diff --git a/mmcls/datasets/builder.py b/mmcls/datasets/builder.py
index ebef4c9b4..fb6028a18 100644
--- a/mmcls/datasets/builder.py
+++ b/mmcls/datasets/builder.py
@@ -1,8 +1,10 @@
 import platform
 import random
+from distutils.version import LooseVersion
 from functools import partial
 
 import numpy as np
+import torch
 from mmcv.parallel import collate
 from mmcv.runner import get_dist_info
 from mmcv.utils import Registry, build_from_cfg
@@ -47,6 +49,8 @@ def build_dataloader(dataset,
                      shuffle=True,
                      round_up=True,
                      seed=None,
+                     pin_memory=True,
+                     persistent_workers=True,
                      **kwargs):
     """Build PyTorch DataLoader.
 
@@ -65,6 +69,13 @@ def build_dataloader(dataset,
             Default: True.
         round_up (bool): Whether to round up the length of dataset by adding
             extra samples to make it evenly divisible. Default: True.
+        pin_memory (bool): Whether to use pin_memory in DataLoader.
+            Default: True
+        persistent_workers (bool): If True, the data loader will not shutdown
+            the worker processes after a dataset has been consumed once.
+            This allows to maintain the workers Dataset instances alive.
+            The argument also has effect in PyTorch>=1.7.0.
+            Default: True
         kwargs: any keyword argument to be used to initialize DataLoader
 
     Returns:
@@ -86,13 +97,16 @@ def build_dataloader(dataset,
         worker_init_fn, num_workers=num_workers, rank=rank,
         seed=seed) if seed is not None else None
 
+    if LooseVersion(torch.__version__) >= LooseVersion('1.7.0'):
+        kwargs['persistent_workers'] = persistent_workers
+
     data_loader = DataLoader(
         dataset,
         batch_size=batch_size,
         sampler=sampler,
         num_workers=num_workers,
         collate_fn=partial(collate, samples_per_gpu=samples_per_gpu),
-        pin_memory=False,
+        pin_memory=pin_memory,
         shuffle=shuffle,
         worker_init_fn=init_fn,
         **kwargs)