ultralytics)
 
@@ -56,7 +56,7 @@ pip install ultralytics
 
 <div align="center">
   <a href="https://www.ultralytics.com/yolo" target="_blank">
-  <img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/yolo-comparison-plots.png"></a>
+  <img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/refs/heads/main/yolo/performance-comparison.png"></a>
 </div>
 
 ## <div align="center">Documentation</div>
@@ -151,9 +151,9 @@ python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml  -
 - [Hyperparameter Evolution](https://docs.ultralytics.com/yolov5/tutorials/hyperparameter_evolution/)
 - [Transfer Learning with Frozen Layers](https://docs.ultralytics.com/yolov5/tutorials/transfer_learning_with_frozen_layers/)
 - [Architecture Summary](https://docs.ultralytics.com/yolov5/tutorials/architecture_description/) 🌟 NEW
-- [Roboflow for Datasets, Labeling, and Active Learning](https://docs.ultralytics.com/yolov5/tutorials/roboflow_datasets_integration/)
-- [ClearML Logging](https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration/) 🌟 NEW
-- [YOLOv5 with Neural Magic's Deepsparse](https://docs.ultralytics.com/yolov5/tutorials/neural_magic_pruning_quantization/) 🌟 NEW
+- [Ultralytics HUB to train and deploy YOLO](https://www.ultralytics.com/hub) 🚀 RECOMMENDED
+- [ClearML Logging](https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration/)
+- [YOLOv5 with Neural Magic's Deepsparse](https://docs.ultralytics.com/yolov5/tutorials/neural_magic_pruning_quantization/)
 - [Comet Logging](https://docs.ultralytics.com/yolov5/tutorials/comet_logging_integration/) 🌟 NEW
 
 </details>

README.zh-CN.md

@@ -42,21 +42,21 @@ YOLOv3 🚀 是世界上最受欢迎的视觉 AI，代表<a href="https://www.ul
 </div>
 </div>
 
-## <div align="center">YOLOv8 🚀 新品</div>
+## <div align="center">YOLO11 🚀 全新发布</div>
 
-我们很高兴宣布 Ultralytics YOLOv8 🚀 的发布，这是我们新推出的领先水平、最先进的（SOTA）模型，发布于 **[https://github.com/ultralytics/ultralytics](https://github.com/ultralytics/ultralytics)**。 YOLOv8 旨在快速、准确且易于使用，使其成为广泛的物体检测、图像分割和图像分类任务的极佳选择。
+我们很高兴宣布推出 Ultralytics YOLO11 🚀，这是我们最先进视觉模型的最新进展！现已在 **[GitHub](https://github.com/ultralytics/ultralytics)** 上发布。YOLO11 在速度、精度和易用性方面进一步提升，无论是处理目标检测、图像分割还是图像分类任务，YOLO11 都具备出色的性能和多功能性，助您在各种应用中脱颖而出。
 
-请查看 [YOLOv8 文档](https://docs.ultralytics.com/)了解详细信息，并开始使用：
+立即开始，解锁 YOLO11 的全部潜力！访问 [Ultralytics 文档](https://docs.ultralytics.com/) 获取全面的指南和资源：
 
 [![PyPI 版本](https://badge.fury.io/py/ultralytics.svg)](https://badge.fury.io/py/ultralytics) [![下载量](https://static.pepy.tech/badge/ultralytics)](https://pepy.tech/project/ultralytics)
 
-```commandline
+```bash
 pip install ultralytics
 ```
 
 <div align="center">
   <a href="https://www.ultralytics.com/yolo" target="_blank">
-  <img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/yolo-comparison-plots.png"></a>
+  <img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/refs/heads/main/yolo/performance-comparison.png"></a>
 </div>
 
 ## <div align="center">文档</div>
@@ -139,22 +139,22 @@ python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml  -
 <details open>
 <summary>教程</summary>
 
-- [训练自定义数据](https://docs.ultralytics.com/yolov5/tutorials/train_custom_data/) 🚀 推荐
-- [获得最佳训练结果的技巧](https://docs.ultralytics.com/guides/model-training-tips/) ☘️
+- [自定义数据训练](https://docs.ultralytics.com/yolov5/tutorials/train_custom_data/) 🚀 **推荐**
+- [最佳训练效果的提示](https://docs.ultralytics.com/guides/model-training-tips/) ☘️
 - [多GPU训练](https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training/)
-- [PyTorch Hub](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading/) 🌟 新
-- [TFLite，ONNX，CoreML，TensorRT导出](https://docs.ultralytics.com/yolov5/tutorials/model_export/) 🚀
-- [NVIDIA Jetson平台部署](https://docs.ultralytics.com/yolov5/tutorials/running_on_jetson_nano/) 🌟 新
+- [PyTorch Hub](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading/) 🌟 **全新**
+- [TFLite, ONNX, CoreML, TensorRT 导出](https://docs.ultralytics.com/yolov5/tutorials/model_export/) 🚀
+- [NVIDIA Jetson 平台部署](https://docs.ultralytics.com/yolov5/tutorials/running_on_jetson_nano/) 🌟 **全新**
 - [测试时增强 (TTA)](https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation/)
 - [模型集成](https://docs.ultralytics.com/yolov5/tutorials/model_ensembling/)
-- [模型剪枝/稀疏](https://docs.ultralytics.com/yolov5/tutorials/model_pruning_and_sparsity/)
+- [模型剪枝/稀疏化](https://docs.ultralytics.com/yolov5/tutorials/model_pruning_and_sparsity/)
 - [超参数进化](https://docs.ultralytics.com/yolov5/tutorials/hyperparameter_evolution/)
 - [冻结层的迁移学习](https://docs.ultralytics.com/yolov5/tutorials/transfer_learning_with_frozen_layers/)
-- [架构概述](https://docs.ultralytics.com/yolov5/tutorials/architecture_description/) 🌟 新
-- [Roboflow用于数据集、标注和主动学习](https://docs.ultralytics.com/yolov5/tutorials/roboflow_datasets_integration/)
-- [ClearML日志记录](https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration/) 🌟 新
-- [使用Neural Magic的Deepsparse的YOLOv5](https://docs.ultralytics.com/yolov5/tutorials/neural_magic_pruning_quantization/) 🌟 新
-- [Comet日志记录](https://docs.ultralytics.com/yolov5/tutorials/comet_logging_integration/) 🌟 新
+- [架构概述](https://docs.ultralytics.com/yolov5/tutorials/architecture_description/) 🌟 **全新**
+- [使用 Ultralytics HUB 进行 YOLO 训练和部署](https://www.ultralytics.com/hub) 🚀 **推荐**
+- [ClearML 日志记录](https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration/)
+- [与 Neural Magic 的 Deepsparse 集成的 YOLOv5](https://docs.ultralytics.com/yolov5/tutorials/neural_magic_pruning_quantization/)
+- [Comet 日志记录](https://docs.ultralytics.com/yolov5/tutorials/comet_logging_integration/) 🌟 **全新**
 
 </details>