diff --git a/TensorFlow/built-in/cv/image_classification/Facenet_ID0122_for_TensorFlow/src/facenet.py b/TensorFlow/built-in/cv/image_classification/Facenet_ID0122_for_TensorFlow/src/facenet.py
index f26031637d7604ffe1bad4d9dc145da1cf3dbb90..b6ccb34c3af3c1289417b8e64c073e7d425b80d1 100644
--- a/TensorFlow/built-in/cv/image_classification/Facenet_ID0122_for_TensorFlow/src/facenet.py
+++ b/TensorFlow/built-in/cv/image_classification/Facenet_ID0122_for_TensorFlow/src/facenet.py
@@ -208,8 +208,8 @@ def train(total_loss, global_step, optimizer, learning_rate, moving_average_deca
         opt = npu_distributed_optimizer_wrapper(opt)
     # -----8P modified end-----
     if use_NPU: 
-        #loss_scale_manager = FixedLossScaleManager(loss_scale=256)
-        loss_scale_manager = ExponentialUpdateLossScaleManager(init_loss_scale=4194304, incr_every_n_steps=2000, decr_every_n_nan_or_inf=1, decr_ratio=0.5)
+        loss_scale_manager = FixedLossScaleManager(loss_scale=2097156, enable_overflow_check=False)
+        #loss_scale_manager = ExponentialUpdateLossScaleManager(init_loss_scale=4194304, incr_every_n_steps=2000, decr_every_n_nan_or_inf=1, decr_ratio=0.5)
         if RANK_SIZE > 1:
             opt = NPULossScaleOptimizer(opt, loss_scale_manager, is_distributed=True)
         else:
diff --git a/TensorFlow/built-in/cv/image_classification/Facenet_ID0122_for_TensorFlow/test/train_full_1p.sh b/TensorFlow/built-in/cv/image_classification/Facenet_ID0122_for_TensorFlow/test/train_full_1p.sh
index 012d534c7d0abe17e53fd6ad271ddf4244ade2df..9328bc68c126f0916d49baae173b357153ce8de7 100644
--- a/TensorFlow/built-in/cv/image_classification/Facenet_ID0122_for_TensorFlow/test/train_full_1p.sh
+++ b/TensorFlow/built-in/cv/image_classification/Facenet_ID0122_for_TensorFlow/test/train_full_1p.sh
@@ -86,7 +86,7 @@ echo "Final Training Duration sec : $e2etime"
 #结果打印，不需要修改
 echo "------------------ Final result ------------------"
 #输出性能FPS，需要模型审视修改
-TrainingTime=`grep  RegLoss $cur_path/test/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log| tail -n +2 | head -4999 | awk '{print $4}' | tr -d s | awk '{sum+=$1} END {print sum/NR}'`
+TrainingTime=`grep  RegLoss $cur_path/test/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | awk '{print $4}' | tr -d s | awk '{sum+=$1} END {print sum/NR}'`
 wait
 FPS=`awk 'BEGIN{printf "%.2f\n",'${batch_size}'/'${TrainingTime}'}'`
 #打印，不需要修改
diff --git a/TensorFlow/built-in/cv/image_classification/Facenet_ID0122_for_TensorFlow/test/train_full_8p.sh b/TensorFlow/built-in/cv/image_classification/Facenet_ID0122_for_TensorFlow/test/train_full_8p.sh
index fd98f04eabdae3dfeaf9e62de2a9a47ee66646a6..ad90c8111eb8153eea0f95f7e35b87ce33eb511f 100644
--- a/TensorFlow/built-in/cv/image_classification/Facenet_ID0122_for_TensorFlow/test/train_full_8p.sh
+++ b/TensorFlow/built-in/cv/image_classification/Facenet_ID0122_for_TensorFlow/test/train_full_8p.sh
@@ -33,6 +33,9 @@ for para in $*
 do
 	if [[ $para == --data_path* ]];then
 		data_path=`echo ${para#*=}`
+  elif [[ $para == --bind_core* ]];then
+    bind_core=`echo ${para#*=}` 
+    name_bind="_bindcore"
 	elif [[ $para == --switch_config* ]];then
 		switch_config=`echo ${para#*=}`
 	fi
@@ -56,7 +59,20 @@ do
 	mkdir -p $cur_path/src/logs/$ASCEND_DEVICE_ID
 	rm -rf ${cur_path}/src/models/$ASCEND_DEVICE_ID
 	mkdir -p ${cur_path}/src/models/$ASCEND_DEVICE_ID
-	nohup python3 ${cur_path}/src/train_softmax.py \
+  # 绑核，不需要的绑核的模型删除，需要的模型审视修改
+  let a=RANK_ID*12
+  let b=RANK_ID+1
+  let c=b*12-1
+
+  corenum=`cat /proc/cpuinfo |grep 'processor' | wc -l`
+  let a=RANK_ID*${corenum}/8
+  let b=RANK_ID+1
+  let c=b*${corenum}/8-1
+  if [ "x${bind_core}" != x ];then
+      bind_core="taskset -c $a-$c"
+  fi
+
+  nohup ${bind_core} python3 ${cur_path}/src/train_softmax.py \
 		--logs_base_dir ${cur_path}/src/logs/$ASCEND_DEVICE_ID \
 		--models_base_dir ${cur_path}/src/models/$ASCEND_DEVICE_ID \
 		--data_dir ${data_path}/CASIA-WebFace_182/ \
@@ -93,7 +109,7 @@ echo "Final Training Duration sec : $e2etime"
 #结果打印，不需要修改
 echo "------------------ Final result ------------------"
 #输出性能FPS，需要模型审视修改
-TrainingTime=`grep  RegLoss $cur_path/test/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log| tail -n +2 | head -2999 | awk '{print $4}' | tr -d s | awk '{sum+=$1} END {print sum/NR}'`
+TrainingTime=`grep  RegLoss $cur_path/test/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log| awk '{print $4}' | tr -d s | awk '{sum+=$1} END {print sum/NR}'`
 wait
 FPS=`awk 'BEGIN{printf "%.2f\n",'${batch_size}'/'${TrainingTime}'}'`
 FPS=$(awk 'BEGIN{print '$FPS'*8}')
@@ -107,7 +123,7 @@ BatchSize=${batch_size}
 #设备类型，自动获取
 DeviceType=`uname -m`
 #用例名称，自动获取
-CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'acc'
+CaseName=${Network}${name_bind}_bs${BatchSize}_${RANK_SIZE}'p'_'acc'
 
 #从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要根据模型审视
 
diff --git a/TensorFlow/built-in/cv/image_classification/Facenet_ID0122_for_TensorFlow/test/train_performance_1p.sh b/TensorFlow/built-in/cv/image_classification/Facenet_ID0122_for_TensorFlow/test/train_performance_1p.sh
index 6da62369e24097c0e251b7c087a059df12eeff8f..6cbe521fd0a5a6ac9e348ec2c9194f552e4dc148 100644
--- a/TensorFlow/built-in/cv/image_classification/Facenet_ID0122_for_TensorFlow/test/train_performance_1p.sh
+++ b/TensorFlow/built-in/cv/image_classification/Facenet_ID0122_for_TensorFlow/test/train_performance_1p.sh
@@ -82,7 +82,7 @@ echo "Final Training Duration sec : $e2etime"
 #结果打印，不需要修改
 echo "------------------ Final result ------------------"
 #输出性能FPS，需要模型审视修改
-TrainingTime=`grep  RegLoss $cur_path/test/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log| tail -n +2 | awk '{print $4}' | tr -d s | awk '{sum+=$1} END {print sum/NR}'`
+TrainingTime=`grep  RegLoss $cur_path/test/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log| awk '{print $4}' | tr -d s | awk '{sum+=$1} END {print sum/NR}'`
 wait
 FPS=`awk 'BEGIN{printf "%.2f\n",'${batch_size}'/'${TrainingTime}'}'`
 #打印，不需要修改
diff --git a/TensorFlow/built-in/cv/image_classification/Facenet_ID0122_for_TensorFlow/test/train_performance_8p.sh b/TensorFlow/built-in/cv/image_classification/Facenet_ID0122_for_TensorFlow/test/train_performance_8p.sh
index 3673d4f41be6db4d13bda562f6931b7b18b490f8..d14ac258bb4ccffa2b3d3540cd59baf5e715aeca 100644
--- a/TensorFlow/built-in/cv/image_classification/Facenet_ID0122_for_TensorFlow/test/train_performance_8p.sh
+++ b/TensorFlow/built-in/cv/image_classification/Facenet_ID0122_for_TensorFlow/test/train_performance_8p.sh
@@ -33,6 +33,9 @@ for para in $*
 do
 	if [[ $para == --data_path* ]];then
 		data_path=`echo ${para#*=}`
+  elif [[ $para == --bind_core* ]];then
+    bind_core=`echo ${para#*=}` 
+    name_bind="_bindcore"
 	elif [[ $para == --switch_config* ]];then
 		switch_config=`echo ${para#*=}`
 	fi
@@ -54,7 +57,20 @@ do
 	export RANK_ID=${ID}
 	mkdir -p $cur_path/test/output/$ASCEND_DEVICE_ID
 	mkdir -p $cur_path/src/logs/$ASCEND_DEVICE_ID
-	nohup python3 ${cur_path}/src/train_softmax.py \
+  # 绑核，不需要的绑核的模型删除，需要的模型审视修改
+  let a=RANK_ID*12
+  let b=RANK_ID+1
+  let c=b*12-1
+
+  corenum=`cat /proc/cpuinfo |grep 'processor' | wc -l`
+  let a=RANK_ID*${corenum}/8
+  let b=RANK_ID+1
+  let c=b*${corenum}/8-1
+  if [ "x${bind_core}" != x ];then
+      bind_core="taskset -c $a-$c"
+  fi
+
+  nohup ${bind_core} python3 ${cur_path}/src/train_softmax.py \
 		--logs_base_dir ${cur_path}/src/logs/$ASCEND_DEVICE_ID \
 		--models_base_dir ${cur_path}/src/models/ \
 		--data_dir ${data_path}/CASIA-WebFace_182/ \
@@ -91,7 +107,7 @@ echo "Final Training Duration sec : $e2etime"
 #结果打印，不需要修改
 echo "------------------ Final result ------------------"
 #输出性能FPS，需要模型审视修改
-TrainingTime=`grep  RegLoss $cur_path/test/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log| tail -n +2 | awk '{print $4}' | tr -d s | awk '{sum+=$1} END {print sum/NR}'`
+TrainingTime=`grep  RegLoss $cur_path/test/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log| awk '{print $4}' | tr -d s | awk '{sum+=$1} END {print sum/NR}'`
 wait
 FPS=`awk 'BEGIN{printf "%.2f\n",'${batch_size}'/'${TrainingTime}'}'`
 FPS=$(awk 'BEGIN{print '$FPS'*8}')
@@ -105,7 +121,7 @@ BatchSize=${batch_size}
 #设备类型，自动获取
 DeviceType=`uname -m`
 #用例名称，自动获取
-CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'
+CaseName=${Network}${name_bind}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'
 
 #从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要根据模型审视