diff --git a/paddlex/cv/models/base.py b/paddlex/cv/models/base.py
index 7d494be8ca627ce563b638839f3c0e4e72b39245..aa982ac4be890ee364433e6659b78ce28e667a55 100644
--- a/paddlex/cv/models/base.py
+++ b/paddlex/cv/models/base.py
@@ -94,8 +94,6 @@ class BaseAPI:
         self.train_inputs, self.train_outputs = self.build_net(mode='train')
         self.train_prog = fluid.default_main_program()
         startup_prog = fluid.default_startup_program()
-        self.train_prog.random_seed = 1000
-        startup_prog.random_seed = 1000
 
         # 构建预测网络
         self.test_prog = fluid.Program()
diff --git a/paddlex/cv/models/yolo_v3.py b/paddlex/cv/models/yolo_v3.py
index f17eb52d0ad0b954bd41e43feb97ffc9a2ae2fac..aca0a845f12b83a8ee96a35ddd886db2ecc79ced 100644
--- a/paddlex/cv/models/yolo_v3.py
+++ b/paddlex/cv/models/yolo_v3.py
@@ -30,10 +30,6 @@ from .base import BaseAPI
 from collections import OrderedDict
 from .utils.detection_eval import eval_results, bbox2out
 
-import random
-random.seed(0)
-np.random.seed(0)
-
 
 class YOLOv3(BaseAPI):
     """构建YOLOv3，并实现其训练、评估、预测和模型导出。
@@ -181,7 +177,7 @@ class YOLOv3(BaseAPI):
             model.max_width = self.max_width
         inputs = model.generate_inputs()
         model_out = model.build_net(inputs)
-        outputs = OrderedDict([('bbox', model_out[0])])
+        outputs = OrderedDict([('bbox', model_out)])
         if mode == 'train':
             self.optimizer.minimize(model_out)
             outputs = OrderedDict([('loss', model_out)])
diff --git a/paddlex/cv/nets/detection/ops.py b/paddlex/cv/nets/detection/ops.py
index 27734271bd71ce99e80e18603a2e8b7c0ccdb9d5..b1ff6823092f52d8f595bc7a49db3dde2d447c7a 100644
--- a/paddlex/cv/nets/detection/ops.py
+++ b/paddlex/cv/nets/detection/ops.py
@@ -50,7 +50,7 @@ def DropBlock(input, block_size, keep_prob, is_test):
 
     input_shape_tmp = fluid.layers.cast(input_shape, dtype="int64")
     random_matrix = fluid.layers.uniform_random(
-        input_shape_tmp, dtype='float32', min=0.0, max=1.0, seed=1000)
+        input_shape_tmp, dtype='float32', min=0.0, max=1.0)
     one_zero_m = fluid.layers.less_than(random_matrix, p)
     one_zero_m.stop_gradient = True
     one_zero_m = fluid.layers.cast(one_zero_m, dtype="float32")
diff --git a/paddlex/cv/transforms/cls_transforms.py b/paddlex/cv/transforms/cls_transforms.py
index 4166cd170ecf1a0f1b840a804b0d0e28615a04ba..fb38c733d9c46bec8356b2a4072dbb0c36ef4542 100644
--- a/paddlex/cv/transforms/cls_transforms.py
+++ b/paddlex/cv/transforms/cls_transforms.py
@@ -46,7 +46,7 @@ class Compose(ClsTransform):
             raise ValueError('The length of transforms ' + \
                             'must be equal or larger than 1!')
         self.transforms = transforms
-
+        self.batch_transforms = None
         # 检查transforms里面的操作，目前支持PaddleX定义的或者是imgaug操作
         for op in self.transforms:
             if not isinstance(op, ClsTransform):
diff --git a/paddlex/cv/transforms/seg_transforms.py b/paddlex/cv/transforms/seg_transforms.py
index 8c92c911b11fb2056817550aba1b2dcdf2c9eda0..6dc3e60f2417118b47d6374a33edb949c4e6a78a 100644
--- a/paddlex/cv/transforms/seg_transforms.py
+++ b/paddlex/cv/transforms/seg_transforms.py
@@ -49,6 +49,7 @@ class Compose(SegTransform):
             raise ValueError('The length of transforms ' + \
                             'must be equal or larger than 1!')
         self.transforms = transforms
+        self.batch_transforms = None
         self.to_rgb = False
         # 检查transforms里面的操作，目前支持PaddleX定义的或者是imgaug操作
         for op in self.transforms: