class_docstr: Default SSD validation transform.
  
  Parameters
  ----------
  width : int
      Image width.
  height : int
      Image height.
  mean : array-like of size 3
      Mean pixel values to be subtracted from image tensor. Default is [0.485, 0.456, 0.406].
  std : array-like of size 3
      Standard deviation to be divided from image. Default is [0.229, 0.224, 0.225].
class_docstr_tok: ["Default","SSD","validation","transform",".","Parameters","--","--","--","--","--","width",":","int","Image","width",".","height",":","int","Image","height",".","mean",":","array-like","of","size","3","Mean","pixel","values","to","be","subtracted","from","image","tensor",".","Default","is","[","0.485",",","0.456",",","0.406","]",".","std",":","array-like","of","size","3","Standard","deviation","to","be","divided","from","image",".","Default","is","[","0.229",",","0.224",",","0.225","]","."]
code: def __init__(
      self,
      width,
      height,
      mean=(0.485, 0.456, 0.406),
      std=(0.229, 0.224, 0.225),
  ):
      self._width = width
      self._height = height
      self._mean = np.array(mean, dtype=np.float32).reshape((1, 1, 3))
      self._std = np.array(std, dtype=np.float32).reshape((1, 1, 3))
  
file_path: gluon-cv/gluoncv/data/transforms/presets/center_net.py
imports: from __future__ import absolute_import
  import numpy
  import mxnet
  from ..None import bbox
  from ..None import image
  from ..None import experimental
  from ....utils.filesystem import try_import_cv2
label_desc: Use image node_id 1 to create a new CenterNetDefaultValTransform object from inherited base classes: object with example: obj = CenterNetDefaultValTransform(width, height, mean, std)