diff --git a/TensorFlow/contrib/cv/YOLOV5_ID0378_for_TensorFlow/test/train_performance_1p.sh b/TensorFlow/contrib/cv/YOLOV5_ID0378_for_TensorFlow/test/train_performance_1p.sh
new file mode 100644
index 0000000000000000000000000000000000000000..e80a3a0c48d3650374c72259dd03fb45ed3d8d97
--- /dev/null
+++ b/TensorFlow/contrib/cv/YOLOV5_ID0378_for_TensorFlow/test/train_performance_1p.sh
@@ -0,0 +1,184 @@
+#!/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/${ASCEND_DEVICE_ID}
+
+# 训练开始时间记录，不需要修改
+start_time=$(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=8
+
+sed -i s#"/home/dingwei/yolov5"#"${data_path}"#g ./2007_train.txt
+sed -i s#"/home/dingwei/yolov5"#"${data_path}"#g ./2007_val.txt
+
+if [ x"${modelarts_flag}" != x ];
+then
+    python3.7 ./train.py --epochs=10 --steps=48 --freeze_flag=False
+else
+    python3.7 ./train.py --epochs=10 --steps=48 --freeze_flag=False 1>${print_log} 2>&1
+fi
+
+# 性能相关数据计算
+StepTime=`grep "ms/step" ${print_log} | tail -n 10 | awk -F"ms" '{print $1}' | awk '{print $NF} | awk '{sum+=$1} END {print sum/NR}'`
+FPS=`awk 'BEGIN{printf "%.2f\n", '${batch_size}'/'${StepTime}'}'`
+
+# 提取所有loss打印信息
+grep "loss:" ${print_log} | awk '{print $NF}' > ./test/output/${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
diff --git a/TensorFlow/contrib/cv/YOLOV5_ID0378_for_TensorFlow/train.py b/TensorFlow/contrib/cv/YOLOV5_ID0378_for_TensorFlow/train.py
index 9d42f872a418cfd2a87cc6c86518d2ef7c9aa4d2..865dd1a2843523b2f221381b26a924d6512c7689 100644
--- a/TensorFlow/contrib/cv/YOLOV5_ID0378_for_TensorFlow/train.py
+++ b/TensorFlow/contrib/cv/YOLOV5_ID0378_for_TensorFlow/train.py
@@ -15,8 +15,18 @@ from utils.dataloader import YoloDatasets
 from utils.utils import get_anchors, get_classes
 from tensorflow.core.protobuf.rewriter_config_pb2 import RewriterConfig
 from npu_bridge.npu_init import *
-  
+
+import argparse
+
 if __name__ == "__main__":
+
+    # 解析输入参数data_url
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--epochs", type=int, default=120)
+    parser.add_argument("--steps", type=int, default=-1)
+    parser.add_argument("--freeze_flag", type=str, default="True")
+    config = parser.parse_args()
+
     #---------------------------------------------------------------------#
     #   classes_path    指向model_data下的txt，与自己训练的数据集相关 
     #                   训练前一定要修改classes_path，使其对应自己的数据集
@@ -107,14 +117,14 @@ if __name__ == "__main__":
     #   UnFreeze_Epoch          模型总共训练的epoch
     #   Unfreeze_batch_size     模型在解冻后的batch_size
     #------------------------------------------------------------------#
-    UnFreeze_Epoch      = 120
+    UnFreeze_Epoch      = config.epochs
     Unfreeze_batch_size = 8
     #------------------------------------------------------------------#
     #   Freeze_Train    是否进行冻结训练
     #                   默认先冻结主干训练后解冻训练。
     #                   如果设置Freeze_Train=False，建议使用优化器为sgd
     #------------------------------------------------------------------#
-    Freeze_Train        = True
+    Freeze_Train        = config.freeze_flag
     
     #------------------------------------------------------------------#
     #   其它训练参数：学习率、优化器、学习率下降有关
@@ -145,7 +155,7 @@ if __name__ == "__main__":
     #------------------------------------------------------------------#
     #   save_period     多少个epoch保存一次权值，默认每个世代都保存
     #------------------------------------------------------------------#
-    save_period         = 1
+    save_period         = 20
     #------------------------------------------------------------------#
     #   save_dir        权值与日志文件保存的文件夹
     #------------------------------------------------------------------#
@@ -259,6 +269,10 @@ if __name__ == "__main__":
         epoch_step          = num_train // batch_size
         epoch_step_val      = num_val // batch_size
 
+        if config.steps != -1:
+            epoch_step = config.steps
+            epoch_step_val = config.steps
+
         if epoch_step == 0 or epoch_step_val == 0:
             raise ValueError('数据集过小，无法进行训练，请扩充数据集。')