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what is the brand of phone?
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[ "nokia", "nokia", "nokia", "nokia", "toshiba", "nokia", "nokia", "nokia", "nokia", "nokia" ]
[ "Belt", "Headphones", "Goggles", "Scale", "Bottle opener", "Mobile phone", "Mirror", "Digital clock", "Television", "Telephone", "Tool", "Wheel", "Camera", "Watch", "Glasses", "Aircraft" ]
train
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what type of plane is this?
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[ "lape", "cargo", "ec-agg", "lape", "lape", "lape", "lape", "lape", "lape", "airplane" ]
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what are the letters on the tail section of the plane?
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[ "ec agg", "ec-agg", "ec", "ec-agg", "ec", "ec", "ec", "ec", "ec goeland", "ec" ]
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who is this copyrighted by?
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[ "simon clancy", "simon ciancy", "simon clancy", "simon clancy", "the brand is bayard", "simon clancy", "simon clancy", "simon clancy", "simon clancy", "simon clancy" ]
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what brand is on the plane?
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[ "virgin is the brand on the plane.", "virgin mobile", "virgin", "virgin", "virgin", "virgin", "virgin", "virgin", "virgin", "virgin" ]
[ "Vehicle", "Tower", "Airplane", "Aircraft" ]
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what year is shown in the photo?
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768
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[ "Person", "Woman", "Man", "Tree", "Clothing", "Airplane", "Human face", "Aircraft" ]
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what type of meeting is it?
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what is the name of this plane?
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[ "g-atco", "g-atco", "g-atco", "g-atco", "g-atco", "g atco", "g-atco", "g-atco", "g-atco", "g-atco" ]
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what is the plane's call sign?
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680
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train
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what letter is on the plane's tail?
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683
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[ "f", "f", "f", "f", "f", "f", "f", "f", "f", "f" ]
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what airline is this?
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[ "finn", "finn", "finn air", "finnair", "finn", "finnair", "finn", "finnair", "finnair", "unanswerable" ]
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what airline is this plane for?
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[ "airfrance", "air france", "airfrance", "air france", "airfrance", "airfrance", "spanish", "air france", "air france", "air france" ]
[ "Vehicle", "Airplane", "Aircraft" ]
train
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what does it say on the plane?
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[ "croatia", "croatia", "roatia", "roatia", "croatia", "croatia", "croatia", "croatia", "roatia", "roatia " ]
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what website is this jet associated with?
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682
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[ "jet2.com", "jet2.com", "jet2.com", "jet2.com", "jet2", "jet2.com", "jet2.com", "jet2.com", "jet2", "jet2.com" ]
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what does this jet advertise as having?
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1,024
682
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[ "friendly low fares", "friendly low fares", "friendly low fares", "friendly low fares", "friendly low fares", "friendly low fares", "friendly low fares", "friendly low fairs", "friendly low fares", "friendly low fares" ]
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train
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what letters are embellished on the parachute?
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1,024
903
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[ "raf", "raf", "raf", "raf", "r a f", "raf", "raf", "raf", "raf", "raf" ]
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train
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what are the letters being displayed?
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1,024
903
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[ "raf", "raf", "raf", "raf", "raf", "raf", "raf", "raf", "raf", "raf" ]
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train
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what is the last number of the plane?
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1,024
683
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[ "3", "3", "3", "traktor", "3", "3", "3", "3", "3", "3" ]
[ "Vehicle", "Ambulance", "Airplane", "Aircraft" ]
train
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what letters are visible on the tail of the helicopter?
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1,024
584
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[ "adx", "danger", "ad", "adx", "adx", "adx", "adx", "adx", "adx", "adx" ]
[ "Person", "Vehicle", "Helicopter", "Footwear", "Airplane", "Aircraft" ]
train
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19
what is the plane name?
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532
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https://c1.staticflickr.鈥85e108e726_z.jpg
[ "728tfw", "f-16", "wp", "wp", "wp", "wp", "wp", "wp", "wp", "unanswerable" ]
[ "Vehicle", "Boat", "Clock", "Airplane", "Aircraft" ]
train
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what is the plane number?
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532
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[ "728", "728tfw", "81", "81728", "728tfw", "unanswerable", "af91728tfw", "728tfw", "8", "wp" ]
[ "Vehicle", "Boat", "Clock", "Airplane", "Aircraft" ]
train
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what does the train say?
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769
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[ "ricklinghausen", "rocklinghausen ", "recklinghausen", "rockinghausen", "rocklinghausen", "recklinghausen ", "rocklinghausen", "rocklinghausen", "recklkinghausen", "recklinghausen " ]
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what organization do these men work for?
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682
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[ "politi", "politi", "politi", "politi", "politi", "politi", "politi", "politi", "politi", "police" ]
[ "Bus", "Ambulance", "Person", "Land vehicle", "Stretcher", "Vehicle", "Auto part", "Van", "Tire", "Car", "Aircraft" ]
train
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what two numbers can be seen on the white post on the right?
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682
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[ "23", "25", "25 50", "unanswerable", "25, 50 ", "25 and 50", "25, 50", "25, 50", "23 50", "25-50" ]
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train
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24
what letters are on the craft?
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1,024
683
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[ "f-pdhv", "f-pdhv", "f-pdhv", "f-pdhv", "f-pdhv", "fpdhv", "f-pdhv", "f-pdhv", "f-pdhv", "f-pdhv" ]
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train
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25
what website is labeled on the plane?
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1,024
683
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[ "fpdhv", "www.verheesengineering.com", "unanswerable", "verhesengineering.com", "unanswerable", "verheesenginering.com", "www.verheesengineering.com", "www.verhwwsengineering.com", "2", "www.verheesengineering.com" ]
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train
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what happens if you pull?
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973
1,024
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[ "stop", "emergency stop", "emergency stop", "stop", "stop", "emergency stop", "emergency stop", "it is an emergency stop", "emergency stop", "unanswerable" ]
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train
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what does the sticker on the window say?
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973
1,024
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[ "aa", "aa", "aa", "aa", "aa", "the sticker says emergency stop pull.", "aa", "emergency stop pull", "aa", "aa" ]
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train
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what made this airplane?
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1,024
682
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https://c1.staticflickr.鈥54199_z.jpg?zz=1
[ "biman", "biman", "biman", "biman", "biman", "biman", "biman", "biman", "biman", "biman" ]
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train
05a4fa87bfdaa243
29
what kind of plane is this?
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1,024
682
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https://c1.staticflickr.鈥54199_z.jpg?zz=1
[ "biman", "biman", "bitman", "biman", "biman", "dc 10-30", "biman", "biman", "biman", "biman" ]
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train
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what are the numbers on the plane?
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681
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[ "n337am", "n337am", "n337am", "n337am", "337", "n337am", "337", "337", "n337am", "337" ]
[ "Vehicle", "Airplane", "Aircraft" ]
train
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what word does one of the hot balloons feature?
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1,024
683
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[ "when", "when", "yamaha", "when", "when", "when pigs fly", "bobby j's", "when", "when", "when apes fly" ]
[ "Vehicle", "Balloon", "Parachute", "Aircraft" ]
train
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32
where is the plane going?
[ "where", "is", "the", "plane", "going" ]
1,024
683
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https://c3.staticflickr.鈥f1827aaff_z.jpg
[ "south african", "worth", "unanswerable", "unanswerable", "unanswerable", "unanswerable", "south africa", "south africa", "south africa", "answering does not require reading text in the image" ]
[ "Tree", "Vehicle", "Airplane", "Aircraft" ]
train
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what type of plane is this?
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1,024
683
https://farm8.staticflic鈥ea4bc6745_o.jpg
https://c3.staticflickr.鈥f1827aaff_z.jpg
[ "south african", "hidehi matsui", "south african", "south african", "south african", "south african", "south african ", "south africa", "south african", "707" ]
[ "Tree", "Vehicle", "Airplane", "Aircraft" ]
train
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what does the white sign say?
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408
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[ "a380", "airbus a380", "unanswerable", "unanswerable", "unanswerable", "airbus a380", "airbus", "airbus", "unanswerable", "airbus" ]
[ "Tree", "Vehicle", "Airplane", "Aircraft" ]
train
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what model of plane is this?
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1,024
408
https://c1.staticflickr.鈥e9ed7d0c3_o.jpg
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[ "airbus", "a380", "airbus a380", "airbus a380", "airbus a380", "macbook air ", "airbus", "airbus a380", "airbus a380", "airbus" ]
[ "Tree", "Vehicle", "Airplane", "Aircraft" ]
train
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36
what number is on the plane?
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1,024
683
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[ "41", "41", "41", "41", "41", "41", "41", "41", "41", "41" ]
[ "Vehicle", "Airplane", "Aircraft" ]
train
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37
is there any other text on the plane?
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1,024
683
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[ "yes", "yes", "41", "yes, luk", "41", "yes", "yes", "yes", "41", "yes" ]
[ "Vehicle", "Airplane", "Aircraft" ]
train
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38
what is the number and letter of the plane?
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1,024
683
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[ "n328kf", "n328kf", "n328kf", "n328kf", "n328kf", "n328kf", "n328kf", "n328kf", "n328kf", "n328kff" ]
[ "Table", "Vehicle", "Airplane", "Aircraft" ]
train
08d8e9951c1ea69e
39
which company owns that aircraft?
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1,024
683
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[ "scaled composites", "spaceshipone", "unanswerable", "scaled composites", "spaceshipone", "spaceshipone", "scaled composites ", "scaled", "space ship one", "scaled" ]
[ "Table", "Vehicle", "Airplane", "Aircraft" ]
train
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40
to which organization does this helicopter belong?
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1,024
683
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[ "department of public safety", "public safety", "department of public safety ", "public safety", "public safety", "public safety", "department of public safety", "department of public safety", "utah department of public safety", "department of public safety" ]
[ "Vehicle", "Helicopter", "Aircraft" ]
train
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what kind of airline is this?
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1,024
683
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[ "lufthansa ", "lufthansa cargo", "lufthansa cargo", "lufthansa cargo", "luthansa cargo", "lufthansa cargo", "lufthansa", "lufthansa cargo", "lufthansa cargo", "luthsana cargo" ]
[ "Doll", "Vehicle", "Airplane", "Aircraft" ]
train
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42
what brand is this phone?
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1,024
768
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[ "fedex", "unanswerable", "fedex", "unanswerable", "unanswerable", "fedex", "unanswerable", "unanswerable", "fedex", "unanswerable" ]
[ "Vehicle", "Airplane", "Aircraft" ]
train
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43
is the word express under the word fedex at the front of the plane?
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1,024
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0b2f523a4e734bec
44
what is the plane number?
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683
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[ "ec-634", "ec-634", "ec-634", "ec-634", "ec-354", "ec-634", "ec-634", "ec-634", "the taste of freedom", "634" ]
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0b2f523a4e734bec
45
what is the name of the plane?
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683
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[ "inta", "ec-634", "inta", "not all those who wander are lost", "inta", "inta", "ec-634", "inta", "inta", "inta ec-634" ]
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0e40cb14d4c4bbbc
46
what is the id number written on the rear of the plane?
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1,024
683
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[ "d-chio", "d-chi0", "0", "0chio", "d-chio", "d-chio", "d-chio", "d-chio", "d-chio", "d-chio" ]
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train
0e45202f3462f604
47
what company is this for?
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768
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[ "java", "java", "java", "java", "java", "java", "java", "java", "jave", "java" ]
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train
0e45202f3462f604
48
what does the sign want to add to java?
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1,024
768
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[ "you", "you", "you", "you", "you", "you", "you", "you", "you", "you" ]
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0fd38fd0f9bf1bee
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the train number is?
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1,024
764
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train
0fef083ac0b5dff6
50
what letter is written on the side of the red propeller plane?
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1,024
768
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0fef083ac0b5dff6
51
what 3 digit number is on the plane in the back?
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1,024
768
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10513fda50da5e6d
52
what is the id number on the tail of the plane?
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768
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what is the word on the side of this plane?
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1,024
682
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[ "team ameristar.com", "teamaerostar.com", "teamaerostar.com", "teamaerostar.com", "teamaerostar.com", "34", "teamaerostar", "teamaerostar.com", "teamaerostar", "teamaerostar.com" ]
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train
1086a31c9367a124
54
what number is this plane?
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1,024
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[ "34", "34", "34", "34", "34", "34", "34", "34", "34", "34" ]
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111d7be56517ed46
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what is written on the side of this airplane?
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683
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111d7be56517ed46
56
what number is on the plane?
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1,024
683
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1139456aa3f70a34
57
what number is on the silver plane?
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1,024
680
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1252873867aa8a86
58
what identification number belongs to the big bomber plane in the center?
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1,024
681
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train
1252873867aa8a86
59
what letter is in the black circle?
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1,024
681
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train
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60
what airline is this plane for?
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1,024
683
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[ "onurair", "onurair", "onurair", "onurair", "onurair", "onurair", "nurair", "onurair", "onurair", "onurair" ]
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12e4f6ee148a112e
61
what is the name of the airline?
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1,024
683
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1334b6b8fcfd8afb
62
what number is on the grey and yellow plane?
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1,024
737
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[ "481", "481", "48i", "481", "481", "481", "481", "481", "481", "481" ]
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13dec395531b458f
63
two letters on the tail?
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1,024
683
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13dec395531b458f
64
what is the number found on the head of the plane?
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1,024
683
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[ "106", "106", "106", "106", "106", "106", "106", "106", "106", "106" ]
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142076607dbdfa59
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is that plane part of the star alliance?
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14fa38ae8fea318d
66
who owns the blimp?
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681
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what number is on the plane?
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what airline does this plane belong to?
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what is this?
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15a48b2dd565998a
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what is the identification number?
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15da5c794efe07b6
71
what is this planes website?
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1,024
582
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15da5c794efe07b6
72
what letter is the symbol on the tale of the plane?
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1,024
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163c6f54edee23ae
73
what does it say on the jet?
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1,024
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163c6f54edee23ae
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what branch of the military is the plane from?
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1665ff941d73a03f
75
what is the plane number?
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1,024
768
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1665ff941d73a03f
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what branch of the military is on the plane?
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1,024
768
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171d86fc6d86d5f0
77
what is the tail number of the jet?
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1,024
627
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[ "13-143", "13-143", "13-143", "13-143", "13-143", "13-143", "13-143", "13 143", "13-143", "13-143" ]
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train
188d67c561540d6b
78
what is the name of the airline?
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1,024
683
https://c4.staticflickr.鈥8c31fbba0_o.jpg
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what number is on the back of this plane?
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1,024
393
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1a2e9a1c8d9432b6
80
what is the name on the plane?
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1,024
529
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[ "iberia ", "iberia", "iberia", "iberia", "iberia", "iberia", "iberia", "iberia", "iberia", "iberia" ]
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train
1a2e9a1c8d9432b6
81
what is the model number of the plane printed on the rear?
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1,024
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train
1b1c4a2fa6175cf0
82
what brand is the white balloon?
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1,024
683
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https://c3.staticflickr.鈥02a1022976_z.jpg
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train
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does this plane state that it's easy?
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train
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84
what is the id number on the bottom of the wing?
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620
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train
1d1b9a571d21441e
85
what is the helicopter's number?
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646
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train
1d1b9a571d21441e
86
which website is listed on the picture?
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1,024
646
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train
1e4fcfbd0bb6e1e1
87
what is the slogan on the side say that life is for?
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1,024
645
https://farm2.staticflic鈥482c4be28c_o.jpg
https://c6.staticflickr.鈥21f204141_z.jpg
[ "sharing ", "life is for sharing", "sharing", "life is for sharing", "lite is for sharing", "sharing", "life is for sharing", "sharing", "airplane", "audite" ]
[ "Vehicle", "Airplane", "Aircraft" ]
train
1e4fcfbd0bb6e1e1
88
what brand is being advertised on the plane?
[ "what", "brand", "is", "being", "advertised", "on", "the", "plane" ]
1,024
645
https://farm2.staticflic鈥482c4be28c_o.jpg
https://c6.staticflickr.鈥21f204141_z.jpg
[ "t mobile", "t mobile", "t mobile", "t-mobile", "t mobile", "t-mobile", "t-mobile", "t-mobile", "t", "t-mobile" ]
[ "Vehicle", "Airplane", "Aircraft" ]
train
20db2e4f0602e5aa
89
what is the number on the tail wing?
[ "what", "is", "the", "number", "on", "the", "tail", "wing" ]
1,024
680
https://c3.staticflickr.鈥9626a83e86_o.jpg
https://c8.staticflickr.鈥5675a3e5f_z.jpg
[ "031", "031", "031", "031", "031", "031", "031", "031", "031", "031" ]
[ "Vehicle", "Airplane", "Aircraft" ]
train
210c80055b9f8e1a
90
what number is on the nose of this aircraft?
[ "what", "number", "is", "on", "the", "nose", "of", "this", "aircraft" ]
1,024
768
https://c3.staticflickr.鈥191bfc86c8_o.jpg
https://c8.staticflickr.鈥9de46_z.jpg?zz=1
[ "202", "202", "202", "202", "202", "202", "202", "202", "202", "202" ]
[ "Vehicle", "Clothing", "Airplane", "Aircraft" ]
train
226d623d0c70664f
91
what is the identification number on the balloon?
[ "what", "is", "the", "identification", "number", "on", "the", "balloon" ]
680
1,024
https://c8.staticflickr.鈥35e54452b3_o.jpg
https://c1.staticflickr.鈥24e91b578f_z.jpg
[ "00-b2w", "00-bzw", "00-bzw", "00-bzw", "00-bzw", "00-9zm", "00-bzw", "00-bzw", "00-bzw", "00-bzw" ]
[ "Toy", "Balloon", "Vehicle", "Clothing", "Aircraft" ]
train
226d623d0c70664f
92
what is the last letter on the balloon?
[ "what", "is", "the", "last", "letter", "on", "the", "balloon" ]
680
1,024
https://c8.staticflickr.鈥35e54452b3_o.jpg
https://c1.staticflickr.鈥24e91b578f_z.jpg
[ "w", "e", "e", "e", "e", "n", "e", "a", "e", "w" ]
[ "Toy", "Balloon", "Vehicle", "Clothing", "Aircraft" ]
train
229c0c1a9abfcd9e
93
what is the airline this plane belongs to?
[ "what", "is", "the", "airline", "this", "plane", "belongs", "to" ]
1,024
683
https://c7.staticflickr.鈥1841cefc3_o.jpg
https://c4.staticflickr.鈥322c7e575_z.jpg
[ "eva air", "eva air", "eva air", "eva air", "eva air", "eva air", "eva air", "eva air", "eva air", "eva air" ]
[ "Vehicle", "Airplane", "Aircraft" ]
train
229c0c1a9abfcd9e
94
what is the 6 digit numbers on the side of the plane?
[ "what", "is", "the", "6", "digit", "numbers", "on", "the", "side", "of", "the", "plane" ]
1,024
683
https://c7.staticflickr.鈥1841cefc3_o.jpg
https://c4.staticflickr.鈥322c7e575_z.jpg
[ "777-300", "777-300", "777300", "777-900", "777-300", "777300", "777 300", "777-300", "777-300", "777-300" ]
[ "Vehicle", "Airplane", "Aircraft" ]
train
233a9ff6b3175939
95
what year was this photograph taken?
[ "what", "year", "was", "this", "photograph", "taken" ]
1,024
790
https://c2.staticflickr.鈥571bf5d618_o.jpg
https://c4.staticflickr.鈥c6a9_z.jpg?zz=1
[ "1949", "1949", "1949", "1949", "1949", "1949", "1949", "1949", "1949", "1949" ]
[ "Tree", "Vehicle", "Airplane", "Aircraft" ]
train
233a9ff6b3175939
96
what is the model of this aircraft?
[ "what", "is", "the", "model", "of", "this", "aircraft" ]
1,024
790
https://c2.staticflickr.鈥571bf5d618_o.jpg
https://c4.staticflickr.鈥c6a9_z.jpg?zz=1
[ "17 gc mcrexf ", "452967", "c-87", "b52", "c-87", "17 gc mcrexf 19jan49 c-87", "452907", "452907", "unanswerable", "452967" ]
[ "Tree", "Vehicle", "Airplane", "Aircraft" ]
train
24456069fd6847a8
97
what is the model airplane model number?
[ "what", "is", "the", "model", "airplane", "model", "number" ]
768
768
https://c1.staticflickr.鈥9a8db5e1b_o.jpg
https://c2.staticflickr.鈥62463523ab_z.jpg
[ "v268", "107", "s107", "s107 or s1076", "s107", "v268", "s107", "s107", "s107", "s107" ]
[ "Person", "Musical instrument", "Musical keyboard", "Vehicle", "Helicopter", "Clothing", "Jeans", "Aircraft" ]
train
2489dc9e42de1b36
98
what number is in red on the plane?
[ "what", "number", "is", "in", "red", "on", "the", "plane" ]
1,024
683
https://farm7.staticflic鈥19b1ec2f0b_o.jpg
https://c3.staticflickr.鈥57c0a3a6c7_z.jpg
[ "4858", "1", "4858", "48592", "4858", "4858", "unanswerable", "48592", "not a question", "4859" ]
[ "Ski", "Vehicle", "Airplane", "Aircraft" ]
train
24ad0e52d2f80d92
99
what's the number written on the plane?
[ "what", "'", "s", "the", "number", "written", "on", "the", "plane" ]
1,024
768
https://farm3.staticflic鈥00d9b5a8b6_o.jpg
https://c2.staticflickr.鈥5a8b6_z.jpg?zz=1
[ "63", "63", "63", "63", "63", "63", "cool stuff", "63", "63", "63" ]
[ "Vehicle", "Airplane", "Aircraft" ]
train

Dataset Card for TextVQA

Dataset Summary

TextVQA requires models to read and reason about text in images to answer questions about them. Specifically, models need to incorporate a new modality of text present in the images and reason over it to answer TextVQA questions. TextVQA dataset contains 45,336 questions over 28,408 images from the OpenImages dataset. The dataset uses VQA accuracy metric for evaluation.

Supported Tasks and Leaderboards

  • visual-question-answering: The dataset can be used for Visual Question Answering tasks where given an image, you have to answer a question based on the image. For the TextVQA dataset specifically, the questions require reading and reasoning about the scene text in the given image.

Languages

The questions in the dataset are in English.

Dataset Structure

Data Instances

A typical sample mainly contains the question in question field, an image object in image field, OpenImage image id in image_id and lot of other useful metadata. 10 answers per questions are contained in the answers attribute. For test set, 10 empty strings are contained in the answers field as the answers are not available for it.

An example look like below:

  {'question': 'who is this copyrighted by?',
   'image_id': '00685bc495504d61',
   'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=384x512 at 0x276021C5EB8>,
   'image_classes': ['Vehicle', 'Tower', 'Airplane', 'Aircraft'],
   'flickr_original_url': 'https://farm2.staticflickr.com/5067/5620759429_4ea686e643_o.jpg',
   'flickr_300k_url': 'https://c5.staticflickr.com/6/5067/5620759429_f43a649fb5_z.jpg',
   'image_width': 786,
   'image_height': 1024,
   'answers': ['simon clancy',
    'simon ciancy',
    'simon clancy',
    'simon clancy',
    'the brand is bayard',
    'simon clancy',
    'simon clancy',
    'simon clancy',
    'simon clancy',
    'simon clancy'],
   'question_tokens': ['who', 'is', 'this', 'copyrighted', 'by'],
   'question_id': 3,
   'set_name': 'train'
  },

Data Fields

  • question: string, the question that is being asked about the image
  • image_id: string, id of the image which is same as the OpenImages id
  • image: A PIL.Image.Image object containing the image about which the question is being asked. Note that when accessing the image column: dataset[0]["image"] the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the "image" column, i.e. dataset[0]["image"] should always be preferred over dataset["image"][0].
  • image_classes: List[str], The OpenImages classes to which the image belongs to.
  • flickr_original_url: string, URL to original image on Flickr
  • flickr_300k_url: string, URL to resized and low-resolution image on Flickr.
  • image_width: int, Width of the original image.
  • image_height: int, Height of the original image.
  • question_tokens: List[str], A pre-tokenized list of question.
  • answers: List[str], List of 10 human-annotated answers for the question. These 10 answers are collected from 10 different users. The list will contain empty strings for test set for which we don't have the answers.
  • question_id: int, Unique id of the question.
  • set_name: string, the set to which this question belongs.

Data Splits

There are three splits. train, validation and test. The train and validation sets share images with OpenImages train set and have their answers available. For test set answers, we return a list of ten empty strings. To get inference results and numbers on test set, you need to go to the EvalAI leaderboard and upload your predictions there. Please see instructions at https://textvqa.org/challenge/.

Dataset Creation

Curation Rationale

From the paper:

Studies have shown that a dominant class of questions asked by visually impaired users on images of their surroundings involves reading text in the image. But today鈥檚 VQA models can not read! Our paper takes a first step towards addressing this problem. First, we introduce a new 鈥淭extVQA鈥 dataset to facilitate progress on this important problem. Existing datasets either have a small proportion of questions about text (e.g., the VQA dataset) or are too small (e.g., the VizWiz dataset). TextVQA contains 45,336 questions on 28,408 images that require reasoning about text to answer.

Source Data

Initial Data Collection and Normalization

The initial images were sourced from OpenImages v4 dataset. These were first filtered based on automatic heuristics using an OCR system where we only took images which had at least some text detected in them. See annotation process section to understand the next stages.

Who are the source language producers?

English Crowdsource Annotators

Annotations

Annotation process

After the automatic process of filter the images that contain text, the images were manually verified using human annotators making sure that they had text. In next stage, the annotators were asked to write questions involving scene text for the image. For some images, in this stage, two questions were collected whenever possible. Finally, in the last stage, ten different human annotators answer the questions asked in last stage.

Who are the annotators?

Annotators are from one of the major data collection platforms such as AMT. Exact details are not mentioned in the paper.

Personal and Sensitive Information

The dataset does have similar PII issues as OpenImages and can at some times contain human faces, license plates, and documents. Using provided image_classes data field is one option to try to filter out some of this information.

Considerations for Using the Data

Social Impact of Dataset

The paper helped realize the importance of scene text recognition and reasoning in general purpose machine learning applications and has led to many follow-up works including TextCaps and TextOCR. Similar datasets were introduced over the time which specifically focus on sight-disabled users such as VizWiz or focusing specifically on the same problem as TextVQA like STVQA, DocVQA and OCRVQA. Currently, most methods train on combined dataset from TextVQA and STVQA to achieve state-of-the-art performance on both datasets.

Discussion of Biases

Question-only bias where a model is able to answer the question without even looking at the image is discussed in the paper which was a major issue with original VQA dataset. The outlier bias in answers is prevented by collecting 10 different answers which are also taken in consideration by the evaluation metric.

Other Known Limitations

  • The dataset is english only but does involve images with non-English latin characters so can involve some multi-lingual understanding.
  • The performance on the dataset is also dependent on the quality of OCR used as the OCR errors can directly lead to wrong answers.
  • The metric used for calculating accuracy is same as VQA accuracy. This involves one-to-one matching with the given answers and thus doesn't allow analyzing one-off errors through OCR.

Additional Information

Dataset Curators

  • Amanpreet Singh
  • Vivek Natarjan
  • Meet Shah
  • Yu Jiang
  • Xinlei Chen
  • Dhruv Batra
  • Devi Parikh
  • Marcus Rohrbach

Licensing Information

CC by 4.0

Citation Information

@inproceedings{singh2019towards,
    title={Towards VQA Models That Can Read},
    author={Singh, Amanpreet and Natarjan, Vivek and Shah, Meet and Jiang, Yu and Chen, Xinlei and Batra, Dhruv and Parikh, Devi and Rohrbach, Marcus},
    booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
    pages={8317-8326},
    year={2019}
}

Contributions

Thanks to @apsdehal for adding this dataset.

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