old_contents: from matplotlib.font_manager import FontProperties
  import matplotlib.pyplot as plot
  import numpy as np
  
  from chainercv.links import FasterRCNNVGG16
  from chainercv.links import SSD300
  from chainercv.links import SSD512
  
  from chainercv.datasets import voc_detection_label_names
  from chainercv.datasets import VOCDetectionDataset
  from chainercv.visualizations import vis_bbox
  
  
  dataset = VOCDetectionDataset(year='2007', split='test')
  models = [
      ('Faster R-CNN', FasterRCNNVGG16(pretrained_model='voc07')),
      ('SSD300', SSD300(pretrained_model='voc0712')),
      ('SSD512', SSD512(pretrained_model='voc0712')),
  ]
  indices = [29, 301, 189, 229]
  
  fig = plot.figure(figsize=(30, 30))
  for i, idx in enumerate(indices):
      for j, (name, model) in enumerate(models):
          img, _, _ = dataset[idx]
          bboxes, labels, scores = model.predict(img[np.newaxis])
          bbox, label, score = bboxes[0], labels[0], scores[0]
  
          ax = fig.add_subplot(4, 3, i * 3 + j + 1)
          vis_bbox(
              img, bbox, label, score,
              label_names=voc_detection_label_names, ax=ax
          )
  
          # Set MatplotLib parameters
          ax.set_aspect('equal')
          if i == 0:
              font = FontProperties()
              font.set_family('serif')
              ax.set_title(name, fontsize=35, y=1.03, fontproperties=font)
          plot.axis('off')
          plot.tight_layout()
  
  plot.show()
  
new_contents: from matplotlib.font_manager import FontProperties
  import matplotlib.pyplot as plot
  
  from chainercv.links import FasterRCNNVGG16
  from chainercv.links import SSD300
  from chainercv.links import SSD512
  
  from chainercv.datasets import voc_detection_label_names
  from chainercv.datasets import VOCDetectionDataset
  from chainercv.visualizations import vis_bbox
  
  
  dataset = VOCDetectionDataset(year='2007', split='test')
  models = [
      ('Faster R-CNN', FasterRCNNVGG16(pretrained_model='voc07')),
      ('SSD300', SSD300(pretrained_model='voc0712')),
      ('SSD512', SSD512(pretrained_model='voc0712')),
  ]
  indices = [29, 301, 189, 229]
  
  fig = plot.figure(figsize=(30, 30))
  for i, idx in enumerate(indices):
      for j, (name, model) in enumerate(models):
          img, _, _ = dataset[idx]
          bboxes, labels, scores = model.predict([img])
          bbox, label, score = bboxes[0], labels[0], scores[0]
  
          ax = fig.add_subplot(4, 3, i * 3 + j + 1)
          vis_bbox(
              img, bbox, label, score,
              label_names=voc_detection_label_names, ax=ax
          )
  
          # Set MatplotLib parameters
          ax.set_aspect('equal')
          if i == 0:
              font = FontProperties()
              font.set_family('serif')
              ax.set_title(name, fontsize=35, y=1.03, fontproperties=font)
          plot.axis('off')
          plot.tight_layout()
  
  plot.show()
  
subject: use [img]