diff --git a/model_zoo/official/cv/deeplabv3/README.md b/model_zoo/official/cv/deeplabv3/README.md
index 6d791e54d1..89304be2d5 100644
--- a/model_zoo/official/cv/deeplabv3/README.md
+++ b/model_zoo/official/cv/deeplabv3/README.md
@@ -104,7 +104,7 @@ For single device training, please config parameters, training script is:
 run_standalone_train.sh
 ```
 
-For 8 devices training, training steps are as follows:
+- For 8 devices training, training steps are as follows:
 
 1. Train s16 with vocaug dataset, finetuning from resnet101 pretrained model, script is:
 
@@ -124,7 +124,7 @@ For 8 devices training, training steps are as follows:
     run_distribute_train_s8_r2.sh
     ```
 
-For evaluation, evaluating steps are as follows:
+- For evaluation, evaluating steps are as follows:
 
 1. Eval s16 with voc val dataset, eval script is:
 
@@ -150,6 +150,238 @@ For evaluation, evaluating steps are as follows:
     run_eval_s8_multiscale_flip.sh
     ```
 
+- Train on ModelArts (If you want to run in modelarts, please check the official documentation of [modelarts](https://support.huaweicloud.com/modelarts/), and you can start training as follows)
+
+1. Train s16 with vocaug dataset on modelarts, finetuning from resnet101 pretrained model, training steps are as follows:
+
+    ```python
+    # (1) Perform a or b.
+    #       a. Set "enable_modelarts=True" on base_config.yaml file.
+    #          Set "data_file='/cache/data/vocaug/vocaug_mindrecord/vocaug_mindrecord0'" on base_config.yaml file.
+    #          Set "checkpoint_url=/The path of checkpoint in S3/" on beta_config.yaml file.
+    #          Set "ckpt_pre_trained=/cache/checkpoint_path/path_to_pretrain/resnet101.ckpt" on base_config.yaml file.
+    #          Set "base_lr=0.08" on base_config.yaml file.
+    #          Set "is_distributed=True" on base_config.yaml file.
+    #          Set "save_steps=410" on base_config.yaml file.
+    #          Set other parameters on base_config.yaml file you need.
+    #       b. Add "enable_modelarts=True" on the website UI interface.
+    #          Add "data_file=/cache/data/vocaug/vocaug_mindrecord/vocaug_mindrecord0" on the website UI interface.
+    #          Add "checkpoint_url=/The path of checkpoint in S3/" on the website UI interface.
+    #          Add "ckpt_pre_trained=/cache/checkpoint_path/path_to_pretrain/resnet101.ckpt" on the website UI interface.
+    #          Add "base_lr=0.08" on the website UI interface.
+    #          Add "is_distributed=True" on the website UI interface.
+    #          Add "save_steps=410" on the website UI interface.
+    #          Add other parameters on the website UI interface.
+    # (2) Upload or copy your pretrained model to S3 bucket.
+    # (3) Upload a zip dataset to S3 bucket. (you could also upload the origin dataset, but it can be so slow.)
+    # (4) Set the code directory to "/path/deeplabv3" on the website UI interface.
+    # (5) Set the startup file to "train.py" on the website UI interface.
+    # (6) Set the "Dataset path" and "Output file path" and "Job log path" to your path on the website UI interface.
+    # (7) Create your job.
+    ```
+
+2. Train s8 with vocaug dataset on modelarts, finetuning from model in previous step, training steps are as follows:
+
+    ```python
+    # (1) Perform a or b.
+    #       a. Set "enable_modelarts=True" on base_config.yaml file.
+    #          Set "model='deeplab_v3_s8'" on base_config.yaml file.
+    #          Set "train_epochs=800" on base_config.yaml file.
+    #          Set "batch_size=16" on base_config.yaml file.
+    #          Set "base_lr=0.02" on base_config.yaml file.
+    #          Set "loss_scale=2048" on base_config.yaml file.
+    #          Set "data_file='/cache/data/vocaug/vocaug_mindrecord/vocaug_mindrecord0'" on base_config.yaml file.
+    #          Set "checkpoint_url=/The path of checkpoint in S3/" on beta_config.yaml file.
+    #          Set "ckpt_pre_trained=/cache/checkpoint_path/path_to_pretrain/deeplab_v3_s16-300_41.ckpt" on base_config.yaml file.
+    #          Set "is_distributed=True" on base_config.yaml file.
+    #          Set "save_steps=820" on base_config.yaml file.
+    #          Set other parameters on base_config.yaml file you need.
+    #       b. Add "enable_modelarts=True" on the website UI interface.
+    #          Add "model='deeplab_v3_s8'" on the website UI interface.
+    #          Add "train_epochs=800" on the website UI interface.
+    #          Add "batch_size=16" on the website UI interface.
+    #          Add "base_lr=0.02" on the website UI interface.
+    #          Add "loss_scale=2048" on the website UI interface.
+    #          Add "data_file='/cache/data/vocaug/vocaug_mindrecord/vocaug_mindrecord0'" on the website UI interface.
+    #          Add "checkpoint_url=/The path of checkpoint in S3/" on the website UI interface.
+    #          Add "ckpt_pre_trained=/cache/checkpoint_path/path_to_pretrain/deeplab_v3_s16-300_41.ckpt" on the website UI interface.
+    #          Add "is_distributed=True" on the website UI interface.
+    #          Add "save_steps=820" on the website UI interface.
+    #          Add other parameters on the website UI interface.
+    # (2) Upload or copy your pretrained model to S3 bucket.
+    # (3) Upload a zip dataset to S3 bucket. (you could also upload the origin dataset, but it can be so slow.)
+    # (4) Set the code directory to "/path/deeplabv3" on the website UI interface.
+    # (5) Set the startup file to "train.py" on the website UI interface.
+    # (6) Set the "Dataset path" and "Output file path" and "Job log path" to your path on the website UI interface.
+    # (7) Create your job.
+    ```
+
+3. Train s8 with voctrain dataset on modelarts, finetuning from model in previous step, training steps are as follows:
+
+    ```python
+    # (1) Perform a or b.
+    #       a. Set "enable_modelarts=True" on base_config.yaml file.
+    #          Set "model='deeplab_v3_s8'" on base_config.yaml file.
+    #          Set "batch_size=16" on base_config.yaml file.
+    #          Set "base_lr=0.008" on base_config.yaml file.
+    #          Set "loss_scale=2048" on base_config.yaml file.
+    #          Set "data_file='/cache/data/vocaug/voctrain_mindrecord/voctrain_mindrecord00'" on base_config.yaml file.
+    #          Set "checkpoint_url=/The path of checkpoint in S3/" on beta_config.yaml file.
+    #          Set "ckpt_pre_trained=/cache/checkpoint_path/path_to_pretrain/deeplab_v3_s8-800_82.ckpt" on base_config.yaml file.
+    #          Set "is_distributed=True" on base_config.yaml file.
+    #          Set "save_steps=110" on base_config.yaml file.
+    #          Set other parameters on base_config.yaml file you need.
+    #       b. Add "enable_modelarts=True" on the website UI interface.
+    #          Add "model='deeplab_v3_s8'" on the website UI interface.
+    #          Add "batch_size=16" on the website UI interface.
+    #          Add "base_lr=0.008" on the website UI interface.
+    #          Add "loss_scale=2048" on the website UI interface.
+    #          Add "data_file='/cache/data/vocaug/voctrain_mindrecord/voctrain_mindrecord00'" on the website UI interface.
+    #          Add "checkpoint_url=/The path of checkpoint in S3/" on the website UI interface.
+    #          Add "ckpt_pre_trained=/cache/checkpoint_path/path_to_pretrain/deeplab_v3_s8-800_82.ckpt" on the website UI interface.
+    #          Add "is_distributed=True" on the website UI interface.
+    #          Add "save_steps=110" on the website UI interface.
+    #          Add other parameters on the website UI interface.
+    # (2) Upload or copy your pretrained model to S3 bucket.
+    # (3) Upload a zip dataset to S3 bucket. (you could also upload the origin dataset, but it can be so slow.)
+    # (4) Set the code directory to "/path/deeplabv3" on the website UI interface.
+    # (5) Set the startup file to "train.py" on the website UI interface.
+    # (6) Set the "Dataset path" and "Output file path" and "Job log path" to your path on the website UI interface.
+    # (7) Create your job.
+    ```
+
+- Eval on ModelArts (If you want to run in modelarts, please check the official documentation of [modelarts](https://support.huaweicloud.com/modelarts/), and you can start evaluating as follows)
+
+1. Eval s16 with voc val dataset on modelarts, evaluating steps are as follows:
+
+    ```python
+    # (1) Perform a or b.
+    #       a. Set "enable_modelarts=True" on base_config.yaml file.
+    #          Set "model='deeplab_v3_s16'" on base_config.yaml file.
+    #          Set "batch_size=32" on base_config.yaml file.
+    #          Set "scales_type=0" on base_config.yaml file.
+    #          Set "freeze_bn=True" on base_config.yaml file.
+    #          Set "data_root='/cache/data/vocaug'" on base_config.yaml file.
+    #          Set "data_lst='/cache/data/vocaug/voc_val_lst.txt'" on base_config.yaml file.
+    #          Set "checkpoint_url=/The path of checkpoint in S3/" on beta_config.yaml file.
+    #          Set "ckpt_path='/cache/checkpoint_path/path_to_pretrain/deeplab_v3_s16-300_41.ckpt'" on base_config.yaml file.
+    #          Set other parameters on base_config.yaml file you need.
+    #       b. Add "enable_modelarts=True" on the website UI interface.
+    #          Add "model=deeplab_v3_s16" on the website UI interface.
+    #          Add "batch_size=32" on the website UI interface.
+    #          Add "scales_type=0" on the website UI interface.
+    #          Add "freeze_bn=True" on the website UI interface.
+    #          Add "data_root=/cache/data/vocaug" on the website UI interface.
+    #          Add "data_lst=/cache/data/vocaug/voc_val_lst.txt" on the website UI interface.
+    #          Add "checkpoint_url=/The path of checkpoint in S3/" on the website UI interface.
+    #          Add "ckpt_path=/cache/checkpoint_path/path_to_pretrain/deeplab_v3_s16-300_41.ckpt" on the website UI interface.
+    #          Add other parameters on the website UI interface.
+    # (2) Upload or copy your pretrained model to S3 bucket.
+    # (3) Upload a zip dataset to S3 bucket. (you could also upload the origin dataset, but it can be so slow.)
+    # (4) Set the code directory to "/path/deeplabv3" on the website UI interface.
+    # (5) Set the startup file to "eval.py" on the website UI interface.
+    # (6) Set the "Dataset path" and "Output file path" and "Job log path" to your path on the website UI interface.
+    # (7) Create your job.
+    ```
+
+2. Eval s8 with voc val dataset on modelarts, evaluating steps are as follows:
+
+    ```python
+    # (1) Perform a or b.
+    #       a. Set "enable_modelarts=True" on base_config.yaml file.
+    #          Set "model='deeplab_v3_s8'" on base_config.yaml file.
+    #          Set "batch_size=16" on base_config.yaml file.
+    #          Set "scales_type=0" on base_config.yaml file.
+    #          Set "freeze_bn=True" on base_config.yaml file.
+    #          Set "data_root='/cache/data/vocaug'" on base_config.yaml file.
+    #          Set "data_lst='/cache/data/vocaug/voc_val_lst.txt'" on base_config.yaml file.
+    #          Set "checkpoint_url='/The path of checkpoint in S3/'" on beta_config.yaml file.
+    #          Set "ckpt_path='/cache/checkpoint_path/path_to_pretrain/deeplab_v3_s8-300_11.ckpt'" on base_config.yaml file.
+    #          Set other parameters on base_config.yaml file you need.
+    #       b. Add "enable_modelarts=True" on the website UI interface.
+    #          Add "model=deeplab_v3_s8" on the website UI interface.
+    #          Add "batch_size=16" on the website UI interface.
+    #          Add "scales_type=0" on the website UI interface.
+    #          Add "freeze_bn=True" on the website UI interface.
+    #          Add "data_root=/cache/data/vocaug" on the website UI interface.
+    #          Add "data_lst=/cache/data/vocaug/voc_val_lst.txt" on the website UI interface.
+    #          Add "checkpoint_url=/The path of checkpoint in S3/" on the website UI interface.
+    #          Add "ckpt_path=/cache/checkpoint_path/path_to_pretrain/deeplab_v3_s8-300_11.ckpt" on the website UI interface.
+    #          Add other parameters on the website UI interface.
+    # (2) Upload or copy your pretrained model to S3 bucket.
+    # (3) Upload a zip dataset to S3 bucket. (you could also upload the origin dataset, but it can be so slow.)
+    # (4) Set the code directory to "/path/deeplabv3" on the website UI interface.
+    # (5) Set the startup file to "eval.py" on the website UI interface.
+    # (6) Set the "Dataset path" and "Output file path" and "Job log path" to your path on the website UI interface.
+    # (7) Create your job.
+    ```
+
+3. Eval s8 multiscale with voc val dataset on modelarts, evaluating steps are as follows:
+
+    ```python
+    # (1) Perform a or b.
+    #       a. Set "enable_modelarts=True" on base_config.yaml file.
+    #          Set "model='deeplab_v3_s8'" on base_config.yaml file.
+    #          Set "batch_size=16" on base_config.yaml file.
+    #          Set "scales_type=1" on base_config.yaml file.
+    #          Set "freeze_bn=True" on base_config.yaml file.
+    #          Set "data_root='/cache/data/vocaug'" on base_config.yaml file.
+    #          Set "data_lst='/cache/data/vocaug/voc_val_lst.txt'" on base_config.yaml file.
+    #          Set "checkpoint_url='/The path of checkpoint in S3/'" on beta_config.yaml file.
+    #          Set "ckpt_path='/cache/checkpoint_path/path_to_pretrain/deeplab_v3_s8-300_11.ckpt'" on base_config.yaml file.
+    #          Set other parameters on base_config.yaml file you need.
+    #       b. Add "enable_modelarts=True" on the website UI interface.
+    #          Add "model=deeplab_v3_s8" on the website UI interface.
+    #          Add "batch_size=16" on the website UI interface.
+    #          Add "scales_type=1" on the website UI interface.
+    #          Add "freeze_bn=True" on the website UI interface.
+    #          Add "data_root=/cache/data/vocaug" on the website UI interface.
+    #          Add "data_lst=/cache/data/vocaug/voc_val_lst.txt" on the website UI interface.
+    #          Add "checkpoint_url=/The path of checkpoint in S3/" on the website UI interface.
+    #          Add "ckpt_path=/cache/checkpoint_path/path_to_pretrain/deeplab_v3_s8-300_11.ckpt" on the website UI interface.
+    #          Add other parameters on the website UI interface.
+    # (2) Upload or copy your pretrained model to S3 bucket.
+    # (3) Upload a zip dataset to S3 bucket. (you could also upload the origin dataset, but it can be so slow.)
+    # (4) Set the code directory to "/path/deeplabv3" on the website UI interface.
+    # (5) Set the startup file to "eval.py" on the website UI interface.
+    # (6) Set the "Dataset path" and "Output file path" and "Job log path" to your path on the website UI interface.
+    # (7) Create your job.
+    ```
+
+4. Eval s8 multiscale and flip with voc val dataset on modelarts, evaluating steps are as follows:
+
+    ```python
+    # (1) Perform a or b.
+    #       a. Set "enable_modelarts=True" on base_config.yaml file.
+    #          Set "model='deeplab_v3_s8'" on base_config.yaml file.
+    #          Set "batch_size=16" on base_config.yaml file.
+    #          Set "scales_type=1" on base_config.yaml file.
+    #          Set "freeze_bn=True" on base_config.yaml file.
+    #          Set "flip=True" on base_config.yaml file.
+    #          Set "data_root='/cache/data/vocaug'" on base_config.yaml file.
+    #          Set "data_lst='/cache/data/vocaug/voc_val_lst.txt'" on base_config.yaml file.
+    #          Set "checkpoint_url='/The path of checkpoint in S3/'" on beta_config.yaml file.
+    #          Set "ckpt_path='/cache/checkpoint_path/path_to_pretrain/deeplab_v3_s8-300_11.ckpt'" on base_config.yaml file.
+    #          Set other parameters on base_config.yaml file you need.
+    #       b. Add "enable_modelarts=True" on the website UI interface.
+    #          Add "model=deeplab_v3_s8" on the website UI interface.
+    #          Add "batch_size=16" on the website UI interface.
+    #          Add "scales_type=1" on the website UI interface.
+    #          Add "freeze_bn=True" on the website UI interface.
+    #          Add "flip=True" on the website UI interface.
+    #          Add "data_root=/cache/data/vocaug" on the website UI interface.
+    #          Add "data_lst=/cache/data/vocaug/voc_val_lst.txt" on the website UI interface.
+    #          Add "checkpoint_url=/The path of checkpoint in S3/" on the website UI interface.
+    #          Add "ckpt_path=/cache/checkpoint_path/path_to_pretrain/deeplab_v3_s8-300_11.ckpt" on the website UI interface.
+    #          Add other parameters on the website UI interface.
+    # (2) Upload or copy your pretrained model to S3 bucket.
+    # (3) Upload a zip dataset to S3 bucket. (you could also upload the origin dataset, but it can be so slow.)
+    # (4) Set the code directory to "/path/deeplabv3" on the website UI interface.
+    # (5) Set the startup file to "eval.py" on the website UI interface.
+    # (6) Set the "Dataset path" and "Output file path" and "Job log path" to your path on the website UI interface.
+    # (7) Create your job.
+    ```
+
 # [Script Description](#contents)
 
 ## [Script and Sample Code](#contents)
diff --git a/model_zoo/official/cv/deeplabv3/README_CN.md b/model_zoo/official/cv/deeplabv3/README_CN.md
index 11209f244b..307ba80cda 100644
--- a/model_zoo/official/cv/deeplabv3/README_CN.md
+++ b/model_zoo/official/cv/deeplabv3/README_CN.md
@@ -119,7 +119,7 @@ Pascal VOC数据集和语义边界数据集（Semantic Boundaries Dataset，SBD
 run_standalone_train.sh
 ```
 
-按照以下训练步骤进行8卡训练：
+- 按照以下训练步骤进行8卡训练：
 
 1. 使用VOCaug数据集训练s16，微调ResNet-101预训练模型。脚本如下：
 
@@ -139,7 +139,7 @@ run_standalone_train.sh
     run_distribute_train_s8_r2.sh
     ```
 
-评估步骤如下：
+- 评估步骤如下：
 
 1. 使用voc val数据集评估s16。评估脚本如下：
 
@@ -165,6 +165,238 @@ run_standalone_train.sh
     run_eval_s8_multiscale_flip.sh
     ```
 
+- 在 ModelArts 进行训练 (如果你想在modelarts上运行，可以参考以下文档 [modelarts](https://support.huaweicloud.com/modelarts/))
+
+1. 在 modelarts 使用VOCaug数据集训练s16，微调ResNet-101预训练模型。训练步骤如下：
+
+    ```python
+    # (1) 执行 a 或者 b.
+    #       a. 在 base_config.yaml 文件中设置 "enable_modelarts=True"
+    #          在 base_config.yaml 文件中设置 "data_file='/cache/data/vocaug/vocaug_mindrecord/vocaug_mindrecord0'"
+    #          在 base_config.yaml 文件中设置 "checkpoint_url=/The path of checkpoint in S3/"
+    #          在 base_config.yaml 文件中设置 "ckpt_pre_trained=/cache/checkpoint_path/path_to_pretrain/resnet101.ckpt"
+    #          在 base_config.yaml 文件中设置 "base_lr=0.08"
+    #          在 base_config.yaml 文件中设置 "is_distributed=True"
+    #          在 base_config.yaml 文件中设置 "save_steps=410"
+    #          在 base_config.yaml 文件中设置 其他参数
+    #       b. 在网页上设置 "enable_modelarts=True"
+    #          在网页上设置 "data_file=/cache/data/vocaug/vocaug_mindrecord/vocaug_mindrecord0"
+    #          在网页上设置 "checkpoint_url=/The path of checkpoint in S3/"
+    #          在网页上设置 "ckpt_pre_trained=/cache/checkpoint_path/path_to_pretrain/resnet101.ckpt"
+    #          在网页上设置 "base_lr=0.08"
+    #          在网页上设置 "is_distributed=True"
+    #          在网页上设置 "save_steps=410"
+    #          在网页上设置 其他参数
+    # (2) 上传你的预训练模型到 S3 桶上
+    # (3) 上传你的压缩数据集到 S3 桶上 (你也可以上传原始的数据集，但那可能会很慢。)
+    # (4) 在网页上设置你的代码路径为 "/path/deeplabv3"
+    # (5) 在网页上设置启动文件为 "train.py"
+    # (6) 在网页上设置"训练数据集"、"训练输出文件路径"、"作业日志路径"等
+    # (7) 创建训练作业
+    ```
+
+2. 使用VOCaug数据集训练s8，微调上一步的模型。训练步骤如下：
+
+    ```python
+    # (1) 执行 a 或者 b.
+    #       a. 在 base_config.yaml 文件中设置 "enable_modelarts=True"
+    #          在 base_config.yaml 文件中设置 "model='deeplab_v3_s8'"
+    #          在 base_config.yaml 文件中设置 "train_epochs=800"
+    #          在 base_config.yaml 文件中设置 "batch_size=16"
+    #          在 base_config.yaml 文件中设置 "base_lr=0.02"
+    #          在 base_config.yaml 文件中设置 "loss_scale=2048"
+    #          在 base_config.yaml 文件中设置 "data_file='/cache/data/vocaug/vocaug_mindrecord/vocaug_mindrecord0'"
+    #          在 base_config.yaml 文件中设置 "checkpoint_url=/The path of checkpoint in S3/"
+    #          在 base_config.yaml 文件中设置 "ckpt_pre_trained=/cache/checkpoint_path/path_to_pretrain/deeplab_v3_s16-300_41.ckpt"
+    #          在 base_config.yaml 文件中设置 "is_distributed=True"
+    #          在 base_config.yaml 文件中设置 "save_steps=820"
+    #          在 base_config.yaml 文件中设置 其他参数
+    #       b. 在网页上设置 "enable_modelarts=True"
+    #          在网页上设置 "model='deeplab_v3_s8'"
+    #          在网页上设置 "train_epochs=800"
+    #          在网页上设置 "batch_size=16"
+    #          在网页上设置 "base_lr=0.02"
+    #          在网页上设置 "loss_scale=2048"
+    #          在网页上设置 "data_file='/cache/data/vocaug/vocaug_mindrecord/vocaug_mindrecord0'"
+    #          在网页上设置 "checkpoint_url=/The path of checkpoint in S3/"
+    #          在网页上设置 "ckpt_pre_trained=/cache/checkpoint_path/path_to_pretrain/deeplab_v3_s16-300_41.ckpt"
+    #          在网页上设置 "is_distributed=True"
+    #          在网页上设置 "save_steps=820"
+    #          在网页上设置 其他参数
+    # (2) 上传你的预训练模型到 S3 桶上
+    # (3) 上传你的压缩数据集到 S3 桶上 (你也可以上传原始的数据集，但那可能会很慢。)
+    # (4) 在网页上设置你的代码路径为 "/path/deeplabv3"
+    # (5) 在网页上设置启动文件为 "train.py"
+    # (6) 在网页上设置"训练数据集"、"训练输出文件路径"、"作业日志路径"等
+    # (7) 创建训练作业
+    ```
+
+3. 使用VOCtrain数据集训练s8，微调上一步的模型。训练步骤如下：
+
+    ```python
+    # (1) 执行 a 或者 b.
+    #       a. 在 base_config.yaml 文件中设置 "enable_modelarts=True"
+    #          在 base_config.yaml 文件中设置 "model='deeplab_v3_s8'"
+    #          在 base_config.yaml 文件中设置 "batch_size=16"
+    #          在 base_config.yaml 文件中设置 "base_lr=0.008"
+    #          在 base_config.yaml 文件中设置 "loss_scale=2048"
+    #          在 base_config.yaml 文件中设置 "data_file='/cache/data/vocaug/voctrain_mindrecord/voctrain_mindrecord00'"
+    #          在 base_config.yaml 文件中设置 "checkpoint_url=/The path of checkpoint in S3/"
+    #          在 base_config.yaml 文件中设置 "ckpt_pre_trained=/cache/checkpoint_path/path_to_pretrain/deeplab_v3_s8-800_82.ckpt"
+    #          在 base_config.yaml 文件中设置 "is_distributed=True"
+    #          在 base_config.yaml 文件中设置 "save_steps=110"
+    #          在 base_config.yaml 文件中设置 其他参数
+    #       b. 在网页上设置 "enable_modelarts=True"
+    #          在网页上设置 "model='deeplab_v3_s8'"
+    #          在网页上设置 "batch_size=16"
+    #          在网页上设置 "base_lr=0.008"
+    #          在网页上设置 "loss_scale=2048"
+    #          在网页上设置 "data_file='/cache/data/vocaug/voctrain_mindrecord/voctrain_mindrecord00'"
+    #          在网页上设置 "checkpoint_url=/The path of checkpoint in S3/"
+    #          在网页上设置 "ckpt_pre_trained=/cache/checkpoint_path/path_to_pretrain/deeplab_v3_s8-800_82.ckpt"
+    #          在网页上设置 "is_distributed=True"
+    #          在网页上设置 "save_steps=110"
+    #          在网页上设置 其他参数
+    # (2) 上传你的预训练模型到 S3 桶上
+    # (3) 上传你的压缩数据集到 S3 桶上 (你也可以上传原始的数据集，但那可能会很慢。)
+    # (4) 在网页上设置你的代码路径为 "/path/deeplabv3"
+    # (5) 在网页上设置启动文件为 "train.py"
+    # (6) 在网页上设置"训练数据集"、"训练输出文件路径"、"作业日志路径"等
+    # (7) 创建训练作业
+    ```
+
+- 在 ModelArts 进行验证 (如果你想在modelarts上运行，可以参考以下文档 [modelarts](https://support.huaweicloud.com/modelarts/))
+
+1. 使用voc val数据集评估s16。评估步骤如下：
+
+    ```python
+    # (1) 执行 a 或者 b.
+    #       a. 在 base_config.yaml 文件中设置 "enable_modelarts=True"
+    #          在 base_config.yaml 文件中设置 "model='deeplab_v3_s16'"
+    #          在 base_config.yaml 文件中设置 "batch_size=32"
+    #          在 base_config.yaml 文件中设置 "scales_type=0"
+    #          在 base_config.yaml 文件中设置 "freeze_bn=True"
+    #          在 base_config.yaml 文件中设置 "data_root='/cache/data/vocaug'"
+    #          在 base_config.yaml 文件中设置 "data_lst='/cache/data/vocaug/voc_val_lst.txt'"
+    #          在 base_config.yaml 文件中设置 "checkpoint_url=/The path of checkpoint in S3/"
+    #          在 base_config.yaml 文件中设置 "ckpt_path='/cache/checkpoint_path/path_to_pretrain/deeplab_v3_s16-300_41.ckpt'"
+    #          在 base_config.yaml 文件中设置 其他参数
+    #       b. 在网页上设置 "enable_modelarts=True"
+    #          在网页上设置 "model=deeplab_v3_s16"
+    #          在网页上设置 "batch_size=32"
+    #          在网页上设置 "scales_type=0"
+    #          在网页上设置 "freeze_bn=True"
+    #          在网页上设置 "data_root=/cache/data/vocaug"
+    #          在网页上设置 "data_lst=/cache/data/vocaug/voc_val_lst.txt"
+    #          在网页上设置 "checkpoint_url=/The path of checkpoint in S3/"
+    #          在网页上设置 "ckpt_path=/cache/checkpoint_path/path_to_pretrain/deeplab_v3_s16-300_41.ckpt"
+    #          在网页上设置 其他参数
+    # (2) 上传你的预训练模型到 S3 桶上
+    # (3) 上传你的压缩数据集到 S3 桶上 (你也可以上传原始的数据集，但那可能会很慢。)
+    # (4) 在网页上设置你的代码路径为 "/path/deeplabv3"
+    # (5) 在网页上设置启动文件为 "eval.py"
+    # (6) 在网页上设置"训练数据集"、"训练输出文件路径"、"作业日志路径"等
+    # (7) 创建训练作业
+    ```
+
+2. 使用voc val数据集评估s8。评估步骤如下：
+
+    ```python
+    # (1) 执行 a 或者 b.
+    #       a. 在 base_config.yaml 文件中设置 "enable_modelarts=True"
+    #          在 base_config.yaml 文件中设置 "model='deeplab_v3_s8'"
+    #          在 base_config.yaml 文件中设置 "batch_size=16"
+    #          在 base_config.yaml 文件中设置 "scales_type=0"
+    #          在 base_config.yaml 文件中设置 "freeze_bn=True"
+    #          在 base_config.yaml 文件中设置 "data_root='/cache/data/vocaug'"
+    #          在 base_config.yaml 文件中设置 "data_lst='/cache/data/vocaug/voc_val_lst.txt'"
+    #          在 base_config.yaml 文件中设置 "checkpoint_url='/The path of checkpoint in S3/'"
+    #          在 base_config.yaml 文件中设置 "ckpt_path='/cache/checkpoint_path/path_to_pretrain/deeplab_v3_s8-300_11.ckpt'"
+    #          在 base_config.yaml 文件中设置 其他参数
+    #       b. 在网页上设置 "enable_modelarts=True"
+    #          在网页上设置 "model=deeplab_v3_s8"
+    #          在网页上设置 "batch_size=16"
+    #          在网页上设置 "scales_type=0"
+    #          在网页上设置 "freeze_bn=True"
+    #          在网页上设置 "data_root=/cache/data/vocaug"
+    #          在网页上设置 "data_lst=/cache/data/vocaug/voc_val_lst.txt"
+    #          在网页上设置 "checkpoint_url=/The path of checkpoint in S3/"
+    #          在网页上设置 "ckpt_path=/cache/checkpoint_path/path_to_pretrain/deeplab_v3_s8-300_11.ckpt"
+    #          在网页上设置 其他参数.
+    # (2) 上传你的预训练模型到 S3 桶上
+    # (3) 上传你的压缩数据集到 S3 桶上 (你也可以上传原始的数据集，但那可能会很慢。)
+    # (4) 在网页上设置你的代码路径为 "/path/deeplabv3"
+    # (5) 在网页上设置启动文件为 "eval.py"
+    # (6) 在网页上设置"训练数据集"、"训练输出文件路径"、"作业日志路径"等
+    # (7) 创建训练作业
+    ```
+
+3. 使用voc val数据集评估多尺度s8。评估步骤如下：
+
+    ```python
+    # (1) 执行 a 或者 b.
+    #       a. 在 base_config.yaml 文件中设置 "enable_modelarts=True"
+    #          在 base_config.yaml 文件中设置 "model='deeplab_v3_s8'"
+    #          在 base_config.yaml 文件中设置 "batch_size=16"
+    #          在 base_config.yaml 文件中设置 "scales_type=1"
+    #          在 base_config.yaml 文件中设置 "freeze_bn=True"
+    #          在 base_config.yaml 文件中设置 "data_root='/cache/data/vocaug'"
+    #          在 base_config.yaml 文件中设置 "data_lst='/cache/data/vocaug/voc_val_lst.txt'"
+    #          在 base_config.yaml 文件中设置 "checkpoint_url='/The path of checkpoint in S3/'"
+    #          在 base_config.yaml 文件中设置 "ckpt_path='/cache/checkpoint_path/path_to_pretrain/deeplab_v3_s8-300_11.ckpt'"
+    #          在 base_config.yaml 文件中设置 其他参数
+    #       b. 在网页上设置 "enable_modelarts=True"
+    #          在网页上设置 "model=deeplab_v3_s8"
+    #          在网页上设置 "batch_size=16"
+    #          在网页上设置 "scales_type=1"
+    #          在网页上设置 "freeze_bn=True"
+    #          在网页上设置 "data_root=/cache/data/vocaug"
+    #          在网页上设置 "data_lst=/cache/data/vocaug/voc_val_lst.txt"
+    #          在网页上设置 "checkpoint_url=/The path of checkpoint in S3/"
+    #          在网页上设置 "ckpt_path=/cache/checkpoint_path/path_to_pretrain/deeplab_v3_s8-300_11.ckpt"
+    #          在网页上设置 其他参数
+    # (2) 上传你的预训练模型到 S3 桶上
+    # (3) 上传你的压缩数据集到 S3 桶上 (你也可以上传原始的数据集，但那可能会很慢。)
+    # (4) 在网页上设置你的代码路径为 "/path/deeplabv3"
+    # (5) 在网页上设置启动文件为 "eval.py"
+    # (6) 在网页上设置"训练数据集"、"训练输出文件路径"、"作业日志路径"等
+    # (7) 创建训练作业
+    ```
+
+4. 使用voc val数据集评估多尺度和翻转s8。评估步骤如下：
+
+    ```python
+    # (1) 执行 a 或者 b.
+    #       a. 在 base_config.yaml 文件中设置 "enable_modelarts=True"
+    #          在 base_config.yaml 文件中设置 "model='deeplab_v3_s8'"
+    #          在 base_config.yaml 文件中设置 "batch_size=16"
+    #          在 base_config.yaml 文件中设置 "scales_type=1"
+    #          在 base_config.yaml 文件中设置 "freeze_bn=True"
+    #          在 base_config.yaml 文件中设置 "flip=True"
+    #          在 base_config.yaml 文件中设置 "data_root='/cache/data/vocaug'"
+    #          在 base_config.yaml 文件中设置 "data_lst='/cache/data/vocaug/voc_val_lst.txt'"
+    #          在 base_config.yaml 文件中设置 "checkpoint_url='/The path of checkpoint in S3/'"
+    #          在 base_config.yaml 文件中设置 "ckpt_path='/cache/checkpoint_path/path_to_pretrain/deeplab_v3_s8-300_11.ckpt'"
+    #          在 base_config.yaml 文件中设置 其他参数
+    #       b. 在网页上设置 "enable_modelarts=True"
+    #          在网页上设置 "model=deeplab_v3_s8"
+    #          在网页上设置 "batch_size=16"
+    #          在网页上设置 "scales_type=1"
+    #          在网页上设置 "freeze_bn=True"
+    #          在网页上设置 "flip=True"
+    #          在网页上设置 "data_root=/cache/data/vocaug"
+    #          在网页上设置 "data_lst=/cache/data/vocaug/voc_val_lst.txt"
+    #          在网页上设置 "checkpoint_url=/The path of checkpoint in S3/"
+    #          在网页上设置 "ckpt_path=/cache/checkpoint_path/path_to_pretrain/deeplab_v3_s8-300_11.ckpt"
+    #          在网页上设置 其他参数
+    # (2) 上传你的预训练模型到 S3 桶上
+    # (3) 上传你的压缩数据集到 S3 桶上 (你也可以上传原始的数据集，但那可能会很慢。)
+    # (4) 在网页上设置你的代码路径为 "/path/deeplabv3"
+    # (5) 在网页上设置启动文件为 "eval.py"
+    # (6) 在网页上设置"训练数据集"、"训练输出文件路径"、"作业日志路径"等
+    # (7) 创建训练作业
+    ```
+
 # 脚本说明
 
 ## 脚本及样例代码
diff --git a/model_zoo/official/cv/deeplabv3/default_config.yaml b/model_zoo/official/cv/deeplabv3/default_config.yaml
new file mode 100644
index 0000000000..4be94ee9d4
--- /dev/null
+++ b/model_zoo/official/cv/deeplabv3/default_config.yaml
@@ -0,0 +1,100 @@
+# Builtin Configurations(DO NOT CHANGE THESE CONFIGURATIONS unless you know exactly what you are doing)
+enable_modelarts: False
+# Url for modelarts
+data_url: ""
+train_url: ""
+checkpoint_url: ""
+# Path for local
+data_path: "/cache/data"
+output_path: "/cache/train"
+load_path: "/cache/checkpoint_path"
+device_target: "Ascend" # ['Ascend', 'CPU']
+
+# ==============================================================================
+# Training options
+train_dir: "/cache/train/ckpt"
+
+# dataset
+need_modelarts_dataset_unzip: True
+data_file: ""
+batch_size: 32
+crop_size: 513
+image_mean: [103.53, 116.28, 123.675]
+image_std: [57.375, 57.120, 58.395]
+min_scale: 0.5
+max_scale: 2.0
+ignore_label: 255
+num_classes: 21
+
+# optimizer
+train_epochs: 300
+lr_type: "cos"
+base_lr: 0.015
+lr_decay_step: 40000
+lr_decay_rate: 0.1
+loss_scale: 3072.0
+
+# model
+model: "deeplab_v3_s16"
+freeze_bn: False
+ckpt_pre_trained: ""
+filter_weight: False
+
+# train
+is_distributed: False
+rank: 0
+group_size: 1
+save_steps: 3000
+keep_checkpoint_max: 1
+
+# eval param
+data_root: ""
+data_lst: ""
+scales: [1.0,]
+scales_list: [[1.0,], [0.5, 0.75, 1.0, 1.25, 1.75]]
+scales_type: 0
+flip: False
+ckpt_path: ""
+input_format: "NCHW" # ["NCHW", "NHWC"]
+
+---
+
+# Help description for each configuration
+enable_modelarts: "Whether training on modelarts, default: False"
+data_url: "Url for modelarts"
+train_url: "Url for modelarts"
+data_path: "The location of the input data."
+output_path: "The location of the output file."
+device_target: 'Target device type'
+train_dir: "where training log and ckpts saved"
+data_file: "path and name of one mindrecord file"
+batch_size: "batch size"
+crop_size: "crop size"
+image_mean: "image mean"
+image_std: "image std"
+min_scale: "minimum scale of data argumentation"
+max_scale: "maximum scale of data argumentation"
+ignore_label: "ignore label"
+num_classes: "number of classes"
+train_epochs: "epoch"
+lr_type: "type of learning rate"
+base_lr: "base learning rate"
+lr_decay_step: "learning rate decay step"
+lr_decay_rate: "learning rate decay rate"
+loss_scale: "loss scale"
+model: "select model"
+freeze_bn: "freeze bn"
+ckpt_pre_trained: "pretrained model"
+filter_weight: "Filter the last weight parameters, default is False."
+is_distributed: "distributed training"
+rank: "local rank of distributed"
+group_size: "world size of distributed"
+save_steps: "steps interval for saving"
+keep_checkpoint_max: "max checkpoint for saving"
+
+data_root: "root path of val data"
+data_lst: "list of val data"
+scales: "scales of evaluation"
+flip: "perform left-right flip"
+ckpt_path: "model to evaluat"
+input_format: "NCHW or NHWC"
\ No newline at end of file
diff --git a/model_zoo/official/cv/deeplabv3/eval.py b/model_zoo/official/cv/deeplabv3/eval.py
index 8ba2fd420e..b532ba4a0e 100644
--- a/model_zoo/official/cv/deeplabv3/eval.py
+++ b/model_zoo/official/cv/deeplabv3/eval.py
@@ -15,7 +15,7 @@
 """eval deeplabv3."""
 
 import os
-import argparse
+import time
 import numpy as np
 import cv2
 from mindspore import Tensor
@@ -25,34 +25,18 @@ import mindspore.ops as ops
 from mindspore import context
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
 from src.nets import net_factory
-context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False,
-                    device_id=int(os.getenv('DEVICE_ID')))
 
+from utils.config import config
+from utils.moxing_adapter import moxing_wrapper
+from utils.device_adapter import get_device_id, get_device_num, get_rank_id
 
-def parse_args():
-    parser = argparse.ArgumentParser('mindspore deeplabv3 eval')
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False,
+                    device_id=get_device_id())
 
-    # val data
-    parser.add_argument('--data_root', type=str, default='', help='root path of val data')
-    parser.add_argument('--data_lst', type=str, default='', help='list of val data')
-    parser.add_argument('--batch_size', type=int, default=16, help='batch size')
-    parser.add_argument('--crop_size', type=int, default=513, help='crop size')
-    parser.add_argument('--image_mean', type=list, default=[103.53, 116.28, 123.675], help='image mean')
-    parser.add_argument('--image_std', type=list, default=[57.375, 57.120, 58.395], help='image std')
-    parser.add_argument('--scales', type=float, action='append', help='scales of evaluation')
-    parser.add_argument('--flip', action='store_true', help='perform left-right flip')
-    parser.add_argument('--ignore_label', type=int, default=255, help='ignore label')
-    parser.add_argument('--num_classes', type=int, default=21, help='number of classes')
 
-    # model
-    parser.add_argument('--model', type=str, default='deeplab_v3_s16', help='select model')
-    parser.add_argument('--freeze_bn', action='store_true', default=False, help='freeze bn')
-    parser.add_argument('--ckpt_path', type=str, default='', help='model to evaluate')
-    parser.add_argument("--input_format", type=str, choices=["NCHW", "NHWC"], default="NCHW",
-                        help="NCHW or NHWC")
+#     parser.add_argument('--scales', type=float, action='append', help='scales of evaluation')
+#     parser.add_argument('--flip', action='store_true', help='perform left-right flip')
 
-    args, _ = parser.parse_known_args()
-    return args
 
 
 def cal_hist(a, b, n):
@@ -153,8 +137,63 @@ def eval_batch_scales(args, eval_net, img_lst, scales,
     return result_msk
 
 
+def modelarts_pre_process():
+    '''modelarts pre process function.'''
+    def unzip(zip_file, save_dir):
+        import zipfile
+        s_time = time.time()
+        if not os.path.exists(os.path.join(save_dir, "vocaug")):
+            zip_isexist = zipfile.is_zipfile(zip_file)
+            if zip_isexist:
+                fz = zipfile.ZipFile(zip_file, 'r')
+                data_num = len(fz.namelist())
+                print("Extract Start...")
+                print("unzip file num: {}".format(data_num))
+                i = 0
+                for file in fz.namelist():
+                    if i % int(data_num / 100) == 0:
+                        print("unzip percent: {}%".format(i / int(data_num / 100)), flush=True)
+                    i += 1
+                    fz.extract(file, save_dir)
+                print("cost time: {}min:{}s.".format(int((time.time() - s_time) / 60),
+                                                     int(int(time.time() - s_time) % 60)))
+                print("Extract Done.")
+            else:
+                print("This is not zip.")
+        else:
+            print("Zip has been extracted.")
+
+    if config.need_modelarts_dataset_unzip:
+        zip_file_1 = os.path.join(config.data_path, "vocaug.zip")
+        save_dir_1 = os.path.join(config.data_path)
+
+        sync_lock = "/tmp/unzip_sync.lock"
+
+        # Each server contains 8 devices as most.
+        if get_device_id() % min(get_device_num(), 8) == 0 and not os.path.exists(sync_lock):
+            print("Zip file path: ", zip_file_1)
+            print("Unzip file save dir: ", save_dir_1)
+            unzip(zip_file_1, save_dir_1)
+            print("===Finish extract data synchronization===")
+            try:
+                os.mknod(sync_lock)
+            except IOError:
+                pass
+
+        while True:
+            if os.path.exists(sync_lock):
+                break
+            time.sleep(1)
+
+        print("Device: {}, Finish sync unzip data from {} to {}.".format(get_device_id(), zip_file_1, save_dir_1))
+
+    config.train_dir = os.path.join(config.output_path, str(get_rank_id()), config.train_dir)
+
+
+@moxing_wrapper(pre_process=modelarts_pre_process)
 def net_eval():
-    args = parse_args()
+    config.scales = config.scales_list[config.scales_type]
+    args = config
 
     # data list
     with open(args.data_lst) as f:
diff --git a/model_zoo/official/cv/deeplabv3/scripts/build_data.sh b/model_zoo/official/cv/deeplabv3/scripts/build_data.sh
index 0d29e8ace7..f94777c186 100644
--- a/model_zoo/official/cv/deeplabv3/scripts/build_data.sh
+++ b/model_zoo/official/cv/deeplabv3/scripts/build_data.sh
@@ -15,7 +15,9 @@
 # ============================================================================
 
 export DEVICE_ID=7
-python /PATH/TO/MODEL_ZOO_CODE/data/build_seg_data.py  --data_root=/PATH/TO/DATA_ROOT  \
+EXECUTE_PATH=$(pwd)
+
+python ${EXECUTE_PATH}/../src/data/build_seg_data.py  --data_root=/PATH/TO/DATA_ROOT  \
                     --data_lst=/PATH/TO/DATA_lst.txt  \
                     --dst_path=/PATH/TO/MINDRECORED_NAME.mindrecord  \
                     --num_shards=8  \
diff --git a/model_zoo/official/cv/deeplabv3/scripts/run_distribute_train_s16_r1.sh b/model_zoo/official/cv/deeplabv3/scripts/run_distribute_train_s16_r1.sh
index 5c490de58e..3bf974ee1f 100644
--- a/model_zoo/official/cv/deeplabv3/scripts/run_distribute_train_s16_r1.sh
+++ b/model_zoo/official/cv/deeplabv3/scripts/run_distribute_train_s16_r1.sh
@@ -14,11 +14,34 @@
 # limitations under the License.
 # ============================================================================
 
+if [ $# != 1 ]
+then
+    echo "Usage: sh run_distribute_train_base.sh [RANK_TABLE_FILE]"
+exit 1
+fi
+
+get_real_path(){
+  if [ "${1:0:1}" == "/" ]; then
+    echo "$1"
+  else
+    echo "$(realpath -m $PWD/$1)"
+  fi
+}
+
+PATH1=$(get_real_path $1)
+echo $PATH1
+
+if [ ! -f $PATH1 ]
+then
+    echo "error: RANK_TABLE_FILE=$PATH1 is not a file"
+exit 1
+fi
+
 ulimit -c unlimited
-train_path=/PATH/TO/EXPERIMENTS_DIR
+EXECUTE_PATH=$(pwd)
+train_path=${EXECUTE_PATH}/s16_aug_train
 export SLOG_PRINT_TO_STDOUT=0
-train_code_path=/PATH/TO/MODEL_ZOO_CODE
-export RANK_TABLE_FILE=${train_code_path}/src/tools/rank_table_8p.json
+export RANK_TABLE_FILE=$PATH1
 export RANK_SIZE=8
 export RANK_START_ID=0
 
@@ -35,8 +58,8 @@ do
     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  \
+    python ${EXECUTE_PATH}/../train.py --train_dir=${train_path}/ckpt  \
+                                               --data_file=/PATH_TO_DATA/vocaug/vocaug_mindrecord/vocaug_mindrecord0  \
                                                --train_epochs=300  \
                                                --batch_size=32  \
                                                --crop_size=513  \
@@ -48,7 +71,7 @@ do
                                                --num_classes=21  \
                                                --model=deeplab_v3_s16  \
                                                --ckpt_pre_trained=/PATH/TO/PRETRAIN_MODEL  \
-                                               --is_distributed  \
+                                               --is_distributed=True  \
                                                --save_steps=410  \
-                                               --keep_checkpoint_max=200 >log 2>&1 &
+                                               --keep_checkpoint_max=1 >log 2>&1 &
 done
diff --git a/model_zoo/official/cv/deeplabv3/scripts/run_distribute_train_s8_r1.sh b/model_zoo/official/cv/deeplabv3/scripts/run_distribute_train_s8_r1.sh
index 2e50fba39a..c0cc37df5b 100644
--- a/model_zoo/official/cv/deeplabv3/scripts/run_distribute_train_s8_r1.sh
+++ b/model_zoo/official/cv/deeplabv3/scripts/run_distribute_train_s8_r1.sh
@@ -14,11 +14,34 @@
 # limitations under the License.
 # ============================================================================
 
+if [ $# != 1 ]
+then
+    echo "Usage: sh run_distribute_train_base.sh [RANK_TABLE_FILE]"
+exit 1
+fi
+
+get_real_path(){
+  if [ "${1:0:1}" == "/" ]; then
+    echo "$1"
+  else
+    echo "$(realpath -m $PWD/$1)"
+  fi
+}
+
+PATH1=$(get_real_path $1)
+echo $PATH1
+
+if [ ! -f $PATH1 ]
+then
+    echo "error: RANK_TABLE_FILE=$PATH1 is not a file"
+exit 1
+fi
+
 ulimit -c unlimited
-train_path=/PATH/TO/EXPERIMENTS_DIR
+EXECUTE_PATH=$(pwd)
+train_path=${EXECUTE_PATH}/s8_aug_train
 export SLOG_PRINT_TO_STDOUT=0
-train_code_path=/PATH/TO/MODEL_ZOO_CODE
-export RANK_TABLE_FILE=${train_code_path}/src/tools/rank_table_8p.json
+export RANK_TABLE_FILE=$PATH1
 export RANK_SIZE=8
 export RANK_START_ID=0
 
@@ -35,8 +58,8 @@ do
     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  \
+    python ${EXECUTE_PATH}/../train.py --train_dir=${train_path}/ckpt  \
+                                               --data_file=/PATH_TO_DATA/vocaug/vocaug_mindrecord/vocaug_mindrecord0  \
                                                --train_epochs=800  \
                                                --batch_size=16  \
                                                --crop_size=513  \
@@ -49,7 +72,7 @@ do
                                                --model=deeplab_v3_s8  \
                                                --loss_scale=2048  \
                                                --ckpt_pre_trained=/PATH/TO/PRETRAIN_MODEL  \
-                                               --is_distributed  \
+                                               --is_distributed=True  \
                                                --save_steps=820  \
-                                               --keep_checkpoint_max=200 >log 2>&1 &
+                                               --keep_checkpoint_max=1 >log 2>&1 &
 done
diff --git a/model_zoo/official/cv/deeplabv3/scripts/run_distribute_train_s8_r2.sh b/model_zoo/official/cv/deeplabv3/scripts/run_distribute_train_s8_r2.sh
index 0a34002ae9..bdcfb44477 100644
--- a/model_zoo/official/cv/deeplabv3/scripts/run_distribute_train_s8_r2.sh
+++ b/model_zoo/official/cv/deeplabv3/scripts/run_distribute_train_s8_r2.sh
@@ -14,11 +14,34 @@
 # limitations under the License.
 # ============================================================================
 
+if [ $# != 1 ]
+then
+    echo "Usage: sh run_distribute_train_base.sh [RANK_TABLE_FILE]"
+exit 1
+fi
+
+get_real_path(){
+  if [ "${1:0:1}" == "/" ]; then
+    echo "$1"
+  else
+    echo "$(realpath -m $PWD/$1)"
+  fi
+}
+
+PATH1=$(get_real_path $1)
+echo $PATH1
+
+if [ ! -f $PATH1 ]
+then
+    echo "error: RANK_TABLE_FILE=$PATH1 is not a file"
+exit 1
+fi
+
 ulimit -c unlimited
-train_path=/PATH/TO/EXPERIMENTS_DIR
+EXECUTE_PATH=$(pwd)
+train_path=${EXECUTE_PATH}/s8_voc_train
 export SLOG_PRINT_TO_STDOUT=0
-train_code_path=/PATH/TO/MODEL_ZOO_CODE
-export RANK_TABLE_FILE=${train_code_path}/src/tools/rank_table_8p.json
+export RANK_TABLE_FILE=$PATH1
 export RANK_SIZE=8
 export RANK_START_ID=0
 
@@ -35,8 +58,8 @@ do
     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  \
+    python ${EXECUTE_PATH}/../train.py --train_dir=${train_path}/ckpt  \
+                                               --data_file=/PATH_TO_DATA/vocaug/voctrain_mindrecord/voctrain_mindrecord00  \
                                                --train_epochs=300  \
                                                --batch_size=16  \
                                                --crop_size=513  \
@@ -49,7 +72,7 @@ do
                                                --model=deeplab_v3_s8  \
                                                --loss_scale=2048  \
                                                --ckpt_pre_trained=/PATH/TO/PRETRAIN_MODEL  \
-                                               --is_distributed  \
+                                               --is_distributed=True  \
                                                --save_steps=110  \
-                                               --keep_checkpoint_max=200 >log 2>&1 &
+                                               --keep_checkpoint_max=1 >log 2>&1 &
 done
diff --git a/model_zoo/official/cv/deeplabv3/scripts/run_eval_s16.sh b/model_zoo/official/cv/deeplabv3/scripts/run_eval_s16.sh
index 66305e860a..88946755df 100644
--- a/model_zoo/official/cv/deeplabv3/scripts/run_eval_s16.sh
+++ b/model_zoo/official/cv/deeplabv3/scripts/run_eval_s16.sh
@@ -14,24 +14,24 @@
 # limitations under the License.
 # ============================================================================
 
-export DEVICE_ID=3
+export DEVICE_ID=0
 export SLOG_PRINT_TO_STDOUT=0
-train_code_path=/PATH/TO/MODEL_ZOO_CODE
-eval_path=/PATH/TO/EVAL
+EXECUTE_PATH=$(pwd)
+eval_path=${EXECUTE_PATH}/s16_eval
 
 if [ -d ${eval_path} ]; then
   rm -rf ${eval_path}
 fi
 mkdir -p ${eval_path}
 
-python ${train_code_path}/eval.py --data_root=/PATH/TO/DATA  \
-                    --data_lst=/PATH/TO/DATA_lst.txt  \
+python ${EXECUTE_PATH}/../eval.py --data_root=/PATH_TO_DATA/vocaug  \
+                    --data_lst=/PATH_TO_DATA/vocaug/voc_val_lst.txt  \
                     --batch_size=32  \
                     --crop_size=513  \
                     --ignore_label=255  \
                     --num_classes=21  \
                     --model=deeplab_v3_s16  \
-                    --scales=1.0  \
-                    --freeze_bn  \
+                    --scales_type=0  \
+                    --freeze_bn=True  \
                     --ckpt_path=/PATH/TO/PRETRAIN_MODEL >${eval_path}/eval_log 2>&1 &
 
diff --git a/model_zoo/official/cv/deeplabv3/scripts/run_eval_s8.sh b/model_zoo/official/cv/deeplabv3/scripts/run_eval_s8.sh
index a189089ceb..98e5f4c958 100644
--- a/model_zoo/official/cv/deeplabv3/scripts/run_eval_s8.sh
+++ b/model_zoo/official/cv/deeplabv3/scripts/run_eval_s8.sh
@@ -14,24 +14,24 @@
 # limitations under the License.
 # ============================================================================
 
-export DEVICE_ID=3
+export DEVICE_ID=1
 export SLOG_PRINT_TO_STDOUT=0
-train_code_path=/PATH/TO/MODEL_ZOO_CODE
-eval_path=/PATH/TO/EVAL
+EXECUTE_PATH=$(pwd)
+eval_path=${EXECUTE_PATH}/s8_eval
 
 if [ -d ${eval_path} ]; then
   rm -rf ${eval_path}
 fi
 mkdir -p ${eval_path}
 
-python ${train_code_path}/eval.py --data_root=/PATH/TO/DATA  \
-                    --data_lst=/PATH/TO/DATA_lst.txt  \
+python ${EXECUTE_PATH}/../eval.py --data_root=/PATH_TO_DATA/vocaug  \
+                    --data_lst=/PATH_TO_DATA/vocaug/voc_val_lst.txt  \
                     --batch_size=16  \
                     --crop_size=513  \
                     --ignore_label=255  \
                     --num_classes=21  \
                     --model=deeplab_v3_s8  \
-                    --scales=1.0  \
-                    --freeze_bn  \
+                    --scales_type=0  \
+                    --freeze_bn=True  \
                     --ckpt_path=/PATH/TO/PRETRAIN_MODEL >${eval_path}/eval_log 2>&1 &
 
diff --git a/model_zoo/official/cv/deeplabv3/scripts/run_eval_s8_multiscale.sh b/model_zoo/official/cv/deeplabv3/scripts/run_eval_s8_multiscale.sh
index 824d539e3a..1a8ec21f4a 100644
--- a/model_zoo/official/cv/deeplabv3/scripts/run_eval_s8_multiscale.sh
+++ b/model_zoo/official/cv/deeplabv3/scripts/run_eval_s8_multiscale.sh
@@ -14,28 +14,24 @@
 # limitations under the License.
 # ============================================================================
 
-export DEVICE_ID=3
+export DEVICE_ID=2
 export SLOG_PRINT_TO_STDOUT=0
-train_code_path=/PATH/TO/MODEL_ZOO_CODE
-eval_path=/PATH/TO/EVAL
+EXECUTE_PATH=$(pwd)
+eval_path=${EXECUTE_PATH}/multiscale_eval
 
 if [ -d ${eval_path} ]; then
   rm -rf ${eval_path}
 fi
 mkdir -p ${eval_path}
 
-python ${train_code_path}/eval.py --data_root=/PATH/TO/DATA  \
-                    --data_lst=/PATH/TO/DATA_lst.txt  \
+python ${EXECUTE_PATH}/../eval.py --data_root=/PATH_TO_DATA/vocaug  \
+                    --data_lst=/PATH_TO_DATA/vocaug/voc_val_lst.txt  \
                     --batch_size=16  \
                     --crop_size=513  \
                     --ignore_label=255  \
                     --num_classes=21  \
                     --model=deeplab_v3_s8  \
-                    --scales=0.5  \
-                    --scales=0.75  \
-                    --scales=1.0  \
-                    --scales=1.25  \
-                    --scales=1.75  \
-                    --freeze_bn  \
+                    --scales_type=1  \
+                    --freeze_bn=True  \
                     --ckpt_path=/PATH/TO/PRETRAIN_MODEL >${eval_path}/eval_log 2>&1 &
 
diff --git a/model_zoo/official/cv/deeplabv3/scripts/run_eval_s8_multiscale_flip.sh b/model_zoo/official/cv/deeplabv3/scripts/run_eval_s8_multiscale_flip.sh
index 88beb11d6f..b0834c65d7 100644
--- a/model_zoo/official/cv/deeplabv3/scripts/run_eval_s8_multiscale_flip.sh
+++ b/model_zoo/official/cv/deeplabv3/scripts/run_eval_s8_multiscale_flip.sh
@@ -16,27 +16,23 @@
 
 export DEVICE_ID=3
 export SLOG_PRINT_TO_STDOUT=0
-train_code_path=/PATH/TO/MODEL_ZOO_CODE
-eval_path=/PATH/TO/EVAL
+EXECUTE_PATH=$(pwd)
+eval_path=${EXECUTE_PATH}/multiscale_flip_eval
 
 if [ -d ${eval_path} ]; then
   rm -rf ${eval_path}
 fi
 mkdir -p ${eval_path}
 
-python ${train_code_path}/eval.py --data_root=/PATH/TO/DATA  \
-                    --data_lst=/PATH/TO/DATA_lst.txt  \
+python ${EXECUTE_PATH}/../eval.py --data_root=/PATH_TO_DATA/vocaug  \
+                    --data_lst=/PATH_TO_DATA/vocaug/voc_val_lst.txt  \
                     --batch_size=16  \
                     --crop_size=513  \
                     --ignore_label=255  \
                     --num_classes=21  \
                     --model=deeplab_v3_s8  \
-                    --scales=0.5  \
-                    --scales=0.75  \
-                    --scales=1.0  \
-                    --scales=1.25  \
-                    --scales=1.75  \
-                    --flip  \
-                    --freeze_bn  \
+                    --scales_type=1  \
+                    --flip=True  \
+                    --freeze_bn=True  \
                     --ckpt_path=/PATH/TO/PRETRAIN_MODEL >${eval_path}/eval_log 2>&1 &
 
diff --git a/model_zoo/official/cv/deeplabv3/scripts/run_standalone_train.sh b/model_zoo/official/cv/deeplabv3/scripts/run_standalone_train.sh
index a9a741ff2a..c3fd3c15c1 100644
--- a/model_zoo/official/cv/deeplabv3/scripts/run_standalone_train.sh
+++ b/model_zoo/official/cv/deeplabv3/scripts/run_standalone_train.sh
@@ -14,10 +14,10 @@
 # limitations under the License.
 # ============================================================================
 
-export DEVICE_ID=5
+export DEVICE_ID=0
 export SLOG_PRINT_TO_STDOUT=0
-train_path=/PATH/TO/EXPERIMENTS_DIR
-train_code_path=/PATH/TO/MODEL_ZOO_CODE
+EXECUTE_PATH=$(pwd)
+train_path=${EXECUTE_PATH}/s16_aug_train_1p
 
 if [ -d ${train_path} ]; then
   rm -rf ${train_path}
@@ -27,7 +27,7 @@ mkdir ${train_path}/device${DEVICE_ID}
 mkdir ${train_path}/ckpt
 cd ${train_path}/device${DEVICE_ID} || exit
 
-python ${train_code_path}/train.py --data_file=/PATH/TO/MINDRECORD_NAME  \
+python ${EXECUTE_PATH}/../train.py --data_file=/PATH_TO_DATA/vocaug/vocaug_mindrecord/vocaug_mindrecord0  \
                     --train_dir=${train_path}/ckpt  \
                     --train_epochs=200  \
                     --batch_size=32  \
diff --git a/model_zoo/official/cv/deeplabv3/scripts/run_standalone_train_cpu.sh b/model_zoo/official/cv/deeplabv3/scripts/run_standalone_train_cpu.sh
index 4f8ae47b1e..4fc0d6ac21 100644
--- a/model_zoo/official/cv/deeplabv3/scripts/run_standalone_train_cpu.sh
+++ b/model_zoo/official/cv/deeplabv3/scripts/run_standalone_train_cpu.sh
@@ -16,8 +16,8 @@
 
 export DEVICE_ID=0
 export SLOG_PRINT_TO_STDOUT=0
-train_path=/PATH/TO/EXPERIMENTS_DIR
-train_code_path=/PATH/TO/MODEL_ZOO_CODE
+EXECUTE_PATH=$(pwd)
+train_path=${EXECUTE_PATH}/s16_aug_train_cpu
 
 if [ -d ${train_path} ]; then
   rm -rf ${train_path}
@@ -27,7 +27,7 @@ mkdir ${train_path}/device${DEVICE_ID}
 mkdir ${train_path}/ckpt
 cd ${train_path}/device${DEVICE_ID} || exit
 
-python ${train_code_path}/train.py --data_file=/PATH/TO/MINDRECORD_NAME  \
+python ${EXECUTE_PATH}/../train.py --data_file=/PATH_TO_DATA/vocaug/vocaug_mindrecord/vocaug_mindrecord0  \
                     --device_target=CPU  \
                     --train_dir=${train_path}/ckpt  \
                     --train_epochs=200  \
diff --git a/model_zoo/official/cv/deeplabv3/train.py b/model_zoo/official/cv/deeplabv3/train.py
index 4139246d01..8207f7c249 100644
--- a/model_zoo/official/cv/deeplabv3/train.py
+++ b/model_zoo/official/cv/deeplabv3/train.py
@@ -15,8 +15,7 @@
 """train deeplabv3."""
 
 import os
-import argparse
-import ast
+import time
 from mindspore import context
 from mindspore.train.model import Model
 from mindspore.context import ParallelMode
@@ -31,6 +30,9 @@ from src.data import dataset as data_generator
 from src.loss import loss
 from src.nets import net_factory
 from src.utils import learning_rates
+from utils.config import config
+from utils.moxing_adapter import moxing_wrapper
+from utils.device_adapter import get_device_id, get_device_num, get_rank_id
 
 set_seed(1)
 
@@ -47,57 +49,68 @@ class BuildTrainNetwork(nn.Cell):
         return net_loss
 
 
-def parse_args():
-    parser = argparse.ArgumentParser('mindspore deeplabv3 training')
-    parser.add_argument('--train_dir', type=str, default='', help='where training log and ckpts saved')
-
-    # dataset
-    parser.add_argument('--data_file', type=str, default='', help='path and name of one mindrecord file')
-    parser.add_argument('--batch_size', type=int, default=32, help='batch size')
-    parser.add_argument('--crop_size', type=int, default=513, help='crop size')
-    parser.add_argument('--image_mean', type=list, default=[103.53, 116.28, 123.675], help='image mean')
-    parser.add_argument('--image_std', type=list, default=[57.375, 57.120, 58.395], help='image std')
-    parser.add_argument('--min_scale', type=float, default=0.5, help='minimum scale of data argumentation')
-    parser.add_argument('--max_scale', type=float, default=2.0, help='maximum scale of data argumentation')
-    parser.add_argument('--ignore_label', type=int, default=255, help='ignore label')
-    parser.add_argument('--num_classes', type=int, default=21, help='number of classes')
-
-    # optimizer
-    parser.add_argument('--train_epochs', type=int, default=300, help='epoch')
-    parser.add_argument('--lr_type', type=str, default='cos', help='type of learning rate')
-    parser.add_argument('--base_lr', type=float, default=0.015, help='base learning rate')
-    parser.add_argument('--lr_decay_step', type=int, default=40000, help='learning rate decay step')
-    parser.add_argument('--lr_decay_rate', type=float, default=0.1, help='learning rate decay rate')
-    parser.add_argument('--loss_scale', type=float, default=3072.0, help='loss scale')
-
-    # model
-    parser.add_argument('--model', type=str, default='deeplab_v3_s16', help='select model')
-    parser.add_argument('--freeze_bn', action='store_true', help='freeze bn')
-    parser.add_argument('--ckpt_pre_trained', type=str, default='', help='pretrained model')
-    parser.add_argument("--filter_weight", type=ast.literal_eval, default=False,
-                        help="Filter the last weight parameters, default is False.")
-
-    # train
-    parser.add_argument('--device_target', type=str, default='Ascend', choices=['Ascend', 'CPU'],
-                        help='device where the code will be implemented. (Default: Ascend)')
-    parser.add_argument('--is_distributed', action='store_true', help='distributed training')
-    parser.add_argument('--rank', type=int, default=0, help='local rank of distributed')
-    parser.add_argument('--group_size', type=int, default=1, help='world size of distributed')
-    parser.add_argument('--save_steps', type=int, default=3000, help='steps interval for saving')
-    parser.add_argument('--keep_checkpoint_max', type=int, default=int, help='max checkpoint for saving')
-
-    args, _ = parser.parse_known_args()
-    return args
-
-
+def modelarts_pre_process():
+    '''modelarts pre process function.'''
+    def unzip(zip_file, save_dir):
+        import zipfile
+        s_time = time.time()
+        if not os.path.exists(os.path.join(save_dir, "vocaug")):
+            zip_isexist = zipfile.is_zipfile(zip_file)
+            if zip_isexist:
+                fz = zipfile.ZipFile(zip_file, 'r')
+                data_num = len(fz.namelist())
+                print("Extract Start...")
+                print("unzip file num: {}".format(data_num))
+                i = 0
+                for file in fz.namelist():
+                    if i % int(data_num / 100) == 0:
+                        print("unzip percent: {}%".format(i / int(data_num / 100)), flush=True)
+                    i += 1
+                    fz.extract(file, save_dir)
+                print("cost time: {}min:{}s.".format(int((time.time() - s_time) / 60),
+                                                     int(int(time.time() - s_time) % 60)))
+                print("Extract Done.")
+            else:
+                print("This is not zip.")
+        else:
+            print("Zip has been extracted.")
+
+    if config.need_modelarts_dataset_unzip:
+        zip_file_1 = os.path.join(config.data_path, "vocaug.zip")
+        save_dir_1 = os.path.join(config.data_path)
+
+        sync_lock = "/tmp/unzip_sync.lock"
+
+        # Each server contains 8 devices as most.
+        if get_device_id() % min(get_device_num(), 8) == 0 and not os.path.exists(sync_lock):
+            print("Zip file path: ", zip_file_1)
+            print("Unzip file save dir: ", save_dir_1)
+            unzip(zip_file_1, save_dir_1)
+            print("===Finish extract data synchronization===")
+            try:
+                os.mknod(sync_lock)
+            except IOError:
+                pass
+
+        while True:
+            if os.path.exists(sync_lock):
+                break
+            time.sleep(1)
+
+        print("Device: {}, Finish sync unzip data from {} to {}.".format(get_device_id(), zip_file_1, save_dir_1))
+
+    config.train_dir = os.path.join(config.output_path, str(get_rank_id()), config.train_dir)
+
+
+@moxing_wrapper(pre_process=modelarts_pre_process)
 def train():
-    args = parse_args()
+    args = config
 
     if args.device_target == "CPU":
         context.set_context(mode=context.GRAPH_MODE, save_graphs=False, device_target="CPU")
     else:
         context.set_context(mode=context.GRAPH_MODE, enable_auto_mixed_precision=True, save_graphs=False,
-                            device_target="Ascend", device_id=int(os.getenv('DEVICE_ID')))
+                            device_target="Ascend", device_id=get_device_id())
 
     # init multicards training
     if args.is_distributed:
diff --git a/model_zoo/official/cv/deeplabv3/utils/__init__.py b/model_zoo/official/cv/deeplabv3/utils/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/model_zoo/official/cv/deeplabv3/utils/config.py b/model_zoo/official/cv/deeplabv3/utils/config.py
new file mode 100644
index 0000000000..2c191e9f74
--- /dev/null
+++ b/model_zoo/official/cv/deeplabv3/utils/config.py
@@ -0,0 +1,127 @@
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+"""Parse arguments"""
+
+import os
+import ast
+import argparse
+from pprint import pprint, pformat
+import yaml
+
+class Config:
+    """
+    Configuration namespace. Convert dictionary to members.
+    """
+    def __init__(self, cfg_dict):
+        for k, v in cfg_dict.items():
+            if isinstance(v, (list, tuple)):
+                setattr(self, k, [Config(x) if isinstance(x, dict) else x for x in v])
+            else:
+                setattr(self, k, Config(v) if isinstance(v, dict) else v)
+
+    def __str__(self):
+        return pformat(self.__dict__)
+
+    def __repr__(self):
+        return self.__str__()
+
+
+def parse_cli_to_yaml(parser, cfg, helper=None, choices=None, cfg_path="default_config.yaml"):
+    """
+    Parse command line arguments to the configuration according to the default yaml.
+
+    Args:
+        parser: Parent parser.
+        cfg: Base configuration.
+        helper: Helper description.
+        cfg_path: Path to the default yaml config.
+    """
+    parser = argparse.ArgumentParser(description="[REPLACE THIS at config.py]",
+                                     parents=[parser])
+    helper = {} if helper is None else helper
+    choices = {} if choices is None else choices
+    for item in cfg:
+        if not isinstance(cfg[item], list) and not isinstance(cfg[item], dict):
+            help_description = helper[item] if item in helper else "Please reference to {}".format(cfg_path)
+            choice = choices[item] if item in choices else None
+            if isinstance(cfg[item], bool):
+                parser.add_argument("--" + item, type=ast.literal_eval, default=cfg[item], choices=choice,
+                                    help=help_description)
+            else:
+                parser.add_argument("--" + item, type=type(cfg[item]), default=cfg[item], choices=choice,
+                                    help=help_description)
+    args = parser.parse_args()
+    return args
+
+
+def parse_yaml(yaml_path):
+    """
+    Parse the yaml config file.
+
+    Args:
+        yaml_path: Path to the yaml config.
+    """
+    with open(yaml_path, 'r') as fin:
+        try:
+            cfgs = yaml.load_all(fin.read(), Loader=yaml.FullLoader)
+            cfgs = [x for x in cfgs]
+            if len(cfgs) == 1:
+                cfg_helper = {}
+                cfg = cfgs[0]
+                cfg_choices = {}
+            elif len(cfgs) == 2:
+                cfg, cfg_helper = cfgs
+                cfg_choices = {}
+            elif len(cfgs) == 3:
+                cfg, cfg_helper, cfg_choices = cfgs
+            else:
+                raise ValueError("At most 3 docs (config, description for help, choices) are supported in config yaml")
+            print(cfg_helper)
+        except:
+            raise ValueError("Failed to parse yaml")
+    return cfg, cfg_helper, cfg_choices
+
+
+def merge(args, cfg):
+    """
+    Merge the base config from yaml file and command line arguments.
+
+    Args:
+        args: Command line arguments.
+        cfg: Base configuration.
+    """
+    args_var = vars(args)
+    for item in args_var:
+        cfg[item] = args_var[item]
+    return cfg
+
+
+def get_config():
+    """
+    Get Config according to the yaml file and cli arguments.
+    """
+    parser = argparse.ArgumentParser(description="default name", add_help=False)
+    current_dir = os.path.dirname(os.path.abspath(__file__))
+    parser.add_argument("--config_path", type=str, default=os.path.join(current_dir, "../default_config.yaml"),
+                        help="Config file path")
+    path_args, _ = parser.parse_known_args()
+    default, helper, choices = parse_yaml(path_args.config_path)
+    pprint(default)
+    args = parse_cli_to_yaml(parser=parser, cfg=default, helper=helper, choices=choices, cfg_path=path_args.config_path)
+    final_config = merge(args, default)
+    return Config(final_config)
+
+config = get_config()
diff --git a/model_zoo/official/cv/deeplabv3/utils/device_adapter.py b/model_zoo/official/cv/deeplabv3/utils/device_adapter.py
new file mode 100644
index 0000000000..7c5d7f837d
--- /dev/null
+++ b/model_zoo/official/cv/deeplabv3/utils/device_adapter.py
@@ -0,0 +1,27 @@
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+"""Device adapter for ModelArts"""
+
+from .config import config
+
+if config.enable_modelarts:
+    from .moxing_adapter import get_device_id, get_device_num, get_rank_id, get_job_id
+else:
+    from .local_adapter import get_device_id, get_device_num, get_rank_id, get_job_id
+
+__all__ = [
+    "get_device_id", "get_device_num", "get_rank_id", "get_job_id"
+]
diff --git a/model_zoo/official/cv/deeplabv3/utils/local_adapter.py b/model_zoo/official/cv/deeplabv3/utils/local_adapter.py
new file mode 100644
index 0000000000..769fa6dc78
--- /dev/null
+++ b/model_zoo/official/cv/deeplabv3/utils/local_adapter.py
@@ -0,0 +1,36 @@
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+"""Local adapter"""
+
+import os
+
+def get_device_id():
+    device_id = os.getenv('DEVICE_ID', '0')
+    return int(device_id)
+
+
+def get_device_num():
+    device_num = os.getenv('RANK_SIZE', '1')
+    return int(device_num)
+
+
+def get_rank_id():
+    global_rank_id = os.getenv('RANK_ID', '0')
+    return int(global_rank_id)
+
+
+def get_job_id():
+    return "Local Job"
diff --git a/model_zoo/official/cv/deeplabv3/utils/moxing_adapter.py b/model_zoo/official/cv/deeplabv3/utils/moxing_adapter.py
new file mode 100644
index 0000000000..25838a7da9
--- /dev/null
+++ b/model_zoo/official/cv/deeplabv3/utils/moxing_adapter.py
@@ -0,0 +1,116 @@
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+"""Moxing adapter for ModelArts"""
+
+import os
+import functools
+from mindspore import context
+from .config import config
+
+_global_sync_count = 0
+
+def get_device_id():
+    device_id = os.getenv('DEVICE_ID', '0')
+    return int(device_id)
+
+
+def get_device_num():
+    device_num = os.getenv('RANK_SIZE', '1')
+    return int(device_num)
+
+
+def get_rank_id():
+    global_rank_id = os.getenv('RANK_ID', '0')
+    return int(global_rank_id)
+
+
+def get_job_id():
+    job_id = os.getenv('JOB_ID')
+    job_id = job_id if job_id != "" else "default"
+    return job_id
+
+def sync_data(from_path, to_path):
+    """
+    Download data from remote obs to local directory if the first url is remote url and the second one is local path
+    Upload data from local directory to remote obs in contrast.
+    """
+    import moxing as mox
+    import time
+    global _global_sync_count
+    sync_lock = "/tmp/copy_sync.lock" + str(_global_sync_count)
+    _global_sync_count += 1
+
+    # Each server contains 8 devices as most.
+    if get_device_id() % min(get_device_num(), 8) == 0 and not os.path.exists(sync_lock):
+        print("from path: ", from_path)
+        print("to path: ", to_path)
+        mox.file.copy_parallel(from_path, to_path)
+        print("===finish data synchronization===")
+        try:
+            os.mknod(sync_lock)
+        except IOError:
+            pass
+        print("===save flag===")
+
+    while True:
+        if os.path.exists(sync_lock):
+            break
+        time.sleep(1)
+
+    print("Finish sync data from {} to {}.".format(from_path, to_path))
+
+
+def moxing_wrapper(pre_process=None, post_process=None):
+    """
+    Moxing wrapper to download dataset and upload outputs.
+    """
+    def wrapper(run_func):
+        @functools.wraps(run_func)
+        def wrapped_func(*args, **kwargs):
+            # Download data from data_url
+            if config.enable_modelarts:
+                if config.data_url:
+                    sync_data(config.data_url, config.data_path)
+                    print("Dataset downloaded: ", os.listdir(config.data_path))
+                if config.checkpoint_url:
+                    sync_data(config.checkpoint_url, config.load_path)
+                    print("Preload downloaded: ", os.listdir(config.load_path))
+                if config.train_url:
+                    sync_data(config.train_url, config.output_path)
+                    print("Workspace downloaded: ", os.listdir(config.output_path))
+
+                context.set_context(save_graphs_path=os.path.join(config.output_path, str(get_rank_id())))
+                config.device_num = get_device_num()
+                config.device_id = get_device_id()
+                if not os.path.exists(config.output_path):
+                    os.makedirs(config.output_path)
+
+                if pre_process:
+                    pre_process()
+
+            # Run the main function
+            run_func(*args, **kwargs)
+
+            # Upload data to train_url
+            if config.enable_modelarts:
+                if post_process:
+                    post_process()
+
+                if config.train_url:
+                    print("Start to copy output directory")
+                    sync_data(config.output_path, config.train_url)
+        return wrapped_func
+    return wrapper