class_docstr: Base class for YOLOv2 and YOLOv3.
  
  A subclass of this class should have :obj:`extractor`,
  :meth:`forward`, and :meth:`_decode`.
docstr: Detect objects from images.
  
  This method predicts objects for each image.
  
  Args:
      imgs (iterable of numpy.ndarray): Arrays holding images.
          All images are in CHW and RGB format
          and the range of their value is :math:`[0, 255]`.
  
  Returns:
     tuple of lists:
     This method returns a tuple of three lists,
     :obj:`(bboxes, labels, scores)`.
  
     * **bboxes**: A list of float arrays of shape :math:`(R, 4)`,                where :math:`R` is the number of bounding boxes in a image.                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,)`.                Each value indicates the class of the bounding box.                Values are in range :math:`[0, L - 1]`, where :math:`L` is the                number of the foreground classes.
     * **scores** : A list of float arrays of shape :math:`(R,)`.                Each value indicates how confident the prediction is.
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code: def predict(self, imgs):
      """Detect objects from images.
  
          This method predicts objects for each image.
  
          Args:
              imgs (iterable of numpy.ndarray): Arrays holding images.
                  All images are in CHW and RGB format
                  and the range of their value is :math:`[0, 255]`.
  
          Returns:
             tuple of lists:
             This method returns a tuple of three lists,
             :obj:`(bboxes, labels, scores)`.
  
             * **bboxes**: A list of float arrays of shape :math:`(R, 4)`, \
                 where :math:`R` is the number of bounding boxes in a image. \
                 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,)`. \
                 Each value indicates the class of the bounding box. \
                 Values are in range :math:`[0, L - 1]`, where :math:`L` is the \
                 number of the foreground classes.
             * **scores** : A list of float arrays of shape :math:`(R,)`. \
                 Each value indicates how confident the prediction is.
  
          """
  
      x = []
      params = []
      for img in imgs:
          _, H, W = img.shape
          img, param = transforms.resize_contain(
              img / 255,
              (self.insize, self.insize),
              fill=0.5,
              return_param=True,
          )
          x.append(self.xp.array(img))
          param["size"] = (H, W)
          params.append(param)
  
      with chainer.using_config(
          "train", False
      ), chainer.function.no_backprop_mode():
          locs, objs, confs = self.forward(self.xp.stack(x))
      locs = locs.array
      objs = objs.array
      confs = confs.array
  
      bboxes = []
      labels = []
      scores = []
      for loc, obj, conf, param in zip(locs, objs, confs, params):
          bbox, label, score = self._decode(loc, obj, conf)
          bbox = cuda.to_cpu(bbox)
          label = cuda.to_cpu(label)
          score = cuda.to_cpu(score)
  
          bbox = transforms.translate_bbox(
              bbox, -self.insize / 2, -self.insize / 2
          )
          bbox = transforms.resize_bbox(
              bbox, param["scaled_size"], param["size"]
          )
          bbox = transforms.translate_bbox(
              bbox, param["size"][0] / 2, param["size"][1] / 2
          )
  
          bboxes.append(bbox)
          labels.append(label)
          scores.append(score)
  
      return bboxes, labels, scores
  
file_path: chainercv/chainercv/links/model/yolo/yolo_base.py
imports: import chainer
  from chainer.backends import cuda
  from chainercv import transforms
label_desc: Use image node_id 3 for calling the YOLOBase obj's underlying member method code with example usage: obj.predict(imgs) and returns: bboxes,  labels,  scores