code: def main():
      parser = argparse.ArgumentParser()
      parser.add_argument(
          "--model", choices=("ssd300", "ssd512"), default="ssd300"
      )
      parser.add_argument("--gpu", type=int, default=-1)
      parser.add_argument("--pretrained-model")
      parser.add_argument("--dataset", choices=("voc",), default="voc")
      parser.add_argument("image")
      args = parser.parse_args()
  
      if args.model == "ssd300":
          cls = SSD300
      elif args.model == "ssd512":
          cls = SSD512
  
      if args.dataset == "voc":
          if args.pretrained_model is None:
              args.pretrained_model = "voc0712"
          label_names = voc_bbox_label_names
  
      model = cls(
          n_fg_class=len(label_names),
          pretrained_model=args.pretrained_model,
      )
  
      if args.gpu >= 0:
          chainer.cuda.get_device_from_id(args.gpu).use()
          model.to_gpu()
  
      img = utils.read_image(args.image, color=True)
      bboxes, labels, scores = model.predict([img])
      bbox, label, score = bboxes[0], labels[0], scores[0]
  
      vis_bbox(img, bbox, label, score, label_names=label_names)
      plt.show()
  
file_path: chainercv/examples/ssd/demo.py
imports: import argparse
  import matplotlib.pyplot
  import chainer
  from chainercv.datasets import voc_bbox_label_names
  from chainercv.links import SSD300
  from chainercv.links import SSD512
  from chainercv import utils
  from chainercv.visualizations import vis_bbox
label_desc: Use image node_id 1 for calling a global function with example usage: main() without return types