docs: Added README."zh-CN".md translation via https://github.com/dephraiim/translate-readme

README.zh-CN.md

@@ -4,9 +4,9 @@
       <img width="850" src="https://raw.githubusercontent.com/ultralytics/assets/master/yolov5/v70/splash.png"></a>
   </p>
 
-[英语](README.md)|[简体中文](README.zh-CN.md)<br>
+[英语](README.md)\|[简体中文](README.zh-CN.md)<br>
 
-<div>
+  <div>
     <a href="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml"><img src="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/badge.svg" alt="YOLOv5 CI"></a>
     <a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv5 Citation"></a>
     <a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a>
@@ -21,7 +21,7 @@ YOLOv5 🚀 是世界上最受欢迎的视觉 AI，代表<a href="https://ultral
 
 要申请企业许可证，请填写表格<a href="https://ultralytics.com/license">Ultralytics 许可</a>.
 
-<div align="center">
+  <div align="center">
     <a href="https://github.com/ultralytics" style="text-decoration:none;">
       <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-github.png" width="2%" alt="" /></a>
     <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="2%" alt="" />
@@ -119,9 +119,7 @@ python segment/predict.py --weights yolov5m-seg.pt --data data/images/bus.jpg
 ```
 
 ```python
-model = torch.hub.load(
+model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5m-seg.pt')  # load from PyTorch Hub (WARNING: inference not yet supported)
-    "ultralytics/yolov5", "custom", "yolov5m-seg.pt"
-)  # load from PyTorch Hub (WARNING: inference not yet supported)
 ```
 
 | ![zidane](https://user-images.githubusercontent.com/26833433/203113421-decef4c4-183d-4a0a-a6c2-6435b33bc5d3.jpg) | ![bus](https://user-images.githubusercontent.com/26833433/203113416-11fe0025-69f7-4874-a0a6-65d0bfe2999a.jpg) |
@@ -164,10 +162,10 @@ YOLOv5[发布](https://github.com/ultralytics/yolov5/releases).
 import torch
 
 # Model
-model = torch.hub.load("ultralytics/yolov5", "yolov5s")  # or yolov5n - yolov5x6, custom
+model = torch.hub.load('ultralytics/yolov5', 'yolov5s')  # or yolov5n - yolov5x6, custom
 
 # Images
-img = "https://ultralytics.com/images/zidane.jpg"  # or file, Path, PIL, OpenCV, numpy, list
+img = 'https://ultralytics.com/images/zidane.jpg'  # or file, Path, PIL, OpenCV, numpy, list
 
 # Inference
 results = model(img)
@@ -316,7 +314,7 @@ YOLOv5 被设计为超级容易上手和简单易学。我们优先考虑现实
   <summary>Table Notes</summary>
 
 -   所有检查点都使用默认设置训练到 300 个时期。纳米和小型型号使用[hyp.scratch-low.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-low.yaml)hyps，所有其他人都使用[hyp.scratch-high.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-high.yaml).
-- \*\*地图<sup>值</sup>\*\*值适用于单模型单尺度[COCO val2017](http://cocodataset.org)数据集。<br>重现者`python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65`
+-   **地图<sup>值</sup>**值适用于单模型单尺度[COCO val2017](http://cocodataset.org)数据集。<br>重现者`python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65`
 -   **速度**使用 a 对 COCO val 图像进行平均[美国销售.Excelerge](https://aws.amazon.com/ec2/instance-types/p3/)实例。 NMS 时间 (~1 ms/img) 不包括在内。<br>重现者`python val.py --data coco.yaml --img 640 --task speed --batch 1`
 -   **电讯局**[测试时间增加](https://github.com/ultralytics/yolov5/issues/303)包括反射和尺度增强。<br>重现者`python val.py --data coco.yaml --img 1536 --iou 0.7 --augment`
 
@@ -333,8 +331,8 @@ YOLOv5[发布 v6.2](https://github.com/ultralytics/yolov5/releases)带来对分
 
 我们使用 4xA100 实例在 ImageNet 上训练了 90 个时期的 YOLOv5-cls 分类模型，我们训练了 ResNet 和 EfficientNet 模型以及相同的默认训练设置以进行比较。我们将所有模型导出到 ONNX FP32 以进行 CPU 速度测试，并导出到 TensorRT FP16 以进行 GPU 速度测试。我们在 Google 上进行了所有速度测试[协作临](https://colab.research.google.com/signup)为了便于重现。
 
-| 模型                                                                                         | 尺寸<br><sup>（像素） | acc<br><sup>top1 | acc<br><sup>烹饪 | 训练<br><sup>90个纪元<br>4xA100（小时） | 速度<br><sup>ONNX 中央处理器<br>（小姐） | 速度<br><sup>TensorRT V100<br>（小姐） | 参数<br><sup>(男) | 失败者<br><sup>@224（二） |
+| 模型                                                                                          | 尺寸<br><sup>（像素） | acc<br><sup>top1 | acc<br><sup>烹饪 | 训练<br><sup>90个纪元<br>4xA100（小时） | 速度<br><sup>ONNX 中央处理器<br>（小姐） | 速度<br><sup>TensorRT V100<br>（小姐） | 参数<br><sup>(男) | 失败者<br><sup>@224（乙） |
-| ------------------------------------------------------------------------------------------ | --------------- | ---------------- | -------------- | ------------------------------ | ----------------------------- | -------------------------------- | -------------- | ------------------- |
+| ------------------------------------------------------------------------------------------- | --------------- | ---------------- | -------------- | ------------------------------ | ----------------------------- | -------------------------------- | -------------- | ------------------- |
 | [YOLOv5n-cls](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5n-cls.pt)  | 224             | 64.6             | 85.4           | 7:59                           | **3.3**                       | **0.5**                          | **2.5**        | **0.5**             |
 | [YOLOv5s-cls](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5s-cls.pt)  | 224             | 71.5             | 90.2           | 8:09                           | 6.6                           | 0.6                              | 5.4            | 1.4                 |
 | [YOLOv5m-cls](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5m-cls.pt)  | 224             | 75.9             | 92.9           | 10:06                          | 15.5                          | 0.9                              | 12.9           | 3.9                 |
@@ -346,7 +344,7 @@ YOLOv5[发布 v6.2](https://github.com/ultralytics/yolov5/releases)带来对分
 | [ResNet50](https://github.com/ultralytics/yolov5/releases/download/v6.2/resnet50.pt)        | 224             | 76.8             | 93.4           | 11:10                          | 23.4                          | 1.0                              | 25.6           | 8.5                 |
 | [ResNet101](https://github.com/ultralytics/yolov5/releases/download/v6.2/resnet101.pt)      | 224             | 78.5             | 94.3           | 17:10                          | 42.1                          | 1.9                              | 44.5           | 15.9                |
 |                                                                                             |                 |                  |                |                                |                               |                                  |                |                     |
-| [高效网络_b0](https://github.com/ultralytics/yolov5/releases/download/v6.2/efficientnet_b0.pt) | 224             | 75.1             | 92.4           | 13:03                          | 12.5                          | 1.3                              | 5.3            | 1.0                 |
+| [高效网络\_b0](https://github.com/ultralytics/yolov5/releases/download/v6.2/efficientnet_b0.pt) | 224             | 75.1             | 92.4           | 13:03                          | 12.5                          | 1.3                              | 5.3            | 1.0                 |
 | [高效网络 b1](https://github.com/ultralytics/yolov5/releases/download/v6.2/efficientnet_b1.pt)  | 224             | 76.4             | 93.2           | 17:04                          | 14.9                          | 1.6                              | 7.8            | 1.5                 |
 | [我们将预测](https://github.com/ultralytics/yolov5/releases/download/v6.2/efficientnet_b2.pt)    | 224             | 76.6             | 93.4           | 17:10                          | 15.9                          | 1.6                              | 9.1            | 1.7                 |
 | [高效Netb3](https://github.com/ultralytics/yolov5/releases/download/v6.2/efficientnet_b3.pt)  | 224             | 77.7             | 94.0           | 19:19                          | 18.9                          | 1.9                              | 12.2           | 2.4                 |
@@ -394,9 +392,7 @@ python classify/predict.py --weights yolov5s-cls.pt --data data/images/bus.jpg
 ```
 
 ```python
-model = torch.hub.load(
+model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s-cls.pt')  # load from PyTorch Hub
-    "ultralytics/yolov5", "custom", "yolov5s-cls.pt"
-)  # load from PyTorch Hub
 ```
 
 ### 出口
@@ -433,6 +429,13 @@ python export.py --weights yolov5s-cls.pt resnet50.pt efficientnet_b0.pt --inclu
     <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-gcp-small.png" width="10%" /></a>
 </div>
 
+## <div align="center">应用程序</div>
+
+在您的 iOS 或 Android 设备上运行 YOLOv5 模型[Ultralytics 应用程序](https://ultralytics.com/app_install)!
+
+<a align="center" href="https://ultralytics.com/app_install" target="_blank">
+<img width="100%" alt="Ultralytics mobile app" src="https://user-images.githubusercontent.com/26833433/202829285-39367043-292a-41eb-bb76-c3e74f38e38e.png">
+
 ## <div align="center">贡献</div>
 
 我们喜欢您的意见！我们希望尽可能简单和透明地为 YOLOv5 做出贡献。请看我们的[投稿指南](CONTRIBUTING.md)开始，并填写[YOLOv5调查](https://ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey)向我们发送您的体验反馈。感谢我们所有的贡献者！