old_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
  
  
  def main():
      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()
  
  
  if __name__ == '__main__':
      main()
  
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
  
  
  def main():
      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(
                  len(indices), len(models), i * len(models) + 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()
  
  
  if __name__ == '__main__':
      main()
  
subject: remove magic