old_contents: """
  Caltech Pedestrian Dataset download/process functions.
  """
  
  
  import os
  import sys
  
  from dbcollection.datasets.dbclass import BaseDataset
  from .detection import Detection2015, Detection2016
  from .captions import Caption2015, Caption2016
  from .keypoints import Keypoints2016
  
  
  class COCO(BaseDataset):
      """ Microsoft COCO Dataset preprocessing/downloading functions """
  
      # download url
      url = [
          'http://msvocds.blob.core.windows.net/coco2014/train2014.zip',
          'http://msvocds.blob.core.windows.net/coco2014/val2014.zip',
          'http://msvocds.blob.core.windows.net/coco2014/test2014.zip',
          'http://msvocds.blob.core.windows.net/coco2015/test2015.zip',
          'http://msvocds.blob.core.windows.net/annotations-1-0-3/instances_train-val2014.zip',
          'http://msvocds.blob.core.windows.net/annotations-1-0-3/person_keypoints_trainval2014.zip',
          'http://msvocds.blob.core.windows.net/annotations-1-0-3/captions_train-val2014.zip',
          'http://msvocds.blob.core.windows.net/annotations-1-0-4/image_info_test2014.zip',
          'http://msvocds.blob.core.windows.net/annotations-1-0-4/image_info_test2015.zip'
      ]
  
      md5_checksum = ''
  
      # some keywords. These are used to classify datasets for easier
      # categorization.
      keywords = ['image_processing', 'detection', 'keypoint', 'captions', 'human', 'pose']
  
      # init tasks
      tasks = {
          "detection" : Detection2015,
          "detection_2015" : Detection2015,
          "detection_2016" : Detection2016,
  
          "caption" : Caption2015,
          "caption_2015" : Caption2015,
          "caption_2016" : Caption2016,
  
          "keypoints" : Keypoints2016,
          "keypoints_2016" : Keypoints2016,
      }
  
      default_task = 'detection'
  
new_contents: """
  Caltech Pedestrian Dataset download/process functions.
  """
  
  
  import os
  import sys
  
  from dbcollection.datasets.dbclass import BaseDataset
  from .detection import Detection2015, Detection2016
  from .captions import Caption2015, Caption2016
  from .keypoints import Keypoints2016
  
  
  class COCO(BaseDataset):
      """ Microsoft COCO Dataset preprocessing/downloading functions """
  
      # download url
      url = [
          'http://msvocds.blob.core.windows.net/coco2014/train2014.zip',
          'http://msvocds.blob.core.windows.net/coco2014/val2014.zip',
          'http://msvocds.blob.core.windows.net/coco2014/test2014.zip',
          'http://msvocds.blob.core.windows.net/coco2015/test2015.zip',
          'http://msvocds.blob.core.windows.net/annotations-1-0-3/instances_train-val2014.zip',
          'http://msvocds.blob.core.windows.net/annotations-1-0-3/person_keypoints_trainval2014.zip',
          'http://msvocds.blob.core.windows.net/annotations-1-0-3/captions_train-val2014.zip',
          'http://msvocds.blob.core.windows.net/annotations-1-0-4/image_info_test2014.zip',
          'http://msvocds.blob.core.windows.net/annotations-1-0-4/image_info_test2015.zip'
      ]
  
      md5_checksum = ''
  
      # some keywords. These are used to classify datasets for easier
      # categorization.
      keywords = ['image_processing', 'detection', 'keypoint', 'captions', 'human', 'pose']
  
      # init tasks
      tasks = {
          "detection_2015" : Detection2015,
          "detection_2016" : Detection2016,
  
          "caption_2015" : Caption2015,
          "caption_2016" : Caption2016,
  
          "keypoints_2016" : Keypoints2016,
      }
  
      default_task = 'detection_2015'
  
subject: Remove unnecessary tasks from the coco dataset