fix error link in model zoo readme files for r1.1

CONTRIBUTING.md

@@ -30,23 +30,24 @@ For individual contributor, please refer to [ICLA online document](https://www.m
 
 Please follow this style to make MindSpore easy to review, maintain and develop.
 
-* Coding guidelines
+- Coding guidelines
 
     The *Python* coding style suggested by [Python PEP 8 Coding Style](https://pep8.org/) and *C++* coding style suggested by [Google C++ Coding Guidelines](http://google.github.io/styleguide/cppguide.html) are used in MindSpore community.
 
-* Unittest guidelines
+- Unittest guidelines
 
-    The *Python* unittest style suggested by [pytest](http://www.pytest.org/en/latest/) and *C++* unittest style suggested by [Googletest Primer](https://github.com/google/googletest/blob/master/googletest/docs/primer.md) are used in MindSpore community.
+    The *Python* unittest style suggested by [pytest](http://www.pytest.org/en/latest/) and *C++* unittest style suggested by [Googletest Primer](https://github.com/google/googletest/blob/master/docs/primer.md) are used in MindSpore community.
 
 ### Fork-Pull development model
 
-* Fork MindSpore repository
+- Fork MindSpore repository
 
     Before submitting code to MindSpore project, please make sure that this project have been forked to your own repository. It means that there will be parallel development between MindSpore repository and your own repository, so be careful to avoid the inconsistency between them.
 
-* Clone the remote repository
+- Clone the remote repository
 
     If you want to download the code to the local machine, `git` is the best way:
+
     ```shell
     # For GitHub
     git clone https://github.com/{insert_your_forked_repo}/mindspore.git
@@ -56,18 +57,20 @@ Please follow this style to make MindSpore easy to review, maintain and develop.
     git remote add upstream https://gitee.com/mindspore/mindspore.git
     ```
 
-* Develop code locally
+- Develop code locally
 
     To avoid inconsistency between multiple branches, checking out to a new branch is `SUGGESTED`:
+
     ```shell
     git checkout -b {new_branch_name} origin/master
     ```
 
     Then you can change the code arbitrarily.
 
-* Push the code to the remote repository
+- Push the code to the remote repository
 
     After updating the code, you should push the update in the formal way:
+
     ```shell
     git add .
     git status # Check the update status
@@ -76,7 +79,7 @@ Please follow this style to make MindSpore easy to review, maintain and develop.
     git push origin {new_branch_name}
     ```
 
-* Pull a request to MindSpore repository
+- Pull a request to MindSpore repository
 
     In the last step, your need to pull a compare request between your new branch and MindSpore `master` branch. After finishing the pull request, the Jenkins CI will be automatically set up for building test.
 
@@ -101,11 +104,11 @@ When reporting issues, refer to this format:
 
 ### Propose PRs
 
-* Raise your idea as an *issue* on [GitHub](https://github.com/mindspore-ai/mindspore/issues) or [Gitee](https://gitee.com/mindspore/mindspore/issues)
-* If it is a new feature that needs lots of design details, a design proposal should also be submitted.
-* After reaching consensus in the issue discussions and design proposal reviews, complete the development on the forked repo and submit a PR.
-* None of PRs is not permitted until it receives **2+ LGTM** from approvers. Please NOTICE that approver is NOT allowed to add *LGTM* on his own PR.
-* After PR is sufficiently discussed, it will get merged, abandoned or rejected depending on the outcome of the discussion.
+- Raise your idea as an *issue* on [GitHub](https://github.com/mindspore-ai/mindspore/issues) or [Gitee](https://gitee.com/mindspore/mindspore/issues)
+- If it is a new feature that needs lots of design details, a design proposal should also be submitted.
+- After reaching consensus in the issue discussions and design proposal reviews, complete the development on the forked repo and submit a PR.
+- None of PRs is not permitted until it receives **2+ LGTM** from approvers. Please NOTICE that approver is NOT allowed to add *LGTM* on his own PR.
+- After PR is sufficiently discussed, it will get merged, abandoned or rejected depending on the outcome of the discussion.
 
 **PRs advisory:**
 

mindspore/nn/probability/README.md

@@ -110,7 +110,7 @@ if __name__ == "__main__":
 
 4. Train the Network
 
-The process of Bayesian network training is basically the same as that of DNN, the only differance is that WithLossCell is replaced with WithBNNLossCell suitable for BNN.
+The process of Bayesian network training is basically the same as that of DNN, the only difference is that WithLossCell is replaced with WithBNNLossCell suitable for BNN.
 Based on the two parameters `backbone` and `loss_fn` in WithLossCell, WithBNNLossCell adds two parameters of `dnn_factor` and `bnn_factor`. Those two parameters are used to trade off backbone's loss and kl loss to prevent kl loss from being too large to cover backbone's loss.
 
 ```python

model_zoo/official/cv/deeptext/README.md

@@ -20,7 +20,7 @@
 
 # [DeepText Description](#contents)
 
-DeepText is a convolutional neural network architecture for text detection in non-specific scenarios. The DeepText system is based on the elegant framwork of Faster R-CNN. This idea was proposed in the paper "DeepText: A new approach for text proposal generation and text detection in natural images.", published in 2017.
+DeepText is a convolutional neural network architecture for text detection in non-specific scenarios. The DeepText system is based on the elegant framework of Faster R-CNN. This idea was proposed in the paper "DeepText: A new approach for text proposal generation and text detection in natural images.", published in 2017.
 
 [Paper](https://arxiv.org/pdf/1605.07314v1.pdf) Zhuoyao Zhong, Lianwen Jin, Shuangping Huang, South China University of Technology (SCUT), Published in ICASSP 2017.
 
@@ -74,7 +74,7 @@ Here we used 4 datasets for training, and 1 datasets for Evaluation.
       ├─anchor_genrator.py              # anchor generator
       ├─bbox_assign_sample.py           # proposal layer for stage 1
       ├─bbox_assign_sample_stage2.py    # proposal layer for stage 2
-      ├─deeptext_vgg16.py               # main network defination
+      ├─deeptext_vgg16.py               # main network definition
       ├─proposal_generator.py           # proposal generator
       ├─rcnn.py                         # rcnn
       ├─roi_align.py                    # roi_align cell wrapper
@@ -83,7 +83,7 @@ Here we used 4 datasets for training, and 1 datasets for Evaluation.
     ├─config.py                       # training configuration
     ├─dataset.py                      # data proprocessing
     ├─lr_schedule.py                  # learning rate scheduler
-    ├─network_define.py               # network defination
+    ├─network_define.py               # network definition
     └─utils.py                        # some functions which is commonly used
   ├─eval.py                           # eval net
   ├─export.py                         # export checkpoint, surpport .onnx, .air, .mindir convert

model_zoo/official/lite/image_segmentation/README.en.md

@@ -140,7 +140,7 @@ In this example, the  download.gradle File configuration auto download `deeplabv
 
 Note: if the automatic download fails, please manually download the relevant library files and put them in the corresponding location.
 
-deeplabv3.ms [deeplabv3.ms]( https://download.mindspore.cn/model_zoo/official/lite/deeplabv3_openimage_lite/deeplabv3.ms)
+deeplabv3.ms [deeplabv3.ms](https://download.mindspore.cn/model_zoo/official/lite/deeplabv3_lite/deeplabv3.ms)
 
 ### Compiling On-Device Inference Code
 
@@ -189,7 +189,7 @@ The inference code process is as follows. For details about the complete code, s
       model.freeBuffer();
       return;
     }
-    // Note: when use model.freeBuffer(), the model can not be complile graph again.
+    // Note: when use model.freeBuffer(), the model can not be compile graph again.
     model.freeBuffer();
     ```
 
@@ -247,7 +247,7 @@ The inference code process is as follows. For details about the complete code, s
     dstBitmap = scaleBitmapAndKeepRatio(dstBitmap, (int) resource_height, (int) resource_weight);
     ```
 
-4. The process of image and output data can refer to methods showing bellow.
+4. The process of image and output data can refer to methods showing below.
 
     ```Java
     Bitmap scaleBitmapAndKeepRatio(Bitmap targetBmp, int reqHeightInPixels, int reqWidthInPixels) {
@@ -304,7 +304,7 @@ The inference code process is as follows. For details about the complete code, s
             float value = inputBuffer.getFloat((y * imageWidth * NUM_CLASSES + x * NUM_CLASSES + i) * 4);
             if (i == 0 || value > maxVal) {
               maxVal = value;
-              // Check wether a pixel belongs to a person whose label is 15.
+              // Check whether a pixel belongs to a person whose label is 15.
               if (i == 15) {
                 mSegmentBits[x][y] = i;
               } else {

model_zoo/official/lite/image_segmentation/README.md

@@ -152,7 +152,7 @@ target_link_libraries(
 
 从MindSpore Model Hub中下载模型文件，本示例程序中使用的终端图像分割模型文件为`deeplabv3.ms`，同样通过download.gradle脚本在APP构建时自动下载，并放置在`app/src/main/assets`工程目录下。
 
-> 若下载失败请手动下载模型文件，deeplabv3.ms [下载链接](https://download.mindspore.cn/model_zoo/official/lite/deeplabv3_openimage_lite/deeplabv3.ms)。
+> 若下载失败请手动下载模型文件，deeplabv3.ms [下载链接](https://download.mindspore.cn/model_zoo/official/lite/deeplabv3_lite/deeplabv3.ms)。
 
 ### 编写端侧推理代码
 
@@ -201,7 +201,7 @@ target_link_libraries(
       model.freeBuffer();
       return;
     }
-    // Note: when use model.freeBuffer(), the model can not be complile graph again.
+    // Note: when use model.freeBuffer(), the model can not be compile graph again.
     model.freeBuffer();
     ```
 
@@ -316,7 +316,7 @@ target_link_libraries(
             float value = inputBuffer.getFloat((y * imageWidth * NUM_CLASSES + x * NUM_CLASSES + i) * 4);
             if (i == 0 || value > maxVal) {
               maxVal = value;
-              // Check wether a pixel belongs to a person whose label is 15.
+              // Check whether a pixel belongs to a person whose label is 15.
               if (i == 15) {
                 mSegmentBits[x][y] = i;
               } else {