metadata
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-*