Fix Chinese README (ultralytics#1965)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

README.md

@@ -18,11 +18,7 @@
   </div>
   <br>
 
-[Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics), developed by [Ultralytics](https://ultralytics.com),
-is a cutting-edge, state-of-the-art (SOTA) model that builds upon the success of previous YOLO versions and introduces
-new features and improvements to further boost performance and flexibility. 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.
+[Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics), developed by [Ultralytics](https://ultralytics.com), is a cutting-edge, state-of-the-art (SOTA) model that builds upon the success of previous YOLO versions and introduces new features and improvements to further boost performance and flexibility. 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.
 
 To request an Enterprise License please complete the form at [Ultralytics Licensing](https://ultralytics.com/license).
 
@@ -51,16 +47,12 @@ To request an Enterprise License please complete the form at [Ultralytics Licens
 
 ## <div align="center">Documentation</div>
 
-See below for a quickstart installation and usage example, and see the [YOLOv8 Docs](https://docs.ultralytics.com) for
-full documentation on training, validation, prediction and deployment.
+See below for a quickstart installation and usage example, and see the [YOLOv8 Docs](https://docs.ultralytics.com) for full documentation on training, validation, prediction and deployment.
 
 <details open>
 <summary>Install</summary>
 
-Pip install the ultralytics package including
-all [requirements](https://github.com/ultralytics/ultralytics/blob/main/requirements.txt) in a
-[**Python>=3.7**](https://www.python.org/) environment with
-[**PyTorch>=1.7**](https://pytorch.org/get-started/locally/).
+Pip install the ultralytics package including all [requirements](https://github.com/ultralytics/ultralytics/blob/main/requirements.txt) in a [**Python>=3.7**](https://www.python.org/) environment with [**PyTorch>=1.7**](https://pytorch.org/get-started/locally/).
 
 ```bash
 pip install ultralytics
@@ -79,13 +71,11 @@ YOLOv8 may be used directly in the Command Line Interface (CLI) with a `yolo` co
 yolo predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg'
 ```
 
-`yolo` can be used for a variety of tasks and modes and accepts additional arguments, i.e. `imgsz=640`. See the YOLOv8
-[CLI Docs](https://docs.ultralytics.com/usage/cli) for examples.
+`yolo` can be used for a variety of tasks and modes and accepts additional arguments, i.e. `imgsz=640`. See the YOLOv8 [CLI Docs](https://docs.ultralytics.com/usage/cli) for examples.
 
 #### Python
 
-YOLOv8 may also be used directly in a Python environment, and accepts the
-same [arguments](https://docs.ultralytics.com/usage/cfg/) as in the CLI example above:
+YOLOv8 may also be used directly in a Python environment, and accepts the same [arguments](https://docs.ultralytics.com/usage/cfg/) as in the CLI example above:
 
 ```python
 from ultralytics import YOLO
@@ -101,21 +91,15 @@ results = model("https://ultralytics.com/images/bus.jpg")  # predict on an image
 success = model.export(format="onnx")  # export the model to ONNX format
 ```
 
-[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models) download automatically from the latest
-Ultralytics [release](https://github.com/ultralytics/assets/releases). See
-YOLOv8 [Python Docs](https://docs.ultralytics.com/usage/python) for more examples.
+[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models) download automatically from the latest Ultralytics [release](https://github.com/ultralytics/assets/releases). See YOLOv8 [Python Docs](https://docs.ultralytics.com/usage/python) for more examples.
 
 </details>
 
 ## <div align="center">Models</div>
 
-All YOLOv8 pretrained models are available here. Detect, Segment and Pose models are pretrained on
-the [COCO](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/coco.yaml) dataset, while Classify
-models are pretrained on
-the [ImageNet](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/ImageNet.yaml) dataset.
+All YOLOv8 pretrained models are available here. Detect, Segment and Pose models are pretrained on the [COCO](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/coco.yaml) dataset, while Classify models are pretrained on the [ImageNet](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/ImageNet.yaml) dataset.
 
-[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models) download automatically from the latest
-Ultralytics [release](https://github.com/ultralytics/assets/releases) on first use.
+[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models) download automatically from the latest Ultralytics [release](https://github.com/ultralytics/assets/releases) on first use.
 
 <details open><summary>Detection</summary>
 
@@ -131,8 +115,7 @@ See [Detection Docs](https://docs.ultralytics.com/tasks/detect/) for usage examp
 
 - **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.
   <br>Reproduce by `yolo val detect data=coco.yaml device=0`
-- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/)
-  instance.
+- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance.
   <br>Reproduce by `yolo val detect data=coco128.yaml batch=1 device=0|cpu`
 
 </details>
@@ -151,8 +134,7 @@ See [Segmentation Docs](https://docs.ultralytics.com/tasks/segment/) for usage e
 
 - **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.
   <br>Reproduce by `yolo val segment data=coco.yaml device=0`
-- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/)
-  instance.
+- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance.
   <br>Reproduce by `yolo val segment data=coco128-seg.yaml batch=1 device=0|cpu`
 
 </details>
@@ -171,15 +153,14 @@ See [Classification Docs](https://docs.ultralytics.com/tasks/classify/) for usag
 
 - **acc** values are model accuracies on the [ImageNet](https://www.image-net.org/) dataset validation set.
   <br>Reproduce by `yolo val classify data=path/to/ImageNet device=0`
-- **Speed** averaged over ImageNet val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/)
-  instance.
+- **Speed** averaged over ImageNet val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance.
   <br>Reproduce by `yolo val classify data=path/to/ImageNet batch=1 device=0|cpu`
 
 </details>
 
 <details><summary>Pose</summary>
 
-See [Pose Docs](https://docs.ultralytics.com/tasks/) for usage examples with these models.
+See [Pose Docs](https://docs.ultralytics.com/tasks/pose) for usage examples with these models.
 
 | Model                                                                                                | size<br><sup>(pixels) | mAP<sup>pose<br>50-95 | mAP<sup>pose<br>50 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
 | ---------------------------------------------------------------------------------------------------- | --------------------- | --------------------- | ------------------ | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
@@ -193,8 +174,7 @@ See [Pose Docs](https://docs.ultralytics.com/tasks/) for usage examples with the
 - **mAP<sup>val</sup>** values are for single-model single-scale on [COCO Keypoints val2017](http://cocodataset.org)
   dataset.
   <br>Reproduce by `yolo val pose data=coco-pose.yaml device=0`
-- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/)
-  instance.
+- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance.
   <br>Reproduce by `yolo val pose data=coco8-pose.yaml batch=1 device=0|cpu`
 
 </details>
@@ -227,20 +207,14 @@ See [Pose Docs](https://docs.ultralytics.com/tasks/) for usage examples with the
 
 ## <div align="center">Ultralytics HUB</div>
 
-Experience seamless AI with [Ultralytics HUB](https://bit.ly/ultralytics_hub) ⭐, the all-in-one solution for data
-visualization, YOLOv5 and YOLOv8 (coming soon) 🚀 model training and deployment, without any coding. Transform images
-into actionable insights and bring your AI visions to life with ease using our cutting-edge platform and
-user-friendly [Ultralytics App](https://ultralytics.com/app_install). Start your journey for **Free** now!
+Experience seamless AI with [Ultralytics HUB](https://bit.ly/ultralytics_hub) ⭐, the all-in-one solution for data visualization, YOLOv5 and YOLOv8 (coming soon) 🚀 model training and deployment, without any coding. Transform images into actionable insights and bring your AI visions to life with ease using our cutting-edge platform and user-friendly [Ultralytics App](https://ultralytics.com/app_install). Start your journey for **Free** now!
 
 <a href="https://bit.ly/ultralytics_hub" target="_blank">
 <img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png"></a>
 
 ## <div align="center">Contribute</div>
 
-We love your input! YOLOv5 and YOLOv8 would not be possible without help from our community. Please see
-our [Contributing Guide](CONTRIBUTING.md) to get started, and fill out
-our [Survey](https://ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey) to send us feedback
-on your experience. Thank you 🙏 to all our contributors!
+We love your input! YOLOv5 and YOLOv8 would not be possible without help from our community. Please see our [Contributing Guide](CONTRIBUTING.md) to get started, and fill out our [Survey](https://ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey) to send us feedback on your experience. Thank you 🙏 to all our contributors!
 
 <!-- SVG image from https://opencollective.com/ultralytics/contributors.svg?width=990 -->
 
@@ -252,15 +226,11 @@ on your experience. Thank you 🙏 to all our contributors!
 YOLOv8 is available under two different licenses:
 
 - **GPL-3.0 License**: See [LICENSE](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) file for details.
-- **Enterprise License**: Provides greater flexibility for commercial product development without the open-source
-  requirements of GPL-3.0. Typical use cases are embedding Ultralytics software and AI models in commercial products and
-  applications. Request an Enterprise License at [Ultralytics Licensing](https://ultralytics.com/license).
+- **Enterprise License**: Provides greater flexibility for commercial product development without the open-source requirements of GPL-3.0. Typical use cases are embedding Ultralytics software and AI models in commercial products and applications. Request an Enterprise License at [Ultralytics Licensing](https://ultralytics.com/license).
 
 ## <div align="center">Contact</div>
 
-For YOLOv8 bug reports and feature requests please
-visit [GitHub Issues](https://github.com/ultralytics/ultralytics/issues) or
-the [Ultralytics Community Forum](https://community.ultralytics.com/).
+For YOLOv8 bug reports and feature requests please visit [GitHub Issues](https://github.com/ultralytics/ultralytics/issues) or the [Ultralytics Community Forum](https://community.ultralytics.com/).
 
 <br>
 <div align="center">