fix error links for master

README.md

@@ -19,7 +19,7 @@ In order to facilitate developers to enjoy the benefits of MindSpore framework,
 | Audio | Speaker Recognition | [ecapa_tdnn](https://gitee.com/mindspore/models/tree/master/official/audio/ecapa_tdnn) |✅|   |   |
 | Audio | Speech Synthesis | [lpcnet](https://gitee.com/mindspore/models/tree/master/official/audio/lpcnet) |✅| ✅ |   |
 | Audio | Speech Synthesis | [melgan](https://gitee.com/mindspore/models/tree/master/official/audio/melgan) |✅| ✅ |   |
-| Audio | Speech Synthesis | [tacotron2](https://gitee.com/mindspore/models/tree/master/research/audio/tacotron2) |✅|   |   |
+| Audio | Speech Synthesis | [tacotron2](https://gitee.com/mindspore/models/tree/master/official/audio/tacotron2) |✅|   |   |
 | Graph Neural Network | Text Classification | [bgcf](https://gitee.com/mindspore/models/tree/master/official/gnn/bgcf) |✅| ✅ |   |
 | Graph Neural Network | Text Classification | [gat](https://gitee.com/mindspore/models/tree/master/official/gnn/gat) |✅| ✅ |   |
 | Graph Neural Network | Text Classification | [gcn](https://gitee.com/mindspore/models/tree/master/official/gnn/gcn) |✅| ✅ |   |
@@ -346,7 +346,7 @@ In order to facilitate developers to enjoy the benefits of MindSpore framework,
 | Image | Image Classification | [snn_mlp](https://gitee.com/mindspore/models/tree/master/research/cv/snn_mlp) | | ✅ |   |
 | Image | Object Detection | [Spnas](https://gitee.com/mindspore/models/tree/master/research/cv/Spnas) |✅|   |   |
 | Image | Image Classification | [SPPNet](https://gitee.com/mindspore/models/tree/master/research/cv/SPPNet) |✅| ✅ |   |
-| Image | Image Classification | [squeezenet1_1](https://gitee.com/mindspore/models/tree/master/research/cv/squeezenet1_1) |✅| ✅ |   |
+| Image | Image Classification | [squeezenet1_1](https://gitee.com/mindspore/models/tree/master/official/cv/squeezenet1_1) |✅| ✅ |   |
 | Image | Image Super Resolution | [sr_ea](https://gitee.com/mindspore/models/tree/master/research/cv/sr_ea) |✅|   |   |
 | Image | Image Super Resolution | [SRGAN](https://gitee.com/mindspore/models/tree/master/research/cv/SRGAN) |✅| ✅ |   |
 | Image | Image Classification | [ssc_resnet50](https://gitee.com/mindspore/models/tree/master/research/cv/ssc_resnet50) |✅| ✅ |   |

README_CN.md

@@ -19,7 +19,7 @@
 | 语音 | 声纹识别 | [ecapa_tdnn](https://gitee.com/mindspore/models/tree/master/official/audio/ecapa_tdnn) |✅|   |   |
 | 语音 | 语音合成 | [lpcnet](https://gitee.com/mindspore/models/tree/master/official/audio/lpcnet) |✅| ✅ |   |
 | 语音 | 语音合成 | [melgan](https://gitee.com/mindspore/models/tree/master/official/audio/melgan) |✅| ✅ |   |
-| 语音 | 语音合成 | [tacotron2](https://gitee.com/mindspore/models/tree/master/research/audio/tacotron2) |✅|   |   |
+| 语音 | 语音合成 | [tacotron2](https://gitee.com/mindspore/models/tree/master/official/audio/tacotron2) |✅|   |   |
 | 图神经网络 | 文本分类 | [bgcf](https://gitee.com/mindspore/models/tree/master/official/gnn/bgcf) |✅| ✅ |   |
 | 图神经网络 | 文本分类 | [gat](https://gitee.com/mindspore/models/tree/master/official/gnn/gat) |✅| ✅ |   |
 | 图神经网络 | 文本分类 | [gcn](https://gitee.com/mindspore/models/tree/master/official/gnn/gcn) |✅| ✅ |   |
@@ -346,7 +346,7 @@
 | 图像 | 图像分类 | [snn_mlp](https://gitee.com/mindspore/models/tree/master/research/cv/snn_mlp) | | ✅ |   |
 | 图像 | 目标检测 | [Spnas](https://gitee.com/mindspore/models/tree/master/research/cv/Spnas) |✅|   |   |
 | 图像 | 图像分类 | [SPPNet](https://gitee.com/mindspore/models/tree/master/research/cv/SPPNet) |✅| ✅ |   |
-| 图像 | 图像分类 | [squeezenet1_1](https://gitee.com/mindspore/models/tree/master/research/cv/squeezenet1_1) |✅| ✅ |   |
+| 图像 | 图像分类 | [squeezenet1_1](https://gitee.com/mindspore/models/tree/master/official/cv/squeezenet1_1) |✅| ✅ |   |
 | 图像 | 图像超分 | [sr_ea](https://gitee.com/mindspore/models/tree/master/research/cv/sr_ea) |✅|   |   |
 | 图像 | 图像超分 | [SRGAN](https://gitee.com/mindspore/models/tree/master/research/cv/SRGAN) |✅| ✅ |   |
 | 图像 | 图像分类 | [ssc_resnet50](https://gitee.com/mindspore/models/tree/master/research/cv/ssc_resnet50) |✅| ✅ |   |

official/audio/tacotron2/README.md

@@ -284,7 +284,7 @@ Parameters for both training and evaluation can be set in [DATASET]_config.yaml
 | Speed|1264ms/step|
 | Total time: training       | 8p: 24h/19m/41s;;                                  |
 | Checkpoint                 | 328.9M (.ckpt file)                                              |
-| Scripts                    | [Tacotron2 script](https://gitee.com/mindspore/models/tree/master/research/audio/tacotron2) |
+| Scripts                    | [Tacotron2 script](https://gitee.com/mindspore/models/tree/master/official/audio/tacotron2) |
 
 ### Inference Performance
 

official/cv/east/infer/README_CN.md

@@ -100,7 +100,7 @@ python export.py \
 --device_id=0 \
 ```
 
-2、aipp配置，详细内容请参照[这里](#https://support.huaweicloud.com/atctool-cann330alphaXinfer/atlasatc_16_0066.html)配置
+2、aipp配置，详细内容请参照[这里](https://www.hiascend.com/document/detail/zh/CANNCommunityEdition/51RC2alpha003/infacldevg/atctool/atlasatc_16_0021.html)配置
 
 ```tex
 aipp_op {

official/cv/squeezenet/README.md

@@ -68,7 +68,7 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
 # [Environment Requirements](#contents)
 
 - Hardware（Ascend/CPU）
-    - Prepare hardware environment with Ascend processor. Squeezenet training on GPU performs is not good now, and it is still in research. See [squeezenet in research](https://gitee.com/mindspore/models/tree/master/research/cv/squeezenet) to get up-to-date details.
+    - Prepare hardware environment with Ascend processor. Squeezenet training on GPU performs is not good now, and it is still in research. See [squeezenet in research](https://gitee.com/mindspore/models/tree/master/official/cv/squeezenet) to get up-to-date details.
 - Framework
     - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：

official/cv/squeezenet/modelarts/README.md

@@ -68,7 +68,7 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
 # [Environment Requirements](#contents)
 
 - Hardware（Ascend/CPU）
-    - Prepare hardware environment with Ascend processor. Squeezenet training on GPU performs is not good now, and it is still in research. See [squeezenet in research](https://gitee.com/mindspore/models/tree/master/research/cv/squeezenet) to get up-to-date details.
+    - Prepare hardware environment with Ascend processor. Squeezenet training on GPU performs is not good now, and it is still in research. See [squeezenet in research](https://gitee.com/mindspore/models/tree/master/official/cv/squeezenet) to get up-to-date details.
 - Framework
     - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：

official/cv/yolov3_resnet18/README.md

@@ -310,7 +310,7 @@ Note the results is two-classification(person and face) used our own annotations
 
 ### Evaluation on Ascend
 
-To eval, run `eval.py` with the dataset `image_dir`, `anno_path`(eval txt), `mindrecord_dir` and `ckpt_path`. `ckpt_path` is the path of [checkpoint](https://www.mindspore.cn/tutorials/en/master/advanced/train/save.html) file.
+To eval, run `eval.py` with the dataset `image_dir`, `anno_path`(eval txt), `mindrecord_dir` and `ckpt_path`. `ckpt_path` is the path of [checkpoint](https://www.mindspore.cn/tutorials/en/master/beginner/save_load.html) file.
 
   ```bash
   bash run_eval.sh 0 yolo.ckpt ./Mindrecord_eval ./dataset ./dataset/eval.txt

official/cv/yolov3_resnet18/README_CN.md

@@ -309,7 +309,7 @@ YOLOv3整体网络架构如下：
 
 ### Ascend评估
 
-运行`eval.py`，数据集为`image_dir`、`anno_path`(评估TXT)、`mindrecord_dir`和`ckpt_path`。`ckpt_path`是[检查点](https://www.mindspore.cn/tutorials/zh-CN/master/advanced/train/save.html)文件的路径。
+运行`eval.py`，数据集为`image_dir`、`anno_path`(评估TXT)、`mindrecord_dir`和`ckpt_path`。`ckpt_path`是[检查点](https://www.mindspore.cn/tutorials/zh-CN/master/beginner/save_load.html)文件的路径。
 
   ```shell script
   bash run_eval.sh 0 yolo.ckpt ./Mindrecord_eval ./dataset ./dataset/eval.txt

official/lite/image_segmentation/README.en.md

@@ -45,7 +45,7 @@ The following describes how to use the MindSpore Lite JAVA APIs and MindSpore Li
 
     - NDK >= 21.3 [NDK](https://developer.android.google.cn/ndk/downloads?hl=zh-cn)
     - CMake >= 3.10.2   [CMake](https://cmake.org/download)
-    - Android SDK >= 26 [SDK](https://developer.microsoft.com/zh-cn/windows/downloads/windows-10-sdk/)
+    - Android SDK >= 26 [SDK](https://developer.microsoft.com/zh-cn/windows/downloads/windows-sdk/)
     - JDK >= 1.8 [JDK](http://jdk.java.net)
 
     ![project_structure](images/project_structure.png)

official/lite/object_detection/README.en.md

@@ -44,7 +44,7 @@ The following describes how to use the MindSpore Lite C++ APIs (Android JNIs) an
 
     - NDK >= 21.3 [NDK](https://developer.android.google.cn/ndk/downloads?hl=zh-cn)
     - CMake >= 3.10.2   [CMake](https://cmake.org/download)
-    - Android SDK >= 26 [SDK](https://developer.microsoft.com/zh-cn/windows/downloads/windows-10-sdk/)
+    - Android SDK >= 26 [SDK](https://developer.microsoft.com/zh-cn/windows/downloads/windows-sdk/)
     - JDK >= 1.8 [JDK](http://jdk.java.net)
 
     ![project_structure](images/project_structure.png)

official/lite/object_detection/README.md

@@ -48,7 +48,7 @@
 
     - NDK >= 21.3 [NDK](https://developer.android.google.cn/ndk/downloads?hl=zh-cn)
     - CMake >= 3.10.2   [CMake](https://cmake.org/download)
-    - Android SDK >= 26 [SDK](https://developer.microsoft.com/zh-cn/windows/downloads/windows-10-sdk/)
+    - Android SDK >= 26 [SDK](https://developer.microsoft.com/zh-cn/windows/downloads/windows-sdk/)
     - JDK >= 1.8 [JDK](http://jdk.java.net)
 
         ![project_structure](images/project_structure.png)

official/lite/posenet/README.en.md

@@ -44,7 +44,7 @@ This sample application demonstrates how to use the MindSpore Lite API and skele
 
     - NDK >= 21.3 [NDK](https://developer.android.google.cn/ndk/downloads?hl=zh-cn)
     - CMake >= 3.10.2   [CMake](https://cmake.org/download)
-    - Android SDK >= 26 [SDK](https://developer.microsoft.com/zh-cn/windows/downloads/windows-10-sdk/)
+    - Android SDK >= 26 [SDK](https://developer.microsoft.com/zh-cn/windows/downloads/windows-sdk/)
     - JDK >= 1.8 [JDK](http://jdk.java.net)
 
     ![project_structure](images/project_structure.png)

official/lite/posenet/README.md

@@ -48,7 +48,7 @@
 
     - NDK >= 21.3 [NDK](https://developer.android.google.cn/ndk/downloads?hl=zh-cn)
     - CMake >= 3.10.2   [CMake](https://cmake.org/download)
-    - Android SDK >= 26 [SDK](https://developer.microsoft.com/zh-cn/windows/downloads/windows-10-sdk/)
+    - Android SDK >= 26 [SDK](https://developer.microsoft.com/zh-cn/windows/downloads/windows-sdk/)
     - JDK >= 1.8 [JDK](http://jdk.java.net)
 
         ![project_structure](images/project_structure.png)

official/lite/scene_detection/README.en.md

@@ -44,7 +44,7 @@ This sample application demonstrates how to use the MindSpore Lite C++ API (Andr
 
     - NDK >= 21.3 [NDK](https://developer.android.google.cn/ndk/downloads?hl=zh-cn)
     - CMake >= 3.10.2   [CMake](https://cmake.org/download)
-    - Android SDK >= 26 [SDK](https://developer.microsoft.com/zh-cn/windows/downloads/windows-10-sdk/)
+    - Android SDK >= 26 [SDK](https://developer.microsoft.com/zh-cn/windows/downloads/windows-sdk/)
     - JDK >= 1.8 [JDK](http://jdk.java.net)
 
     ![project_structure](images/project_structure.png)

official/lite/scene_detection/README.md

@@ -48,7 +48,7 @@
 
     - NDK >= 21.3 [NDK](https://developer.android.google.cn/ndk/downloads?hl=zh-cn)
     - CMake >= 3.10.2   [CMake](https://cmake.org/download)
-    - Android SDK >= 26 [SDK](https://developer.microsoft.com/zh-cn/windows/downloads/windows-10-sdk/)
+    - Android SDK >= 26 [SDK](https://developer.microsoft.com/zh-cn/windows/downloads/windows-sdk/)
     - JDK >= 1.8 [JDK](http://jdk.java.net)
 
         ![project_structure](images/project_structure.png)

official/lite/style_transfer/README.en.md

@@ -44,7 +44,7 @@ This sample application demonstrates how to use the MindSpore Lite API and MindS
 
     - NDK >= 21.3 [NDK](https://developer.android.google.cn/ndk/downloads?hl=zh-cn)
     - CMake >= 3.10.2   [CMake](https://cmake.org/download)
-    - Android SDK >= 26 [SDK](https://developer.microsoft.com/zh-cn/windows/downloads/windows-10-sdk/)
+    - Android SDK >= 26 [SDK](https://developer.microsoft.com/zh-cn/windows/downloads/windows-sdk/)
     - JDK >= 1.8 [JDK](http://jdk.java.net)
 
     ![project_structure](images/project_structure.png)

official/lite/style_transfer/README.md

@@ -48,7 +48,7 @@
 
     - NDK >= 21.3 [NDK](https://developer.android.google.cn/ndk/downloads?hl=zh-cn)
     - CMake >= 3.10.2   [CMake](https://cmake.org/download)
-    - Android SDK >= 26 [SDK](https://developer.microsoft.com/zh-cn/windows/downloads/windows-10-sdk/)
+    - Android SDK >= 26 [SDK](https://developer.microsoft.com/zh-cn/windows/downloads/windows-sdk/)
     - JDK >= 1.8 [JDK](http://jdk.java.net)
 
         ![project_structure](images/project_structure.png)

research/cv/OSVOS/README_CN.md

@@ -335,7 +335,7 @@ epoch time: 240.559 ms, per step time: 240.559 ms
 | 输出 | 视频序列分割掩膜 |
 | 性能 | 695ms/step（单卡）;722ms/step（八卡） |
 | 总时长 | 8.1h（单卡）;1h（八卡） |
-| 脚本 | [链接](https://gitee.com/mindspore/models/tree/master/research/cv/osvos) |
+| 脚本 | [链接](https://gitee.com/mindspore/models/tree/master/research/cv/OSVOS) |
 
 # ModelZoo主页
 

research/cv/rbpn/README.md

@@ -226,7 +226,7 @@ If you set --future_frame=True , then it is recommended to delete the first thre
 | Speed                     | 1pc(Ascend): 277 ms/step                                     |
 | Total time                | 8pcs: 81h                                                    |
 | Checkpoint of RBPN models | 49M (.ckpt file)                                             |
-| Scripts                   | [RBPN script](https://gitee.com/mindspore/models/tree/master/research/cv/RBPN) |
+| Scripts                   | [RBPN script](https://gitee.com/mindspore/models/tree/master/research/cv/rbpn) |
 
 ### evaling Performance
 

research/cv/squeezenet1_1/README.md

@@ -63,7 +63,7 @@ Dataset used: [ImageNet2012](http://www.image-net.org/)
 # [Environment Requirements](#contents)
 
 - Hardware（Ascend）
-    - Prepare hardware environment with Ascend processor. Squeezenet1_1 training on GPU performs is not good now, and it is still in research. See [squeezenet in research](https://gitee.com/mindspore/models/tree/master/research/cv/squeezenet1_1) to get up-to-date details.
+    - Prepare hardware environment with Ascend processor. Squeezenet1_1 training on GPU performs is not good now, and it is still in research. See [squeezenet in research](https://gitee.com/mindspore/models/tree/master/official/cv/squeezenet1_1) to get up-to-date details.
 - Framework
     - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：