diff --git a/model_zoo/official/cv/FCN8s/README.md b/model_zoo/official/cv/FCN8s/README.md
index e0dc23af100..46545f407e1 100644
--- a/model_zoo/official/cv/FCN8s/README.md
+++ b/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：
diff --git a/model_zoo/official/cv/centerface/README.md b/model_zoo/official/cv/centerface/README.md
index 119d37f04d9..a0140e89c94 100644
--- a/model_zoo/official/cv/centerface/README.md
+++ b/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
diff --git a/model_zoo/official/cv/cnnctc/README.md b/model_zoo/official/cv/cnnctc/README.md
index 535e5f3c10c..aa287823310 100644
--- a/model_zoo/official/cv/cnnctc/README.md
+++ b/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)
diff --git a/model_zoo/official/cv/crnn/README.md b/model_zoo/official/cv/crnn/README.md
index 3b3247ecd7d..e110c022faf 100644
--- a/model_zoo/official/cv/crnn/README.md
+++ b/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：
diff --git a/model_zoo/official/cv/crnn_seq2seq_ocr/README.md b/model_zoo/official/cv/crnn_seq2seq_ocr/README.md
index 668d20bf875..00a9f0e6caa 100755
--- a/model_zoo/official/cv/crnn_seq2seq_ocr/README.md
+++ b/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：
diff --git a/model_zoo/official/cv/ctpn/README.md b/model_zoo/official/cv/ctpn/README.md
index 1c0a8de70b7..69bc893fb69 100644
--- a/model_zoo/official/cv/ctpn/README.md
+++ b/model_zoo/official/cv/ctpn/README.md
@@ -1,9 +1,9 @@
-
-
-
+
# CTPN for Ascend
+
+
- [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：
diff --git a/model_zoo/official/cv/deeplabv3/README.md b/model_zoo/official/cv/deeplabv3/README.md
index 41b53bba1e4..cc0c5452f4e 100644
--- a/model_zoo/official/cv/deeplabv3/README.md
+++ b/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).
\ No newline at end of file
+Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).
diff --git a/model_zoo/official/cv/deeplabv3/README_CN.md b/model_zoo/official/cv/deeplabv3/README_CN.md
index 97661945bd9..688124f2b9b 100644
--- a/model_zoo/official/cv/deeplabv3/README_CN.md
+++ b/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
diff --git a/model_zoo/official/cv/deeptext/README.md b/model_zoo/official/cv/deeptext/README.md
index bb6d20b6c5a..77fefad87d4 100644
--- a/model_zoo/official/cv/deeptext/README.md
+++ b/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：
diff --git a/model_zoo/official/cv/densenet/README.md b/model_zoo/official/cv/densenet/README.md
index 90462eaed84..43b5b6fb385 100644
--- a/model_zoo/official/cv/densenet/README.md
+++ b/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：
diff --git a/model_zoo/official/cv/densenet/README_CN.md b/model_zoo/official/cv/densenet/README_CN.md
index 07e00f4513c..11316c3136d 100644
--- a/model_zoo/official/cv/densenet/README_CN.md
+++ b/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：
diff --git a/model_zoo/official/cv/dpn/README.md b/model_zoo/official/cv/dpn/README.md
index 9a5358b357d..123a7381150 100644
--- a/model_zoo/official/cv/dpn/README.md
+++ b/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
diff --git a/model_zoo/official/cv/faster_rcnn/README.md b/model_zoo/official/cv/faster_rcnn/README.md
index 78226698bcd..91276ea26fa 100644
--- a/model_zoo/official/cv/faster_rcnn/README.md
+++ b/model_zoo/official/cv/faster_rcnn/README.md
@@ -48,7 +48,7 @@ Dataset used: [COCO2017]()
# 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)
diff --git a/model_zoo/official/cv/faster_rcnn/README_CN.md b/model_zoo/official/cv/faster_rcnn/README_CN.md
index a9d01444b34..c9a859edcd1 100644
--- a/model_zoo/official/cv/faster_rcnn/README_CN.md
+++ b/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)
diff --git a/model_zoo/official/cv/googlenet/README.md b/model_zoo/official/cv/googlenet/README.md
index b0a39d7defb..0c392063deb 100644
--- a/model_zoo/official/cv/googlenet/README.md
+++ b/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：
diff --git a/model_zoo/official/cv/googlenet/README_CN.md b/model_zoo/official/cv/googlenet/README_CN.md
index 03df3c1a8dd..fc114e74e20 100644
--- a/model_zoo/official/cv/googlenet/README_CN.md
+++ b/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：
diff --git a/model_zoo/official/cv/inceptionv3/README.md b/model_zoo/official/cv/inceptionv3/README.md
index bfba3390aa0..49b8044b99b 100644
--- a/model_zoo/official/cv/inceptionv3/README.md
+++ b/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：
diff --git a/model_zoo/official/cv/inceptionv3/README_CN.md b/model_zoo/official/cv/inceptionv3/README_CN.md
index 7a295db95b8..ce35335e7b2 100644
--- a/model_zoo/official/cv/inceptionv3/README_CN.md
+++ b/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：
diff --git a/model_zoo/official/cv/inceptionv4/README.md b/model_zoo/official/cv/inceptionv4/README.md
index eac92df58d9..d19faf842dc 100644
--- a/model_zoo/official/cv/inceptionv4/README.md
+++ b/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)
diff --git a/model_zoo/official/cv/maskrcnn/README.md b/model_zoo/official/cv/maskrcnn/README.md
index aef4277d6be..88d082e9731 100644
--- a/model_zoo/official/cv/maskrcnn/README.md
+++ b/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
diff --git a/model_zoo/official/cv/maskrcnn/README_CN.md b/model_zoo/official/cv/maskrcnn/README_CN.md
index 0065d2a3a4f..4be4834db40 100644
--- a/model_zoo/official/cv/maskrcnn/README_CN.md
+++ b/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
diff --git a/model_zoo/official/cv/maskrcnn_mobilenetv1/README.md b/model_zoo/official/cv/maskrcnn_mobilenetv1/README.md
index 0be61ab07ec..2e18640ce97 100644
--- a/model_zoo/official/cv/maskrcnn_mobilenetv1/README.md
+++ b/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:
diff --git a/model_zoo/official/cv/mobilenetv1/README.md b/model_zoo/official/cv/mobilenetv1/README.md
index c971774e0e5..15b858359c2 100644
--- a/model_zoo/official/cv/mobilenetv1/README.md
+++ b/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：
diff --git a/model_zoo/official/cv/mobilenetv2/README.md b/model_zoo/official/cv/mobilenetv2/README.md
index 2c5d4b8fa45..e0a29eed30d 100644
--- a/model_zoo/official/cv/mobilenetv2/README.md
+++ b/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：
diff --git a/model_zoo/official/cv/mobilenetv2/README_CN.md b/model_zoo/official/cv/mobilenetv2/README_CN.md
index 12d55fd6c3d..027a4a8ee65 100644
--- a/model_zoo/official/cv/mobilenetv2/README_CN.md
+++ b/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：
diff --git a/model_zoo/official/cv/mobilenetv2_quant/README_CN.md b/model_zoo/official/cv/mobilenetv2_quant/README_CN.md
index 6a45135fbd3..6471eb45cf1 100644
--- a/model_zoo/official/cv/mobilenetv2_quant/README_CN.md
+++ b/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
diff --git a/model_zoo/official/cv/mobilenetv2_quant/Readme.md b/model_zoo/official/cv/mobilenetv2_quant/Readme.md
index 5a22ea8eea5..241aade01e8 100644
--- a/model_zoo/official/cv/mobilenetv2_quant/Readme.md
+++ b/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
diff --git a/model_zoo/official/cv/openpose/README.md b/model_zoo/official/cv/openpose/README.md
index 68798789dd8..d9760f795f6 100644
--- a/model_zoo/official/cv/openpose/README.md
+++ b/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:
diff --git a/model_zoo/official/cv/psenet/README.md b/model_zoo/official/cv/psenet/README.md
index 5b473f888ad..02784a84be5 100644
--- a/model_zoo/official/cv/psenet/README.md
+++ b/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：
diff --git a/model_zoo/official/cv/psenet/README_CN.md b/model_zoo/official/cv/psenet/README_CN.md
index c2f40f8f8d1..a76810a6efe 100644
--- a/model_zoo/official/cv/psenet/README_CN.md
+++ b/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)
diff --git a/model_zoo/official/cv/resnet/README.md b/model_zoo/official/cv/resnet/README.md
index 724b2460849..a677b9e5b5f 100644
--- a/model_zoo/official/cv/resnet/README.md
+++ b/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：
diff --git a/model_zoo/official/cv/resnet/README_CN.md b/model_zoo/official/cv/resnet/README_CN.md
index 6affe2445df..5d5cb5d3892 100755
--- a/model_zoo/official/cv/resnet/README_CN.md
+++ b/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：
diff --git a/model_zoo/official/cv/resnet152/README-CN.md b/model_zoo/official/cv/resnet152/README-CN.md
index 75e7f9e2f99..7f1898a4d4a 100644
--- a/model_zoo/official/cv/resnet152/README-CN.md
+++ b/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：
diff --git a/model_zoo/official/cv/resnet50_quant/README.md b/model_zoo/official/cv/resnet50_quant/README.md
index 2c08da8b993..6c75d0d9f47 100644
--- a/model_zoo/official/cv/resnet50_quant/README.md
+++ b/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：
diff --git a/model_zoo/official/cv/resnet50_quant/README_CN.md b/model_zoo/official/cv/resnet50_quant/README_CN.md
index e3a8300a2e1..ac0f43332d9 100644
--- a/model_zoo/official/cv/resnet50_quant/README_CN.md
+++ b/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)
diff --git a/model_zoo/official/cv/resnet_thor/README.md b/model_zoo/official/cv/resnet_thor/README.md
index cba50a97ef6..2a8a934d0b2 100644
--- a/model_zoo/official/cv/resnet_thor/README.md
+++ b/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)
diff --git a/model_zoo/official/cv/resnet_thor/README_CN.md b/model_zoo/official/cv/resnet_thor/README_CN.md
index 58ff735d8d5..f1bb862cf77 100644
--- a/model_zoo/official/cv/resnet_thor/README_CN.md
+++ b/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)
diff --git a/model_zoo/official/cv/resnext101/README_CN.md b/model_zoo/official/cv/resnext101/README_CN.md
index e88c873e24e..a28bcdad911 100644
--- a/model_zoo/official/cv/resnext101/README_CN.md
+++ b/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 |
-
diff --git a/model_zoo/official/cv/resnext50/README.md b/model_zoo/official/cv/resnext50/README.md
index 0fcd8e632c3..afb4e5bed12 100644
--- a/model_zoo/official/cv/resnext50/README.md
+++ b/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：
diff --git a/model_zoo/official/cv/resnext50/README_CN.md b/model_zoo/official/cv/resnext50/README_CN.md
index 41da9b0836e..6dbf0ed0d87 100644
--- a/model_zoo/official/cv/resnext50/README_CN.md
+++ b/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：
diff --git a/model_zoo/official/cv/retinanet/README_CN.md b/model_zoo/official/cv/retinanet/README_CN.md
index 02bc29479d0..989e8c6317e 100644
--- a/model_zoo/official/cv/retinanet/README_CN.md
+++ b/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：
diff --git a/model_zoo/official/cv/shufflenetv1/README_CN.md b/model_zoo/official/cv/shufflenetv1/README_CN.md
index e4998b4e36a..6d29a1e177b 100644
--- a/model_zoo/official/cv/shufflenetv1/README_CN.md
+++ b/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：
diff --git a/model_zoo/official/cv/simple_pose/README.md b/model_zoo/official/cv/simple_pose/README.md
index 9dde9dc5373..3c3c6911b76 100644
--- a/model_zoo/official/cv/simple_pose/README.md
+++ b/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
diff --git a/model_zoo/official/cv/squeezenet/README.md b/model_zoo/official/cv/squeezenet/README.md
index bf529f69337..441ca62ca33 100644
--- a/model_zoo/official/cv/squeezenet/README.md
+++ b/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：
diff --git a/model_zoo/official/cv/tinydarknet/README.md b/model_zoo/official/cv/tinydarknet/README.md
index d3153fe2a9b..77012ed8ee3 100644
--- a/model_zoo/official/cv/tinydarknet/README.md
+++ b/model_zoo/official/cv/tinydarknet/README.md
@@ -56,7 +56,7 @@ Dataset used can refer to [paper]()
# [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：
diff --git a/model_zoo/official/cv/yolov3_resnet18/README_CN.md b/model_zoo/official/cv/yolov3_resnet18/README_CN.md
index 991837dfc2d..b4409ee9331 100644
--- a/model_zoo/official/cv/yolov3_resnet18/README_CN.md
+++ b/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：
diff --git a/model_zoo/official/cv/yolov4/README.md b/model_zoo/official/cv/yolov4/README.md
index f852aa2f016..279de662684 100644
--- a/model_zoo/official/cv/yolov4/README.md
+++ b/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：
diff --git a/model_zoo/official/gnn/gcn/README.md b/model_zoo/official/gnn/gcn/README.md
index 738857484db..6dffd848d77 100644
--- a/model_zoo/official/gnn/gcn/README.md
+++ b/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：
diff --git a/model_zoo/official/gnn/gcn/README_CN.md b/model_zoo/official/gnn/gcn/README_CN.md
index 49709a6edbe..dcc6105acf8 100644
--- a/model_zoo/official/gnn/gcn/README_CN.md
+++ b/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：
diff --git a/model_zoo/official/nlp/bert/README.md b/model_zoo/official/nlp/bert/README.md
index af1e717136b..4108f19611d 100644
--- a/model_zoo/official/nlp/bert/README.md
+++ b/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：
diff --git a/model_zoo/official/nlp/bert/README_CN.md b/model_zoo/official/nlp/bert/README_CN.md
index 4b9ca2f79c5..98f0fb5e775 100644
--- a/model_zoo/official/nlp/bert/README_CN.md
+++ b/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：
diff --git a/model_zoo/official/nlp/bert_thor/README.md b/model_zoo/official/nlp/bert_thor/README.md
index 35dcaf77cf8..24f042c884c 100644
--- a/model_zoo/official/nlp/bert_thor/README.md
+++ b/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：
diff --git a/model_zoo/official/nlp/bert_thor/README_CN.md b/model_zoo/official/nlp/bert_thor/README_CN.md
index 6db8bd6740c..304272c938b 100644
--- a/model_zoo/official/nlp/bert_thor/README_CN.md
+++ b/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：
diff --git a/model_zoo/official/nlp/fasttext/README.md b/model_zoo/official/nlp/fasttext/README.md
index 24fa09f67e7..cc63e79473b 100644
--- a/model_zoo/official/nlp/fasttext/README.md
+++ b/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：
diff --git a/model_zoo/official/nlp/gnmt_v2/README.md b/model_zoo/official/nlp/gnmt_v2/README.md
index 89a6ef9d2dc..3c7409eab82 100644
--- a/model_zoo/official/nlp/gnmt_v2/README.md
+++ b/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:
diff --git a/model_zoo/official/nlp/gpt/README.md b/model_zoo/official/nlp/gpt/README.md
index de96560bc95..49ac0e716db 100644
--- a/model_zoo/official/nlp/gpt/README.md
+++ b/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：
diff --git a/model_zoo/official/nlp/gru/README.md b/model_zoo/official/nlp/gru/README.md
index 9958dec152c..53876b42720 100644
--- a/model_zoo/official/nlp/gru/README.md
+++ b/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：
diff --git a/model_zoo/official/nlp/lstm/README.md b/model_zoo/official/nlp/lstm/README.md
index c8889a76af9..360195e1cfb 100644
--- a/model_zoo/official/nlp/lstm/README.md
+++ b/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：
diff --git a/model_zoo/official/nlp/mass/README.md b/model_zoo/official/nlp/mass/README.md
index 4b33fba44e5..9ae73502eaa 100644
--- a/model_zoo/official/nlp/mass/README.md
+++ b/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：
diff --git a/model_zoo/official/nlp/mass/README_CN.md b/model_zoo/official/nlp/mass/README_CN.md
index a07f812b44f..9a0ec500a6c 100644
--- a/model_zoo/official/nlp/mass/README_CN.md
+++ b/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：
diff --git a/model_zoo/official/nlp/prophetnet/README.md b/model_zoo/official/nlp/prophetnet/README.md
index 6c8220824df..4aa46ea46c0 100644
--- a/model_zoo/official/nlp/prophetnet/README.md
+++ b/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：
diff --git a/model_zoo/official/nlp/textcnn/README.md b/model_zoo/official/nlp/textcnn/README.md
index 7110530368f..47f5cec53fd 100644
--- a/model_zoo/official/nlp/textcnn/README.md
+++ b/model_zoo/official/nlp/textcnn/README.md
@@ -40,7 +40,7 @@ Dataset used: [Movie Review Data]()
# [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：
diff --git a/model_zoo/research/nlp/dscnn/README.md b/model_zoo/research/nlp/dscnn/README.md
index 22cfa5347c1..8863494606a 100644
--- a/model_zoo/research/nlp/dscnn/README.md
+++ b/model_zoo/research/nlp/dscnn/README.md
@@ -57,7 +57,7 @@ Dataset used: [Speech commands dataset version 2](https://arxiv.org/abs/1804.032
# [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)
- Third party open source package（if have）
diff --git a/model_zoo/research/recommend/autodis/README.md b/model_zoo/research/recommend/autodis/README.md
index d904ea0909b..18e39c358ee 100644
--- a/model_zoo/research/recommend/autodis/README.md
+++ b/model_zoo/research/recommend/autodis/README.md
@@ -37,7 +37,7 @@ AutoDis leverages a set of meta-embeddings for each numerical field, which are s
# [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：
diff --git a/model_zoo/research/rl/ldp_linucb/README.md b/model_zoo/research/rl/ldp_linucb/README.md
index 9c37d2068e7..1d7f7d940d8 100644
--- a/model_zoo/research/rl/ldp_linucb/README.md
+++ b/model_zoo/research/rl/ldp_linucb/README.md
@@ -35,7 +35,7 @@ Dataset used: [MovieLens 100K](https://grouplens.org/datasets/movielens/100k/)
# [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：