text: import argparse
  
  import matplotlib.pyplot as plt
  
  from mxnet import nd, image
  from mxnet.gluon.data.vision import transforms
  
  from gluoncv.model_zoo import get_model
  
  parser = argparse.ArgumentParser(description='Predict ImageNet classes from a given image')
  parser.add_argument('--model', type=str, required=True,
                      help='name of the model to use')
  parser.add_argument('--saved-params', type=str, default='',
                      help='path to the saved model parameters')
  parser.add_argument('--input-pic', type=str, required=True,
                      help='path to the input picture')
  opt = parser.parse_args()
  
  classes = 1000
  with open('imagenet_labels.txt', 'r') as f:
      class_names = [l.strip() for l in f.readlines()]
  
  # Load Model
  model_name = opt.model
  pretrained = True if opt.saved_params == '' else False
  kwargs = {'classes': classes, 'pretrained': pretrained}
  net = get_model(model_name, **kwargs)
  
  if not pretrained:
      net.load_params(opt.saved_params)
  
  # Load Images
  img = image.imread(opt.input_pic)
  
  # Transform
  transform_fn = transforms.Compose([
      transforms.Resize(256),
      transforms.CenterCrop(224),
      transforms.ToTensor(),
      transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
      # transforms.Normalize([0.485, 0.456, 0.406], [1, 1, 1])
  ])
  
  img = transform_fn(img)
  pred = net(img.expand_dims(0))
  
  topK = 5
  ind = nd.topk(pred, k=topK)[0].astype('int')
  print('The input picture is classified to be')
  for i in range(topK):
      print('\t[%s], with probability %.3f.'%
            (class_names[ind[i].asscalar()], nd.softmax(pred)[0][ind[i]].asscalar()))
  