def prepare_boxlist(self, boxes, scores, image_shape):
    '\n        Returns BoxList from `boxes` and adds probability scores information\n        as an extra field\n        `boxes` has shape (#detections, 4 * #classes), where each row represents\n        a list of predicted bounding boxes for each of the object classes in the\n        dataset (including the background class). The detections in each row\n        originate from the same object proposal.\n        `scores` has shape (#detection, #classes), where each row represents a list\n        of object detection confidence scores for each of the object classes in the\n        dataset (including the background class). `scores[i, j]`` corresponds to the\n        box at `boxes[i, j * 4:(j + 1) * 4]`.\n        '
    boxes = boxes.reshape((- 1), 4)
    scores = scores.reshape((- 1))
    boxlist = BoxList(boxes, image_shape, mode='xyxy')
    boxlist.add_field('scores', scores)
    return boxlist
Returns BoxList from `boxes` and adds probability scores information
as an extra field
`boxes` has shape (#detections, 4 * #classes), where each row represents
a list of predicted bounding boxes for each of the object classes in the
dataset (including the background class). The detections in each row
originate from the same object proposal.
`scores` has shape (#detection, #classes), where each row represents a list
of object detection confidence scores for each of the object classes in the
dataset (including the background class). `scores[i, j]`` corresponds to the
box at `boxes[i, j * 4:(j + 1) * 4]`.
def prepare_boxlist(self, boxes, scores, image_shape):
    '\n        Returns BoxList from `boxes` and adds probability scores information\n        as an extra field\n        `boxes` has shape (#detections, 4 * #classes), where each row represents\n        a list of predicted bounding boxes for each of the object classes in the\n        dataset (including the background class). The detections in each row\n        originate from the same object proposal.\n        `scores` has shape (#detection, #classes), where each row represents a list\n        of object detection confidence scores for each of the object classes in the\n        dataset (including the background class). `scores[i, j]`` corresponds to the\n        box at `boxes[i, j * 4:(j + 1) * 4]`.\n        '
    boxes = boxes.reshape((- 1), 4)
    scores = scores.reshape((- 1))
    boxlist = BoxList(boxes, image_shape, mode='xyxy')
    boxlist.add_field('scores', scores)
    return boxlist