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diff --git a/TensorFlow/contrib/cv/DifferentiableBinarization/test/train_full_1p.sh b/TensorFlow/contrib/cv/DifferentiableBinarization/test/train_full_1p.sh
new file mode 100644
index 0000000000000000000000000000000000000000..a68051cd2031f0eafa71310b8d40fd46299f1076
--- /dev/null
+++ b/TensorFlow/contrib/cv/DifferentiableBinarization/test/train_full_1p.sh
@@ -0,0 +1,195 @@
+#!/bin/bash
+
+##########################################################
+#########第3行 至 100行，请一定不要、不要、不要修改##########
+#########第3行 至 100行，请一定不要、不要、不要修改##########
+#########第3行 至 100行，请一定不要、不要、不要修改##########
+##########################################################
+# shell脚本所在路径
+cur_path=`echo $(cd $(dirname $0);pwd)`
+
+# 判断当前shell是否是performance
+perf_flag=`echo $0 | grep performance | wc -l`
+
+# 当前执行网络的名称
+Network=`echo $(cd $(dirname $0);pwd) | awk -F"/" '{print $(NF-1)}'`
+
+export RANK_SIZE=1
+export RANK_ID=0
+export JOB_ID=10087
+
+# 路径参数初始化
+data_path=""
+output_path=""
+
+# 帮助信息，不需要修改
+if [[ $1 == --help || $1 == -h ]];then
+    echo"usage:./train_performance_1P.sh <args>"
+    echo " "
+    echo "parameter explain:
+    --data_path              # dataset of training
+    --output_path            # output of training
+    --train_steps            # max_step for training
+    --train_epochs           # max_epoch for training
+    --batch_size             # batch size
+    -h/--help                show help message
+    "
+    exit 1
+fi
+
+# 参数校验，不需要修改
+for para in $*
+do
+    if [[ $para == --data_path* ]];then
+        data_path=`echo ${para#*=}`
+    elif [[ $para == --output_path* ]];then
+        output_path=`echo ${para#*=}`
+    elif [[ $para == --train_steps* ]];then
+        train_steps=`echo ${para#*=}`
+    elif [[ $para == --train_epochs* ]];then
+        train_epochs=`echo ${para#*=}`
+    elif [[ $para == --batch_size* ]];then
+        batch_size=`echo ${para#*=}`
+    fi
+done
+
+# 校验是否传入data_path,不需要修改
+if [[ $data_path == "" ]];then
+    echo "[Error] para \"data_path\" must be config"
+    exit 1
+fi
+
+# 校验是否传入output_path,不需要修改
+if [[ $output_path == "" ]];then
+    output_path="./test/output/${ASCEND_DEVICE_ID}"
+fi
+
+# 设置打屏日志文件名，请保留，文件名为${print_log}
+print_log="./test/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log"
+modelarts_flag=${MODELARTS_MODEL_PATH}
+if [ x"${modelarts_flag}" != x ];
+then
+    echo "running without etp..."
+    print_log_name=`ls /home/ma-user/modelarts/log/ | grep proc-rank`
+    print_log="/home/ma-user/modelarts/log/${print_log_name}"
+fi
+echo "### get your log here : ${print_log}"
+
+CaseName=""
+function get_casename()
+{
+    if [ x"${perf_flag}" = x1 ];
+    then
+        CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'perf'
+    else
+        CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'acc'
+    fi
+}
+
+# 跳转到code目录
+cd ${cur_path}/../
+rm -rf ./test/output/${ASCEND_DEVICE_ID}
+mkdir -p ./test/output/${ASCEND_DEVICE_ID}
+
+sed -i "s#dave/db_48_2.0216_2.5701.h5#${data_path}/DifferentiableBinarization/datasets/total_text/dave/db_48_2.0216_2.5701.h5#g" inference.py
+sed -i "s#datasets/total_text/test_images#${data_path}/DifferentiableBinarization/datasets/total_text/test_images#g" inference.py
+
+# 训练开始时间记录，不需要修改
+start_time=$(date +%s)
+##########################################################
+#########第3行 至 100行，请一定不要、不要、不要修改##########
+#########第3行 至 100行，请一定不要、不要、不要修改##########
+#########第3行 至 100行，请一定不要、不要、不要修改##########
+##########################################################
+
+#=========================================================
+#=========================================================
+#========训练执行命令，需要根据您的网络进行修改==============
+#=========================================================
+#=========================================================
+# 基础参数，需要模型审视修改
+# 您的训练数据集在${data_path}路径下，请直接使用这个变量获取
+# 您的训练输出目录在${output_path}路径下，请直接使用这个变量获取
+# 您的其他基础参数，可以自定义增加，但是batch_size请保留，并且设置正确的值
+batch_size=16
+train_epoch=100
+
+if [ x"${modelarts_flag}" != x ];
+then
+    python3.7 ./train.py \
+        --train_generator=${data_path}/DifferentiableBinarization/datasets/total_text \
+        --val_generator=${data_path}/DifferentiableBinarization/datasets/total_text \
+        --epochs=${train_epoch} \
+        --output_path=${output_path} 1>${print_log} 2>&1
+else
+    python3.7 ./train.py \
+        --train_generator=${data_path}/DifferentiableBinarization/datasets/total_text \
+        --val_generator=${data_path}/DifferentiableBinarization/datasets/total_text \
+        --epochs=${train_epoch} \
+        --output_path=${output_path} 1>${print_log} 2>&1
+fi
+# 性能相关数据
+StepTime=`grep "200/200" ${print_log} | awk '{print $5}' | tr -d "s/step" | tail -n +2 | awk '{sum+=$1} END {print sum/NR}'`
+FPS=`awk 'BEGIN{printf "%.2f\n", '${batch_size}'/'${StepTime}'}'`
+
+# 精度相关数据计算
+train_accuracy=`grep "200/200" ${print_log} | awk '{print $8}' | awk 'END {print $NF}'`
+# 提取所有loss打印信息
+grep "200/200" ${print_log} | awk '{print $8}' > ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt
+
+
+###########################################################
+#########后面的所有内容请不要修改###########################
+#########后面的所有内容请不要修改###########################
+#########后面的所有内容请不要修改###########################
+###########################################################
+
+# 判断本次执行是否正确使用Ascend NPU
+use_npu_flag=`grep "The model has been compiled on the Ascend AI processor" ${print_log} | wc -l`
+if [ x"${use_npu_flag}" == x0 ];
+then
+    echo "------------------ ERROR NOTICE START ------------------"
+    echo "ERROR, your task haven't used Ascend NPU, please check your npu Migration."
+    echo "------------------ ERROR NOTICE END------------------"
+else
+    echo "------------------ INFO NOTICE START------------------"
+    echo "INFO, your task have used Ascend NPU, please check your result."
+    echo "------------------ INFO NOTICE END------------------"
+fi
+
+# 获取最终的casename，请保留，case文件名为${CaseName}
+get_casename
+
+# 重命名loss文件
+if [ -f ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ];
+then
+    mv ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ./test/output/${ASCEND_DEVICE_ID}/${CaseName}_loss.txt
+fi
+
+# 训练端到端耗时
+end_time=$(date +%s)
+e2e_time=$(( $end_time - $start_time ))
+
+echo "------------------ Final result ------------------"
+# 输出性能FPS/单step耗时/端到端耗时
+echo "Final Performance images/sec : $FPS"
+echo "Final Performance sec/step : $StepTime"
+echo "E2E Training Duration sec : $e2e_time"
+
+# 输出训练精度
+echo "Final Train Accuracy : ${train_accuracy}"
+
+# 最后一个迭代loss值，不需要修改
+ActualLoss=(`awk 'END {print $NF}' $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}_loss.txt`)
+
+#关键信息打印到${CaseName}.log中，不需要修改
+echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "BatchSize = ${batch_size}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "DeviceType = `uname -m`" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "ActualFPS = ${FPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "TrainingTime = ${StepTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "TrainAccuracy = ${train_accuracy}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
\ No newline at end of file
diff --git a/TensorFlow/contrib/cv/DifferentiableBinarization/test/train_performance_1p.sh b/TensorFlow/contrib/cv/DifferentiableBinarization/test/train_performance_1p.sh
new file mode 100644
index 0000000000000000000000000000000000000000..3061385f554ca27358b24ff45a9ecb4153313a34
--- /dev/null
+++ b/TensorFlow/contrib/cv/DifferentiableBinarization/test/train_performance_1p.sh
@@ -0,0 +1,195 @@
+#!/bin/bash
+
+##########################################################
+#########第3行 至 100行，请一定不要、不要、不要修改##########
+#########第3行 至 100行，请一定不要、不要、不要修改##########
+#########第3行 至 100行，请一定不要、不要、不要修改##########
+##########################################################
+# shell脚本所在路径
+cur_path=`echo $(cd $(dirname $0);pwd)`
+
+# 判断当前shell是否是performance
+perf_flag=`echo $0 | grep performance | wc -l`
+
+# 当前执行网络的名称
+Network=`echo $(cd $(dirname $0);pwd) | awk -F"/" '{print $(NF-1)}'`
+
+export RANK_SIZE=1
+export RANK_ID=0
+export JOB_ID=10087
+
+# 路径参数初始化
+data_path=""
+output_path=""
+
+# 帮助信息，不需要修改
+if [[ $1 == --help || $1 == -h ]];then
+    echo"usage:./train_performance_1P.sh <args>"
+    echo " "
+    echo "parameter explain:
+    --data_path              # dataset of training
+    --output_path            # output of training
+    --train_steps            # max_step for training
+    --train_epochs           # max_epoch for training
+    --batch_size             # batch size
+    -h/--help                show help message
+    "
+    exit 1
+fi
+
+# 参数校验，不需要修改
+for para in $*
+do
+    if [[ $para == --data_path* ]];then
+        data_path=`echo ${para#*=}`
+    elif [[ $para == --output_path* ]];then
+        output_path=`echo ${para#*=}`
+    elif [[ $para == --train_steps* ]];then
+        train_steps=`echo ${para#*=}`
+    elif [[ $para == --train_epochs* ]];then
+        train_epochs=`echo ${para#*=}`
+    elif [[ $para == --batch_size* ]];then
+        batch_size=`echo ${para#*=}`
+    fi
+done
+
+# 校验是否传入data_path,不需要修改
+if [[ $data_path == "" ]];then
+    echo "[Error] para \"data_path\" must be config"
+    exit 1
+fi
+
+# 校验是否传入output_path,不需要修改
+if [[ $output_path == "" ]];then
+    output_path="./test/output/${ASCEND_DEVICE_ID}"
+fi
+
+# 设置打屏日志文件名，请保留，文件名为${print_log}
+print_log="./test/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log"
+modelarts_flag=${MODELARTS_MODEL_PATH}
+if [ x"${modelarts_flag}" != x ];
+then
+    echo "running without etp..."
+    print_log_name=`ls /home/ma-user/modelarts/log/ | grep proc-rank`
+    print_log="/home/ma-user/modelarts/log/${print_log_name}"
+fi
+echo "### get your log here : ${print_log}"
+
+CaseName=""
+function get_casename()
+{
+    if [ x"${perf_flag}" = x1 ];
+    then
+        CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'perf'
+    else
+        CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'acc'
+    fi
+}
+
+# 跳转到code目录
+cd ${cur_path}/../
+rm -rf ./test/output/${ASCEND_DEVICE_ID}
+mkdir -p ./test/output/${ASCEND_DEVICE_ID}
+
+sed -i "s#dave/db_48_2.0216_2.5701.h5#${data_path}/DifferentiableBinarization/datasets/total_text/dave/db_48_2.0216_2.5701.h5#g" inference.py
+sed -i "s#datasets/total_text/test_images#${data_path}/DifferentiableBinarization/datasets/total_text/test_images#g" inference.py
+
+# 训练开始时间记录，不需要修改
+start_time=$(date +%s)
+##########################################################
+#########第3行 至 100行，请一定不要、不要、不要修改##########
+#########第3行 至 100行，请一定不要、不要、不要修改##########
+#########第3行 至 100行，请一定不要、不要、不要修改##########
+##########################################################
+
+#=========================================================
+#=========================================================
+#========训练执行命令，需要根据您的网络进行修改==============
+#=========================================================
+#=========================================================
+# 基础参数，需要模型审视修改
+# 您的训练数据集在${data_path}路径下，请直接使用这个变量获取
+# 您的训练输出目录在${output_path}路径下，请直接使用这个变量获取
+# 您的其他基础参数，可以自定义增加，但是batch_size请保留，并且设置正确的值
+batch_size=16
+train_epoch=3
+
+if [ x"${modelarts_flag}" != x ];
+then
+    python3.7 ./train.py \
+        --train_generator=${data_path}/DifferentiableBinarization/datasets/total_text \
+        --val_generator=${data_path}/DifferentiableBinarization/datasets/total_text \
+        --epochs=${train_epoch} \
+        --output_path=${output_path} 1>${print_log} 2>&1
+else
+    python3.7 ./train.py \
+        --train_generator=${data_path}/DifferentiableBinarization/datasets/total_text \
+        --val_generator=${data_path}/DifferentiableBinarization/datasets/total_text \
+        --epochs=${train_epoch} \
+        --output_path=${output_path} 1>${print_log} 2>&1
+fi
+# 性能相关数据
+StepTime=`grep "200/200" ${print_log} | awk '{print $5}' | tr -d "s/step" | tail -n +2 | awk '{sum+=$1} END {print sum/NR}'`
+FPS=`awk 'BEGIN{printf "%.2f\n", '${batch_size}'/'${StepTime}'}'`
+
+# 精度相关数据计算
+train_accuracy=`grep "200/200" ${print_log} | awk '{print $8}' | awk 'END {print $NF}'`
+# 提取所有loss打印信息
+grep "200/200" ${print_log} | awk '{print $8}' > ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt
+
+
+###########################################################
+#########后面的所有内容请不要修改###########################
+#########后面的所有内容请不要修改###########################
+#########后面的所有内容请不要修改###########################
+###########################################################
+
+# 判断本次执行是否正确使用Ascend NPU
+use_npu_flag=`grep "The model has been compiled on the Ascend AI processor" ${print_log} | wc -l`
+if [ x"${use_npu_flag}" == x0 ];
+then
+    echo "------------------ ERROR NOTICE START ------------------"
+    echo "ERROR, your task haven't used Ascend NPU, please check your npu Migration."
+    echo "------------------ ERROR NOTICE END------------------"
+else
+    echo "------------------ INFO NOTICE START------------------"
+    echo "INFO, your task have used Ascend NPU, please check your result."
+    echo "------------------ INFO NOTICE END------------------"
+fi
+
+# 获取最终的casename，请保留，case文件名为${CaseName}
+get_casename
+
+# 重命名loss文件
+if [ -f ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ];
+then
+    mv ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ./test/output/${ASCEND_DEVICE_ID}/${CaseName}_loss.txt
+fi
+
+# 训练端到端耗时
+end_time=$(date +%s)
+e2e_time=$(( $end_time - $start_time ))
+
+echo "------------------ Final result ------------------"
+# 输出性能FPS/单step耗时/端到端耗时
+echo "Final Performance images/sec : $FPS"
+echo "Final Performance sec/step : $StepTime"
+echo "E2E Training Duration sec : $e2e_time"
+
+# 输出训练精度
+echo "Final Train Accuracy : ${train_accuracy}"
+
+# 最后一个迭代loss值，不需要修改
+ActualLoss=(`awk 'END {print $NF}' $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}_loss.txt`)
+
+#关键信息打印到${CaseName}.log中，不需要修改
+echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "BatchSize = ${batch_size}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "DeviceType = `uname -m`" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "ActualFPS = ${FPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "TrainingTime = ${StepTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "TrainAccuracy = ${train_accuracy}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
\ No newline at end of file
diff --git a/TensorFlow/contrib/cv/DifferentiableBinarization/train.py b/TensorFlow/contrib/cv/DifferentiableBinarization/train.py
index 16fd7c8efe2abc3afd21d98ab7b713e9c2ef8650..a6e40f6cd4aa5963bf2a02fe6d80a555caeca637 100644
--- a/TensorFlow/contrib/cv/DifferentiableBinarization/train.py
+++ b/TensorFlow/contrib/cv/DifferentiableBinarization/train.py
@@ -7,15 +7,25 @@ from keras.utils import get_file
 import os
 from generator import generate
 from model import dbnet
-checkpoints_dir = f'checkpoints/{datetime.date.today()}'
+import argparse
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--train_generator", default='./datasets/total_text', type=str)
+parser.add_argument("--val_generator", default='./datasets/total_text', type=str)
+parser.add_argument("--epochs", default=100, type=int)
+parser.add_argument("--output_path", default='./output', type=str)
+
+args = parser.parse_args()
+today = datetime.date.today()
+checkpoints_dir = f'checkpoints/{today}'
 
 batch_size = 16
 
 if not osp.exists(checkpoints_dir):
     os.makedirs(checkpoints_dir)
 
-train_generator = generate('datasets/total_text', batch_size=batch_size, is_training=True)
-val_generator = generate('datasets/total_text', batch_size=batch_size, is_training=False)
+train_generator = generate(args.train_generator, batch_size=batch_size, is_training=True)
+val_generator = generate(args.val_generator, batch_size=batch_size, is_training=False)
 
 model, prediction_model = dbnet()
 resnet_filename = 'ResNet-50-model.keras.h5'
@@ -27,17 +37,16 @@ model.compile(optimizer=optimizers.Adam(lr=1e-3), loss={'db_loss': lambda y_true
 checkpoint = callbacks.ModelCheckpoint(
     osp.join(checkpoints_dir, 'db_{epoch:02d}_{loss:.4f}_{val_loss:.4f}.h5'),
     verbose=1,
+    mode="min",
 )
 """开始训练"""
 model.fit_generator(
     generator=train_generator,
     steps_per_epoch=200,
     initial_epoch=0,
-    epochs=100,
+    epochs=args.epochs,
     verbose=1,
     callbacks=[checkpoint],
     validation_data=val_generator,
     validation_steps=19
 )
-
-