---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: mask
    dtype: image
  - name: sents
    sequence: string
  - name: img_size
    sequence: int64
  - name: img_path
    dtype: string
  - name: num_sents
    dtype: int64
  - name: cat
    dtype:
      class_label:
        names:
          '0': person
          '1': bicycle
          '2': car
          '3': motorcycle
          '4': airplane
          '5': bus
          '6': train
          '7': truck
          '8': boat
          '9': traffic light
          '10': fire hydrant
          '11': street sign
          '12': stop sign
          '13': parking meter
          '14': bench
          '15': bird
          '16': cat
          '17': dog
          '18': horse
          '19': sheep
          '20': cow
          '21': elephant
          '22': bear
          '23': zebra
          '24': giraffe
          '25': hat
          '26': backpack
          '27': umbrella
          '28': shoe
          '29': eye glasses
          '30': handbag
          '31': tie
          '32': suitcase
          '33': frisbee
          '34': skis
          '35': snowboard
          '36': sports ball
          '37': kite
          '38': baseball bat
          '39': baseball glove
          '40': skateboard
          '41': surfboard
          '42': tennis racket
          '43': bottle
          '44': plate
          '45': wine glass
          '46': cup
          '47': fork
          '48': knife
          '49': spoon
          '50': bowl
          '51': banana
          '52': apple
          '53': sandwich
          '54': orange
          '55': broccoli
          '56': carrot
          '57': hot dog
          '58': pizza
          '59': donut
          '60': cake
          '61': chair
          '62': couch
          '63': potted plant
          '64': bed
          '65': mirror
          '66': dining table
          '67': window
          '68': desk
          '69': toilet
          '70': door
          '71': tv
          '72': laptop
          '73': mouse
          '74': remote
          '75': keyboard
          '76': cell phone
          '77': microwave
          '78': oven
          '79': toaster
          '80': sink
          '81': refrigerator
          '82': blender
          '83': book
          '84': clock
          '85': vase
          '86': scissors
          '87': teddy bear
          '88': hair drier
          '89': toothbrush
          '90': hair brush
          '91': banner
          '92': blanket
          '93': branch
          '94': bridge
          '95': building-other
          '96': bush
          '97': cabinet
          '98': cage
          '99': cardboard
          '100': carpet
          '101': ceiling-other
          '102': ceiling-tile
          '103': cloth
          '104': clothes
          '105': clouds
          '106': counter
          '107': cupboard
          '108': curtain
          '109': desk-stuff
          '110': dirt
          '111': door-stuff
          '112': fence
          '113': floor-marble
          '114': floor-other
          '115': floor-stone
          '116': floor-tile
          '117': floor-wood
          '118': flower
          '119': fog
          '120': food-other
          '121': fruit
          '122': furniture-other
          '123': grass
          '124': gravel
          '125': ground-other
          '126': hill
          '127': house
          '128': leaves
          '129': light
          '130': mat
          '131': metal
          '132': mirror-stuff
          '133': moss
          '134': mountain
          '135': mud
          '136': napkin
          '137': net
          '138': paper
          '139': pavement
          '140': pillow
          '141': plant-other
          '142': plastic
          '143': platform
          '144': playingfield
          '145': railing
          '146': railroad
          '147': river
          '148': road
          '149': rock
          '150': roof
          '151': rug
          '152': salad
          '153': sand
          '154': sea
          '155': shelf
          '156': sky-other
          '157': skyscraper
          '158': snow
          '159': solid-other
          '160': stairs
          '161': stone
          '162': straw
          '163': structural-other
          '164': table
          '165': tent
          '166': textile-other
          '167': towel
          '168': tree
          '169': vegetable
          '170': wall-brick
          '171': wall-concrete
          '172': wall-other
          '173': wall-panel
          '174': wall-stone
          '175': wall-tile
          '176': wall-wood
          '177': water-other
          '178': waterdrops
          '179': window-blind
          '180': window-other
          '181': wood
  - name: seg_id
    dtype: int64
  - name: mask_path
    dtype: string
  splits:
  - name: testA_object_part
    num_bytes: 5922400482.75
    num_examples: 11641
  - name: testB_part_only
    num_bytes: 2174144982.25
    num_examples: 4119
  - name: testA_part_only
    num_bytes: 4919679761.5
    num_examples: 9666
  - name: val_part_only
    num_bytes: 6590282037.25
    num_examples: 12691
  download_size: 2358360855
  dataset_size: 19606507263.75
configs:
- config_name: default
  data_files:
  - split: testA_part_only
    path: data/testA_part_only-*
  - split: testB_part_only
    path: data/testB_part_only-*
  - split: val_part_only
    path: data/val_part_only-*
  - split: testA_object_part
    path: data/testA_object_part-*
---