diff --git a/model_zoo/official/cv/resnet/eval.py b/model_zoo/official/cv/resnet/eval.py
index 1536a36f898..756d76257cf 100755
--- a/model_zoo/official/cv/resnet/eval.py
+++ b/model_zoo/official/cv/resnet/eval.py
@@ -49,7 +49,7 @@ if args_opt.net in ("resnet18", "resnet50"):
 elif args_opt.net == "resnet34":
     from src.resnet import resnet34 as resnet
     from src.config import config_resnet34 as config
-    from src.dataset import create_dataset_resnet34 as create_dataset
+    from src.dataset import create_dataset2 as create_dataset
 elif args_opt.net == "resnet101":
     from src.resnet import resnet101 as resnet
     from src.config import config3 as config
diff --git a/model_zoo/official/cv/resnet/src/dataset.py b/model_zoo/official/cv/resnet/src/dataset.py
index 4aaeb1da594..98b4fa7d376 100755
--- a/model_zoo/official/cv/resnet/src/dataset.py
+++ b/model_zoo/official/cv/resnet/src/dataset.py
@@ -400,65 +400,6 @@ def create_dataset4(dataset_path, do_train, repeat_num=1, batch_size=32, target=
 
     return data_set
 
-def create_dataset_resnet34(dataset_path, do_train, repeat_num=1, batch_size=32):
-    """
-        create a train or eval imagenet2012 dataset for resnet34
-
-    Args:
-        dataset_path(string): the path of dataset.
-        do_train(bool): whether dataset is used for train or eval.
-        repeat_num(int): the repeat times of dataset. Default: 1
-        batch_size(int): the batch size of dataset. Default: 32
-
-    Returns:
-        data_set
-    """
-    device_id = int(os.getenv('DEVICE_ID'))
-    device_num = int(os.getenv('RANK_SIZE'))
-
-    if device_num == 1:
-        data_set = ds.ImageFolderDataset(dataset_path)
-    else:
-        if do_train:
-            data_set = ds.ImageFolderDataset(dataset_path, shuffle=True,
-                                             num_shards=device_num, shard_id=device_id)
-        else:
-            data_set = ds.ImageFolderDataset(dataset_path)
-
-    image_size = 224
-    mean = [0.485 * 255, 0.456 * 255, 0.406 * 255]
-    std = [0.229 * 255, 0.224 * 255, 0.225 * 255]
-
-    # define map operations
-    if do_train:
-        trans = [
-            C.RandomCropDecodeResize(image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
-            C.RandomHorizontalFlip(prob=0.5),
-            C.Normalize(mean=mean, std=std),
-            C.HWC2CHW()
-        ]
-    else:
-        trans = [
-            C.Decode(),
-            C.Resize(256),
-            C.CenterCrop(image_size),
-            C.Normalize(mean=mean, std=std),
-            C.HWC2CHW()
-        ]
-
-    type_cast_op = C2.TypeCast(mstype.int32)
-
-    data_set = data_set.map(operations=trans, input_columns="image", num_parallel_workers=8)
-    data_set = data_set.map(operations=type_cast_op, input_columns="label", num_parallel_workers=8)
-
-    # apply batch operations
-    data_set = data_set.batch(batch_size, drop_remainder=True)
-
-    # apply dataset repeat operation
-    data_set = data_set.repeat(repeat_num)
-
-    return data_set
-
 def _get_rank_info():
     """
     get rank size and rank id
diff --git a/model_zoo/official/cv/resnet/train.py b/model_zoo/official/cv/resnet/train.py
index dc3250a9532..642a7426392 100755
--- a/model_zoo/official/cv/resnet/train.py
+++ b/model_zoo/official/cv/resnet/train.py
@@ -86,7 +86,7 @@ if args_opt.net in ("resnet18", "resnet50"):
 elif args_opt.net == "resnet34":
     from src.resnet import resnet34 as resnet
     from src.config import config_resnet34 as config
-    from src.dataset import create_dataset_resnet34 as create_dataset
+    from src.dataset import create_dataset2 as create_dataset
 elif args_opt.net == "resnet101":
     from src.resnet import resnet101 as resnet
     from src.config import config3 as config