diff --git a/docs/apis/deploy.md b/docs/apis/deploy.md
index 3f924ebee2893cfca77cad459f4cb9c7a6b2acb1..5b906239b9fb45f92e9bca1ba450c028817885ff 100644
--- a/docs/apis/deploy.md
+++ b/docs/apis/deploy.md
@@ -45,7 +45,7 @@ predict(image, topk=1)
 
 ### batch_predict 接口
 ```
-batch_predict(image_list, topk=1, thread_num=2)
+batch_predict(image_list, topk=1)
 ```
 批量图片预测接口。
 
@@ -53,4 +53,3 @@ batch_predict(image_list, topk=1, thread_num=2)
 >
 > > * **image_list** (list|tuple): 对列表（或元组）中的图像同时进行预测，列表中的元素可以是图像路径或numpy数组(HWC排列，BGR格式)。
 > > * **topk** (int): 图像分类时使用的参数，表示预测前topk个可能的分类。
-> > * **thread_num** (int): 并发执行各图像预处理时的线程数。
diff --git a/docs/apis/models/classification.md b/docs/apis/models/classification.md
index 793a889568f8cb597fdea650310acada6512a1e9..96f76b8b5d49d800ad12439eefdb46530fc4c834 100755
--- a/docs/apis/models/classification.md
+++ b/docs/apis/models/classification.md
@@ -62,7 +62,7 @@ evaluate(self, eval_dataset, batch_size=1, epoch_id=None, return_details=False)
 ### predict
 
 ```python
-predict(self, img_file, transforms=None, topk=5)
+predict(self, img_file, transforms=None, topk=1)
 ```
 
 > 分类模型预测接口。需要注意的是，只有在训练过程中定义了eval_dataset，模型在保存时才会将预测时的图像处理流程保存在`ResNet50.test_transforms`和`ResNet50.eval_transforms`中。如未在训练时定义eval_dataset，那在调用预测`predict`接口时，用户需要再重新定义test_transforms传入给`predict`接口。
@@ -81,7 +81,7 @@ predict(self, img_file, transforms=None, topk=5)
 ### batch_predict
 
 ```python
-batch_predict(self, img_file_list, transforms=None, topk=5, thread_num=2)
+batch_predict(self, img_file_list, transforms=None, topk=1)
 ```
 
 > 分类模型批量预测接口。需要注意的是，只有在训练过程中定义了eval_dataset，模型在保存时才会将预测时的图像处理流程保存在`ResNet50.test_transforms`和`ResNet50.eval_transforms`中。如未在训练时定义eval_dataset，那在调用预测`batch_predict`接口时，用户需要再重新定义test_transforms传入给`batch_predict`接口。
@@ -91,7 +91,6 @@ batch_predict(self, img_file_list, transforms=None, topk=5, thread_num=2)
 > > - **img_file_list** (list|tuple): 对列表（或元组）中的图像同时进行预测，列表中的元素可以是图像路径或numpy数组(HWC排列，BGR格式)。
 > > - **transforms** (paddlex.cls.transforms): 数据预处理操作。
 > > - **topk** (int): 预测时前k个最大值。
-> > - **thread_num** (int): 并发执行各图像预处理时的线程数。
 
 > **返回值**
 >
diff --git a/docs/apis/models/detection.md b/docs/apis/models/detection.md
index b3873ce5eba6516c4296d13d2d99510d5e3e6e45..3cc911377bc45d3831faacfb160a760bcc1cd8e2 100755
--- a/docs/apis/models/detection.md
+++ b/docs/apis/models/detection.md
@@ -108,7 +108,7 @@ predict(self, img_file, transforms=None)
 ### batch_predict
 
 ```python
-batch_predict(self, img_file_list, transforms=None, thread_num=2)
+batch_predict(self, img_file_list, transforms=None)
 ```
 
 > PPYOLO模型批量预测接口。需要注意的是，只有在训练过程中定义了eval_dataset，模型在保存时才会将预测时的图像处理流程保存在`YOLOv3.test_transforms`和`YOLOv3.eval_transforms`中。如未在训练时定义eval_dataset，那在调用预测`batch_predict`接口时，用户需要再重新定义`test_transforms`传入给`batch_predict`接口
@@ -117,7 +117,6 @@ batch_predict(self, img_file_list, transforms=None, thread_num=2)
 >
 > > - **img_file_list** (str|np.ndarray): 对列表（或元组）中的图像同时进行预测，列表中的元素是预测图像路径或numpy数组(HWC排列，BGR格式)。
 > > - **transforms** (paddlex.det.transforms): 数据预处理操作。
-> > - **thread_num** (int): 并发执行各图像预处理时的线程数。
 >
 > **返回值**
 >
@@ -222,7 +221,7 @@ predict(self, img_file, transforms=None)
 ### batch_predict
 
 ```python
-batch_predict(self, img_file_list, transforms=None, thread_num=2)
+batch_predict(self, img_file_list, transforms=None)
 ```
 
 > YOLOv3模型批量预测接口。需要注意的是，只有在训练过程中定义了eval_dataset，模型在保存时才会将预测时的图像处理流程保存在`YOLOv3.test_transforms`和`YOLOv3.eval_transforms`中。如未在训练时定义eval_dataset，那在调用预测`batch_predict`接口时，用户需要再重新定义`test_transforms`传入给`batch_predict`接口
@@ -231,7 +230,6 @@ batch_predict(self, img_file_list, transforms=None, thread_num=2)
 >
 > > - **img_file_list** (str|np.ndarray): 对列表（或元组）中的图像同时进行预测，列表中的元素是预测图像路径或numpy数组(HWC排列，BGR格式)。
 > > - **transforms** (paddlex.det.transforms): 数据预处理操作。
-> > - **thread_num** (int): 并发执行各图像预处理时的线程数。
 >
 > **返回值**
 >
@@ -327,7 +325,7 @@ predict(self, img_file, transforms=None)
 ### batch_predict
 
 ```python
-batch_predict(self, img_file_list, transforms=None, thread_num=2)
+batch_predict(self, img_file_list, transforms=None)
 ```
 
 > FasterRCNN模型批量预测接口。需要注意的是，只有在训练过程中定义了eval_dataset，模型在保存时才会将预测时的图像处理流程保存在`FasterRCNN.test_transforms`和`FasterRCNN.eval_transforms`中。如未在训练时定义eval_dataset，那在调用预测`batch_predict`接口时，用户需要再重新定义test_transforms传入给`batch_predict`接口。
@@ -336,7 +334,6 @@ batch_predict(self, img_file_list, transforms=None, thread_num=2)
 >
 > > - **img_file_list** (list|tuple): 对列表（或元组）中的图像同时进行预测，列表中的元素是预测图像路径或numpy数组(HWC排列，BGR格式)。
 > > - **transforms** (paddlex.det.transforms): 数据预处理操作。
-> > - **thread_num** (int): 并发执行各图像预处理时的线程数。
 >
 > **返回值**
 >
diff --git a/docs/apis/models/instance_segmentation.md b/docs/apis/models/instance_segmentation.md
index 494cde32a1888897b5771e6d94d8691d6ff79ce8..3c86096cccef3d66dcad15a2a496023102750386 100755
--- a/docs/apis/models/instance_segmentation.md
+++ b/docs/apis/models/instance_segmentation.md
@@ -88,7 +88,7 @@ predict(self, img_file, transforms=None)
 #### batch_predict
 
 ```python
-batch_predict(self, img_file_list, transforms=None, thread_num=2)
+batch_predict(self, img_file_list, transforms=None)
 ```
 
 > MaskRCNN模型批量预测接口。需要注意的是，只有在训练过程中定义了eval_dataset，模型在保存时才会将预测时的图像处理流程保存在`FasterRCNN.test_transforms`和`FasterRCNN.eval_transforms`中。如未在训练时定义eval_dataset，那在调用预测`batch_predict`接口时，用户需要再重新定义test_transforms传入给`batch_predict`接口。
@@ -97,7 +97,6 @@ batch_predict(self, img_file_list, transforms=None, thread_num=2)
 >
 > > - **img_file_list** (list|tuple): 对列表（或元组）中的图像同时进行预测，列表中的元素可以是预测图像路径或numpy数组(HWC排列，BGR格式)。
 > > - **transforms** (paddlex.det.transforms): 数据预处理操作。
-> > - **thread_num** (int): 并发执行各图像预处理时的线程数。
 >
 > **返回值**
 >
diff --git a/docs/apis/models/semantic_segmentation.md b/docs/apis/models/semantic_segmentation.md
index 82b758d98f243e6f653c5e8d39d181b45e150587..c7d2872498acfadf8bf1d5a24dc32b85ae5b9a76 100755
--- a/docs/apis/models/semantic_segmentation.md
+++ b/docs/apis/models/semantic_segmentation.md
@@ -95,7 +95,7 @@ predict(self, img_file, transforms=None):
 ### batch_predict
 
 ```
-batch_predict(self, img_file_list, transforms=None, thread_num=2):
+batch_predict(self, img_file_list, transforms=None):
 ```
 
 > DeepLabv3p模型批量预测接口。需要注意的是，只有在训练过程中定义了eval_dataset，模型在保存时才会将预测时的图像处理流程保存在`DeepLabv3p.test_transforms`和`DeepLabv3p.eval_transforms`中。如未在训练时定义eval_dataset，那在调用预测`batch_predict`接口时，用户需要再重新定义test_transforms传入给`batch_predict`接口。
@@ -104,7 +104,6 @@ batch_predict(self, img_file_list, transforms=None, thread_num=2):
 > >
 > > - **img_file_list** (list|tuple): 对列表（或元组）中的图像同时进行预测，列表中的元素可以是预测图像路径或numpy数组(HWC排列，BGR格式)。
 > > - **transforms** (paddlex.seg.transforms): 数据预处理操作。
-> > - **thread_num** (int): 并发执行各图像预处理时的线程数。
 
 > **返回值**
 > >
diff --git a/docs/examples/meter_reader.md b/docs/examples/meter_reader.md
index 670d7d1399b55c672b17ed903663bf26c8a6ef84..114046d16ba4db837e143a63d47b1fcdfdd26680 100644
--- a/docs/examples/meter_reader.md
+++ b/docs/examples/meter_reader.md
@@ -70,7 +70,6 @@ cd PaddleX/examples/meter_reader/
 | save_dir	| 保存可视化结果的路径, 默认值为"output"|
 | score_threshold | 检测模型输出结果中，预测得分低于该阈值的框将被滤除，默认值为0.5|
 | seg_batch_size | 分割的批量大小，默认为2 |
-| seg_thread_num	| 分割预测的线程数，默认为cpu处理器个数 |
 | use_camera | 是否使用摄像头采集图片，默认为False |
 | camera_id | 摄像头设备ID，默认值为0 |
 | use_erode | 是否使用图像腐蚀对分割预测图进行细分，默认为False |
diff --git a/examples/meter_reader/README.md b/examples/meter_reader/README.md
index ce5666f5afeecb0dc97dd78429ae132ae52a7723..a5cae4af0cc3ace2ee80b10ccc44c2fff79ea0cc 100644
--- a/examples/meter_reader/README.md
+++ b/examples/meter_reader/README.md
@@ -79,7 +79,6 @@ cd PaddleX/examples/meter_reader/
 | save_dir	| 保存可视化结果的路径, 默认值为"output"|
 | score_threshold | 检测模型输出结果中，预测得分低于该阈值的框将被滤除，默认值为0.5|
 | seg_batch_size | 分割的批量大小，默认为2 |
-| seg_thread_num	| 分割预测的线程数，默认为cpu处理器个数 |
 | use_camera | 是否使用摄像头采集图片，默认为False |
 | camera_id | 摄像头设备ID，默认值为0 |
 | use_erode | 是否使用图像腐蚀对分割预测图进行细分，默认为False |
diff --git a/examples/meter_reader/deploy/python/reader_deploy.py b/examples/meter_reader/deploy/python/reader_deploy.py
index a5f5d18b0edad902217b6392cfc53dfb4709daf9..1006ff83a732bf0a12f586e37aa1ad99e932ccb5 100644
--- a/examples/meter_reader/deploy/python/reader_deploy.py
+++ b/examples/meter_reader/deploy/python/reader_deploy.py
@@ -105,12 +105,6 @@ def parse_args():
         help="Segmentation batch size",
         type=int,
         default=2)
-    parser.add_argument(
-        '--seg_thread_num',
-        dest='seg_thread_num',
-        help="Thread number of segmentation preprocess",
-        type=int,
-        default=2)
 
     return parser.parse_args()
 
@@ -143,8 +137,7 @@ class MeterReader:
                 use_erode=True,
                 erode_kernel=4,
                 score_threshold=0.5,
-                seg_batch_size=2,
-                seg_thread_num=2):
+                seg_batch_size=2):
         if isinstance(im_file, str):
             im = cv2.imread(im_file).astype('float32')
         else:
@@ -190,8 +183,7 @@ class MeterReader:
                 meter_images.append(resized_meters[j - i])
             result = self.segmenter.batch_predict(
                 transforms=self.seg_transforms,
-                img_file_list=meter_images,
-                thread_num=seg_thread_num)
+                img_file_list=meter_images)
             if use_erode:
                 kernel = np.ones((erode_kernel, erode_kernel), np.uint8)
                 for i in range(len(result)):
@@ -334,7 +326,7 @@ def infer(args):
         for im_file in image_lists:
             meter_reader.predict(im_file, args.save_dir, args.use_erode,
                                  args.erode_kernel, args.score_threshold,
-                                 args.seg_batch_size, args.seg_thread_num)
+                                 args.seg_batch_size)
     elif args.use_camera:
         cap_video = cv2.VideoCapture(args.camera_id)
         if not cap_video.isOpened():
@@ -347,7 +339,7 @@ def infer(args):
             if ret:
                 meter_reader.predict(frame, args.save_dir, args.use_erode,
                                      args.erode_kernel, args.score_threshold,
-                                     args.seg_batch_size, args.seg_thread_num)
+                                     args.seg_batch_size)
                 if cv2.waitKey(1) & 0xFF == ord('q'):
                     break
             else:
diff --git a/examples/meter_reader/reader_infer.py b/examples/meter_reader/reader_infer.py
index c7f7d7367a7ef3d0b6bba4fd1c6a3258cd5145ac..58108bbacfc02422bd9e80dd93d53252694cec1e 100644
--- a/examples/meter_reader/reader_infer.py
+++ b/examples/meter_reader/reader_infer.py
@@ -105,12 +105,6 @@ def parse_args():
         help="Segmentation batch size",
         type=int,
         default=2)
-    parser.add_argument(
-        '--seg_thread_num',
-        dest='seg_thread_num',
-        help="Thread number of segmentation preprocess",
-        type=int,
-        default=2)
 
     return parser.parse_args()
 
@@ -143,8 +137,7 @@ class MeterReader:
                 use_erode=True,
                 erode_kernel=4,
                 score_threshold=0.5,
-                seg_batch_size=2,
-                seg_thread_num=2):
+                seg_batch_size=2):
         if isinstance(im_file, str):
             im = cv2.imread(im_file).astype('float32')
         else:
@@ -190,8 +183,7 @@ class MeterReader:
                 meter_images.append(resized_meters[j - i])
             result = self.segmenter.batch_predict(
                 transforms=self.seg_transforms,
-                img_file_list=meter_images,
-                thread_num=seg_thread_num)
+                img_file_list=meter_images)
             if use_erode:
                 kernel = np.ones((erode_kernel, erode_kernel), np.uint8)
                 for i in range(len(result)):
@@ -334,7 +326,7 @@ def infer(args):
         for im_file in image_lists:
             meter_reader.predict(im_file, args.save_dir, args.use_erode,
                                  args.erode_kernel, args.score_threshold,
-                                 args.seg_batch_size, args.seg_thread_num)
+                                 args.seg_batch_size)
     elif args.use_camera:
         cap_video = cv2.VideoCapture(args.camera_id)
         if not cap_video.isOpened():
@@ -347,7 +339,7 @@ def infer(args):
             if ret:
                 meter_reader.predict(frame, args.save_dir, args.use_erode,
                                      args.erode_kernel, args.score_threshold,
-                                     args.seg_batch_size, args.seg_thread_num)
+                                     args.seg_batch_size)
                 if cv2.waitKey(1) & 0xFF == ord('q'):
                     break
             else:
diff --git a/paddlex/__init__.py b/paddlex/__init__.py
index 25fd9f4ec65108feae0cb62743d91468967b88c4..3adb77d460c71140d744bfe698073767658a7499 100644
--- a/paddlex/__init__.py
+++ b/paddlex/__init__.py
@@ -56,4 +56,4 @@ log_level = 2
 
 from . import interpret
 
-__version__ = '1.1.1'
+__version__ = '1.1.4'
diff --git a/paddlex/command.py b/paddlex/command.py
index 4fde4b879c55eb4a278b9089fa4b4b9b0d38c7a5..a433cbcfb8f308470e27b0bc31d4d55ef7a3c29c 100644
--- a/paddlex/command.py
+++ b/paddlex/command.py
@@ -189,7 +189,7 @@ def main():
 
     if args.split_dataset:
         assert args.dataset_dir is not None, "--dataset_dir should be defined while spliting dataset"
-        assert args.format is not None, "--form should be defined while spliting dataset"
+        assert args.format is not None, "--format should be defined while spliting dataset"
         assert args.val_value is not None, "--val_value should be defined while spliting dataset"
 
         dataset_dir = args.dataset_dir
diff --git a/paddlex/cv/models/base.py b/paddlex/cv/models/base.py
index 19bf4f034a2fb2c0c42126843913517f8c7cb56a..899d1d3b64e4d166fcdf432fadaf4eaebe189710 100644
--- a/paddlex/cv/models/base.py
+++ b/paddlex/cv/models/base.py
@@ -23,6 +23,7 @@ import yaml
 import copy
 import json
 import functools
+import multiprocessing as mp
 import paddlex.utils.logging as logging
 from paddlex.utils import seconds_to_hms
 from paddlex.utils.utils import EarlyStop
@@ -76,6 +77,16 @@ class BaseAPI:
         self.completed_epochs = 0
         self.scope = fluid.global_scope()
 
+        # 线程池，在模型在预测时用于对输入数据以图片为单位进行并行处理
+        # 主要用于batch_predict接口
+        thread_num = mp.cpu_count() if mp.cpu_count() < 8 else 8
+        self.thread_pool = mp.pool.ThreadPool(thread_num)
+
+    def reset_thread_pool(self, thread_num):
+        self.thread_pool.close()
+        self.thread_pool.join()
+        self.thread_pool = mp.pool.ThreadPool(thread_num)
+
     def _get_single_card_bs(self, batch_size):
         if batch_size % len(self.places) == 0:
             return int(batch_size // len(self.places))
@@ -356,23 +367,13 @@ class BaseAPI:
         ]
         test_outputs = list(self.test_outputs.values())
         with fluid.scope_guard(self.scope):
-            if self.__class__.__name__ == 'MaskRCNN':
-                from paddlex.utils.save import save_mask_inference_model
-                save_mask_inference_model(
-                    dirname=save_dir,
-                    executor=self.exe,
-                    params_filename='__params__',
-                    feeded_var_names=test_input_names,
-                    target_vars=test_outputs,
-                    main_program=self.test_prog)
-            else:
-                fluid.io.save_inference_model(
-                    dirname=save_dir,
-                    executor=self.exe,
-                    params_filename='__params__',
-                    feeded_var_names=test_input_names,
-                    target_vars=test_outputs,
-                    main_program=self.test_prog)
+            fluid.io.save_inference_model(
+                dirname=save_dir,
+                executor=self.exe,
+                params_filename='__params__',
+                feeded_var_names=test_input_names,
+                target_vars=test_outputs,
+                main_program=self.test_prog)
         model_info = self.get_model_info()
         model_info['status'] = 'Infer'
 
diff --git a/paddlex/cv/models/classifier.py b/paddlex/cv/models/classifier.py
index 7f1c3527d8c681e8737e6a65a898ec083495bf4b..41590ad99e5c645744324c467b131b2c6271c13b 100644
--- a/paddlex/cv/models/classifier.py
+++ b/paddlex/cv/models/classifier.py
@@ -279,16 +279,18 @@ class BaseClassifier(BaseAPI):
         return metrics
 
     @staticmethod
-    def _preprocess(images, transforms, model_type, class_name, thread_num=1):
+    def _preprocess(images, transforms, model_type, class_name, thread_pool=None):
         arrange_transforms(
             model_type=model_type,
             class_name=class_name,
             transforms=transforms,
             mode='test')
-        pool = ThreadPool(thread_num)
-        batch_data = pool.map(transforms, images)
-        pool.close()
-        pool.join()
+        if thread_pool is not None:
+            batch_data = thread_pool.map(transforms, images)
+        else:
+            batch_data = list()
+            for image in images:
+                batch_data.append(transforms(image))
         padding_batch = generate_minibatch(batch_data)
         im = np.array([data[0] for data in padding_batch])
 
@@ -344,15 +346,13 @@ class BaseClassifier(BaseAPI):
     def batch_predict(self,
                       img_file_list,
                       transforms=None,
-                      topk=1,
-                      thread_num=2):
+                      topk=1):
         """预测。
         Args:
             img_file_list(list|tuple): 对列表（或元组）中的图像同时进行预测，列表中的元素可以是图像路径
                 也可以是解码后的排列格式为（H，W，C）且类型为float32且为BGR格式的数组。
             transforms (paddlex.cls.transforms): 数据预处理操作。
             topk (int): 预测时前k个最大值。
-            thread_num (int): 并发执行各图像预处理时的线程数。
         Returns:
             list: 每个元素都为列表，表示各图像的预测结果。在各图像的预测列表中，其中元素均为字典。字典的关键字为'category_id'、'category'、'score'，
             分别对应预测类别id、预测类别标签、预测得分。
@@ -367,7 +367,7 @@ class BaseClassifier(BaseAPI):
             transforms = self.test_transforms
         im = BaseClassifier._preprocess(img_file_list, transforms,
                                         self.model_type,
-                                        self.__class__.__name__, thread_num)
+                                        self.__class__.__name__, self.thread_pool)
 
         with fluid.scope_guard(self.scope):
             result = self.exe.run(self.test_prog,
diff --git a/paddlex/cv/models/deeplabv3p.py b/paddlex/cv/models/deeplabv3p.py
index fe1c294ae61d5d7e6e18696e56ff22909d8cc6c8..4103ae879a983758af4f03ae76bf50cf3354ac2e 100644
--- a/paddlex/cv/models/deeplabv3p.py
+++ b/paddlex/cv/models/deeplabv3p.py
@@ -443,16 +443,18 @@ class DeepLabv3p(BaseAPI):
         return metrics
 
     @staticmethod
-    def _preprocess(images, transforms, model_type, class_name, thread_num=1):
+    def _preprocess(images, transforms, model_type, class_name, thread_pool=None):
         arrange_transforms(
             model_type=model_type,
             class_name=class_name,
             transforms=transforms,
             mode='test')
-        pool = ThreadPool(thread_num)
-        batch_data = pool.map(transforms, images)
-        pool.close()
-        pool.join()
+        if thread_pool is not None:
+            batch_data = thread_pool.map(transforms, images)
+        else:
+            batch_data = list()
+            for image in images:
+                batch_data.append(transforms(image))
         padding_batch = generate_minibatch(batch_data)
         im = np.array(
             [data[0] for data in padding_batch],
@@ -517,13 +519,12 @@ class DeepLabv3p(BaseAPI):
         preds = DeepLabv3p._postprocess(result, im_info)
         return preds[0]
 
-    def batch_predict(self, img_file_list, transforms=None, thread_num=2):
+    def batch_predict(self, img_file_list, transforms=None):
         """预测。
         Args:
             img_file_list(list|tuple): 对列表（或元组）中的图像同时进行预测，列表中的元素可以是图像路径
                 也可以是解码后的排列格式为（H，W，C）且类型为float32且为BGR格式的数组。
             transforms(paddlex.cv.transforms): 数据预处理操作。
-            thread_num (int): 并发执行各图像预处理时的线程数。
 
         Returns:
             list: 每个元素都为列表，表示各图像的预测结果。各图像的预测结果用字典表示，包含关键字'label_map'和'score_map', 'label_map'存储预测结果灰度图，
@@ -538,7 +539,7 @@ class DeepLabv3p(BaseAPI):
             transforms = self.test_transforms
         im, im_info = DeepLabv3p._preprocess(
             img_file_list, transforms, self.model_type,
-            self.__class__.__name__, thread_num)
+            self.__class__.__name__, self.thread_pool)
 
         with fluid.scope_guard(self.scope):
             result = self.exe.run(self.test_prog,
diff --git a/paddlex/cv/models/faster_rcnn.py b/paddlex/cv/models/faster_rcnn.py
index 3ab4da52899a7d122a68d2de17666addc8ae4849..f09ca047903964576517553a4b3705ce85d0dc12 100644
--- a/paddlex/cv/models/faster_rcnn.py
+++ b/paddlex/cv/models/faster_rcnn.py
@@ -376,16 +376,18 @@ class FasterRCNN(BaseAPI):
         return metrics
 
     @staticmethod
-    def _preprocess(images, transforms, model_type, class_name, thread_num=1):
+    def _preprocess(images, transforms, model_type, class_name, thread_pool=None):
         arrange_transforms(
             model_type=model_type,
             class_name=class_name,
             transforms=transforms,
             mode='test')
-        pool = ThreadPool(thread_num)
-        batch_data = pool.map(transforms, images)
-        pool.close()
-        pool.join()
+        if thread_pool is not None:
+            batch_data = thread_pool.map(transforms, images)
+        else:
+            batch_data = list()
+            for image in images:
+                batch_data.append(transforms(image))
         padding_batch = generate_minibatch(batch_data)
         im = np.array([data[0] for data in padding_batch])
         im_resize_info = np.array([data[1] for data in padding_batch])
@@ -453,14 +455,13 @@ class FasterRCNN(BaseAPI):
 
         return preds[0]
 
-    def batch_predict(self, img_file_list, transforms=None, thread_num=2):
+    def batch_predict(self, img_file_list, transforms=None):
         """预测。
 
         Args:
             img_file_list(list|tuple): 对列表（或元组）中的图像同时进行预测，列表中的元素可以是图像路径
                 也可以是解码后的排列格式为（H，W，C）且类型为float32且为BGR格式的数组。
             transforms (paddlex.det.transforms): 数据预处理操作。
-            thread_num (int): 并发执行各图像预处理时的线程数。
 
         Returns:
             list: 每个元素都为列表，表示各图像的预测结果。在各图像的预测结果列表中，每个预测结果由预测框类别标签、
@@ -477,7 +478,7 @@ class FasterRCNN(BaseAPI):
             transforms = self.test_transforms
         im, im_resize_info, im_shape = FasterRCNN._preprocess(
             img_file_list, transforms, self.model_type,
-            self.__class__.__name__, thread_num)
+            self.__class__.__name__, self.thread_pool)
 
         with fluid.scope_guard(self.scope):
             result = self.exe.run(self.test_prog,
diff --git a/paddlex/cv/models/mask_rcnn.py b/paddlex/cv/models/mask_rcnn.py
index 7f31cd530ff0d6660e65661531b442941c88a336..0869fec29ac9ff1cc503a9aa2d6ee2446eddb855 100644
--- a/paddlex/cv/models/mask_rcnn.py
+++ b/paddlex/cv/models/mask_rcnn.py
@@ -408,14 +408,13 @@ class MaskRCNN(FasterRCNN):
 
         return preds[0]
 
-    def batch_predict(self, img_file_list, transforms=None, thread_num=2):
+    def batch_predict(self, img_file_list, transforms=None):
         """预测。
 
         Args:
             img_file_list(list|tuple): 对列表（或元组）中的图像同时进行预测，列表中的元素可以是图像路径
                 也可以是解码后的排列格式为（H，W，C）且类型为float32且为BGR格式的数组。
             transforms (paddlex.det.transforms): 数据预处理操作。
-            thread_num (int): 并发执行各图像预处理时的线程数。
         Returns:
             dict: 每个元素都为列表，表示各图像的预测结果。在各图像的预测结果列表中，每个预测结果由预测框类别标签、预测框类别名称、
                   预测框坐标(坐标格式为[xmin, ymin, w, h]）、
@@ -432,7 +431,7 @@ class MaskRCNN(FasterRCNN):
             transforms = self.test_transforms
         im, im_resize_info, im_shape = FasterRCNN._preprocess(
             img_file_list, transforms, self.model_type,
-            self.__class__.__name__, thread_num)
+            self.__class__.__name__, self.thread_pool)
 
         with fluid.scope_guard(self.scope):
             result = self.exe.run(self.test_prog,
diff --git a/paddlex/cv/models/ppyolo.py b/paddlex/cv/models/ppyolo.py
index e82dea4b10b4857d4aeea86e1c4998fdaa7358dc..e1159276d46829f44f40214bab60fefa6d453be3 100644
--- a/paddlex/cv/models/ppyolo.py
+++ b/paddlex/cv/models/ppyolo.py
@@ -447,16 +447,18 @@ class PPYOLO(BaseAPI):
         return evaluate_metrics
 
     @staticmethod
-    def _preprocess(images, transforms, model_type, class_name, thread_num=1):
+    def _preprocess(images, transforms, model_type, class_name, thread_pool=None):
         arrange_transforms(
             model_type=model_type,
             class_name=class_name,
             transforms=transforms,
             mode='test')
-        pool = ThreadPool(thread_num)
-        batch_data = pool.map(transforms, images)
-        pool.close()
-        pool.join()
+        if thread_pool is not None:
+            batch_data = thread_pool.map(transforms, images)
+        else:
+            batch_data = list()
+            for image in images:
+                batch_data.append(transforms(image))
         padding_batch = generate_minibatch(batch_data)
         im = np.array(
             [data[0] for data in padding_batch],
@@ -520,14 +522,13 @@ class PPYOLO(BaseAPI):
                                     len(images), self.num_classes, self.labels)
         return preds[0]
 
-    def batch_predict(self, img_file_list, transforms=None, thread_num=2):
+    def batch_predict(self, img_file_list, transforms=None):
         """预测。
 
         Args:
             img_file_list (list|tuple): 对列表（或元组）中的图像同时进行预测，列表中的元素可以是图像路径，也可以是解码后的排列格式为（H，W，C）
                 且类型为float32且为BGR格式的数组。
             transforms (paddlex.det.transforms): 数据预处理操作。
-            thread_num (int): 并发执行各图像预处理时的线程数。
         Returns:
             list: 每个元素都为列表，表示各图像的预测结果。在各图像的预测结果列表中，每个预测结果由预测框类别标签、
               预测框类别名称、预测框坐标(坐标格式为[xmin, ymin, w, h]）、
@@ -543,7 +544,7 @@ class PPYOLO(BaseAPI):
             transforms = self.test_transforms
         im, im_size = PPYOLO._preprocess(img_file_list, transforms,
                                          self.model_type,
-                                         self.__class__.__name__, thread_num)
+                                         self.__class__.__name__, self.thread_pool)
 
         with fluid.scope_guard(self.scope):
             result = self.exe.run(self.test_prog,
diff --git a/paddlex/deploy.py b/paddlex/deploy.py
index 6e529b52257ed6bc6c089dbdfabc2045ae37c1e3..e7a9264240ff52007ad3480ed794064cc171320f 100644
--- a/paddlex/deploy.py
+++ b/paddlex/deploy.py
@@ -16,6 +16,7 @@ import os.path as osp
 import cv2
 import numpy as np
 import yaml
+import multiprocessing as mp
 import paddlex
 import paddle.fluid as fluid
 from paddlex.cv.transforms import build_transforms
@@ -79,6 +80,15 @@ class Predictor:
         self.predictor = self.create_predictor(use_gpu, gpu_id, use_mkl,
                                                mkl_thread_num, use_trt,
                                                use_glog, memory_optimize)
+        # 线程池，在模型在预测时用于对输入数据以图片为单位进行并行处理
+        # 主要用于batch_predict接口
+        thread_num = mp.cpu_count() if mp.cpu_count() < 8 else 8
+        self.thread_pool = mp.pool.ThreadPool(thread_num)
+
+    def reset_thread_pool(self, thread_num):
+        self.thread_pool.close()
+        self.thread_pool.join()
+        self.thread_pool = mp.pool.ThreadPool(thread_num)
 
     def create_predictor(self,
                          use_gpu=True,
@@ -115,7 +125,7 @@ class Predictor:
         predictor = fluid.core.create_paddle_predictor(config)
         return predictor
 
-    def preprocess(self, image, thread_num=1):
+    def preprocess(self, image, thread_pool=None):
         """ 对图像做预处理
 
             Args:
@@ -129,7 +139,7 @@ class Predictor:
                 self.transforms,
                 self.model_type,
                 self.model_name,
-                thread_num=thread_num)
+                thread_pool=thread_pool)
             res['image'] = im
         elif self.model_type == "detector":
             if self.model_name in ["PPYOLO", "YOLOv3"]:
@@ -138,7 +148,7 @@ class Predictor:
                     self.transforms,
                     self.model_type,
                     self.model_name,
-                    thread_num=thread_num)
+                    thread_pool=thread_pool)
                 res['image'] = im
                 res['im_size'] = im_size
             if self.model_name.count('RCNN') > 0:
@@ -147,7 +157,7 @@ class Predictor:
                     self.transforms,
                     self.model_type,
                     self.model_name,
-                    thread_num=thread_num)
+                    thread_pool=thread_pool)
                 res['image'] = im
                 res['im_info'] = im_resize_info
                 res['im_shape'] = im_shape
@@ -157,7 +167,7 @@ class Predictor:
                 self.transforms,
                 self.model_type,
                 self.model_name,
-                thread_num=thread_num)
+                thread_pool=thread_pool)
             res['image'] = im
             res['im_info'] = im_info
         return res
@@ -254,17 +264,16 @@ class Predictor:
 
         return results[0]
 
-    def batch_predict(self, image_list, topk=1, thread_num=2):
+    def batch_predict(self, image_list, topk=1):
         """ 图片预测
 
             Args:
                 image_list(list|tuple): 对列表（或元组）中的图像同时进行预测，列表中的元素可以是图像路径
                     也可以是解码后的排列格式为（H，W，C）且类型为float32且为BGR格式的数组。
-                thread_num (int): 并发执行各图像预处理时的线程数。
 
                 topk(int): 分类预测时使用，表示预测前topk的结果
         """
-        preprocessed_input = self.preprocess(image_list)
+        preprocessed_input = self.preprocess(image_list, self.thread_pool)
         model_pred = self.raw_predict(preprocessed_input)
         im_shape = None if 'im_shape' not in preprocessed_input else preprocessed_input[
             'im_shape']
diff --git a/setup.py b/setup.py
index edcee85e8f42edbda41a7feb7557f6f3c5524d34..ade3cd22e3aa27582dc8ece38cc391d50885f31e 100644
--- a/setup.py
+++ b/setup.py
@@ -19,7 +19,7 @@ long_description = "PaddlePaddle Entire Process Development Toolkit"
 
 setuptools.setup(
     name="paddlex",
-    version='1.1.1',
+    version='1.1.4',
     author="paddlex",
     author_email="paddlex@baidu.com",
     description=long_description,
@@ -30,7 +30,7 @@ setuptools.setup(
     setup_requires=['cython', 'numpy'],
     install_requires=[
         "pycocotools;platform_system!='Windows'", 'pyyaml', 'colorama', 'tqdm',
-        'paddleslim==1.0.1', 'visualdl>=2.0.0b', 'paddlehub>=1.6.2',
+        'paddleslim==1.0.1', 'visualdl>=2.0.0b', 'paddlehub>=1.8.2',
         'shapely>=1.7.0', "opencv-python"
     ],
     classifiers=[