!6587 update links of README

Merge pull request !6587 from TingWang/update-readme-links

mindspore/lite/README_CN.md

@@ -9,7 +9,7 @@ MindSpore Lite是MindSpore推出的端云协同的、轻量化、高性能AI推
 
 <img src="../../docs/MindSpore-Lite-architecture.png" alt="MindSpore Lite Architecture" width="600"/>
 
-欲了解更多详情，请查看我们的[MindSpore Lite 总体架构](https://www.mindspore.cn/lite/doc/note/zh-CN/master/design/mindspore/architecture_lite.html)。
+欲了解更多详情，请查看我们的[MindSpore Lite 总体架构](https://www.mindspore.cn/doc/note/zh-CN/master/design/mindspore/architecture_lite.html)。
 
 ## MindSpore Lite技术特点
 

model_zoo/official/cv/resnet50_quant/Readme.md

@@ -46,7 +46,7 @@ Dataset used: [imagenet](http://www.image-net.org/)
 
 ## [Mixed Precision](#contents)
 
-The [mixed precision](https://www.mindspore.cn/tutorial/training/master/advanced_use/enable_mixed_precision.html) training method accelerates the deep learning neural network training process by using both the single-precision and half-precision data formats, and maintains the network precision achieved by the single-precision training at the same time. Mixed precision training can accelerate the computation process, reduce memory usage, and enable a larger model or batch size to be trained on specific hardware.
+The [mixed precision](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/enable_mixed_precision.html) training method accelerates the deep learning neural network training process by using both the single-precision and half-precision data formats, and maintains the network precision achieved by the single-precision training at the same time. Mixed precision training can accelerate the computation process, reduce memory usage, and enable a larger model or batch size to be trained on specific hardware.
 For FP16 operators, if the input data type is FP32, the backend of MindSpore will automatically handle it with reduced precision. Users could check the reduced-precision operators by enabling INFO log and then searching ‘reduce precision’.
 
 # [Environment Requirements](#contents)

model_zoo/official/cv/resnext50/README.md

@@ -47,7 +47,7 @@ Dataset used: [imagenet](http://www.image-net.org/)
 
 ## [Mixed Precision](#contents)
 
-The [mixed precision](https://www.mindspore.cn/tutorial/training/master/advanced_use/enable_mixed_precision.html) training method accelerates the deep learning neural network training process by using both the single-precision and half-precision data formats, and maintains the network precision achieved by the single-precision training at the same time. Mixed precision training can accelerate the computation process, reduce memory usage, and enable a larger model or batch size to be trained on specific hardware.
+The [mixed precision](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/enable_mixed_precision.html) training method accelerates the deep learning neural network training process by using both the single-precision and half-precision data formats, and maintains the network precision achieved by the single-precision training at the same time. Mixed precision training can accelerate the computation process, reduce memory usage, and enable a larger model or batch size to be trained on specific hardware.
  
 For FP16 operators, if the input data type is FP32, the backend of MindSpore will automatically handle it with reduced precision. Users could check the reduced-precision operators by enabling INFO log and then searching ‘reduce precision’.
 

model_zoo/official/cv/ssd/README.md

@@ -147,7 +147,7 @@ sh run_eval.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID]
 
 ### Training on Ascend
 
-To train the model, run `train.py`. If the `mindrecord_dir` is empty, it will generate [mindrecord](https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/converse_datasets.html) files by `coco_root`(coco dataset) or `iamge_dir` and `anno_path`(own dataset). **Note if mindrecord_dir isn't empty, it will use mindrecord_dir instead of raw images.**
+To train the model, run `train.py`. If the `mindrecord_dir` is empty, it will generate [mindrecord](https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/converse_dataset.html) files by `coco_root`(coco dataset) or `iamge_dir` and `anno_path`(own dataset). **Note if mindrecord_dir isn't empty, it will use mindrecord_dir instead of raw images.**
 
 
 - Distribute mode

model_zoo/official/cv/unet/README.md

@@ -236,7 +236,7 @@ step: 300, loss is 0.18949677, fps is 57.63118508760329
 ## [How to use](#contents)
 ### Inference
 
-If you need to use the trained model to perform inference on multiple hardware platforms, such as Ascend 910 or Ascend 310, you can refer to this [Link](https://www.mindspore.cn/tutorial/training/master/advanced_use/migrate_3rd_scripts.html). Following the steps below, this is a simple example:
+If you need to use the trained model to perform inference on multiple hardware platforms, such as Ascend 910 or Ascend 310, you can refer to this [Link](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/migrate_3rd_scripts.html). Following the steps below, this is a simple example:
 
 - Running on Ascend
   

model_zoo/official/cv/yolov3_resnet18/README.md

@@ -135,7 +135,7 @@ After installing MindSpore via the official website, you can start training and
 ## [Training Process](#contents)
 
 ### Training on Ascend
-To train the model, run `train.py` with the dataset `image_dir`, `anno_path` and `mindrecord_dir`. If the `mindrecord_dir` is empty, it wil generate [mindrecord](https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/converse_datasets.html) file by `image_dir` and `anno_path`(the absolute image path is joined by the `image_dir` and the relative path in `anno_path`). **Note if `mindrecord_dir` isn't empty, it will use `mindrecord_dir` rather than `image_dir` and `anno_path`.**
+To train the model, run `train.py` with the dataset `image_dir`, `anno_path` and `mindrecord_dir`. If the `mindrecord_dir` is empty, it wil generate [mindrecord](https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/converse_dataset.html) file by `image_dir` and `anno_path`(the absolute image path is joined by the `image_dir` and the relative path in `anno_path`). **Note if `mindrecord_dir` isn't empty, it will use `mindrecord_dir` rather than `image_dir` and `anno_path`.**
 
 - Stand alone mode
 

model_zoo/research/cv/ghostnet_quant/Readme.md

@@ -50,8 +50,8 @@ Dataset used: [Oxford-IIIT Pet](https://www.robots.ox.ac.uk/~vgg/data/pets/)
 - Framework
   - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：
-  - [MindSpore tutorials](https://www.mindspore.cn/tutorial/zh-CN/master/index.html)
-  - [MindSpore API](https://www.mindspore.cn/api/zh-CN/master/index.html)
+  - [MindSpore Tutorials](https://www.mindspore.cn/tutorial/training/en/master/index.html)
+  - [MindSpore Python API](https://www.mindspore.cn/doc/api_python/en/master/index.html)
 
 # [Script description](#contents)
 

model_zoo/research/cv/ssd_ghostnet/README.md

@@ -134,7 +134,7 @@ python eval.py --device_id 0 --dataset coco --checkpoint_path LOG4/ssd-500_458.c
 
 ### Training on Ascend
 
-To train the model, run `train.py`. If the `mindrecord_dir` is empty, it will generate [mindrecord](https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/converse_datasets.html) files by `coco_root`(coco dataset) or `iamge_dir` and `anno_path`(own dataset). **Note if mindrecord_dir isn't empty, it will use mindrecord_dir instead of raw images.**
+To train the model, run `train.py`. If the `mindrecord_dir` is empty, it will generate [mindrecord](https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/converse_dataset.html) files by `coco_root`(coco dataset) or `iamge_dir` and `anno_path`(own dataset). **Note if mindrecord_dir isn't empty, it will use mindrecord_dir instead of raw images.**
 
 
 - Distribute mode