fix typo, 'bounding box'

chainercv/experimental/links/model/fcis/utils/proposal_target_creator.py

@@ -85,7 +85,7 @@ def __call__(
             bbox (array): The coordinates of ground truth bounding boxes.
                 Its shape is :math:`(R', 4)`.
             loc_normalize_mean (tuple of four floats): Mean values to normalize
-                coordinates of bouding boxes.
+                coordinates of bounding boxes.
             loc_normalize_std (tuple of four floats): Standard deviation of
                 the coordinates of bounding boxes.
             mask_size (tuple of int or int): Generated mask size, which is

chainercv/links/model/faster_rcnn/faster_rcnn.py

@@ -258,7 +258,7 @@ def predict(self, imgs):
 
            * **bboxes**: A list of float arrays of shape :math:`(R, 4)`, \
                where :math:`R` is the number of bounding boxes in a image. \
-               Each bouding box is organized by \
+               Each bounding box is organized by \
                :math:`(y_{min}, x_{min}, y_{max}, x_{max})` \
                in the second axis.
            * **labels** : A list of integer arrays of shape :math:`(R,)`. \

chainercv/links/model/faster_rcnn/utils/proposal_target_creator.py

@@ -73,7 +73,7 @@ def __call__(self, roi, bbox, label,
                 is :math:`(R',)`. Its range is :math:`[0, L - 1]`, where
                 :math:`L` is the number of foreground classes.
             loc_normalize_mean (tuple of four floats): Mean values to normalize
-                coordinates of bouding boxes.
+                coordinates of bounding boxes.
             loc_normalize_std (tuple of four floats): Standard deviation of
                 the coordinates of bounding boxes.
 

chainercv/links/model/ssd/multibox_coder.py

@@ -49,7 +49,7 @@ class MultiboxCoder(object):
             Each integer indicates the size of a feature map.
         aspect_ratios (iterable of tuples of ints):
             An iterable of tuples of integers
-            used to compute the default bouding boxes.
+            used to compute the default bounding boxes.
             Each tuple indicates the aspect ratios of
             the default bounding boxes at each feature maps.
             The length of this iterable should be :obj:`len(grids)`.
@@ -117,7 +117,7 @@ def encode(self, bbox, label, iou_thresh=0.5):
         Args:
             bbox (array): A float array of shape :math:`(R, 4)`,
                 where :math:`R` is the number of bounding boxes in an image.
-                Each bouding box is organized by
+                Each bounding box is organized by
                 :math:`(y_{min}, x_{min}, y_{max}, x_{max})`
                 in the second axis.
             label (array) : An integer array of shape :math:`(R,)`.
@@ -211,7 +211,7 @@ def decode(self, mb_loc, mb_conf, nms_thresh=0.45, score_thresh=0.6):
 
             * **bbox**: A float array of shape :math:`(R, 4)`, \
                 where :math:`R` is the number of bounding boxes in a image. \
-                Each bouding box is organized by \
+                Each bounding box is organized by \
                 :math:`(y_{min}, x_{min}, y_{max}, x_{max})` \
                 in the second axis.
             * **label** : An integer array of shape :math:`(R,)`. \

chainercv/links/model/ssd/ssd.py

@@ -53,7 +53,7 @@ class SSD(chainer.Chain):
             for each feature map. This value is used by
             :class:`~chainercv.links.model.ssd.MultiboxCoder`.
         variance (tuple of floats): Two coefficients for decoding
-            the locations of bounding boxe.
+            the locations of bounding boxes.
             This value is used by
             :class:`~chainercv.links.model.ssd.MultiboxCoder`.
             The default value is :obj:`(0.1, 0.2)`.
@@ -179,7 +179,7 @@ def predict(self, imgs):
 
            * **bboxes**: A list of float arrays of shape :math:`(R, 4)`, \
                where :math:`R` is the number of bounding boxes in a image. \
-               Each bouding box is organized by \
+               Each bounding box is organized by \
                :math:`(y_{min}, x_{min}, y_{max}, x_{max})` \
                in the second axis.
            * **labels** : A list of integer arrays of shape :math:`(R,)`. \

chainercv/links/model/yolo/yolo_base.py

@@ -59,7 +59,7 @@ def predict(self, imgs):
 
            * **bboxes**: A list of float arrays of shape :math:`(R, 4)`, \
                where :math:`R` is the number of bounding boxes in a image. \
-               Each bouding box is organized by \
+               Each bounding box is organized by \
                :math:`(y_{min}, x_{min}, y_{max}, x_{max})` \
                in the second axis.
            * **labels** : A list of integer arrays of shape :math:`(R,)`. \