diff --git a/ppcls/utils/gallery2fc.py b/ppcls/utils/gallery2fc.py
index 3629507d0fa0690b195a47248d05bca1ae62a920..e2f49de3c882dd366a4287d666a81c287f4d9105 100644
--- a/ppcls/utils/gallery2fc.py
+++ b/ppcls/utils/gallery2fc.py
@@ -1,5 +1,6 @@
 import os
 import paddle
+import cv2
 
 from ppcls.arch import build_model
 from ppcls.arch.gears.identity_head import IdentityHead
@@ -9,8 +10,8 @@ from ppcls.utils.logger import init_logger
 from ppcls.data import transform, create_operators
 
 
-def build_gallery_feature(configs, feature_extractor):
-    transform_configs = configs["Infer"]["transforms"]
+def build_gallery_layer(configs, feature_extractor):
+    transform_configs = configs["IndexProcess"]["transform_ops"]
     preprocess_ops = create_operators(transform_configs)
 
     embedding_size = configs["Arch"]["Head"]["embedding_size"]
@@ -35,13 +36,34 @@ def build_gallery_feature(configs, feature_extractor):
 
             gallery_images.append(image_file)
             gallery_docs.append(ori_line.strip())
-
-
-
-
-
-def save_fuse_model(fuse_model):
-    pass
+    batch_index = 0
+    gallery_feature = paddle.zeros((len(gallery_images), embedding_size))
+    for i, image_path in enumerate(gallery_images):
+        image = cv2.imread(image_path)
+        for op in preprocess_ops:
+            image = op(image)
+        input_tensor[batch_index] = image
+        batch_index += 1
+        if batch_index == batch_size or i == len(gallery_images) - 1:
+            batch_feature = feature_extractor(input_tensor)
+            for j in range(batch_index):
+                feature = batch_feature[j]
+                norm_feature = paddle.nn.functional.normalize(feature)
+                gallery_feature[i + batch_index - j] = norm_feature
+    gallery_layer = paddle.nn.Linear(embedding_size, len(gallery_images), weight_attr=gallery_feature, bias_attr=False)
+    return gallery_layer
+
+
+def export_fuse_model(model, config):
+    model.eval()
+    model.quanter.save_quantized_model(
+        model.base_model,
+        save_path,
+        input_spec=[
+            paddle.static.InputSpec(
+                shape=[None] + config["Global"]["image_shape"],
+                dtype='float32')
+        ])
 
 
 class FuseModel(paddle.nn.Layer):
@@ -50,11 +72,11 @@ class FuseModel(paddle.nn.Layer):
         self.feature_extractor = build_model(configs)
         load_dygraph_pretrain(self.feature_extractor, configs["Global"]["pretrained_model"])
         self.feature_extractor.head = IdentityHead()
-        self.gallery_layer = build_gallery_feature(configs, self.feature_extractor)
+        self.gallery_layer = build_gallery_layer(configs, self.feature_extractor)
 
     def forward(self, x):
         x = self.feature_model(x)["features"]
-        x = paddle.norm(x)
+        x = paddle.nn.functional.normalize(x)
         x = self.gallery_layer(x)
         return x