diff --git a/paddleclas.py b/paddleclas.py
index d7eb3f9430871a19c432f36789feeb3cabfce7a5..a1e3b2d77d23393be04b3b96e8715e239ef26841 100644
--- a/paddleclas.py
+++ b/paddleclas.py
@@ -33,6 +33,7 @@ from .ppcls.utils import logger
 
 from .deploy.python.predict_cls import ClsPredictor
 from .deploy.python.predict_system import SystemPredictor
+from .deploy.python.build_gallery import GalleryBuilder
 from .deploy.utils.get_image_list import get_image_list
 from .deploy.utils import config
 
@@ -227,7 +228,9 @@ class InputModelError(Exception):
 
 def init_config(model_type, model_name, inference_model_dir, **kwargs):
 
-    if model_type == "pulc":
+    if kwargs.get("build_gallery", False):
+        cfg_path = "deploy/configs/inference_general.yaml"
+    elif model_type == "pulc":
         cfg_path = f"deploy/configs/PULC/{model_name}/inference_{model_name}.yaml"
     elif model_type == "shitu":
         cfg_path = "deploy/configs/inference_general.yaml"
@@ -236,7 +239,8 @@ def init_config(model_type, model_name, inference_model_dir, **kwargs):
 
     __dir__ = os.path.dirname(__file__)
     cfg_path = os.path.join(__dir__, cfg_path)
-    cfg = config.get_config(cfg_path, show=False)
+    cfg = config.get_config(
+        cfg_path, overrides=kwargs.get("override", None), show=False)
     if cfg.Global.get("inference_model_dir"):
         cfg.Global.inference_model_dir = inference_model_dir
     else:
@@ -337,10 +341,15 @@ def args_cfg():
     parser.add_argument(
         "--infer_imgs",
         type=str,
-        required=True,
+        required=False,
         help="The image(s) to be predicted.")
     parser.add_argument(
         "--model_name", type=str, help="The model name to be used.")
+    parser.add_argument(
+        "--predict_type",
+        type=str,
+        default="cls",
+        help="The predict type to be selected.")
     parser.add_argument(
         "--inference_model_dir",
         type=str,
@@ -395,7 +404,17 @@ def args_cfg():
     parser.add_argument(
         "--resize_short", type=int, help="Resize according to short size.")
     parser.add_argument("--crop_size", type=int, help="Centor crop size.")
-
+    parser.add_argument(
+        "--build_gallery",
+        type=str2bool,
+        default=False,
+        help="Whether build gallery.")
+    parser.add_argument(
+        '-o',
+        '--override',
+        action='append',
+        default=[],
+        help='config options to be overridden')
     args = parser.parse_args()
     return vars(args)
 
@@ -549,14 +568,27 @@ class PaddleClas(object):
         """
         super().__init__()
 
-        self.model_type, inference_model_dir = self._check_input_model(
-            model_name, inference_model_dir)
-        self._config = init_config(self.model_type, model_name,
-                                   inference_model_dir, **kwargs)
-        if self.model_type == "shitu":
-            self.predictor = SystemPredictor(self._config)
+        if kwargs.get("build_gallery", False):
+            self.model_type, inference_model_dir = self._check_input_model(
+                model_name
+                if model_name else "PP-ShiTuV2", inference_model_dir)
+            self._config = init_config(self.model_type, model_name
+                                       if model_name else "PP-ShiTuV2",
+                                       inference_model_dir, **kwargs)
+
+            logger.info("Building Gallery...")
+            GalleryBuilder(self._config)
+
         else:
-            self.predictor = ClsPredictor(self._config)
+            self.model_type, inference_model_dir = self._check_input_model(
+                model_name, inference_model_dir)
+            self._config = init_config(self.model_type, model_name,
+                                       inference_model_dir, **kwargs)
+
+            if self.model_type == "shitu":
+                self.predictor = SystemPredictor(self._config)
+            else:
+                self.predictor = ClsPredictor(self._config)
 
     def get_config(self):
         """Get the config.
@@ -700,6 +732,9 @@ class PaddleClas(object):
                 prediction result(s) is zipped as a dict, that includs topk "class_ids", "scores" and "label_names".
                 The format of batch prediction result(s) is as follow: [{"class_ids": [...], "scores": [...], "label_names": [...]}, ...]
         """
+        if input_data == None and self._config.Global.infer_imgs:
+            input_data = self._config.Global.infer_imgs
+
         if isinstance(input_data, np.ndarray):
             yield self.predictor.predict(input_data)
         elif isinstance(input_data, str):
@@ -742,6 +777,8 @@ class PaddleClas(object):
                 input_data: Union[str, np.array],
                 print_pred: bool=False,
                 predict_type="cls"):
+        assert predict_type in ["cls", "shitu"
+                                ], "Predict type should be 'cls' or 'shitu'."
         if predict_type == "cls":
             return self.predict_cls(input_data, print_pred)
         elif predict_type == "shitu":
@@ -760,13 +797,14 @@ def main():
     print_info()
     cfg = args_cfg()
     clas_engine = PaddleClas(**cfg)
-    res = clas_engine.predict(
-        cfg["infer_imgs"],
-        print_pred=True,
-        predict_type="cls" if "PP-ShiTu" not in cfg["model_name"] else "shitu")
-    for _ in res:
-        pass
-    logger.info("Predict complete!")
+    if cfg["build_gallery"] == False:
+        res = clas_engine.predict(
+            cfg["infer_imgs"],
+            print_pred=True,
+            predict_type=cfg["predict_type"])
+        for _ in res:
+            pass
+        logger.info("Predict complete!")
     return