diff --git a/model_zoo/research/cv/FaceQualityAssessment/src/dataset.py b/model_zoo/research/cv/FaceQualityAssessment/src/dataset.py
index a96a08fa92..2e797cccbc 100644
--- a/model_zoo/research/cv/FaceQualityAssessment/src/dataset.py
+++ b/model_zoo/research/cv/FaceQualityAssessment/src/dataset.py
@@ -20,7 +20,6 @@ from PIL import Image, ImageFile
 
 import mindspore.dataset as ds
 import mindspore.dataset.vision.py_transforms as F
-import mindspore.dataset.transforms.py_transforms as F2
 
 warnings.filterwarnings('ignore')
 ImageFile.LOAD_TRUNCATED_IMAGES = True
@@ -72,7 +71,7 @@ class MdFaceDataset():
         landmarks = self._trans_cor(path_label_info[4:14], x_length, y_length)
         eulers = np.array([e / 90. for e in list(map(float, path_label_info[1:4]))])
         labels = np.concatenate([eulers, landmarks], axis=0)
-        sample = image
+        sample = F.ToTensor()(image)
 
         return sample, labels
 
@@ -107,14 +106,10 @@ class DistributedSampler():
 
 def faceqa_dataset(imlist, per_batch_size, local_rank, world_size):
     '''faceqa dataset'''
-    transform_img = F2.Compose([F.ToTensor()])
     dataset = MdFaceDataset(imlist)
     sampler = DistributedSampler(dataset, local_rank, world_size)
-    de_dataset = ds.GeneratorDataset(dataset, ["image", "label"], sampler=sampler, num_parallel_workers=8,
+    de_dataset = ds.GeneratorDataset(dataset, ["image", "label"], sampler=sampler, num_parallel_workers=16,
                                      python_multiprocessing=True)
-
-    de_dataset = de_dataset.map(input_columns="image", operations=transform_img, num_parallel_workers=8,
-                                python_multiprocessing=True)
-    de_dataset = de_dataset.batch(per_batch_size, drop_remainder=True)
+    de_dataset = de_dataset.batch(per_batch_size, drop_remainder=True, num_parallel_workers=4)
 
     return de_dataset