old_contents: # --------------------------------------------------------
  # Fast R-CNN
  # Copyright (c) 2015 Microsoft
  # Licensed under The MIT License [see LICENSE for details]
  # Written by Ross Girshick
  # --------------------------------------------------------
  
  """Factory method for easily getting imdbs by name."""
  
  __sets = {}
  
  import numpy as np
  
  from .pascal_voc import pascal_voc
  from .imagenet3d import imagenet3d
  from .kitti import kitti
  from .kitti_tracking import kitti_tracking
  from .nthu import nthu
  from .coco import coco
  from .kittivoc import kittivoc
  
  def _selective_search_IJCV_top_k(split, year, top_k):
      """Return an imdb that uses the top k proposals from the selective search
      IJCV code.
      """
      imdb = pascal_voc(split, year)
      imdb.roidb_handler = imdb.selective_search_IJCV_roidb
      imdb.config['top_k'] = top_k
      return imdb
  
  # Set up voc_<year>_<split> using selective search "fast" mode
  for year in ['2007', '2012']:
      for split in ['train', 'val', 'trainval', 'test']:
          name = 'voc_{}_{}'.format(year, split)
          __sets[name] = (lambda split=split, year=year:
                  pascal_voc(split, year))
  
  
  # Set up kittivoc
      for split in ['train', 'val', 'trainval', 'test']:
          name = 'kittivoc_{}'.format(split)
          print name
          __sets[name] = (lambda split=split: kittivoc(split))
  
  # # KITTI dataset
  # for split in ['train', 'val', 'trainval', 'test']:
  #     name = 'kitti_{}'.format(split)
  #     print name
  #     __sets[name] = (lambda split=split: kitti(split))
  
  # Set up coco_2014_<split>
  for year in ['2014']:
      for split in ['train', 'val', 'minival', 'valminusminival']:
          name = 'coco_{}_{}'.format(year, split)
          __sets[name] = (lambda split=split, year=year: coco(split, year))
  
  # Set up coco_2015_<split>
  for year in ['2015']:
      for split in ['test', 'test-dev']:
          name = 'coco_{}_{}'.format(year, split)
          __sets[name] = (lambda split=split, year=year: coco(split, year))
  
  # NTHU dataset
  for split in ['71', '370']:
      name = 'nthu_{}'.format(split)
      print name
      __sets[name] = (lambda split=split: nthu(split))
  
  
  def get_imdb(name):
      """Get an imdb (image database) by name."""
      if not __sets.has_key(name):
          print (list_imdbs())
          raise KeyError('Unknown dataset: {}'.format(name))
      return __sets[name]()
  
  def list_imdbs():
      """List all registered imdbs."""
      return __sets.keys()
  
new_contents: # --------------------------------------------------------
  # Fast R-CNN
  # Copyright (c) 2015 Microsoft
  # Licensed under The MIT License [see LICENSE for details]
  # Written by Ross Girshick
  # --------------------------------------------------------
  
  """Factory method for easily getting imdbs by name."""
  
  __sets = {}
  
  import numpy as np
  
  from .pascal_voc import pascal_voc
  from .imagenet3d import imagenet3d
  from .kitti import kitti
  from .kitti_tracking import kitti_tracking
  from .nthu import nthu
  from .coco import coco
  from .kittivoc import kittivoc
  
  def _selective_search_IJCV_top_k(split, year, top_k):
      """Return an imdb that uses the top k proposals from the selective search
      IJCV code.
      """
      imdb = pascal_voc(split, year)
      imdb.roidb_handler = imdb.selective_search_IJCV_roidb
      imdb.config['top_k'] = top_k
      return imdb
  
  # Set up voc_<year>_<split> using selective search "fast" mode
  for year in ['2007', '2012', '0712']:
      for split in ['train', 'val', 'trainval', 'test']:
          name = 'voc_{}_{}'.format(year, split)
          __sets[name] = (lambda split=split, year=year:
                  pascal_voc(split, year))
  
  
  # Set up kittivoc
      for split in ['train', 'val', 'trainval', 'test']:
          name = 'kittivoc_{}'.format(split)
          print name
          __sets[name] = (lambda split=split: kittivoc(split))
  
  # # KITTI dataset
  # for split in ['train', 'val', 'trainval', 'test']:
  #     name = 'kitti_{}'.format(split)
  #     print name
  #     __sets[name] = (lambda split=split: kitti(split))
  
  # Set up coco_2014_<split>
  for year in ['2014']:
      for split in ['train', 'val', 'minival', 'valminusminival']:
          name = 'coco_{}_{}'.format(year, split)
          __sets[name] = (lambda split=split, year=year: coco(split, year))
  
  # Set up coco_2015_<split>
  for year in ['2015']:
      for split in ['test', 'test-dev']:
          name = 'coco_{}_{}'.format(year, split)
          __sets[name] = (lambda split=split, year=year: coco(split, year))
  
  # NTHU dataset
  for split in ['71', '370']:
      name = 'nthu_{}'.format(split)
      print name
      __sets[name] = (lambda split=split: nthu(split))
  
  
  def get_imdb(name):
      """Get an imdb (image database) by name."""
      if not __sets.has_key(name):
          print (list_imdbs())
          raise KeyError('Unknown dataset: {}'.format(name))
      return __sets[name]()
  
  def list_imdbs():
      """List all registered imdbs."""
      return __sets.keys()
  
subject: add 0712 data