diff --git a/docs/en/whl_en.md b/docs/en/whl_en.md
index 05248cb188730a0ef0c8a4cf5d867732676e8ccb..e791ef08cb22139f8aab771be0ecce4883cd151c 100644
--- a/docs/en/whl_en.md
+++ b/docs/en/whl_en.md
@@ -5,7 +5,7 @@
 * installing from pypi
 
 ```bash
-pip3 install paddleclas==2.2.0
+pip3 install paddleclas==2.2.1
 ```
 
 * build own whl package and install
diff --git a/docs/zh_CN/whl.md b/docs/zh_CN/whl.md
index 58051c52db51b49c18cdc60c5ef541339a5fddeb..2a138c7fa1e9e575e61440308de74dba36abcc4d 100644
--- a/docs/zh_CN/whl.md
+++ b/docs/zh_CN/whl.md
@@ -5,7 +5,7 @@
 * pip安装
 
 ```bash
-pip3 install paddleclas==2.2.0
+pip3 install paddleclas==2.2.1
 ```
 
 * 本地构建并安装
diff --git a/paddleclas.py b/paddleclas.py
index d0bd6b14a1386c908a77c25dcb93a4ff60a5efe6..91cd030abcb5fb24cece4ac508c7f0eb46e779fc 100644
--- a/paddleclas.py
+++ b/paddleclas.py
@@ -18,6 +18,7 @@ __dir__ = os.path.dirname(__file__)
 sys.path.append(os.path.join(__dir__, ""))
 sys.path.append(os.path.join(__dir__, "deploy"))
 
+from typing import Union, Generator
 import argparse
 import shutil
 import textwrap
@@ -356,7 +357,7 @@ def download_with_progressbar(url, save_path):
 
 
 def check_model_file(model_name):
-    """Check the model files exist and download and untar when no exist. 
+    """Check the model files exist and download and untar when no exist.
     """
     storage_directory = partial(os.path.join, BASE_INFERENCE_MODEL_DIR,
                                 model_name)
@@ -410,11 +411,11 @@ class PaddleClas(object):
         """Init PaddleClas with config.
 
         Args:
-            model_name: The model name supported by PaddleClas, default by None. If specified, override config.
-            inference_model_dir: The directory that contained model file and params file to be used, default by None. If specified, override config.
-            use_gpu: Whether use GPU, default by None. If specified, override config.
-            batch_size: The batch size to pridict, default by None. If specified, override config.
-            topk: Return the top k prediction results with the highest score.
+            model_name (str, optional): The model name supported by PaddleClas. If specified, override config. Defaults to None.
+            inference_model_dir (str, optional): The directory that contained model file and params file to be used. If specified, override config. Defaults to None.
+            use_gpu (bool, optional): Whether use GPU. If specified, override config. Defaults to True.
+            batch_size (int, optional): The batch size to pridict. If specified, override config. Defaults to 1.
+            topk (int, optional): Return the top k prediction results with the highest score. Defaults to 5.
         """
         super().__init__()
         self._config = init_config(model_name, inference_model_dir, use_gpu,
@@ -459,20 +460,26 @@ class PaddleClas(object):
             raise InputModelError(err)
         return
 
-    def predict(self, input_data, print_pred=False):
+    def predict(self, input_data: Union[str, np.array],
+                print_pred: bool=False) -> Generator[list, None, None]:
         """Predict input_data.
 
         Args:
-            input_data (str | NumPy.array): The path of image, or the directory containing images, or the URL of image from Internet.
-            print_pred (bool, optional): Whether print the prediction result. Defaults to False.
+            input_data (Union[str, np.array]): 
+                When the type is str, it is the path of image, or the directory containing images, or the URL of image from Internet.
+                When the type is np.array, it is the image data whose channel order is RGB.
+            print_pred (bool, optional): Whether print the prediction result. Defaults to False. Defaults to False.
 
         Raises:
             ImageTypeError: Illegal input_data.
 
         Yields:
-            list: The prediction result(s) of input_data by batch_size. For every one image, prediction result(s) is zipped as a dict, that includs topk "class_ids", "scores" and "label_names". The format is as follow:
-            [{"class_ids": [...], "scores": [...], "label_names": [...]}, ...]
+            Generator[list, None, None]: 
+                The prediction result(s) of input_data by batch_size. For every one image, 
+                prediction result(s) is zipped as a dict, that includs topk "class_ids", "scores" and "label_names". 
+                The format is as follow: [{"class_ids": [...], "scores": [...], "label_names": [...]}, ...]
         """
+
         if isinstance(input_data, np.ndarray):
             outputs = self.cls_predictor.predict(input_data)
             yield self.cls_predictor.postprocess(outputs)
@@ -502,6 +509,7 @@ class PaddleClas(object):
                         f"Image file failed to read and has been skipped. The path: {img_path}"
                     )
                     continue
+                img = img[:, :, ::-1]
                 img_list.append(img)
                 img_path_list.append(img_path)
                 cnt += 1