diff --git a/python/paddle/v2/fluid/layers/detection.py b/python/paddle/v2/fluid/layers/detection.py
index 6af5c8388b7f51563cf7208b89565c5aea2db71f..5ae4da1ea31d036217c5595f8b30842403896a7c 100644
--- a/python/paddle/v2/fluid/layers/detection.py
+++ b/python/paddle/v2/fluid/layers/detection.py
@@ -23,7 +23,6 @@ import nn
 import math
 
 __all__ = [
-    'prior_box',
     'multi_box_head',
     'bipartite_match',
     'target_assign',
@@ -133,219 +132,6 @@ def detection_output(scores,
     return nmsed_outs
 
 
-def prior_box(inputs,
-              image,
-              min_ratio,
-              max_ratio,
-              aspect_ratios,
-              base_size,
-              steps=None,
-              step_w=None,
-              step_h=None,
-              offset=0.5,
-              variance=[0.1, 0.1, 0.1, 0.1],
-              flip=False,
-              clip=False,
-              min_sizes=None,
-              max_sizes=None,
-              name=None):
-    """
-    **Prior_boxes**
-
-    Generate prior boxes for SSD(Single Shot MultiBox Detector)
-    algorithm. The details of this algorithm, please refer the
-    section 2.2 of SSD paper (SSD: Single Shot MultiBox Detector)
-    <https://arxiv.org/abs/1512.02325>`_ .
-
-    Args:
-       inputs(list|tuple): The list of input Variables, the format
-            of all Variables is NCHW.
-       image(Variable): The input image data of PriorBoxOp,
-            the layout is NCHW.
-       min_ratio(int): the min ratio of generated prior boxes.
-       max_ratio(int): the max ratio of generated prior boxes.
-       aspect_ratios(list|tuple): the aspect ratios of generated prior
-            boxes. The length of input and aspect_ratios must be equal.
-       base_size(int): the base_size is used to get min_size
-            and max_size according to min_ratio and max_ratio.
-       step_w(list|tuple|None): Prior boxes step
-            across width. If step_w[i] == 0.0, the prior boxes step
-            across width of the inputs[i] will be automatically calculated.
-       step_h(list|tuple|None): Prior boxes step
-            across height, If step_h[i] == 0.0, the prior boxes
-            step across height of the inputs[i] will be automatically calculated.
-       offset(float, optional, default=0.5): Prior boxes center offset.
-       variance(list|tuple|[0.1, 0.1, 0.1, 0.1]): the variances
-            to be encoded in prior boxes.
-       flip(bool|False): Whether to flip
-            aspect ratios.
-       clip(bool, optional, default=False): Whether to clip
-            out-of-boundary boxes.
-       min_sizes(list|tuple|None): If `len(inputs) <=2`,
-            min_sizes must be set up, and the length of min_sizes
-            should equal to the length of inputs.
-       max_sizes(list|tuple|None): If `len(inputs) <=2`,
-            max_sizes must be set up, and the length of min_sizes
-            should equal to the length of inputs.
-       name(str|None): Name of the prior box layer.
-
-    Returns:
-        boxes(Variable): the output prior boxes of PriorBox.
-             The layout is [num_priors, 4]. num_priors is the total
-             box count of each position of inputs.
-        Variances(Variable): the expanded variances of PriorBox.
-             The layout is [num_priors, 4]. num_priors is the total
-             box count of each position of inputs
-
-    Examples:
-        .. code-block:: python
-
-          prior_box(
-             inputs = [conv1, conv2, conv3, conv4, conv5, conv6],
-             image = data,
-             min_ratio = 20, # 0.20
-             max_ratio = 90, # 0.90
-             offset = 0.5,
-             base_size = 300,
-             variance = [0.1,0.1,0.1,0.1],
-             aspect_ratios = [[2.], [2., 3.], [2., 3.], [2., 3.], [2.], [2.]],
-             flip=True,
-             clip=True)
-    """
-
-    def _prior_box_(input,
-                    image,
-                    min_sizes,
-                    max_sizes,
-                    aspect_ratios,
-                    variance,
-                    flip=False,
-                    clip=False,
-                    step_w=0.0,
-                    step_h=0.0,
-                    offset=0.5,
-                    name=None):
-        helper = LayerHelper("prior_box", **locals())
-        dtype = helper.input_dtype()
-
-        box = helper.create_tmp_variable(dtype)
-        var = helper.create_tmp_variable(dtype)
-        helper.append_op(
-            type="prior_box",
-            inputs={"Input": input,
-                    "Image": image},
-            outputs={"Boxes": box,
-                     "Variances": var},
-            attrs={
-                'min_sizes': min_sizes,
-                'max_sizes': max_sizes,
-                'aspect_ratios': aspect_ratios,
-                'variances': variance,
-                'flip': flip,
-                'clip': clip,
-                'step_w': step_w,
-                'step_h': step_h,
-                'offset': offset
-            })
-        return box, var
-
-    def _reshape_with_axis_(input, axis=1):
-        if not (axis > 0 and axis < len(input.shape)):
-            raise ValueError("The axis should be smaller than "
-                             "the arity of input and bigger than 0.")
-        new_shape = [
-            -1, reduce(lambda x, y: x * y, input.shape[axis:len(input.shape)])
-        ]
-        out = ops.reshape(x=input, shape=new_shape)
-        return out
-
-    def _is_list_or_tuple_(data):
-        return (isinstance(data, list) or isinstance(data, tuple))
-
-    def _is_list_or_tuple_and_equal(data, length, err_info):
-        if not (_is_list_or_tuple_(data) and len(data) == length):
-            raise ValueError(err_info)
-
-    if not _is_list_or_tuple_(inputs):
-        raise ValueError('inputs should be a list or tuple.')
-
-    num_layer = len(inputs)
-
-    if num_layer <= 2:
-        assert min_sizes is not None and max_sizes is not None
-        assert len(min_sizes) == num_layer and len(max_sizes) == num_layer
-    else:
-        min_sizes = []
-        max_sizes = []
-        step = int(math.floor(((max_ratio - min_ratio)) / (num_layer - 2)))
-        for ratio in xrange(min_ratio, max_ratio + 1, step):
-            min_sizes.append(base_size * ratio / 100.)
-            max_sizes.append(base_size * (ratio + step) / 100.)
-        min_sizes = [base_size * .10] + min_sizes
-        max_sizes = [base_size * .20] + max_sizes
-
-    if aspect_ratios:
-        _is_list_or_tuple_and_equal(
-            aspect_ratios, num_layer,
-            'aspect_ratios should be list or tuple, and the length of inputs '
-            'and aspect_ratios should be the same.')
-    if step_h:
-        _is_list_or_tuple_and_equal(
-            step_h, num_layer,
-            'step_h should be list or tuple, and the length of inputs and '
-            'step_h should be the same.')
-    if step_w:
-        _is_list_or_tuple_and_equal(
-            step_w, num_layer,
-            'step_w should be list or tuple, and the length of inputs and '
-            'step_w should be the same.')
-    if steps:
-        _is_list_or_tuple_and_equal(
-            steps, num_layer,
-            'steps should be list or tuple, and the length of inputs and '
-            'step_w should be the same.')
-        step_w = steps
-        step_h = steps
-
-    box_results = []
-    var_results = []
-    for i, input in enumerate(inputs):
-        min_size = min_sizes[i]
-        max_size = max_sizes[i]
-        aspect_ratio = []
-        if not _is_list_or_tuple_(min_size):
-            min_size = [min_size]
-        if not _is_list_or_tuple_(max_size):
-            max_size = [max_size]
-        if aspect_ratios:
-            aspect_ratio = aspect_ratios[i]
-            if not _is_list_or_tuple_(aspect_ratio):
-                aspect_ratio = [aspect_ratio]
-
-        box, var = _prior_box_(input, image, min_size, max_size, aspect_ratio,
-                               variance, flip, clip, step_w[i]
-                               if step_w else 0.0, step_h[i]
-                               if step_w else 0.0, offset)
-
-        box_results.append(box)
-        var_results.append(var)
-
-    if len(box_results) == 1:
-        box = box_results[0]
-        var = var_results[0]
-    else:
-        reshaped_boxes = []
-        reshaped_vars = []
-        for i in range(len(box_results)):
-            reshaped_boxes.append(_reshape_with_axis_(box_results[i], axis=3))
-            reshaped_vars.append(_reshape_with_axis_(var_results[i], axis=3))
-
-        box = tensor.concat(reshaped_boxes)
-        var = tensor.concat(reshaped_vars)
-
-    return box, var
-
-
 def bipartite_match(dist_matrix, name=None):
     """
     **Bipartite matchint operator**
@@ -672,106 +458,162 @@ def ssd_loss(location,
 
 
 def multi_box_head(inputs,
+                   image,
+                   base_size,
                    num_classes,
+                   aspect_ratios,
+                   min_ratio,
+                   max_ratio,
                    min_sizes=None,
                    max_sizes=None,
-                   min_ratio=None,
-                   max_ratio=None,
-                   aspect_ratios=None,
+                   steps=None,
+                   step_w=None,
+                   step_h=None,
+                   offset=0.5,
+                   variance=[0.1, 0.1, 0.1, 0.1],
                    flip=False,
-                   share_location=True,
+                   clip=False,
                    kernel_size=1,
-                   pad=1,
+                   pad=0,
                    stride=1,
-                   use_batchnorm=False,
-                   base_size=None):
+                   name=None):
     """
-    **Multi Box Head**
+    **Prior_boxes**
 
-    Generate prior boxes' location and confidence for SSD(Single
-    Shot MultiBox Detector)algorithm. The details of this algorithm,
-    please refer the section 2.1 of SSD paper (SSD: Single Shot
-    MultiBox Detector)<https://arxiv.org/abs/1512.02325>`_ .
+    Generate prior boxes for SSD(Single Shot MultiBox Detector)
+    algorithm. The details of this algorithm, please refer the
+    section 2.2 of SSD paper (SSD: Single Shot MultiBox Detector)
+    <https://arxiv.org/abs/1512.02325>`_ .
 
     Args:
        inputs(list|tuple): The list of input Variables, the format
             of all Variables is NCHW.
-       num_classes(int): The number of classes.
-       min_sizes(list|tuple|None): The number of
-            min_sizes is used to compute the number of predicted box.
-            If the min_size is None, it will be computed according
-            to min_ratio and max_ratio.
-       max_sizes(list|tuple|None): The number of max_sizes
-            is used to compute the the number of predicted box.
-       min_ratio(int|None): If the min_sizes is None, min_ratio and max_ratio
-            will be used to compute the min_sizes and max_sizes.
-       max_ratio(int|None): If the min_sizes is None, max_ratio and min_ratio
-            will be used to compute the min_sizes and max_sizes.
-       aspect_ratios(list|tuple): The number of the aspect ratios is used to
-            compute the number of prior box.
+       image(Variable): The input image data of PriorBoxOp,
+            the layout is NCHW.
        base_size(int): the base_size is used to get min_size
             and max_size according to min_ratio and max_ratio.
-       flip(bool|False): Whether to flip
-            aspect ratios.
-       name(str|None): Name of the prior box layer.
+       num_classes(int): The number of classes.
+       aspect_ratios(list|tuple): the aspect ratios of generated prior
+            boxes. The length of input and aspect_ratios must be equal.
+       min_ratio(int): the min ratio of generated prior boxes.
+       max_ratio(int): the max ratio of generated prior boxes.
+       min_sizes(list|tuple|None): If `len(inputs) <=2`,
+            min_sizes must be set up, and the length of min_sizes
+            should equal to the length of inputs. Default: None.
+       max_sizes(list|tuple|None): If `len(inputs) <=2`,
+            max_sizes must be set up, and the length of min_sizes
+            should equal to the length of inputs. Default: None.
+       steps(list|tuple): If step_w and step_h are the same,
+            step_w and step_h can be replaced by steps.
+       step_w(list|tuple): Prior boxes step
+            across width. If step_w[i] == 0.0, the prior boxes step
+            across width of the inputs[i] will be automatically
+            calculated. Default: None.
+       step_h(list|tuple): Prior boxes step across height, If
+            step_h[i] == 0.0, the prior boxes step across height of
+            the inputs[i] will be automatically calculated. Default: None.
+       offset(float): Prior boxes center offset. Default: 0.5
+       variance(list|tuple): the variances to be encoded in prior boxes.
+            Default:[0.1, 0.1, 0.1, 0.1].
+       flip(bool): Whether to flip aspect ratios. Default:False.
+       clip(bool): Whether to clip out-of-boundary boxes. Default: False.
+       kernel_size(int): The kernel size of conv2d. Default: 1.
+       pad(int|list|tuple): The padding of conv2d. Default:0.
+       stride(int|list|tuple): The stride of conv2d. Default:1,
+       name(str): Name of the prior box layer. Default: None.
 
     Returns:
-
         mbox_loc(list): The predicted boxes' location of the inputs.
              The layout of each element is [N, H, W, Priors]. Priors
              is the number of predicted boxof each position of each input.
         mbox_conf(list): The predicted boxes' confidence of the inputs.
              The layout of each element is [N, H, W, Priors]. Priors
              is the number of predicted box of each position of each input.
+        boxes(Variable): the output prior boxes of PriorBox.
+             The layout is [num_priors, 4]. num_priors is the total
+             box count of each position of inputs.
+        Variances(Variable): the expanded variances of PriorBox.
+             The layout is [num_priors, 4]. num_priors is the total
+             box count of each position of inputs
+
 
     Examples:
         .. code-block:: python
-
-            mbox_locs, mbox_confs = detection.multi_box_head(
-                inputs=[conv1, conv2, conv3, conv4, conv5, conv5],
-                num_classes=21,
-                min_ratio=20,
-                max_ratio=90,
-                aspect_ratios=[[2.], [2., 3.], [2., 3.], [2., 3.], [2.], [2.]],
-                base_size=300,
-                flip=True)
+          mbox_locs, mbox_confs, box, var = layers.multi_box_head(
+            inputs=[conv1, conv2, conv3, conv4, conv5, conv5],
+            image=images,
+            num_classes=21,
+            min_ratio=20,
+            max_ratio=90,
+            aspect_ratios=[[2.], [2., 3.], [2., 3.], [2., 3.], [2.], [2.]],
+            base_size=300,
+            offset=0.5,
+            flip=True,
+            clip=True)
     """
 
-    def _is_equal_(len1, len2, err_info):
-        if not (len1 == len2):
-            raise ValueError(err_info)
+    def _prior_box_(input,
+                    image,
+                    min_sizes,
+                    max_sizes,
+                    aspect_ratios,
+                    variance,
+                    flip=False,
+                    clip=False,
+                    step_w=0.0,
+                    step_h=0.0,
+                    offset=0.5,
+                    name=None):
+        helper = LayerHelper("prior_box", **locals())
+        dtype = helper.input_dtype()
+
+        box = helper.create_tmp_variable(dtype)
+        var = helper.create_tmp_variable(dtype)
+        helper.append_op(
+            type="prior_box",
+            inputs={"Input": input,
+                    "Image": image},
+            outputs={"Boxes": box,
+                     "Variances": var},
+            attrs={
+                'min_sizes': min_sizes,
+                'max_sizes': max_sizes,
+                'aspect_ratios': aspect_ratios,
+                'variances': variance,
+                'flip': flip,
+                'clip': clip,
+                'step_w': step_w,
+                'step_h': step_h,
+                'offset': offset
+            })
+        return box, var
+
+    def _reshape_with_axis_(input, axis=1):
+        if not (axis > 0 and axis < len(input.shape)):
+            raise ValueError("The axis should be smaller than "
+                             "the arity of input and bigger than 0.")
+        new_shape = [
+            -1, reduce(lambda x, y: x * y, input.shape[axis:len(input.shape)])
+        ]
+        out = ops.reshape(x=input, shape=new_shape)
+        return out
 
     def _is_list_or_tuple_(data):
         return (isinstance(data, list) or isinstance(data, tuple))
 
+    def _is_list_or_tuple_and_equal(data, length, err_info):
+        if not (_is_list_or_tuple_(data) and len(data) == length):
+            raise ValueError(err_info)
+
     if not _is_list_or_tuple_(inputs):
         raise ValueError('inputs should be a list or tuple.')
 
-    if min_sizes is not None:
-        _is_equal_(
-            len(inputs),
-            len(min_sizes), 'the length of min_sizes '
-            'and inputs should be equal.')
-
-    if max_sizes is not None:
-        _is_equal_(
-            len(inputs),
-            len(max_sizes), 'the length of max_sizes '
-            'and inputs should be equal.')
-
-    if aspect_ratios is not None:
-        _is_equal_(
-            len(inputs),
-            len(aspect_ratios), 'the length of aspect_ratios '
-            'and inputs should be equal.')
-
-    if min_sizes is None:
-        # If min_sizes is None, min_sizes and max_sizes
-        #  will be set according to max_ratio and min_ratio.
-        num_layer = len(inputs)
-        assert max_ratio is not None and min_ratio is not None,\
-            'max_ratio and min_ratio must be not None.'
-        assert num_layer >= 3, 'The length of the input data is at least three.'
+    num_layer = len(inputs)
+
+    if num_layer <= 2:
+        assert min_sizes is not None and max_sizes is not None
+        assert len(min_sizes) == num_layer and len(max_sizes) == num_layer
+    else:
         min_sizes = []
         max_sizes = []
         step = int(math.floor(((max_ratio - min_ratio)) / (num_layer - 2)))
@@ -781,21 +623,43 @@ def multi_box_head(inputs,
         min_sizes = [base_size * .10] + min_sizes
         max_sizes = [base_size * .20] + max_sizes
 
+    if aspect_ratios:
+        _is_list_or_tuple_and_equal(
+            aspect_ratios, num_layer,
+            'aspect_ratios should be list or tuple, and the length of inputs '
+            'and aspect_ratios should be the same.')
+    if step_h:
+        _is_list_or_tuple_and_equal(
+            step_h, num_layer,
+            'step_h should be list or tuple, and the length of inputs and '
+            'step_h should be the same.')
+    if step_w:
+        _is_list_or_tuple_and_equal(
+            step_w, num_layer,
+            'step_w should be list or tuple, and the length of inputs and '
+            'step_w should be the same.')
+    if steps:
+        _is_list_or_tuple_and_equal(
+            steps, num_layer,
+            'steps should be list or tuple, and the length of inputs and '
+            'step_w should be the same.')
+        step_w = steps
+        step_h = steps
+
     mbox_locs = []
     mbox_confs = []
+    box_results = []
+    var_results = []
     for i, input in enumerate(inputs):
         min_size = min_sizes[i]
+        max_size = max_sizes[i]
+
         if not _is_list_or_tuple_(min_size):
             min_size = [min_size]
-
-        max_size = []
-        if max_sizes is not None:
-            max_size = max_sizes[i]
-            if not _is_list_or_tuple_(max_size):
-                max_size = [max_size]
-            _is_equal_(
-                len(max_size),
-                len(min_size),
+        if not _is_list_or_tuple_(max_size):
+            max_size = [max_size]
+        if not (len(max_size) == len(min_size)):
+            raise ValueError(
                 'the length of max_size and min_size should be equal.')
 
         aspect_ratio = []
@@ -804,23 +668,18 @@ def multi_box_head(inputs,
             if not _is_list_or_tuple_(aspect_ratio):
                 aspect_ratio = [aspect_ratio]
 
-        # get the number of prior box on each location
-        num_priors_per_location = 0
-        if max_sizes is not None:
-            num_priors_per_location = len(min_size) + \
-                                      len(aspect_ratio) * len(min_size) +\
-                                      len(max_size)
-        else:
-            num_priors_per_location = len(min_size) +\
-                                      len(aspect_ratio) * len(min_size)
-        if flip:
-            num_priors_per_location += len(aspect_ratio) * len(min_size)
-
-        # get mbox_loc
-        num_loc_output = num_priors_per_location * 4
-        if share_location:
-            num_loc_output *= num_classes
+        box, var = _prior_box_(input, image, min_size, max_size, aspect_ratio,
+                               variance, flip, clip, step_w[i]
+                               if step_w else 0.0, step_h[i]
+                               if step_w else 0.0, offset)
+
+        box_results.append(box)
+        var_results.append(var)
+
+        num_boxes = box.shape[2]
 
+        # get box_loc
+        num_loc_output = num_boxes * num_classes * 4
         mbox_loc = nn.conv2d(
             input=input,
             num_filters=num_loc_output,
@@ -832,7 +691,7 @@ def multi_box_head(inputs,
         mbox_locs.append(mbox_loc)
 
         # get conf_loc
-        num_conf_output = num_priors_per_location * num_classes
+        num_conf_output = num_boxes * num_classes
         conf_loc = nn.conv2d(
             input=input,
             num_filters=num_conf_output,
@@ -842,4 +701,17 @@ def multi_box_head(inputs,
         conf_loc = nn.transpose(conf_loc, perm=[0, 2, 3, 1])
         mbox_confs.append(conf_loc)
 
-    return mbox_locs, mbox_confs
+    if len(box_results) == 1:
+        box = box_results[0]
+        var = var_results[0]
+    else:
+        reshaped_boxes = []
+        reshaped_vars = []
+        for i in range(len(box_results)):
+            reshaped_boxes.append(_reshape_with_axis_(box_results[i], axis=3))
+            reshaped_vars.append(_reshape_with_axis_(var_results[i], axis=3))
+
+        box = tensor.concat(reshaped_boxes)
+        var = tensor.concat(reshaped_vars)
+
+    return mbox_locs, mbox_confs, box, var
diff --git a/python/paddle/v2/fluid/tests/test_detection.py b/python/paddle/v2/fluid/tests/test_detection.py
index dd28a05313cd38665a6390552fc3247b84949f04..8c0ca0d8fc57c87e3a4a473fa062f211eb949e58 100644
--- a/python/paddle/v2/fluid/tests/test_detection.py
+++ b/python/paddle/v2/fluid/tests/test_detection.py
@@ -109,16 +109,19 @@ class TestDetection(unittest.TestCase):
         print(str(program))
 
 
-class TestPriorBox(unittest.TestCase):
-    def test_prior_box(self):
+class TestMultiBoxHead(unittest.TestCase):
+    def test_multi_box_head(self):
         data_shape = [3, 224, 224]
-        box, var = self.prior_box_output(data_shape)
+        mbox_locs, mbox_confs, box, var = self.multi_box_head_output(data_shape)
 
         assert len(box.shape) == 2
         assert box.shape == var.shape
         assert box.shape[1] == 4
 
-    def prior_box_output(self, data_shape):
+        for loc, conf in zip(mbox_locs, mbox_confs):
+            assert loc.shape[1:3] == conf.shape[1:3]
+
+    def multi_box_head_output(self, data_shape):
         images = fluid.layers.data(
             name='pixel', shape=data_shape, dtype='float32')
         conv1 = fluid.layers.conv2d(images, 3, 3, 2)
@@ -127,46 +130,19 @@ class TestPriorBox(unittest.TestCase):
         conv4 = fluid.layers.conv2d(conv3, 3, 3, 2)
         conv5 = fluid.layers.conv2d(conv4, 3, 3, 2)
 
-        box, var = layers.prior_box(
+        mbox_locs, mbox_confs, box, var = layers.multi_box_head(
             inputs=[conv1, conv2, conv3, conv4, conv5, conv5],
             image=images,
+            num_classes=21,
             min_ratio=20,
             max_ratio=90,
-            # steps=[8, 16, 32, 64, 100, 300],
             aspect_ratios=[[2.], [2., 3.], [2., 3.], [2., 3.], [2.], [2.]],
             base_size=300,
             offset=0.5,
             flip=True,
             clip=True)
-        return box, var
-
-
-class TestMultiBoxHead(unittest.TestCase):
-    def test_prior_box(self):
-        data_shape = [3, 224, 224]
-        mbox_locs, mbox_confs = self.multi_box_output(data_shape)
-
-        for loc, conf in zip(mbox_locs, mbox_confs):
-            assert loc.shape[1:3] == conf.shape[1:3]
-
-    def multi_box_output(self, data_shape):
-        images = fluid.layers.data(
-            name='pixel', shape=data_shape, dtype='float32')
-        conv1 = fluid.layers.conv2d(images, 3, 3, 2)
-        conv2 = fluid.layers.conv2d(conv1, 3, 3, 2)
-        conv3 = fluid.layers.conv2d(conv2, 3, 3, 2)
-        conv4 = fluid.layers.conv2d(conv3, 3, 3, 2)
-        conv5 = fluid.layers.conv2d(conv4, 3, 3, 2)
 
-        mbox_locs, mbox_confs = detection.multi_box_head(
-            inputs=[conv1, conv2, conv3, conv4, conv5, conv5],
-            num_classes=21,
-            min_ratio=20,
-            max_ratio=90,
-            aspect_ratios=[[2.], [2., 3.], [2., 3.], [2., 3.], [2.], [2.]],
-            base_size=300,
-            flip=True)
-        return mbox_locs, mbox_confs
+        return mbox_locs, mbox_confs, box, var
 
 
 if __name__ == '__main__':