diff --git a/python/mxnet/io.py b/python/mxnet/io.py
index c07c7a2b8062..fdb32408d191 100644
--- a/python/mxnet/io.py
+++ b/python/mxnet/io.py
@@ -477,7 +477,7 @@ def _infer_column_shape(self, sarray):
             else:
                 return (lengths.max(), )
         elif dtype is gl.Image:
-            first_image = sarray.dropna()[0]
+            first_image = sarray.head(1)[0]
             return (first_image.channels, first_image.height, first_image.width)
 
     def infer_shape(self):
diff --git a/src/ndarray/ndarray.cc b/src/ndarray/ndarray.cc
index 1a88d7528be8..23ca1cc8a674 100644
--- a/src/ndarray/ndarray.cc
+++ b/src/ndarray/ndarray.cc
@@ -584,7 +584,7 @@ void NDArray::SyncCopyFromSFrame(const graphlab::flexible_type *data, size_t siz
   auto type = data[0].get_type();
   if (type == graphlab::flex_type_enum::IMAGE) {
     CHECK_EQ(size, 1) << "Image data only support one input field";
-    graphlab::image_type img = data[0].get<graphlab::flex_image>();
+    const graphlab::image_type& img = data[0].get<graphlab::flex_image>();
     mshadow::Tensor<cpu, 4> batch_tensor = dst.GetWithShape<cpu, 4, float>(
         mshadow::Shape4(dshape[0], img.m_channels, img.m_height, img.m_width));
 
@@ -604,16 +604,22 @@ void NDArray::SyncCopyFromSFrame(const graphlab::flexible_type *data, size_t siz
       } else if (img.m_format == graphlab::Format::PNG) {
         graphlab::decode_png((const char*)img.get_image_data(), img.m_image_data_size, &buf, length);
       }
-      img.m_image_data.reset(buf);
-      img.m_image_data_size = length;
-      img.m_format = graphlab::Format::RAW_ARRAY;
-    }
-    size_t cnt = 0;
-    const unsigned char* raw_data = img.get_image_data();
-    for (size_t i = 0; i < img.m_height; ++i) {
-      for (size_t j = 0; j < img.m_width; ++j) {
-        for (size_t k = 0; k < img.m_channels; ++k) {
-          batch_tensor[idx][k][i][j] = raw_data[cnt++];
+      size_t cnt = 0;
+      for (size_t i = 0; i < img.m_height; ++i) {
+        for (size_t j = 0; j < img.m_width; ++j) {
+          for (size_t k = 0; k < img.m_channels; ++k) {
+            batch_tensor[idx][k][i][j] = buf[cnt++];
+          }
+        }
+      }
+    } else {
+      size_t cnt = 0;
+      const unsigned char* raw_data = img.get_image_data();
+      for (size_t i = 0; i < img.m_height; ++i) {
+        for (size_t j = 0; j < img.m_width; ++j) {
+          for (size_t k = 0; k < img.m_channels; ++k) {
+            batch_tensor[idx][k][i][j] = raw_data[cnt++];
+          }
         }
       }
     }