!13892 removed the useless link of apply form

From: @oacjiewen
Reviewed-by: @c_34,@liangchenghui
Signed-off-by: @c_34

model_zoo/official/cv/FCN8s/README.md

@@ -41,7 +41,7 @@ Dataset used:
 # [环境要求](#contents)
 
 - 硬件（Ascend/GPU）
-    - 需要准备具有Ascend或GPU处理能力的硬件环境. 如需使用Ascend，可以发送 [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) 到ascend@huawei.com。一旦批准，你就可以使用此资源
+    - 需要准备具有Ascend或GPU处理能力的硬件环境.
 - 框架
     - [MindSpore](https://www.mindspore.cn/install/en)
 - 如需获取更多信息，请查看如下链接：

model_zoo/official/cv/centerface/README.md

@@ -82,7 +82,7 @@ other datasets need to use the same format as WiderFace.
 # [Environment Requirements](#contents)
 
 - Hardware（Ascend）
-    - Prepare hardware environment with Ascend processor. If you want to try Ascend, please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
+    - Prepare hardware environment with Ascend processor.
 - Framework
     - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：
@@ -229,53 +229,53 @@ sh eval_all.sh
 
 1. train scripts parameters
 
-the command is: python train.py [train parameters]
-Major parameters train.py as follows:
+    the command is: python train.py [train parameters]
+    Major parameters train.py as follows:
 
-```text
---lr: learning rate
---per_batch_size: batch size on each device
---is_distributed: multi-device or not
---t_max: for cosine lr_scheduler
---max_epoch: training epochs
---warmup_epochs: warmup_epochs, not needed for adam, needed for sgd
---lr scheduler: learning rate scheduler, default is multistep
---lr_epochs: decrease lr steps
---lr_gamma: decrease lr by a factor
---weight_decay: weight decay
---loss_scale: mix precision training
---pretrained_backbone: pretrained mobilenet_v2 model path
---data_dir: data dir
---annot_path: annotations path
---img_dir: img dir in data_dir
-```
+    ```text
+    --lr: learning rate
+    --per_batch_size: batch size on each device
+    --is_distributed: multi-device or not
+    --t_max: for cosine lr_scheduler
+    --max_epoch: training epochs
+    --warmup_epochs: warmup_epochs, not needed for adam, needed for sgd
+    --lr scheduler: learning rate scheduler, default is multistep
+    --lr_epochs: decrease lr steps
+    --lr_gamma: decrease lr by a factor
+    --weight_decay: weight decay
+    --loss_scale: mix precision training
+    --pretrained_backbone: pretrained mobilenet_v2 model path
+    --data_dir: data dir
+    --annot_path: annotations path
+    --img_dir: img dir in data_dir
+    ```
 
 2. centerface unique configs: in config.py; not recommend user to change
 
 3. test scripts parameters:
 
-the command is: python test.py [test parameters]
-Major parameters test.py as follows:
+    the command is: python test.py [test parameters]
+    Major parameters test.py as follows:
 
-```python
-test_script_path: test.py path;
---is_distributed: multi-device or not
---data_dir: img dir
---test_model: test model dir
---ground_truth_mat: ground_truth file, mat type
---save_dir: save_path for evaluate
---rank: use device id
---ckpt_name: test model name
-# blow are used for calculate ckpt/model name
-# model/ckpt name is "0-" + str(ckpt_num) + "_" + str(steps_per_epoch*ckpt_num) + ".ckpt";
-# ckpt_num is epoch number, can be calculated by device_num
-# detail can be found in "test.py"
-# if ckpt is specified not need below 4 parameter
---device_num: training device number
---steps_per_epoch: steps for each epoch
---start: start loop number, used to calculate first epoch number
---end: end loop number, used to calculate last epoch number
-```
+    ```python
+    test_script_path: test.py path;
+    --is_distributed: multi-device or not
+    --data_dir: img dir
+    --test_model: test model dir
+    --ground_truth_mat: ground_truth file, mat type
+    --save_dir: save_path for evaluate
+    --rank: use device id
+    --ckpt_name: test model name
+    # blow are used for calculate ckpt/model name
+    # model/ckpt name is "0-" + str(ckpt_num) + "_" + str(steps_per_epoch*ckpt_num) + ".ckpt";
+    # ckpt_num is epoch number, can be calculated by device_num
+    # detail can be found in "test.py"
+    # if ckpt is specified not need below 4 parameter
+    --device_num: training device number
+    --steps_per_epoch: steps for each epoch
+    --start: start loop number, used to calculate first epoch number
+    --end: end loop number, used to calculate last epoch number
+    ```
 
 4. eval scripts parameters:
 
@@ -384,18 +384,18 @@ mkdir [SAVE_PATH]
 
 1. test a single ckpt file
 
-```python
-# you need to change the parameter in test.sh
-# or use symbolic link as quick start
-# or use the command as follow:
-#   MODEL_PATH: ckpt path saved during training
-#   DATASET: img dir
-#   GROUND_TRUTH_MAT: ground_truth file, mat type
-#   SAVE_PATH: save_path for evaluate
-#   DEVICE_ID: use device id
-#   CKPT: test model name
-sh test.sh [MODEL_PATH] [DATASET] [GROUND_TRUTH_MAT] [SAVE_PATH] [DEVICE_ID] [CKPT]
-```
+    ```python
+    # you need to change the parameter in test.sh
+    # or use symbolic link as quick start
+    # or use the command as follow:
+    #   MODEL_PATH: ckpt path saved during training
+    #   DATASET: img dir
+    #   GROUND_TRUTH_MAT: ground_truth file, mat type
+    #   SAVE_PATH: save_path for evaluate
+    #   DEVICE_ID: use device id
+    #   CKPT: test model name
+    sh test.sh [MODEL_PATH] [DATASET] [GROUND_TRUTH_MAT] [SAVE_PATH] [DEVICE_ID] [CKPT]
+    ```
 
 2. test many out ckpt for user to choose the best one
 
@@ -433,19 +433,19 @@ cd ../../../scripts;
 
 1. eval a single testing output
 
-```python
-# you need to change the parameter in eval.sh
-# default eval the ckpt saved in ./scripts/output/centerface/999
-sh eval.sh
-```
+    ```python
+    # you need to change the parameter in eval.sh
+    # default eval the ckpt saved in ./scripts/output/centerface/999
+    sh eval.sh
+    ```
 
 2. eval many testing output for user to choose the best one
 
-```python
-# you need to change the parameter in eval_all.sh
-# default eval the ckpt saved in ./scripts/output/centerface/[89-140]
-sh eval_all.sh
-```
+    ```python
+    # you need to change the parameter in eval_all.sh
+    # default eval the ckpt saved in ./scripts/output/centerface/[89-140]
+    sh eval_all.sh
+    ```
 
 3. test+eval
 

model_zoo/official/cv/cnnctc/README.md

@@ -96,7 +96,7 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
 
 - Hardware（Ascend）
 
-    - Prepare hardware environment with Ascend processor. If you want to try Ascend  , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
+    - Prepare hardware environment with Ascend processor.
 - Framework
 
     - [MindSpore](https://www.mindspore.cn/install/en)

model_zoo/official/cv/crnn/README.md

@@ -58,7 +58,7 @@ We provide `convert_ic03.py`, `convert_iiit5k.py`, `convert_svt.py` as exmples f
 ## [Environment Requirements](#contents)
 
 - Hardware（Ascend）
-    - Prepare hardware environment with Ascend processor. If you want to try Ascend, please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. You will be able to have access to related resources once approved.
+    - Prepare hardware environment with Ascend processor.
 - Framework
     - [MindSpore](https://gitee.com/mindspore/mindspore)
 - For more information, please check the resources below：

model_zoo/official/cv/crnn_seq2seq_ocr/README.md

@@ -38,7 +38,7 @@ For training and evaluation, we use the French Street Name Signs (FSNS) released
 ## [Environment Requirements](#contents)
 
 - Hardware（Ascend）
-    - Prepare hardware environment with Ascend processor. If you want to try Ascend, please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. You will be able to have access to related resources once approved.
+    - Prepare hardware environment with Ascend processor.
 - Framework
     - [MindSpore](https://gitee.com/mindspore/mindspore)
 - For more information, please check the resources below：

model_zoo/official/cv/ctpn/README.md

@@ -1,9 +1,9 @@
-![](https://www.mindspore.cn/static/img/logo_black.6a5c850d.png)
-
-<!-- TOC -->
+![logo](https://www.mindspore.cn/static/img/logo_black.6a5c850d.png)
 
 # CTPN for Ascend
 
+<!-- TOC -->
+
 - [CTPN Description](#CTPN-description)
 - [Model Architecture](#model-architecture)
 - [Dataset](#dataset)
@@ -57,7 +57,7 @@ Here we used 6 datasets for training, and 1 datasets for Evaluation.
 # [Environment Requirements](#contents)
 
 - Hardware（Ascend）
-    - Prepare hardware environment with Ascend processor. If you want to try Ascend  , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
+    - Prepare hardware environment with Ascend processor.
 - Framework
     - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：

model_zoo/official/cv/deeplabv3/README.md

@@ -74,7 +74,7 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
 # [Environment Requirements](#contents)
 
 - Hardware（Ascend）
-- Prepare hardware environment with Ascend. If you want to try Ascend , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
+- Prepare hardware environment with Ascend.
 - Framework
     - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：
@@ -83,7 +83,7 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
 - Install python packages in requirements.txt
 - Generate config json file for 8pcs training
 
-     ```
+     ```bash
      # From the root of this project
      cd src/tools/
      python3 get_multicards_json.py 10.111.*.*
@@ -108,47 +108,47 @@ For 8 devices training, training steps are as follows:
 
 1. Train s16 with vocaug dataset, finetuning from resnet101 pretrained model, script is:
 
-```shell
-run_distribute_train_s16_r1.sh
-```
+    ```shell
+    run_distribute_train_s16_r1.sh
+    ```
 
 2. Train s8 with vocaug dataset, finetuning from model in previous step, training script is:
 
-```shell
-run_distribute_train_s8_r1.sh
-```
+    ```shell
+    run_distribute_train_s8_r1.sh
+    ```
 
 3. Train s8 with voctrain dataset, finetuning from model in previous step, training script is:
 
-```shell
-run_distribute_train_s8_r2.sh
-```
+    ```shell
+    run_distribute_train_s8_r2.sh
+    ```
 
 For evaluation, evaluating steps are as follows:
 
 1. Eval s16 with voc val dataset, eval script is:
 
-```shell
-run_eval_s16.sh
-```
+    ```shell
+    run_eval_s16.sh
+    ```
 
 2. Eval s8 with voc val dataset, eval script is:
 
-```shell
-run_eval_s8.sh
-```
+    ```shell
+    run_eval_s8.sh
+    ```
 
 3. Eval s8 multiscale with voc val dataset, eval script is:
 
-```shell
-run_eval_s8_multiscale.sh
-```
+    ```shell
+    run_eval_s8_multiscale.sh
+    ```
 
 4. Eval s8 multiscale and flip with voc val dataset, eval script is:
 
-```shell
-run_eval_s8_multiscale_flip.sh
-```
+    ```shell
+    run_eval_s8_multiscale_flip.sh
+    ```
 
 # [Script Description](#contents)
 
@@ -245,64 +245,64 @@ For 8 devices training, training steps are as follows:
 
 1. Train s16 with vocaug dataset, finetuning from resnet101 pretrained model, script is as follows:
 
-```shell
-# run_distribute_train_s16_r1.sh
-for((i=0;i<=$RANK_SIZE-1;i++));
-do
-    export RANK_ID=${i}
-    export DEVICE_ID=$((i + RANK_START_ID))
-    echo 'start rank='${i}', device id='${DEVICE_ID}'...'
-    mkdir ${train_path}/device${DEVICE_ID}
-    cd ${train_path}/device${DEVICE_ID} || exit
-    python ${train_code_path}/train.py --train_dir=${train_path}/ckpt  \
-                                               --data_file=/PATH/TO/MINDRECORD_NAME  \
-                                               --train_epochs=300  \
-                                               --batch_size=32  \
-                                               --crop_size=513  \
-                                               --base_lr=0.08  \
-                                               --lr_type=cos  \
-                                               --min_scale=0.5  \
-                                               --max_scale=2.0  \
-                                               --ignore_label=255  \
-                                               --num_classes=21  \
-                                               --model=deeplab_v3_s16  \
-                                               --ckpt_pre_trained=/PATH/TO/PRETRAIN_MODEL  \
-                                               --is_distributed  \
-                                               --save_steps=410  \
-                                               --keep_checkpoint_max=200 >log 2>&1 &
-done
-```
+    ```shell
+    # run_distribute_train_s16_r1.sh
+    for((i=0;i<=$RANK_SIZE-1;i++));
+    do
+        export RANK_ID=${i}
+        export DEVICE_ID=$((i + RANK_START_ID))
+        echo 'start rank='${i}', device id='${DEVICE_ID}'...'
+        mkdir ${train_path}/device${DEVICE_ID}
+        cd ${train_path}/device${DEVICE_ID} || exit
+        python ${train_code_path}/train.py --train_dir=${train_path}/ckpt  \
+                                                --data_file=/PATH/TO/MINDRECORD_NAME  \
+                                                --train_epochs=300  \
+                                                --batch_size=32  \
+                                                --crop_size=513  \
+                                                --base_lr=0.08  \
+                                                --lr_type=cos  \
+                                                --min_scale=0.5  \
+                                                --max_scale=2.0  \
+                                                --ignore_label=255  \
+                                                --num_classes=21  \
+                                                --model=deeplab_v3_s16  \
+                                                --ckpt_pre_trained=/PATH/TO/PRETRAIN_MODEL  \
+                                                --is_distributed  \
+                                                --save_steps=410  \
+                                                --keep_checkpoint_max=200 >log 2>&1 &
+    done
+    ```
 
 2. Train s8 with vocaug dataset, finetuning from model in previous step, training script is as follows:
 
-```shell
-# run_distribute_train_s8_r1.sh
-for((i=0;i<=$RANK_SIZE-1;i++));
-do
-    export RANK_ID=${i}
-    export DEVICE_ID=$((i + RANK_START_ID))
-    echo 'start rank='${i}', device id='${DEVICE_ID}'...'
-    mkdir ${train_path}/device${DEVICE_ID}
-    cd ${train_path}/device${DEVICE_ID} || exit
-    python ${train_code_path}/train.py --train_dir=${train_path}/ckpt  \
-                                               --data_file=/PATH/TO/MINDRECORD_NAME  \
-                                               --train_epochs=800  \
-                                               --batch_size=16  \
-                                               --crop_size=513  \
-                                               --base_lr=0.02  \
-                                               --lr_type=cos  \
-                                               --min_scale=0.5  \
-                                               --max_scale=2.0  \
-                                               --ignore_label=255  \
-                                               --num_classes=21  \
-                                               --model=deeplab_v3_s8  \
-                                               --loss_scale=2048  \
-                                               --ckpt_pre_trained=/PATH/TO/PRETRAIN_MODEL  \
-                                               --is_distributed  \
-                                               --save_steps=820  \
-                                               --keep_checkpoint_max=200 >log 2>&1 &
-done
-```
+    ```shell
+    # run_distribute_train_s8_r1.sh
+    for((i=0;i<=$RANK_SIZE-1;i++));
+    do
+        export RANK_ID=${i}
+        export DEVICE_ID=$((i + RANK_START_ID))
+        echo 'start rank='${i}', device id='${DEVICE_ID}'...'
+        mkdir ${train_path}/device${DEVICE_ID}
+        cd ${train_path}/device${DEVICE_ID} || exit
+        python ${train_code_path}/train.py --train_dir=${train_path}/ckpt  \
+                                                --data_file=/PATH/TO/MINDRECORD_NAME  \
+                                                --train_epochs=800  \
+                                                --batch_size=16  \
+                                                --crop_size=513  \
+                                                --base_lr=0.02  \
+                                                --lr_type=cos  \
+                                                --min_scale=0.5  \
+                                                --max_scale=2.0  \
+                                                --ignore_label=255  \
+                                                --num_classes=21  \
+                                                --model=deeplab_v3_s8  \
+                                                --loss_scale=2048  \
+                                                --ckpt_pre_trained=/PATH/TO/PRETRAIN_MODEL  \
+                                                --is_distributed  \
+                                                --save_steps=820  \
+                                                --keep_checkpoint_max=200 >log 2>&1 &
+    done
+    ```
 
 3. Train s8 with voctrain dataset, finetuning from model in previous step, training script is as follows:
 
@@ -566,4 +566,4 @@ In dataset.py, we set the seed inside "create_dataset" function. We also use ran
 
 # [ModelZoo Homepage](#contents)
 
- Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).
+Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).

model_zoo/official/cv/deeplabv3/README_CN.md

@@ -71,7 +71,7 @@ Pascal VOC数据集和语义边界数据集（Semantic Boundaries Dataset，SBD
 
 - 配置并运行build_data.sh，将数据集转换为MindRecords。scripts/build_data.sh中的参数：
 
-     ```
+     ```bash
      --data_root                 训练数据的根路径
      --data_lst                  训练数据列表（如上准备）
      --dst_path                  MindRecord所在路径
@@ -89,7 +89,7 @@ Pascal VOC数据集和语义边界数据集（Semantic Boundaries Dataset，SBD
 # 环境要求
 
 - 硬件（Ascend）
-    - 准备Ascend处理器搭建硬件环境。如需试用Ascend处理器，请发送[申请表](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx)至ascend@huawei.com，审核通过即可获得资源。
+    - 准备Ascend处理器搭建硬件环境。
 - 框架
     - [MindSpore](https://www.mindspore.cn/install)
 - 如需查看详情，请参见如下资源：
@@ -98,7 +98,7 @@ Pascal VOC数据集和语义边界数据集（Semantic Boundaries Dataset，SBD
 - 安装requirements.txt中的python包。
 - 生成config json文件用于8卡训练。
 
-     ```
+     ```bash
      # 从项目根目录进入
      cd src/tools/
      python3 get_multicards_json.py 10.111.*.*
@@ -123,47 +123,47 @@ run_standalone_train.sh
 
 1. 使用VOCaug数据集训练s16，微调ResNet-101预训练模型。脚本如下：
 
-```bash
-run_distribute_train_s16_r1.sh
-```
+    ```bash
+    run_distribute_train_s16_r1.sh
+    ```
 
 2. 使用VOCaug数据集训练s8，微调上一步的模型。脚本如下：
 
-```bash
-run_distribute_train_s8_r1.sh
-```
+    ```bash
+    run_distribute_train_s8_r1.sh
+    ```
 
 3. 使用VOCtrain数据集训练s8，微调上一步的模型。脚本如下：
 
-```bash
-run_distribute_train_s8_r2.sh
-```
+    ```bash
+    run_distribute_train_s8_r2.sh
+    ```
 
 评估步骤如下：
 
 1. 使用voc val数据集评估s16。评估脚本如下：
 
-```bash
-run_eval_s16.sh
-```
+    ```bash
+    run_eval_s16.sh
+    ```
 
 2. 使用voc val数据集评估s8。评估脚本如下：
 
-```bash
-run_eval_s8.sh
-```
+    ```bash
+    run_eval_s8.sh
+    ```
 
 3. 使用voc val数据集评估多尺度s8。评估脚本如下：
 
-```bash
-run_eval_s8_multiscale.sh
-```
+    ```bash
+    run_eval_s8_multiscale.sh
+    ```
 
 4. 使用voc val数据集评估多尺度和翻转s8。评估脚本如下：
 
-```bash
-run_eval_s8_multiscale_flip.sh
-```
+    ```bash
+    run_eval_s8_multiscale_flip.sh
+    ```
 
 # 脚本说明
 

model_zoo/official/cv/deeptext/README.md

@@ -49,7 +49,7 @@ Here we used 4 datasets for training, and 1 datasets for Evaluation.
 # [Environment Requirements](#contents)
 
 - Hardware（Ascend）
-    - Prepare hardware environment with Ascend processor. If you want to try Ascend  , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
+    - Prepare hardware environment with Ascend processor.
 - Framework
     - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：

model_zoo/official/cv/densenet/README.md

@@ -78,7 +78,7 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
 # [Environment Requirements](#contents)
 
 - Hardware（Ascend/GPU）
-    - Prepare hardware environment with Ascend or GPU processor. If you want to try Ascend  , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
+    - Prepare hardware environment with Ascend or GPU processor.
 - Framework
     - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：

model_zoo/official/cv/densenet/README_CN.md

@@ -82,7 +82,7 @@ DenseNet-100使用的数据集： Cifar-10
 # 环境要求
 
 - 硬件（Ascend/GPU）
-- 准备Ascend或GPU处理器搭建硬件环境。如需试用昇腾处理器，请发送[申请表](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx)至ascend@huawei.com，审核通过即可获得资源。
+- 准备Ascend或GPU处理器搭建硬件环境。
 - 框架
 - [MindSpore](https://www.mindspore.cn/install)
 - 如需查看详情，请参见如下资源：

model_zoo/official/cv/dpn/README.md

@@ -70,7 +70,7 @@ The [mixed precision](https://www.mindspore.cn/tutorial/training/en/master/advan
 To run the python scripts in the repository, you need to prepare the environment as follow:
 
 - Hardware
-    - Prepare hardware environment with Ascend or GPU processor. If you want to try Ascend , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to [ascend@huawei.com](mailto:ascend@huawei.com). Once approved, you can get the resources.
+    - Prepare hardware environment with Ascend or GPU processor.
 - Python and dependencies
     - Python3.7
     - Mindspore 1.1.0

model_zoo/official/cv/faster_rcnn/README.md

@@ -48,7 +48,7 @@ Dataset used: [COCO2017](<https://cocodataset.org/>)
 # Environment Requirements
 
 - Hardware（Ascend/GPU）
-    - Prepare hardware environment with Ascend processor. If you want to try Ascend, please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
+    - Prepare hardware environment with Ascend processor.
 
 - Docker base image
     - [Ascend Hub](ascend.huawei.com/ascendhub/#/home)

model_zoo/official/cv/faster_rcnn/README_CN.md

@@ -49,7 +49,7 @@ Faster R-CNN是一个两阶段目标检测网络，该网络采用RPN，可以
 # 环境要求
 
 - 硬件（Ascend/GPU）
-    - 使用Ascend处理器来搭建硬件环境。如需试用Ascend处理器，请发送[申请表](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx)至ascend@huawei.com，审核通过即可获得资源。
+    - 使用Ascend处理器来搭建硬件环境。
 
 - 获取基础镜像
     - [Ascend Hub](https://ascend.huawei.com/ascendhub/#/home)

model_zoo/official/cv/googlenet/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/GPU）
-    - Prepare hardware environment with Ascend or GPU processor. If you want to try Ascend  , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
+    - Prepare hardware environment with Ascend or GPU processor.
 - Framework
     - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：

model_zoo/official/cv/googlenet/README_CN.md

@@ -75,7 +75,7 @@ GoogleNet由多个inception模块串联起来，可以更加深入。  降维的
 # 环境要求
 
 - 硬件（Ascend/GPU）
-    - 使用Ascend或GPU处理器来搭建硬件环境。如需试用Ascend处理器，请发送[申请表](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx)至ascend@huawei.com，审核通过即可获得资源。
+    - 使用Ascend或GPU处理器来搭建硬件环境。
 - 框架
     - [MindSpore](https://www.mindspore.cn/install/en)
 - 如需查看详情，请参见如下资源：

model_zoo/official/cv/inceptionv3/README.md

@@ -59,7 +59,7 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
 # [Environment Requirements](#contents)
 
 - Hardware（Ascend）
-- Prepare hardware environment with Ascend processor. If you want to try Ascend, please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
+- Prepare hardware environment with Ascend processor.
 - Framework
     - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：

model_zoo/official/cv/inceptionv3/README_CN.md

@@ -70,7 +70,7 @@ InceptionV3的总体网络架构如下：
 # 环境要求
 
 - 硬件（Ascend）
-- 使用Ascend来搭建硬件环境。如需试用Ascend处理器，请发送[申请表](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx)至ascend@huawei.com，审核通过即可获得资源。
+- 使用Ascend来搭建硬件环境。
 - 框架
 - [MindSpore](https://www.mindspore.cn/install/en)
 - 如需查看详情，请参见如下资源：

model_zoo/official/cv/inceptionv4/README.md

@@ -51,7 +51,7 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
 # [Environment Requirements](#contents)
 
 - Hardware（Ascend/GPU）
-    - Prepare hardware environment with Ascend processor. If you want to try Ascend, please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
+    - Prepare hardware environment with Ascend processor.
     - or prepare GPU processor.
 - Framework
     - [MindSpore](https://www.mindspore.cn/install/en)

model_zoo/official/cv/maskrcnn/README.md

@@ -53,7 +53,7 @@ Note that you can run the scripts based on the dataset mentioned in original pap
 # [Environment Requirements](#contents)
 
 - Hardware（Ascend）
-    - Prepare hardware environment with Ascend processor. If you want to try Ascend, please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
+    - Prepare hardware environment with Ascend processor.
 - Framework
     - [MindSpore](https://gitee.com/mindspore/mindspore)
 - Docker base image

model_zoo/official/cv/maskrcnn/README_CN.md

@@ -55,7 +55,7 @@ MaskRCNN是一个两级目标检测网络，作为FasterRCNN的扩展模型，
 # 环境要求
 
 - 硬件（昇腾处理器）
-    - 采用昇腾处理器搭建硬件环境。如需试用昇腾处理器，请发送[申请表](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx)至ascend@huawei.com，审核通过即可获得资源。
+    - 采用昇腾处理器搭建硬件环境。
 - 框架
     - [MindSpore](https://gitee.com/mindspore/mindspore)
 - 获取基础镜像

model_zoo/official/cv/maskrcnn_mobilenetv1/README.md

@@ -54,7 +54,7 @@ Note that you can run the scripts based on the dataset mentioned in original pap
 # [Environment Requirements](#contents)
 
 - Hardware（Ascend）
-    - Prepare hardware environment with Ascend processor. If you want to try Ascend  , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
+    - Prepare hardware environment with Ascend processor.
 - Framework
     - [MindSpore](https://gitee.com/mindspore/mindspore)
 - For more information, please check the resources below:

model_zoo/official/cv/mobilenetv1/README.md

@@ -64,7 +64,7 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
 ## Environment Requirements
 
 - Hardware（Ascend）
-    - Prepare hardware environment with Ascend. If you want to try Ascend, please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
+    - Prepare hardware environment with Ascend.
 - Framework
     - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：

model_zoo/official/cv/mobilenetv2/README.md

@@ -50,7 +50,7 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
 # [Environment Requirements](#contents)
 
 - Hardware（Ascend/GPU/CPU）
-    - Prepare hardware environment with Ascend, GPU or CPU processor. If you want to try Ascend, please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
+    - Prepare hardware environment with Ascend, GPU or CPU processor.
 - Framework
     - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：

model_zoo/official/cv/mobilenetv2/README_CN.md

@@ -56,7 +56,7 @@ MobileNetV2总体网络架构如下：
 # 环境要求
 
 - 硬件（Ascend/GPU/CPU）
-    - 使用Ascend、GPU或CPU处理器来搭建硬件环境。如需试用Ascend处理器，请发送[申请表](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx)至ascend@huawei.com，审核通过即可获得资源。
+    - 使用Ascend、GPU或CPU处理器来搭建硬件环境。
 - 框架
     - [MindSpore](https://www.mindspore.cn/install)
 - 如需查看详情，请参见如下资源：

model_zoo/official/cv/mobilenetv2_quant/README_CN.md

@@ -65,7 +65,7 @@ MobileNetV2总体网络架构如下：
 # 环境要求
 
 - 硬件：昇腾处理器（Ascend）
-    - 使用昇腾处理器来搭建硬件环境。如需试用昇腾处理器，请发送[申请表](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx)至ascend@huawei.com，审核通过即可获得资源。
+    - 使用昇腾处理器来搭建硬件环境。
 - 框架
     - [MindSpore](https://www.mindspore.cn/install)
 - 如需查看详情，请参见如下资源

model_zoo/official/cv/mobilenetv2_quant/Readme.md

@@ -52,7 +52,7 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
 # [Environment Requirements](#contents)
 
 - Hardware:Ascend
-    - Prepare hardware environment with Ascend. If you want to try Ascend  , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
+    - Prepare hardware environment with Ascend.
 - Framework
   - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below

model_zoo/official/cv/openpose/README.md

@@ -75,7 +75,7 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
 # [Environment Requirements](#contents)
 
 - Hardware (Ascend)
-    - Prepare hardware environment with Ascend. If you want to try, please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
+    - Prepare hardware environment with Ascend.
 - Framework
     - [MindSpore](https://www.mindspore.cn/install/en)
 - Download the VGG19 model of the MindSpore version:

model_zoo/official/cv/psenet/README.md

@@ -46,7 +46,7 @@ A testing set containing about 2000 readable words
 # [Environment Requirements](#contents)
 
 - Hardware（Ascend）
-    - Prepare hardware environment with Ascend processor. If you want to try Ascend  , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
+    - Prepare hardware environment with Ascend processor.
 - Framework
     - [MindSpore](http://www.mindspore.cn/install/en)
 - For more information, please check the resources below：

model_zoo/official/cv/psenet/README_CN.md

@@ -47,7 +47,7 @@
 # 环境要求
 
 - 硬件：昇腾处理器（Ascend）
-    - 使用Ascend处理器来搭建硬件环境。如需试用昇腾处理器，请发送[申请表](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx)至ascend@huawei.com，审核通过即可获得资源。
+    - 使用Ascend处理器来搭建硬件环境。
 
 - 框架
     - [MindSpore](https://www.mindspore.cn/install)

model_zoo/official/cv/resnet/README.md

@@ -82,7 +82,7 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
 # [Environment Requirements](#contents)
 
 - Hardware（Ascend/GPU/CPU）
-    - Prepare hardware environment with Ascend, GPU or CPU processor. If you want to try Ascend  , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
+    - Prepare hardware environment with Ascend, GPU or CPU processor.
 - Framework
     - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：

model_zoo/official/cv/resnet/README_CN.md

@@ -85,7 +85,7 @@ ResNet的总体网络架构如下：
 # 环境要求
 
 - 硬件(Ascend/GPU)
-    - 准备Ascend或GPU处理器搭建硬件环境。如需试用昇腾处理器，请发送[申请表](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx)至ascend@huawei.com，审核通过即可获得资源。
+    - 准备Ascend或GPU处理器搭建硬件环境。
 - 框架
     - [MindSpore](https://www.mindspore.cn/install/en)
 - 如需查看详情，请参见如下资源：

model_zoo/official/cv/resnet152/README-CN.md

@@ -35,7 +35,7 @@ ResNet152的总体网络架构如下：[链接](https://arxiv.org/pdf/1512.03385
 # 环境要求
 
 - 硬件
-    - 准备Ascend处理器搭建硬件环境。如需试用昇腾处理器，请发送[申请表](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx)至ascend@huawei.com，审核通过即可获得资源。
+    - 准备Ascend处理器搭建硬件环境。
 - 框架
     - [MindSpore](https://www.mindspore.cn/install/en)
 - 如需查看详情，请参见如下资源：

model_zoo/official/cv/resnet50_quant/README.md

@@ -59,7 +59,7 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
 # [Environment Requirements](#contents)
 
 - Hardware:Ascend
-    - Prepare hardware environment with Ascend. If you want to try Ascend  , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
+    - Prepare hardware environment with Ascend.
 - Framework
   - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：

model_zoo/official/cv/resnet50_quant/README_CN.md

@@ -64,7 +64,7 @@ ResNet-50总体网络架构如下：
 # 环境要求
 
 - 硬件：昇腾处理器（Ascend）
-    - 使用昇腾处理器来搭建硬件环境。如需试用昇腾处理器，请发送[申请表](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx)至ascend@huawei.com，审核通过即可获得资源。
+    - 使用昇腾处理器来搭建硬件环境。
 
 - 框架
   - [MindSpore](https://www.mindspore.cn/install)

model_zoo/official/cv/resnet_thor/README.md

@@ -52,7 +52,7 @@ The classical first-order optimization algorithm, such as SGD, has a small amoun
 ## Environment Requirements
 
 - Hardware（Ascend/GPU）
-    - Prepare hardware environment with Ascend or GPU processor. If you want to try Ascend  , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
+    - Prepare hardware environment with Ascend or GPU processor.
 
 - Framework
     - [MindSpore](https://www.mindspore.cn/install/en)

model_zoo/official/cv/resnet_thor/README_CN.md

@@ -57,7 +57,7 @@ ResNet-50的总体网络架构如下：[链接](https://arxiv.org/pdf/1512.03385
 ## 环境要求
 
 - 硬件：昇腾处理器（Ascend或GPU）
-    - 使用Ascend或GPU处理器搭建硬件环境。如需试用昇腾处理器，请发送[申请表](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) 至ascend@huawei.com，审核通过即可获得资源。
+    - 使用Ascend或GPU处理器搭建硬件环境。
 
 - 框架
     - [MindSpore](https://www.mindspore.cn/install)

model_zoo/official/cv/resnext101/README_CN.md

@@ -1,4 +1,4 @@
-# ResNext101-64x4d for MindSpore
+# ResNext101-64x4d
 
 本仓库提供了ResNeXt101-64x4d模型的训练脚本和超参配置，以达到论文中的准确性。
 
@@ -65,7 +65,7 @@ ResNeXt是ResNet网络的改进版本，比ResNet的网络多了块多了cardina
 
 ## 快速入门指南
 
-目录说明，代码参考了Modelzoo上的[ResNext50_for_MindSpore](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/resnext50)
+目录说明，代码参考了Modelzoo上的[ResNext50](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/resnext50)
 
 ```path
 .
@@ -221,4 +221,3 @@ python export.py --device_target [PLATFORM] --ckpt_file [CKPT_PATH] --file_forma
 | **NPUs** | train performance |
 | :------: | :---------------: |
 |    1     |   196.33image/sec   |
-

model_zoo/official/cv/resnext50/README.md

@@ -53,7 +53,7 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
 # [Environment Requirements](#contents)
 
 - Hardware（Ascend/GPU）
-- Prepare hardware environment with Ascend or GPU processor. If you want to try Ascend, please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
+- Prepare hardware environment with Ascend or GPU processor.
 - Framework
 - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：

model_zoo/official/cv/resnext50/README_CN.md

@@ -58,7 +58,7 @@ ResNeXt整体网络架构如下：
 # 环境要求
 
 - 硬件（Ascend或GPU）
-    - 准备Ascend或GPU处理器搭建硬件环境。如需试用昇腾处理器，请发送[申请表](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx)至ascend@huawei.com，审核通过即可获得资源。
+    - 准备Ascend或GPU处理器搭建硬件环境。
 - 框架
     - [MindSpore](https://www.mindspore.cn/install)
 - 如需查看详情，请参见如下资源：

model_zoo/official/cv/retinanet/README_CN.md

@@ -58,7 +58,7 @@ MSCOCO2017
 ## [环境要求](#content)
 
 - 硬件（Ascend）
-    - 使用Ascend处理器准备硬件环境。如果您想使用Ascend，请发送[申请表](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx)至ascend@huawei.com。一旦获得批准，您就可以获取资源。
+    - 使用Ascend处理器准备硬件环境。
 - 架构
     - [MindSpore](https://www.mindspore.cn/install/en)
 - 想要获取更多信息，请检查以下资源：

model_zoo/official/cv/shufflenetv1/README_CN.md

@@ -42,7 +42,7 @@ ShuffleNetV1的核心部分被分成三个阶段，每个阶段重复堆积了
 # 环境要求
 
 - 硬件（Ascend）
-    - 使用Ascend来搭建硬件环境。如需试用Ascend处理器，请发送[申请表](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx)至ascend@huawei.com，审核通过即可获得资源。
+    - 使用Ascend来搭建硬件环境。
 - 框架
     - [MindSpore](https://www.mindspore.cn/install)
 - 如需查看详情，请参见如下资源：

model_zoo/official/cv/simple_pose/README.md

@@ -60,7 +60,7 @@ The [mixed precision](https://www.mindspore.cn/tutorial/training/en/master/advan
 To run the python scripts in the repository, you need to prepare the environment as follow:
 
 - Hardware
-    - Prepare hardware environment with Ascend. If you want to try Ascend, please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to [ascend@huawei.com](mailto:ascend@huawei.com). Once approved, you can get the resources.
+    - Prepare hardware environment with Ascend.
 - Python and dependencies
     - python 3.7
     - mindspore 1.0.1

model_zoo/official/cv/squeezenet/README.md

@@ -63,7 +63,7 @@ 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. If you want to try Ascend, please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources. Squeezenet training on GPU performs badly now, and it is still in research. See [squeezenet in research](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/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/mindspore/tree/master/model_zoo/research/cv/squeezenet) to get up-to-date details.
 - Framework
     - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：

model_zoo/official/cv/tinydarknet/README.md

@@ -56,7 +56,7 @@ Dataset used can refer to [paper](<https://ieeexplore.ieee.org/abstract/document
 # [Environment Requirements](#contents)
 
 - Hardware（Ascend）
-    - Prepare hardware environment with Ascend or GPU processor. If you want to try Ascend , please send the [application](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) form to ascend@huawei.com.
+    - Prepare hardware environment with Ascend or GPU processor.
 - Framework
     - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information,please check the resources below：

model_zoo/official/cv/tinydarknet/README_CN.md

@@ -64,7 +64,7 @@ Tiny-DarkNet是Joseph Chet Redmon等人提出的一个16层的针对于经典的
 # [环境要求](#目录)
 
 - 硬件（Ascend）
-    - 请准备具有Ascend处理器的硬件环境.如果想使用Ascend资源，请发送[申请表](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) 至ascend@huawei.com. 当收到许可即可使用Ascend资源.
+    - 请准备具有Ascend处理器的硬件环境.
 - 框架
     - [MindSpore](https://www.mindspore.cn/install/en)
 - 更多的信息请访问以下链接：

model_zoo/official/cv/unet/README.md

@@ -58,7 +58,7 @@ We also support cell nuclei dataset which is used in [Unet++ original paper](htt
 ## [Environment Requirements](#contents)
 
 - Hardware（Ascend）
-    - Prepare hardware environment with Ascend processor. If you want to try Ascend, please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
+    - Prepare hardware environment with Ascend processor.
 - Framework
     - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：

model_zoo/official/cv/unet/README_CN.md

@@ -62,7 +62,7 @@ UNet++是U-Net的增强版本，使用了新的跨层链接方式和深层监督
 ## 环境要求
 
 - 硬件（Ascend）
-    - 准备Ascend处理器搭建硬件环境。如需试用昇腾处理器，请发送[申请表](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx)至ascend@huawei.com，审核通过即可获得资源。
+    - 准备Ascend处理器搭建硬件环境。
 - 框架
     - [MindSpore](https://www.mindspore.cn/install)
 - 如需查看详情，请参见如下资源：

model_zoo/official/cv/vgg16/README.md

@@ -87,7 +87,7 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
 ## [Environment Requirements](#contents)
 
 - Hardware（Ascend/GPU）
-    - Prepare hardware environment with Ascend or GPU processor. If you want to try Ascend  , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
+    - Prepare hardware environment with Ascend or GPU processor.
 - Framework
     - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：

model_zoo/official/cv/vgg16/README_CN.md

@@ -94,7 +94,7 @@ VGG 16网络主要由几个基本模块（包括卷积层和池化层）和三
 ## 环境要求
 
 - 硬件（Ascend或GPU）
-    - 准备Ascend或GPU处理器搭建硬件环境。如需试用昇腾处理器，请发送[申请表](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx)至ascend@huawei.com，审核通过即可获得资源。
+    - 准备Ascend或GPU处理器搭建硬件环境。
 - 框架
     - [MindSpore](https://www.mindspore.cn/install)
 - 如需查看详情，请参见如下资源：

model_zoo/official/cv/warpctc/README.md

@@ -38,7 +38,7 @@ The dataset is self-generated using a third-party library called [captcha](https
 ## [Environment Requirements](#contents)
 
 - Hardware（Ascend/GPU）
-    - Prepare hardware environment with Ascend or GPU processor. If you want to try Ascend, please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. You will be able to have access to related resources once approved.
+    - Prepare hardware environment with Ascend or GPU processor.
 - Framework
     - [MindSpore](https://gitee.com/mindspore/mindspore)
 - For more information, please check the resources below：

model_zoo/official/cv/warpctc/README_CN.md

@@ -43,7 +43,7 @@ WarpCTC是带有一层FC神经网络的二层堆叠LSTM模型。详细信息请
 ## 环境要求
 
 - 硬件（Ascend/GPU）
-    - 使用Ascend或GPU处理器来搭建硬件环境。如需试用昇腾处理器，请发送[申请表](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx)至ascend@huawie,审核通过即可获得资源。
+    - 使用Ascend或GPU处理器来搭建硬件环境。
 - 框架
     - [MindSpore](https://gitee.com/mindspore/mindspore)
 - 如需查看详情，请参见如下资源：

model_zoo/official/cv/xception/README.md

@@ -58,7 +58,7 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
 # [Environment Requirements](#contents)
 
 - Hardware（Ascend）
-    - Prepare hardware environment with Ascend. If you want to try Ascend  , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
+    - Prepare hardware environment with Ascend.
 - Framework
     - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：

model_zoo/official/cv/yolov3_darknet53/README.md

@@ -68,7 +68,7 @@ Dataset used: [COCO2014](https://cocodataset.org/#download)
 ## [Environment Requirements](#contents)
 
 - Hardware（Ascend/GPU）
-- Prepare hardware environment with Ascend or GPU processor. If you want to try Ascend  , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
+- Prepare hardware environment with Ascend or GPU processor.
 - Framework
     - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：

model_zoo/official/cv/yolov3_darknet53/README_CN.md

@@ -70,7 +70,7 @@ YOLOv3使用DarkNet53执行特征提取，这是YOLOv2中的Darknet-19和残差
 # 环境要求
 
 - 硬件（Ascend/GPU）
-    - 使用Ascend或GPU处理器来搭建硬件环境。如需试用Ascend处理器，请发送[申请表](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) 至ascend@huawei.com，审核通过即可获得资源。
+    - 使用Ascend或GPU处理器来搭建硬件环境。
 - 框架
     - [MindSpore](https://www.mindspore.cn/install)
 - 如需查看详情，请参见如下资源：

model_zoo/official/cv/yolov3_darknet53_quant/README.md

@@ -54,7 +54,7 @@ Dataset used: [COCO2014](https://cocodataset.org/#download)
 ## [Environment Requirements](#contents)
 
 - Hardware（Ascend）
-    - Prepare hardware environment with Ascend processor. If you want to try Ascend  , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
+    - Prepare hardware environment with Ascend processor.
 - Framework
     - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：

model_zoo/official/cv/yolov3_darknet53_quant/README_CN.md

@@ -56,7 +56,7 @@ YOLOv3使用DarkNet53执行特征提取，这是YOLOv2中的Darknet-19和残差
 ## 环境要求
 
 - 硬件（Ascend处理器）
-    - 准备Ascend或GPU处理器搭建硬件环境。如需试用Ascend处理器，请发送[申请表](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx)至ascend@huawei.com，审核通过即可获得资源。
+    - 准备Ascend或GPU处理器搭建硬件环境。
 - 框架
     - [MindSpore](https://www.mindspore.cn/install/)
 - 如需查看详情，请参见如下资源：

model_zoo/official/cv/yolov3_resnet18/README.md

@@ -66,7 +66,7 @@ Dataset used: [COCO2017](<http://images.cocodataset.org/>)
 # [Environment Requirements](#contents)
 
 - Hardware（Ascend）
-    - Prepare hardware environment with Ascend processor. If you want to try Ascend  , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
+    - Prepare hardware environment with Ascend processor.
 - Framework
   - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：

model_zoo/official/cv/yolov3_resnet18/README_CN.md

@@ -69,7 +69,7 @@ YOLOv3整体网络架构如下：
 # 环境要求
 
 - 硬件（Ascend处理器）
-    - 准备Ascend处理器搭建硬件环境。如需试用Ascend处理器，请发送[申请表](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx)至ascend@huawei.com，审核通过即可获得资源。
+    - 准备Ascend处理器搭建硬件环境。
 - 框架
     - [MindSpore](https://www.mindspore.cn/install)
 - 如需查看详情，请参见如下资源：

model_zoo/official/cv/yolov4/README.md

@@ -62,7 +62,7 @@ other datasets need to use the same format as MS COCO.
 # [Environment Requirements](#contents)
 
 - Hardware（Ascend）
-    - Prepare hardware environment with Ascend processor. If you want to try Ascend, please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
+    - Prepare hardware environment with Ascend processor.
 - Framework
     - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：

model_zoo/official/gnn/gcn/README.md

@@ -36,7 +36,7 @@ Note that you can run the scripts based on the dataset mentioned in original pap
 ## [Environment Requirements](#contents)
 
 - Hardware（Ascend）
-    - Prepare hardware environment with Ascend processor. If you want to try Ascend  , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
+    - Prepare hardware environment with Ascend processor.
 - Framework
     - [MindSpore](https://gitee.com/mindspore/mindspore)
 - For more information, please check the resources below：

model_zoo/official/gnn/gcn/README_CN.md

@@ -44,7 +44,7 @@ GCN包含两个图卷积层。每一层以节点特征和邻接矩阵为输入
 ## 环境要求
 
 - 硬件（Ascend处理器）
-    - 准备Ascend或GPU处理器搭建硬件环境。如需试用昇腾处理器，请发送[申请表](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx)至ascend@huawei，审核通过即可获得资源。
+    - 准备Ascend或GPU处理器搭建硬件环境。
 - 框架
     - [MindSpore](https://gitee.com/mindspore/mindspore)
 - 如需查看详情，请参见如下资源：

model_zoo/official/nlp/bert/README.md

@@ -56,7 +56,7 @@ The backbone structure of BERT is transformer. For BERT_base, the transformer co
 # [Environment Requirements](#contents)
 
 - Hardware（Ascend/GPU）
-    - Prepare hardware environment with Ascend/GPU processor. If you want to try Ascend, please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get access to the resources.
+    - Prepare hardware environment with Ascend/GPU processor.
 - Framework
     - [MindSpore](https://gitee.com/mindspore/mindspore)
 - For more information, please check the resources below：

model_zoo/official/nlp/bert/README_CN.md

@@ -59,7 +59,7 @@ BERT的主干结构为Transformer。对于BERT_base，Transformer包含12个编
 # 环境要求
 
 - 硬件（Ascend处理器）
-    - 准备Ascend或GPU处理器搭建硬件环境。如需试用昇腾处理器，请发送[申请表](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx)至ascend@huawei.com，申请通过后，即可获得资源。
+    - 准备Ascend或GPU处理器搭建硬件环境。
 - 框架
     - [MindSpore](https://gitee.com/mindspore/mindspore)
 - 更多关于Mindspore的信息，请查看以下资源：

model_zoo/official/nlp/bert_thor/README.md

@@ -50,7 +50,7 @@ The classical first-order optimization algorithm, such as SGD, has a small amoun
 ## Environment Requirements
 
 - Hardware（Ascend）
-    - Prepare hardware environment with Ascend. If you want to try Ascend  , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
+    - Prepare hardware environment with Ascend.
 - Framework
     - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：

model_zoo/official/nlp/bert_thor/README_CN.md

@@ -56,7 +56,7 @@ BERT的总体架构包含3个嵌入层，用于查找令牌嵌入、位置嵌入
 环境要求
 
 - 硬件（Ascend）
-    - 使用Ascend处理器准备硬件环境。- 如需试用昇腾处理器，请发送[申请表](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx)至ascend@huawei.com，申请通过即可获得资源。
+    - 使用Ascend处理器准备硬件环境。
 - 框架
     - [MindSpore](https://www.mindspore.cn/install)
 - 更多关于Mindspore的信息，请查看以下资源：

model_zoo/official/nlp/fasttext/README.md

@@ -50,7 +50,7 @@ architecture. In the following sections, we will introduce how to run the script
 # [Environment Requirements](#content)
 
 - Hardware（Ascend）
-    - Prepare hardware environment with Ascend processor. If you want to try Ascend  , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
+    - Prepare hardware environment with Ascend processor.
 - Framework
     - [MindSpore](https://gitee.com/mindspore/mindspore)
 - For more information, please check the resources below：

model_zoo/official/nlp/gnmt_v2/README.md

@@ -47,7 +47,7 @@ Note that you can run the scripts based on the dataset mentioned in original pap
 ## Platform
 
 - Hardware (Ascend)
-    - Prepare hardware environment with Ascend processor. If you want to try Ascend, please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you could get the resources for trial.
+    - Prepare hardware environment with Ascend processor.
 - Framework
     - Install [MindSpore](https://www.mindspore.cn/install/en).
 - For more information, please check the resources below:

model_zoo/official/nlp/gpt/README.md

@@ -30,7 +30,7 @@ GPT3 stacks many layers of decoder of transformer. According to the layer number
 # [Environment Requirements](#contents)
 
 - Hardware（Ascend）
-    - Prepare hardware environment with Ascend processor. If you want to try Ascend, please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get access to the resources.
+    - Prepare hardware environment with Ascend processor.
 - Framework
     - [MindSpore](https://gitee.com/mindspore/mindspore)
 - For more information, please check the resources below：

model_zoo/official/nlp/gru/README.md

@@ -45,7 +45,7 @@ In this model, we use the Multi30K dataset as our train and test dataset.As trai
 # [Environment Requirements](#content)
 
 - Hardware（Ascend）
-    - Prepare hardware environment with Ascend processor. If you want to try Ascend  , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
+    - Prepare hardware environment with Ascend processor.
 - Framework
     - [MindSpore](https://gitee.com/mindspore/mindspore)
 - For more information, please check the resources below：

model_zoo/official/nlp/lstm/README.md

@@ -39,7 +39,7 @@ Note that you can run the scripts based on the dataset mentioned in original pap
 # [Environment Requirements](#contents)
 
 - Hardware（GPU/CPU/Ascend）
-    - If you want to try Ascend, please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you could get the resources for trial.
+    - Prepare hardware environment with Ascend, GPU or CPU processor.
 - Framework
     - [MindSpore](https://gitee.com/mindspore/mindspore)
 - For more information, please check the resources below：

model_zoo/official/nlp/mass/README.md

@@ -488,7 +488,7 @@ More detail about LR scheduler could be found in `src/utils/lr_scheduler.py`.
 ## Platform
 
 - Hardware（Ascend/GPU）
-    - Prepare hardware environment with Ascend or GPU processor. If you want to try Ascend  , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
+    - Prepare hardware environment with Ascend or GPU processor.
 - Framework
     - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：

model_zoo/official/nlp/mass/README_CN.md

@@ -487,7 +487,7 @@ python weights_average.py --input_files your_checkpoint_list --output_file model
 ## 平台
 
 - 硬件（Ascend或GPU）
-    - 使用Ascend或GPU处理器准备硬件环境。- 如需试用昇腾处理器，请发送[申请表](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx)至ascend@huawei.com，申请通过即可获得资源。
+    - 使用Ascend或GPU处理器准备硬件环境。
 - 框架
     - [MindSpore](https://www.mindspore.cn/install)
 - 更多关于Mindspore的信息，请查看以下资源：

model_zoo/official/nlp/prophetnet/README.md

@@ -546,7 +546,7 @@ The comparisons between MASS and other baseline methods in terms of PPL on Corne
 ## Platform
 
 - Hardware(Ascend)
-    - Prepare hardware environment with Ascend processor. If you want to try Ascend, please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you could get the resources for trial.
+    - Prepare hardware environment with Ascend processor.
 - Framework
     - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：

model_zoo/official/nlp/textcnn/README.md

@@ -40,7 +40,7 @@ Dataset used: [Movie Review Data](<http://www.cs.cornell.edu/people/pabo/movie-r
 # [Environment Requirements](#contents)
 
 - Hardware（Ascend）
-    - Prepare hardware environment with Ascend processor. If you want to try Ascend  , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
+    - Prepare hardware environment with Ascend processor.
 - Framework
     - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：

model_zoo/official/nlp/tinybert/README.md

@@ -51,7 +51,7 @@ The backbone structure of TinyBERT is transformer, the transformer contains four
 # [Environment Requirements](#contents)
 
 - Hardware（Ascend/GPU）
-    - Prepare hardware environment with Ascend or GPU processor. If you want to try Ascend, please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
+    - Prepare hardware environment with Ascend or GPU processor.
 - Framework
     - [MindSpore](https://gitee.com/mindspore/mindspore)
 - For more information, please check the resources below：

model_zoo/official/nlp/tinybert/README_CN.md

@@ -56,7 +56,7 @@ TinyBERT模型的主干结构是转换器，转换器包含四个编码器模块
 # 环境要求
 
 - 硬件（Ascend或GPU）
-    - 使用Ascend或GPU处理器准备硬件环境。如需试用昇腾处理器，请发送[申请表](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx)到ascend@huawei.com。申请通过后，即可获得资源。
+    - 使用Ascend或GPU处理器准备硬件环境。
 - 框架
     - [MindSpore](https://gitee.com/mindspore/mindspore)
 - 更多关于Mindspore的信息，请查看以下资源：

model_zoo/official/nlp/transformer/README.md

@@ -40,7 +40,7 @@ Note that you can run the scripts based on the dataset mentioned in original pap
 ## [Environment Requirements](#contents)
 
 - Hardware（Ascend/GPU）
-    - Prepare hardware environment with Ascend or GPU processor. If you want to try Ascend  , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
+    - Prepare hardware environment with Ascend or GPU processor.
 - Framework
     - [MindSpore](https://gitee.com/mindspore/mindspore)
 - For more information, please check the resources below：

model_zoo/official/nlp/transformer/README_CN.md

@@ -46,7 +46,7 @@ Transformer具体包括六个编码模块和六个解码模块。每个编码模
 ## 环境要求
 
 - 硬件（Ascend处理器）
-    - 使用Ascend处理器准备硬件环境。- 如需试用昇腾处理器，请发送[申请表](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx)至ascend@huawei，申请通过后，即可获得资源。
+    - 使用Ascend处理器准备硬件环境。
 - 框架
     - [MindSpore](https://gitee.com/mindspore/mindspore)
 - 如需查看详情，请参见如下资源：

model_zoo/official/recommend/deepfm/README.md

@@ -38,7 +38,7 @@ The FM and deep component share the same input raw feature vector, which enables
 # [Environment Requirements](#contents)
 
 - Hardware（Ascend/GPU/CPU）
-    - Prepare hardware environment with Ascend, GPU, or CPU processor. If you want to try Ascend, please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
+    - Prepare hardware environment with Ascend, GPU, or CPU processor.
 - Framework
     - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：

model_zoo/official/recommend/deepfm/README_CN.md

@@ -43,7 +43,7 @@ FM和深度学习部分拥有相同的输入原样特征向量，让DeepFM能从
 ## 环境要求
 
 - 硬件（Ascend或GPU）
-    - 使用Ascend或GPU处理器准备硬件环境。如需试用昇腾处理器，请发送[申请表](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx)至ascend@huawei.com，申请通过后，即可获得资源。
+    - 使用Ascend或GPU处理器准备硬件环境。
 - 框架
     - [MindSpore](https://www.mindspore.cn/install)
 - 如需查看详情，请参见如下资源：

model_zoo/official/recommend/naml/README.md

@@ -38,7 +38,7 @@ You can download the dataset and put the directory in structure as follows:
 # [Environment Requirements](#contents)
 
 - Hardware（Ascend/GPU）
-    - Prepare hardware environment with Ascend, GPU processor. If you want to try Ascend, please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
+    - Prepare hardware environment with Ascend, GPU processor.
 - Framework
     - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：

model_zoo/official/recommend/ncf/README.md

@@ -75,7 +75,7 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
 # [Environment Requirements](#contents)
 
 - Hardware（Ascend/GPU）
-    - Prepare hardware environment with Ascend or GPU processor. If you want to try Ascend  , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
+    - Prepare hardware environment with Ascend or GPU processor.
 - Framework
     - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：

model_zoo/official/recommend/wide_and_deep/README.md

@@ -43,7 +43,7 @@ Currently we support host-device mode with column partition and  parameter serve
 # [Environment Requirements](#contents)
 
 - Hardware（Ascend or GPU）
-    - Prepare hardware environment with Ascend processor. If you want to try Ascend  , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
+    - Prepare hardware environment with Ascend processor.
 - Framework
     - [MindSpore](https://gitee.com/mindspore/mindspore)
 - For more information, please check the resources below：

model_zoo/official/recommend/wide_and_deep/README_CN.md

@@ -45,7 +45,7 @@ Wide&Deep模型训练了宽线性模型和深度学习神经网络，结合了
 # 环境要求
 
 - 硬件（Ascend或GPU）
-    - 准备Ascend或GPU处理器搭建硬件环境。- 如需试用昇腾处理器，请发送[申请表](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx)至ascend@huawei.com,  申请通过即可获得资源。
+    - 准备Ascend或GPU处理器搭建硬件环境。
 - 框架
     - [MindSpore](https://gitee.com/mindspore/mindspore)
 - 如需查看详情，请参见如下资源：

model_zoo/official/recommend/wide_and_deep_multitable/README.md

@@ -35,7 +35,7 @@ Wide&Deep model jointly trained wide linear models and deep neural network, whic
 ## [Environment Requirements](#contents)
 
 - Hardware（Ascend or GPU）
-    - Prepare hardware environment with Ascend processor. If you want to try Ascend  , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
+    - Prepare hardware environment with Ascend processor.
 - Framework
     - [MindSpore](https://gitee.com/mindspore/mindspore)
 - For more information, please check the resources below：

model_zoo/official/recommend/wide_and_deep_multitable/README_CN.md

@@ -38,7 +38,7 @@ Wide&Deep模型训练了宽线性模型和深度学习神经网络，结合了
 ## 环境要求
 
 - 硬件（Ascend或GPU）
-    - 准备Ascend或GPU处理器搭建硬件环境。如需试用昇腾处理器，请发送[申请表](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx)至ascend@huawei.com，申请通过即可获得资源。
+    - 准备Ascend或GPU处理器搭建硬件环境。
 - 框架
     - [MindSpore](https://gitee.com/mindspore/mindspore)
 - 更多关于Mindspore的信息，请查看以下资源：

model_zoo/official/rl/dqn/README.md

@@ -30,7 +30,7 @@ The overall network architecture of DQN is show below:
 ## [Requirements](#content)
 
 - Hardware（Ascend/GPU/CPU）
-    - Prepare hardware environment with Ascend or GPU processor. If you want to try Ascend, please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
+    - Prepare hardware environment with Ascend or GPU processor.
 - Framework
     - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：

model_zoo/research/audio/fcn-4/README.md

@@ -38,7 +38,7 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
 ## [Environment Requirements](#contents)
 
 - Hardware（Ascend
-    - If you want to try Ascend  , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
+    - Prepare hardware environment with Ascend processor.
 - Framework
     - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：

model_zoo/research/cv/FaceAttribute/README.md

@@ -86,7 +86,7 @@ We use about 91K face images as training dataset and 11K as evaluating dataset i
 # [Environment Requirements](#contents)
 
 - Hardware(Ascend)
-    - Prepare hardware environment with Ascend processor. If you want to try Ascend, please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
+    - Prepare hardware environment with Ascend processor.
 - Framework
     - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below:

model_zoo/research/cv/FaceDetection/README.md

@@ -70,7 +70,7 @@ We use about 13K images as training dataset and 3K as evaluating dataset in this
 # [Environment Requirements](#contents)
 
 - Hardware（Ascend）
-    - Prepare hardware environment with Ascend processor. If you want to try Ascend  , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
+    - Prepare hardware environment with Ascend processor.
 - Framework
     - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：

model_zoo/research/cv/FaceQualityAssessment/README.md

@@ -68,7 +68,7 @@ We use about 122K face images as training dataset and 2K as evaluating dataset i
 # [Environment Requirements](#contents)
 
 - Hardware(Ascend)
-    - Prepare hardware environment with Ascend processor. If you want to try Ascend, please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
+    - Prepare hardware environment with Ascend processor.
 - Framework
     - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below:

model_zoo/research/cv/FaceRecognition/README.md

@@ -56,7 +56,7 @@ The directory structure is as follows:
 # [Environment Requirements](#contents)
 
 - Hardware（Ascend）
-    - Prepare hardware environment with Ascend processor. If you want to get Ascend , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
+    - Prepare hardware environment with Ascend processor.
 - Framework
     - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：

model_zoo/research/cv/FaceRecognitionForTracking/README.md

@@ -56,7 +56,7 @@ The directory structure is as follows:
 # [Environment Requirements](#contents)
 
 - Hardware(Ascend)
-    - Prepare hardware environment with Ascend processor. If you want to try Ascend, please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
+    - Prepare hardware environment with Ascend processor.
 - Framework
     - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below:

model_zoo/research/cv/MaskedFaceRecognition/README.md

@@ -66,7 +66,7 @@ The directory structure is as follows:
 ## [Environment Requirements](#contents)
 
 - Hardware（Ascend）
-    - Prepare hardware environment with Ascend processor. If you want to get Ascend , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
+    - Prepare hardware environment with Ascend processor.
 - Framework
     - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：

model_zoo/research/cv/centernet/README.md

@@ -77,7 +77,7 @@ Dataset used: [COCO2017](https://cocodataset.org/)
 # [Environment Requirements](#contents)
 
 - Hardware（Ascend）
-    - Prepare hardware environment with Ascend processor. If you want to try Ascend, please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
+    - Prepare hardware environment with Ascend processor.
 - Framework
     - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：

model_zoo/research/cv/ghostnet/Readme.md

@@ -41,7 +41,7 @@ Dataset used: [Oxford-IIIT Pet](https://www.robots.ox.ac.uk/~vgg/data/pets/)
 ## [Environment Requirements](#contents)
 
 - Hardware（Ascend/GPU)
-    - Prepare hardware environment with Ascend or GPU. If you want to try Ascend, please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
+    - Prepare hardware environment with Ascend or GPU.
 - Framework
     - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：

model_zoo/research/cv/ghostnet_quant/Readme.md

@@ -46,7 +46,7 @@ Dataset used: [Oxford-IIIT Pet](https://www.robots.ox.ac.uk/~vgg/data/pets/)
 ## [Environment Requirements](#contents)
 
 - Hardware（Ascend/GPU）
-    - Prepare hardware environment with Ascend or GPU processor. If you want to try Ascend, please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
+    - Prepare hardware environment with Ascend or GPU processor.
 - Framework
     - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：

model_zoo/research/cv/resnet50_adv_pruning/Readme.md

@@ -43,7 +43,7 @@ Dataset used: [Oxford-IIIT Pet](https://www.robots.ox.ac.uk/~vgg/data/pets/)
 # [Environment Requirements](#contents)
 
 - Hardware（Ascend/GPU）
-    - Prepare hardware environment with Ascend or GPU processor. If you want to try Ascend, please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
+    - Prepare hardware environment with Ascend or GPU processor.
 - Framework
     - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：

model_zoo/research/cv/squeezenet/README.md

@@ -63,7 +63,7 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
 # [Environment Requirements](#contents)
 
 - Hardware（Ascend/GPU）
-    - Prepare hardware environment with Ascend or GPU processor. If you want to try Ascend  , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources. Squeezenet training on GPU performs badly now, and it is still in research.
+    - Prepare hardware environment with Ascend or GPU processor. Squeezenet training on GPU performs not well now, and it is still in research.
 - Framework
     - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：