diff --git a/ACL_TensorFlow/built-in/cv/Facenet_for_ACL/README.md b/ACL_TensorFlow/built-in/cv/Facenet_for_ACL/README.md
index 4b581197385b3ff6f90f2d11107275d2ccf33b53..348b9bcf73d500373cc18d4845e1cdda9f1e3320 100644
--- a/ACL_TensorFlow/built-in/cv/Facenet_for_ACL/README.md
+++ b/ACL_TensorFlow/built-in/cv/Facenet_for_ACL/README.md
@@ -50,7 +50,28 @@ python3 pb_to_pbtxt.py 20180408-102900.pb
 
 1.删除第一个节点batch_size
 
-2.第二个节点phase_train的op修改为const
+2.第二个节点phase_train修改为:
+node {
+  name: "phase_train"
+  op: "Const"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_BOOL
+    }
+  }
+  attr {
+    key: "value"
+    value {
+      tensor {
+        dtype: DT_BOOL
+        tensor_shape {
+        }
+        bool_val: false
+      }
+    }
+  }
+}
 
 3.删除第三个节点batch_join/fifo_queue
 
@@ -134,11 +155,11 @@ pip3 install amct_tensorflow-2.16.8-py3-none-linux_x86_64.tar.gz
 
 2.重新导出一份原始数据集用于量化
 
-python3 pre_process_data_forquant.py Path_of_Data_after_face_alignment  Outpath_of_Data_after_face_alignment  --use_fixed_image_standardization --lfw_batch_size 1 --use_flipped_images
+python3 pre_process_data_fp32.py Path_of_Data_after_face_alignment  Outpath_of_Data_after_face_alignment  --use_fixed_image_standardization --lfw_batch_size 1 --use_flipped_images
 
 3.量化模型
 
-python3 amct_python.py ./facenet_20180408-102900.pb ./datasets_bin/data_image_bin_original ./quant
+python3 amct_python.py ./facenet_20180408-102900.pb ./datasets_bin/data_image_bin_fp32 ./quant
 
 4.pb转om
 
diff --git a/ACL_TensorFlow/built-in/cv/Facenet_for_ACL/script/preprocess_data.py b/ACL_TensorFlow/built-in/cv/Facenet_for_ACL/script/preprocess_data.py
index 5d839bb5a818290bab2c40deaca94c0a965ec0ad..0eedf0c7eed64693aee1ddcc94192fabcad8d6c7 100644
--- a/ACL_TensorFlow/built-in/cv/Facenet_for_ACL/script/preprocess_data.py
+++ b/ACL_TensorFlow/built-in/cv/Facenet_for_ACL/script/preprocess_data.py
@@ -149,7 +149,7 @@ def main(args):
             control_placeholder = tf.compat.v1.placeholder(tf.int32, shape=(None, 1), name='control')
             phase_train_placeholder = tf.compat.v1.placeholder(tf.bool, name='phase_train')
 
-            nrof_preprocess_threads = 4
+            nrof_preprocess_threads = 1
             image_size = (args.image_size, args.image_size)
             eval_input_queue = data_flow_ops.FIFOQueue(capacity=2000000,
                                                        dtypes=[tf.string, tf.int32, tf.int32],
diff --git a/ACL_TensorFlow/built-in/cv/Facenet_for_ACL/script/preprocess_data_forquant.py b/ACL_TensorFlow/built-in/cv/Facenet_for_ACL/script/preprocess_data_fp32.py
similarity index 94%
rename from ACL_TensorFlow/built-in/cv/Facenet_for_ACL/script/preprocess_data_forquant.py
rename to ACL_TensorFlow/built-in/cv/Facenet_for_ACL/script/preprocess_data_fp32.py
index 8563095c50cc224d5390af262ca4e61253925334..5c9b8be2bb5f4542cc8d026f8f98fdd5fa10ba7d 100644
--- a/ACL_TensorFlow/built-in/cv/Facenet_for_ACL/script/preprocess_data_forquant.py
+++ b/ACL_TensorFlow/built-in/cv/Facenet_for_ACL/script/preprocess_data_fp32.py
@@ -113,15 +113,15 @@ def evaluate(sess,output_path, enqueue_op,image_paths_placeholder,labels_placeho
     assert nrof_images % batch_size == 0, 'The number of LFW images must be an integer multiple of the LFW batch size'
     nrof_batches = nrof_images // batch_size
     print("#############nrof_batches",nrof_batches)
-    if not os.path.exists(os.path.join(output_path ,"data_image_bin_original/")):
-        os.makedirs(os.path.join(output_path ,"data_image_bin_original/"))
-    if not os.path.exists(os.path.join(output_path ,"data_label_bin_original/")):
-        os.makedirs(os.path.join(output_path ,"data_label_bin_original/"))
+    if not os.path.exists(os.path.join(output_path ,"data_image_bin_fp32/")):
+        os.makedirs(os.path.join(output_path ,"data_image_bin_fp32/"))
+    if not os.path.exists(os.path.join(output_path ,"data_label_bin_fp32/")):
+        os.makedirs(os.path.join(output_path ,"data_label_bin_fp32/"))
     for i in range(nrof_batches):
         ###########save  bin ###############
         feed_dict2 = {batch_size_placeholder:batch_size}
-        bin_image2 = os.path.join(output_path,"data_image_bin_original/{}.bin".format(str(i).zfill(6)))
-        bin_label2 = os.path.join(output_path,"data_label_bin_original/{}.bin".format(str(i).zfill(6)))
+        bin_image2 = os.path.join(output_path,"data_image_bin_fp32/{}.bin".format(str(i).zfill(6)))
+        bin_label2 = os.path.join(output_path,"data_label_bin_fp32/{}.bin".format(str(i).zfill(6)))
         emb, lab  = sess.run([image_batch,labels],feed_dict=feed_dict2)
         #emb.astype(np.uint8).tofile(bin_image2)
         emb.tofile(bin_image2)
@@ -150,7 +150,7 @@ def main(args):
             control_placeholder = tf.compat.v1.placeholder(tf.int32, shape=(None, 1), name='control')
             phase_train_placeholder = tf.compat.v1.placeholder(tf.bool, name='phase_train')
 
-            nrof_preprocess_threads = 4
+            nrof_preprocess_threads = 1
             image_size = (args.image_size, args.image_size)
             eval_input_queue = data_flow_ops.FIFOQueue(capacity=2000000,
                                                        dtypes=[tf.string, tf.int32, tf.int32],