--- a
+++ b/FastRCNN/utils/visualization.py
@@ -0,0 +1,108 @@
+import numpy as np
+
+
+def vis_image(img, ax=None):
+    """Visualize a color image.
+
+    Args:
+        img (~numpy.ndarray): An array of shape :math:`(3, height, width)`.
+            This is in RGB format and the range of its value is
+            :math:`[0, 255]`.
+        ax (matplotlib.axes.Axis): The visualization is displayed on this
+            axis. If this is :obj:`None` (default), a new axis is created.
+
+    Returns:
+        ~matploblib.axes.Axes:
+        Returns the Axes object with the plot for further tweaking.
+
+    """
+    from matplotlib import pyplot as plot
+    if ax is None:
+        fig = plot.figure()
+        ax = fig.add_subplot(1, 1, 1)
+    # CHW -> HWC
+    img = img.transpose((1, 2, 0))[:, :, 0]
+    ax.imshow(img.astype(np.uint8), cmap='gray')
+    return ax
+
+
+def vis_bbox(img, bbox, label=None, score=None, label_names=None, ax=None):
+    """Visualize bounding boxes inside image.
+
+    Example:
+
+        >>> from chainercv.datasets import VOCDetectionDataset
+        >>> from chainercv.datasets import voc_bbox_label_names
+        >>> from chainercv.visualizations import vis_bbox
+        >>> import matplotlib.pyplot as plot
+        >>> dataset = VOCDetectionDataset()
+        >>> img, bbox, label = dataset[60]
+        >>> vis_bbox(img, bbox, label,
+        ...         label_names=voc_bbox_label_names)
+        >>> plot.show()
+
+    Args:
+        img (~numpy.ndarray): An array of shape :math:`(3, height, width)`.
+            This is in RGB format and the range of its value is
+            :math:`[0, 255]`.
+        bbox (~numpy.ndarray): An array of shape :math:`(R, 4)`, where
+            :math:`R` is the number of bounding boxes in the image.
+            Each element is organized
+            by :math:`(y_{min}, x_{min}, y_{max}, x_{max})` in the second axis.
+        label (~numpy.ndarray): An integer array of shape :math:`(R,)`.
+            The values correspond to id for label names stored in
+            :obj:`label_names`. This is optional.
+        score (~numpy.ndarray): A float array of shape :math:`(R,)`.
+             Each value indicates how confident the prediction is.
+             This is optional.
+        label_names (iterable of strings): Name of labels ordered according
+            to label ids. If this is :obj:`None`, labels will be skipped.
+        ax (matplotlib.axes.Axis): The visualization is displayed on this
+            axis. If this is :obj:`None` (default), a new axis is created.
+
+    Returns:
+        ~matploblib.axes.Axes:
+        Returns the Axes object with the plot for further tweaking.
+
+    """
+    from matplotlib import pyplot as plot
+
+    #if label is not None and not len(bbox) == len(label):
+    #    raise ValueError('The length of label must be same as that of bbox')
+    #if score is not None and not len(bbox) == len(score):
+    #    raise ValueError('The length of score must be same as that of bbox')
+
+    # Returns newly instantiated matplotlib.axes.Axes object if ax is None
+    ax = vis_image(img, ax=ax)
+
+    # If there is no bounding box to display, visualize the image and exit.
+    if len(bbox) == 0:
+        return ax
+
+    if len(bbox) != 0:
+        #print(bbox)
+        bb = bbox[0]
+        i = 0
+        xy = (bb[1], bb[0])
+        height = bb[2] - bb[0]
+        width = bb[3] - bb[1]
+        ax.add_patch(plot.Rectangle(
+            xy, width, height, fill=False, edgecolor='red', linewidth=3))
+
+        caption = list()
+
+        if label is not None and label_names is not None:
+            lb = label[i]
+            if not (0 <= lb < len(label_names)):
+                raise ValueError('No corresponding name is given')
+            caption.append(label_names[lb])
+        if score is not None:
+            sc = score[i]
+            caption.append('{:.2f}'.format(sc))
+
+        if len(caption) > 0:
+            ax.text(bb[1], bb[0],
+                    ': '.join(caption),
+                    style='italic')
+#                     bbox={'facecolor': 'white', 'alpha': 0.7, 'pad': 5})
+    return ax
\ No newline at end of file