diff --git a/scripts/instance/mask_rcnn/train_mask_rcnn.py b/scripts/instance/mask_rcnn/train_mask_rcnn.py
index 4910be5afd..09bff75f04 100644
--- a/scripts/instance/mask_rcnn/train_mask_rcnn.py
+++ b/scripts/instance/mask_rcnn/train_mask_rcnn.py
@@ -113,6 +113,11 @@ def parse_args():
     parser.add_argument('--kv-store', type=str, default='nccl',
                         help='KV store options. local, device, nccl, dist_sync, dist_device_sync, '
                              'dist_async are available.')
+    parser.add_argument('--use-pinned', action='store_true',
+                        help='Whether to use pinned memory buffers to stage the input data.')
+    parser.add_argument('--pinned-buffer-size', type=int, default=0,
+                        help='Size of the staged memory buffers for input data. If the value is <= 0, '
+                        'the buffers will be dynamically reshaped. Default is 0.')
 
     args = parser.parse_args()
     if args.horovod:
@@ -124,6 +129,8 @@ def parse_args():
     args.lr = float(args.lr) if args.lr else 0.01
     args.lr_warmup = args.lr_warmup if args.lr_warmup else 1000
     args.wd = float(args.wd) if args.wd else 1e-4
+    global use_pinned, pinned_buffer_size
+    use_pinned, pinned_buffer_size = args.use_pinned, args.pinned_buffer_size
     return args
 
 
@@ -175,6 +182,7 @@ def save_params(net, logger, best_map, current_map, epoch, save_interval, prefix
 
 
 def _stage_data(i, data, ctx_list, pinned_data_stage):
+    global pinned_buffer_size
     def _get_chunk(data, storage):
         s = storage.reshape(shape=(storage.size,))
         s = s[:data.size]
@@ -192,7 +200,12 @@ def _get_chunk(data, storage):
 
     for j in range(len(storage)):
         if data[j].size > storage[j].size:
-            storage[j] = data[j].as_in_context(mx.cpu_pinned())
+            if data[j].size > pinned_buffer_size:
+                storage[j] = data[j].as_in_context(mx.cpu_pinned())
+            else:
+                storage[j] = mx.nd.zeros(shape=(pinned_buffer_size),
+                                         dtype=data[j].dtype,
+                                         ctx=mx.cpu_pinned())
 
     return [_get_chunk(d, s) for d, s in zip(data, storage)]
 
@@ -204,14 +217,19 @@ def split_and_load(batch, ctx_list):
     """Split data to 1 batch each device."""
     new_batch = []
     for i, data in enumerate(batch):
-        if isinstance(data, (list, tuple)):
-            new_data = [x.as_in_context(ctx) for x, ctx in zip(data, ctx_list)]
+        if not isinstance(data, (list, tuple)):
+            data = [data]
+        global use_pinned
+        if use_pinned:
+            staged_data = _stage_data(i, data, ctx_list, pinned_data_stage)
+            new_data = [x.as_in_context(ctx) for x, ctx in zip(staged_data, ctx_list)]
         else:
-            new_data = [data.as_in_context(ctx_list[0])]
+            new_data = [x.as_in_context(ctx) for x, ctx in zip(data, ctx_list)]
         new_batch.append(new_data)
     return new_batch
 
 
+
 def validate(net, val_data, ctx, eval_metric, args):
     """Test on validation dataset."""
     clipper = gcv.nn.bbox.BBoxClipToImage()