old_contents: from matplotlib.font_manager import FontProperties
  import matplotlib.pyplot as plt
  
  from chainercv.links import FasterRCNNVGG16
  from chainercv.links import SSD300
  from chainercv.links import SSD512
  from chainercv.links import YOLOv2
  from chainercv.links import YOLOv3
  
  from chainercv.datasets import voc_bbox_label_names
  from chainercv.datasets import VOCBboxDataset
  from chainercv.visualizations import vis_bbox
  
  
  def main():
      dataset = VOCBboxDataset(year='2007', split='test') \
          .slice[[29, 301, 189, 229], 'img']
      models = [
          ('Faster R-CNN', FasterRCNNVGG16(pretrained_model='voc07')),
          ('SSD300', SSD300(pretrained_model='voc0712')),
          ('SSD512', SSD512(pretrained_model='voc0712')),
          ('YOLOv2', YOLOv2(pretrained_model='voc0712')),
          ('YOLOv3', YOLOv3(pretrained_model='voc0712')),
      ]
  
      fig = plt.figure(figsize=(30, 30))
      for i, img in enumerate(dataset):
          for j, (name, model) in enumerate(models):
              bboxes, labels, scores = model.predict([img])
              bbox, label, score = bboxes[0], labels[0], scores[0]
  
              ax = fig.add_subplot(
                  len(dataset), len(models), i * len(models) + j + 1)
              vis_bbox(
                  img, bbox, label, score,
                  label_names=voc_bbox_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)
              plt.axis('off')
              plt.tight_layout()
  
      plt.show()
  
  
  if __name__ == '__main__':
      main()
  
new_contents: from matplotlib.font_manager import FontProperties
  import matplotlib.pyplot as plt
  
  from chainercv.links import FasterRCNNVGG16
  from chainercv.links import SSD300
  from chainercv.links import SSD512
  from chainercv.links import YOLOv2
  from chainercv.links import YOLOv3
  
  from chainercv.datasets import voc_bbox_label_names
  from chainercv.datasets import VOCBboxDataset
  from chainercv.visualizations import vis_bbox
  
  
  def main():
      dataset = VOCBboxDataset(year='2007', split='test') \
          .slice[[29, 301, 189, 229], 'img']
      models = [
          ('Faster R-CNN', FasterRCNNVGG16(pretrained_model='voc07')),
          ('SSD300', SSD300(pretrained_model='voc0712')),
          ('SSD512', SSD512(pretrained_model='voc0712')),
          ('YOLOv2', YOLOv2(pretrained_model='voc0712')),
          ('YOLOv3', YOLOv3(pretrained_model='voc0712')),
      ]
  
      fig = plt.figure(figsize=(30, 30))
      for i, img in enumerate(dataset):
          for j, (name, model) in enumerate(models):
              bboxes, labels, scores = model.predict([img])
              bbox, label, score = bboxes[0], labels[0], scores[0]
  
              ax = fig.add_subplot(
                  len(dataset), len(models), i * len(models) + j + 1)
              vis_bbox(
                  img, bbox, label, score,
                  label_names=voc_bbox_label_names, ax=ax
              )
  
              # Set MatplotLib parameters
              ax.set_aspect('equal')
              if i == 0:
                  font = FontProperties()
                  font.set_family('serif')
                  font.set_size(35)
                  ax.set_title(name, y=1.03, fontproperties=font)
              plt.axis('off')
              plt.tight_layout()
  
      plt.show()
  
  
  if __name__ == '__main__':
      main()
  
subject: fix param