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Among the players whose total NHL games played in their first 7 years of NHL career is no less than 500, what is the name of the player who committed the most rule violations? | total NHL games played in their first 7 years of NHL career is no less than 500 refers to sum_7yr_GP > 500; name of the player refers to PlayerName; committed the most rule violations refers to MAX(PIM); | height of over 6'2" inches refers to height_in_inch > '6''2"'; born in Sweden refers to nation = 'Sweden' ; | players refers to PlayerName; drafted by the Toronto Maple Leafs refers to overallby = 'Toronto Maple Leafs'; percentage = MULTIPLY(DIVIDE(SUM(nation = 'Eastern Europe'), COUNT(ELITEID) WHERE overallby = 'Toronto Maple Leafs'), 100); from Eastern Europe refers to nation in ('Belarus', 'Bulgaria', 'Czech Republic', 'Hungary', 'Moldova', 'Poland', 'Romania', 'Slovakia', 'Ukraine'); countries in a continent can be identified by referring to https://worldpopulationreview.com/country-rankings/list-of-countries-by-continent; | names of the players refers to PlayerName; team Avangard Omsk refers to TEAM = 'Avangard Omsk'; 2000-2001 season refers to SEASON = '2000-2001'; | drafted by Anaheim Ducks refers to overallby = 'Anaheim Ducks'; in 2008 refers to draftyear = 2008; played for U.S. National U18 Team refers to TEAM = 'U.S. National U18 Team'; | heigh in inches refers to height_in_inch; | committed the highest rule violations or penalty minutes refers to MAX(PIM); 2000-2001 season refers to SEASON = '2000-2001' | how much taller = SUBTRACT(SUM(height_in_cm WHERE PlayerName = 'David Bornhammar'), SUM(height_in_cm WHERE PlayerName = 'Pauli Levokari')); height in centimeters refers to height_in_cm; | weigh more than 90 kg refers to weight_in_kg > 90; | weight in kilograms refers to weight_in_kg; highest number of goal differential of all time refers to MAX(PLUSMINUS); | youngest player refers to MAX(birthdate); 1997-1998 season refers to SEASON = '1997-1998'; OHL league refers to LEAGUE = 'OHL'; |
Identify the players who weigh 120 kg. | players refers to PlayerName; weigh 120 kg refers to weight_in_kg = 120; | born in 1980 refers to birthyear = 1980; weigh 185 in pounds refers to weight_in_lbs = 185; | OHL league refers to LEAGUE = 'OHL'; who refers to PlayerName; regular season refers to GAMETYPE = 'Regular Season'; most number of assist refers to MAX(A); 2007-2008 season refers to SEASON = '2007-2008'; | height of over 6'2" inches refers to height_in_inch > '6''2"'; born in Sweden refers to nation = 'Sweden' ; | names of the players refers to PlayerName; Avangard Omsk refers to TEAM = 'Avangard Omsk'; playoffs refers to GAMETYPE = 'Playoffs'; 2000-2001 season refers to SEASON = '2000-2001'; | weight in kilograms refers to weight_in_kg; highest number of goal differential of all time refers to MAX(PLUSMINUS); | most valuable player refers to MAX(P); 2000-2001 season refers to SEASON = '2000-2001'; International league refers to LEAGUE = 'International'; | average = AVG(height_in_cm); players refers to PlayerName; position of defense refers to position_info = 'D' ; | goals scored refers to G; Calgary Hitmen refers to TEAM = 'Calgary Hitmen'; percentage = MULTIPLY(DIVIDE(SUM(G WHERE PlayerName = 'Ian Schultz'), SUM(G)), 100); 2007-2008 season refers to SEASON = '2007-2008'; | heigh in inches refers to height_in_inch; | tallest refers to MAX(height_in_cm);
player refers to PlayerName; team USA U20 refers to TEAM = 'USA U20'; |
List out the name of players who have a height of 5'8". | name of players refers to PlayerName; height of 5'8" refers to height_in_inch = '5''8"'; | weigh in kilograms refers to weight_in_kg; | right-shooted refers to shoots = 'R'; weigh over 90 kg refers to weight_in_kg > 90; | how much taller = SUBTRACT(SUM(height_in_cm WHERE PlayerName = 'David Bornhammar'), SUM(height_in_cm WHERE PlayerName = 'Pauli Levokari')); height in centimeters refers to height_in_cm; | oldest player refers to MIN(birthdate); Avangard Omsk refers to TEAM = 'Avangard Omsk'; regular season refers to GAMETYPE = 'Regular Season'; 2000-2001 season refers to SEASON = '2000-2001';
| name of the player refers to PlayerName; most goals refers to MAX(G); team Rimouski Oceanic refers to TEAM = 'Rimouski Oceanic'; playoff refers to GAMETYPE = 'Playoffs'; | penalty minutes refers to PIM; Ak Bars Kazan refers to TEAM = 'Ak Bars Kazan'; percentage = MULTIPLY(DIVIDE(SUM(PIM WHERE PlayerName = 'Yevgeni Muratov'), SUM(PIM)), 100.0); 1999-2000 season refers to SEASON = '1999-2000'; | heigh in inches refers to height_in_inch; | name of the player refers to PlayerName; position of the player refers to position_info; committed the most rule violations refers to MAX(PIM); | weight in kilograms refers to weight_in_kg; longest time on ice in the player's first 7 years of NHL career refers to MAX(sum_7yr_TOI); | average = AVG(height_in_cm); players refers to PlayerName; position of defense refers to position_info = 'D' ; |
Who is the tallest player in team USA U20? | tallest refers to MAX(height_in_cm);
player refers to PlayerName; team USA U20 refers to TEAM = 'USA U20'; | penalty minutes refers to PIM; Ak Bars Kazan refers to TEAM = 'Ak Bars Kazan'; percentage = MULTIPLY(DIVIDE(SUM(PIM WHERE PlayerName = 'Yevgeni Muratov'), SUM(PIM)), 100.0); 1999-2000 season refers to SEASON = '1999-2000'; | how much taller = SUBTRACT(SUM(height_in_cm WHERE PlayerName = 'David Bornhammar'), SUM(height_in_cm WHERE PlayerName = 'Pauli Levokari')); height in centimeters refers to height_in_cm; | right-shooted players refers to shoots = 'R'; height of 5'7'' refers to height_in_inch = '5''7"'; | weight in kilograms refers to weight_in_kg; longest time on ice in the player's first 7 years of NHL career refers to MAX(sum_7yr_TOI); | USA refers to nation = 'USA' ; players refers to PlayerName; lightest weight refers to MIN(weight_in_lbs);
| weight in kilograms refers to weight_in_kg; highest number of goal differential of all time refers to MAX(PLUSMINUS); | most valuable player refers to MAX(P); 2000-2001 season refers to SEASON = '2000-2001'; International league refers to LEAGUE = 'International'; | born in 1982 refers to birthyear = 1982; height above 182cm refers to height_in_cm > 182 ; | names of the players refers to PlayerName; team Avangard Omsk refers to TEAM = 'Avangard Omsk'; 2000-2001 season refers to SEASON = '2000-2001'; | type of game refers to GAMETYPE; |
What is the average height in centimeters of all the players in the position of defense? | average = AVG(height_in_cm); players refers to PlayerName; position of defense refers to position_info = 'D' ; | name of the player refers to PlayerName; playoffs refers to GAMETYPE = 'Playoffs'; highest points refers to MAX(P); 2006-2007 season refers to SEASON = '2006-2007'; SuperElit league refers to LEAGUE = 'SuperElit'; | average weight in pounds = AVG(weight_in_lbs); weight in pounds refers to weight_in_lbs; players refers to PlayerName; drafted by Arizona Coyotes refers to overallby = 'Arizona Coyotes'; | total NHL games played in their first 7 years of NHL career is no less than 500 refers to sum_7yr_GP > 500; name of the player refers to PlayerName; committed the most rule violations refers to MAX(PIM); | who refers to PlayerName; drafted by Arizona Coyotes refers to overallby = 'Arizona Coyotes'; committed the highest rule violations refers to MAX(PIM); in 2000 refers to draftyear = 2000; | name of players refers to PlayerName; height of 5'8" refers to height_in_inch = '5''8"'; | right-shooted players refers to shoots = 'R'; height of 5'7'' refers to height_in_inch = '5''7"'; | players refers to PlayerName; drafted by the Toronto Maple Leafs refers to overallby = 'Toronto Maple Leafs'; highest prospects for the draft refers to MAX(CSS_rank); | name of the player refers to PlayerName; most goals refers to MAX(G); team Rimouski Oceanic refers to TEAM = 'Rimouski Oceanic'; playoff refers to GAMETYPE = 'Playoffs'; | playoffs refers to GAMETYPE = 'Playoffs'; | name of the player refers to PlayerName; most NHL points in draft year refers to MAX(P); |
What is the percentage of Russian players who have a height of under 200 inch? | percentage = MULTIPLY(DIVIDE(SUM(nation = 'Russia' WHERE height_in_cm < 200), COUNT(ELITEID)), 100); Russian refers to nation = 'Russia'; players refers to PlayerName; height of under 200 inch refers to height_in_cm < 200; | right-shooted refers to shoots = 'R'; weigh over 90 kg refers to weight_in_kg > 90; | average weight in pounds = AVG(weight_in_lbs); weight in pounds refers to weight_in_lbs; players refers to PlayerName; drafted by Arizona Coyotes refers to overallby = 'Arizona Coyotes'; | born in 1980 refers to birthyear = 1980; weigh 185 in pounds refers to weight_in_lbs = 185; | committed the highest rule violations or penalty minutes refers to MAX(PIM); 2000-2001 season refers to SEASON = '2000-2001' | height of over 6'2" inches refers to height_in_inch > '6''2"'; born in Sweden refers to nation = 'Sweden' ; | tallest refers to MAX(height_in_cm);
player refers to PlayerName; team USA U20 refers to TEAM = 'USA U20'; | heaviest player refers to MAX(weight_in_lb); drafted by Arizona Coyotes refers to overallby = 'Arizona Coyotes'; | height in inches refers to height_in_inch; players refers to PlayerName; team Oshawa Generals refers to TEAM = 'Oshawa Generals'; | players refers to PlayerName; weigh 120 kg refers to weight_in_kg = 120; | percentage = MULTIPLY(DIVIDE(SUM(nation = 'Sweden'), COUNT(ELITEID) WHERE SEASON = '1997-2000'), 100); Swedish refers to nation = 'Sweden'; players refers to PlayerName; playoffs games refers to GAMETYPE = 'Playoffs'; 1997-2000 season refers to 3 consecutive SEASONs : '1997-1998', '1998-1999', '1999-2000'; |
How many players who were born in 1980 weigh 185 in pounds? | born in 1980 refers to birthyear = 1980; weigh 185 in pounds refers to weight_in_lbs = 185; | weigh more than 90 kg refers to weight_in_kg > 90; | heaviest player refers to MAX(weight_in_lb); drafted by Arizona Coyotes refers to overallby = 'Arizona Coyotes'; | FALSE; | total NHL games played in their first 7 years of NHL career is no less than 500 refers to sum_7yr_GP > 500; name of the player refers to PlayerName; committed the most rule violations refers to MAX(PIM); | committed the highest rule violations or penalty minutes refers to MAX(PIM); 2000-2001 season refers to SEASON = '2000-2001' | right-shooted players refers to shoots = 'R'; height of 5'7'' refers to height_in_inch = '5''7"'; | weight in kilograms refers to weight_in_kg; longest time on ice in the player's first 7 years of NHL career refers to MAX(sum_7yr_TOI); | percentage = MULTIPLY(DIVIDE(SUM(nation = 'Sweden'), COUNT(ELITEID) WHERE SEASON = '1997-2000'), 100); Swedish refers to nation = 'Sweden'; players refers to PlayerName; playoffs games refers to GAMETYPE = 'Playoffs'; 1997-2000 season refers to 3 consecutive SEASONs : '1997-1998', '1998-1999', '1999-2000'; | most valuable player refers to MAX(P); 2000-2001 season refers to SEASON = '2000-2001'; International league refers to LEAGUE = 'International'; | heigh in inches refers to height_in_inch; |
How many players, who were drafted by Anaheim Ducks in 2008, have played for U.S. National U18 Team? | drafted by Anaheim Ducks refers to overallby = 'Anaheim Ducks'; in 2008 refers to draftyear = 2008; played for U.S. National U18 Team refers to TEAM = 'U.S. National U18 Team'; | who refers to PlayerName; drafted by Arizona Coyotes refers to overallby = 'Arizona Coyotes'; committed the highest rule violations refers to MAX(PIM); in 2000 refers to draftyear = 2000; | average weight in pounds = AVG(weight_in_lbs); weight in pounds refers to weight_in_lbs; players refers to PlayerName; drafted by Arizona Coyotes refers to overallby = 'Arizona Coyotes'; | born in 1982 refers to birthyear = 1982; height above 182cm refers to height_in_cm > 182 ; | percentage = MULTIPLY(DIVIDE(SUM(nation = 'Sweden'), COUNT(ELITEID) WHERE SEASON = '1997-2000'), 100); Swedish refers to nation = 'Sweden'; players refers to PlayerName; playoffs games refers to GAMETYPE = 'Playoffs'; 1997-2000 season refers to 3 consecutive SEASONs : '1997-1998', '1998-1999', '1999-2000'; | tallest refers to MAX(height_in_cm);
player refers to PlayerName; team USA U20 refers to TEAM = 'USA U20'; | name of the player refers to PlayerName; playoffs refers to GAMETYPE = 'Playoffs'; highest points refers to MAX(P); 2006-2007 season refers to SEASON = '2006-2007'; SuperElit league refers to LEAGUE = 'SuperElit'; | youngest player refers to MAX(birthdate); 1997-1998 season refers to SEASON = '1997-1998'; OHL league refers to LEAGUE = 'OHL'; | name of the player refers to PlayerName; most NHL points in draft year refers to MAX(P); | drafted by the Toronto Maple Leafs refers to overallby = 'Toronto Maple Leafs'; played over 300 games in their first 7 years of the NHL career refers to sum_7yr_GP > 300; | percentage = MULTIPLY(DIVIDE(SUM(nation = 'Russia' WHERE height_in_cm < 200), COUNT(ELITEID)), 100); Russian refers to nation = 'Russia'; players refers to PlayerName; height of under 200 inch refers to height_in_cm < 200; |
How many playoffs did Per Mars participate in? | playoffs refers to GAMETYPE = 'Playoffs'; | played the most game plays refers to MAX(GP); 2000-2001 season refers to SEASON = '2000-2001'; International league refers to LEAGUE = 'International'; | youngest player refers to MAX(birthdate); 1997-1998 season refers to SEASON = '1997-1998'; OHL league refers to LEAGUE = 'OHL'; | difference = SUBTRACT(SUM(G WHERE GAMETYPE = 'Regular Season'), SUM(G WHERE GAMETYPE = 'Playoffs') WHERE SEASON = '1998-1999'); number of goals scored refers to G; regular season refers to GAMETYPE = 'Regular Season'; playoffs refers to GAMETYPE = 'Playoffs'; 1998-1999 season refers to SEASON = '1998-1999'; | FALSE; | players refers to PlayerName; drafted by the Toronto Maple Leafs refers to overallby = 'Toronto Maple Leafs'; percentage = MULTIPLY(DIVIDE(SUM(nation = 'Eastern Europe'), COUNT(ELITEID) WHERE overallby = 'Toronto Maple Leafs'), 100); from Eastern Europe refers to nation in ('Belarus', 'Bulgaria', 'Czech Republic', 'Hungary', 'Moldova', 'Poland', 'Romania', 'Slovakia', 'Ukraine'); countries in a continent can be identified by referring to https://worldpopulationreview.com/country-rankings/list-of-countries-by-continent; | heigh in inches refers to height_in_inch; | name of the player refers to PlayerName; playoffs refers to GAMETYPE = 'Playoffs'; highest points refers to MAX(P); 2006-2007 season refers to SEASON = '2006-2007'; SuperElit league refers to LEAGUE = 'SuperElit'; | name of the player refers to PlayerName; most goals refers to MAX(G); team Rimouski Oceanic refers to TEAM = 'Rimouski Oceanic'; playoff refers to GAMETYPE = 'Playoffs'; | tallest player refers to MAX(height_in_cm); | average = AVG(height_in_cm); players refers to PlayerName; position of defense refers to position_info = 'D' ; |
Mention the type of game that Matthias Trattnig played. | type of game refers to GAMETYPE; | name of the player refers to PlayerName; position of the player refers to position_info; committed the most rule violations refers to MAX(PIM); | OHL league refers to LEAGUE = 'OHL'; who refers to PlayerName; regular season refers to GAMETYPE = 'Regular Season'; most number of assist refers to MAX(A); 2007-2008 season refers to SEASON = '2007-2008'; | name of the player refers to PlayerName; most goals refers to MAX(G); team Rimouski Oceanic refers to TEAM = 'Rimouski Oceanic'; playoff refers to GAMETYPE = 'Playoffs'; | penalty minutes refers to PIM; Ak Bars Kazan refers to TEAM = 'Ak Bars Kazan'; percentage = MULTIPLY(DIVIDE(SUM(PIM WHERE PlayerName = 'Yevgeni Muratov'), SUM(PIM)), 100.0); 1999-2000 season refers to SEASON = '1999-2000'; | USA refers to nation = 'USA' ; players refers to PlayerName; lightest weight refers to MIN(weight_in_lbs);
| committed the highest rule violations or penalty minutes refers to MAX(PIM); 2000-2001 season refers to SEASON = '2000-2001' | playoffs refers to GAMETYPE = 'Playoffs'; | players refers to PlayerName; drafted by the Toronto Maple Leafs refers to overallby = 'Toronto Maple Leafs'; percentage = MULTIPLY(DIVIDE(SUM(nation = 'Eastern Europe'), COUNT(ELITEID) WHERE overallby = 'Toronto Maple Leafs'), 100); from Eastern Europe refers to nation in ('Belarus', 'Bulgaria', 'Czech Republic', 'Hungary', 'Moldova', 'Poland', 'Romania', 'Slovakia', 'Ukraine'); countries in a continent can be identified by referring to https://worldpopulationreview.com/country-rankings/list-of-countries-by-continent; | weight in kilograms refers to weight_in_kg; highest number of goal differential of all time refers to MAX(PLUSMINUS); | drafted by Anaheim Ducks refers to overallby = 'Anaheim Ducks'; in 2008 refers to draftyear = 2008; played for U.S. National U18 Team refers to TEAM = 'U.S. National U18 Team'; |
Among the players who played in OHL league during the regular season in 2007-2008, who is the player that attained the most number of assist? | OHL league refers to LEAGUE = 'OHL'; who refers to PlayerName; regular season refers to GAMETYPE = 'Regular Season'; most number of assist refers to MAX(A); 2007-2008 season refers to SEASON = '2007-2008'; | played the most game plays refers to MAX(GP); 2000-2001 season refers to SEASON = '2000-2001'; International league refers to LEAGUE = 'International'; | drafted by Anaheim Ducks refers to overallby = 'Anaheim Ducks'; in 2008 refers to draftyear = 2008; played for U.S. National U18 Team refers to TEAM = 'U.S. National U18 Team'; | weigh in kilograms refers to weight_in_kg; | height in inches refers to height_in_inch; players refers to PlayerName; team Oshawa Generals refers to TEAM = 'Oshawa Generals'; | born in 1980 refers to birthyear = 1980; weigh 185 in pounds refers to weight_in_lbs = 185; | average weight in pounds = AVG(weight_in_lbs); weight in pounds refers to weight_in_lbs; players refers to PlayerName; drafted by Arizona Coyotes refers to overallby = 'Arizona Coyotes'; | names of the players refers to PlayerName; team Avangard Omsk refers to TEAM = 'Avangard Omsk'; 2000-2001 season refers to SEASON = '2000-2001'; | name of the player refers to PlayerName; playoffs refers to GAMETYPE = 'Playoffs'; highest points refers to MAX(P); 2006-2007 season refers to SEASON = '2006-2007'; SuperElit league refers to LEAGUE = 'SuperElit'; | FALSE; | weight in kilograms refers to weight_in_kg; highest number of goal differential of all time refers to MAX(PLUSMINUS); |
Among all goals scored by Calgary Hitmen in the 2007-2008 season, identify the percentage scored by Ian Schultz. | goals scored refers to G; Calgary Hitmen refers to TEAM = 'Calgary Hitmen'; percentage = MULTIPLY(DIVIDE(SUM(G WHERE PlayerName = 'Ian Schultz'), SUM(G)), 100); 2007-2008 season refers to SEASON = '2007-2008'; | names of the players refers to PlayerName; team Avangard Omsk refers to TEAM = 'Avangard Omsk'; 2000-2001 season refers to SEASON = '2000-2001'; | tallest refers to MAX(height_in_cm);
player refers to PlayerName; team USA U20 refers to TEAM = 'USA U20'; | name of the player refers to PlayerName; playoffs refers to GAMETYPE = 'Playoffs'; highest points refers to MAX(P); 2006-2007 season refers to SEASON = '2006-2007'; SuperElit league refers to LEAGUE = 'SuperElit'; | average = AVG(height_in_cm); players refers to PlayerName; position of defense refers to position_info = 'D' ; | most valuable player refers to MAX(P); 2000-2001 season refers to SEASON = '2000-2001'; International league refers to LEAGUE = 'International'; | FALSE; | how much taller = SUBTRACT(SUM(height_in_cm WHERE PlayerName = 'David Bornhammar'), SUM(height_in_cm WHERE PlayerName = 'Pauli Levokari')); height in centimeters refers to height_in_cm; | youngest player refers to MAX(birthdate); 1997-1998 season refers to SEASON = '1997-1998'; OHL league refers to LEAGUE = 'OHL'; | players refers to PlayerName; weigh 120 kg refers to weight_in_kg = 120; | drafted by the Toronto Maple Leafs refers to overallby = 'Toronto Maple Leafs'; played over 300 games in their first 7 years of the NHL career refers to sum_7yr_GP > 300; |
Name the player who had the most goals for team Rimouski Oceanic in playoff. | name of the player refers to PlayerName; most goals refers to MAX(G); team Rimouski Oceanic refers to TEAM = 'Rimouski Oceanic'; playoff refers to GAMETYPE = 'Playoffs'; | heaviest player refers to MAX(weight_in_lb); drafted by Arizona Coyotes refers to overallby = 'Arizona Coyotes'; | oldest player refers to MIN(birthdate); Avangard Omsk refers to TEAM = 'Avangard Omsk'; regular season refers to GAMETYPE = 'Regular Season'; 2000-2001 season refers to SEASON = '2000-2001';
| name of the player refers to PlayerName; most NHL points in draft year refers to MAX(P); | weight in kilograms refers to weight_in_kg; highest number of goal differential of all time refers to MAX(PLUSMINUS); | name of the player refers to PlayerName; position of the player refers to position_info; committed the most rule violations refers to MAX(PIM); | playoffs refers to GAMETYPE = 'Playoffs'; | weigh in kilograms refers to weight_in_kg; | born in 1980 refers to birthyear = 1980; weigh 185 in pounds refers to weight_in_lbs = 185; | goals scored refers to G; Calgary Hitmen refers to TEAM = 'Calgary Hitmen'; percentage = MULTIPLY(DIVIDE(SUM(G WHERE PlayerName = 'Ian Schultz'), SUM(G)), 100); 2007-2008 season refers to SEASON = '2007-2008'; | name of players refers to PlayerName; height of 5'8" refers to height_in_inch = '5''8"'; |
Indicate the height of all players from team Oshawa Generals in inches. | height in inches refers to height_in_inch; players refers to PlayerName; team Oshawa Generals refers to TEAM = 'Oshawa Generals'; | weight in kilograms refers to weight_in_kg; highest number of goal differential of all time refers to MAX(PLUSMINUS); | tallest player refers to MAX(height_in_cm); | playoffs refers to GAMETYPE = 'Playoffs'; | players refers to PlayerName; drafted by the Toronto Maple Leafs refers to overallby = 'Toronto Maple Leafs'; percentage = MULTIPLY(DIVIDE(SUM(nation = 'Eastern Europe'), COUNT(ELITEID) WHERE overallby = 'Toronto Maple Leafs'), 100); from Eastern Europe refers to nation in ('Belarus', 'Bulgaria', 'Czech Republic', 'Hungary', 'Moldova', 'Poland', 'Romania', 'Slovakia', 'Ukraine'); countries in a continent can be identified by referring to https://worldpopulationreview.com/country-rankings/list-of-countries-by-continent; | name of the player refers to PlayerName; position of the player refers to position_info; committed the most rule violations refers to MAX(PIM); | right-shooted refers to shoots = 'R'; weigh over 90 kg refers to weight_in_kg > 90; | USA refers to nation = 'USA' ; players refers to PlayerName; lightest weight refers to MIN(weight_in_lbs);
| how much taller = SUBTRACT(SUM(height_in_cm WHERE PlayerName = 'David Bornhammar'), SUM(height_in_cm WHERE PlayerName = 'Pauli Levokari')); height in centimeters refers to height_in_cm; | players refers to PlayerName; weigh 120 kg refers to weight_in_kg = 120; | tallest refers to MAX(height_in_cm);
player refers to PlayerName; team USA U20 refers to TEAM = 'USA U20'; |
What is the height of David Bornhammar in inches? | heigh in inches refers to height_in_inch; | name of the player refers to PlayerName; most goals refers to MAX(G); team Rimouski Oceanic refers to TEAM = 'Rimouski Oceanic'; playoff refers to GAMETYPE = 'Playoffs'; | right-shooted refers to shoots = 'R'; weigh over 90 kg refers to weight_in_kg > 90; | played the most game plays refers to MAX(GP); 2000-2001 season refers to SEASON = '2000-2001'; International league refers to LEAGUE = 'International'; | penalty minutes refers to PIM; Ak Bars Kazan refers to TEAM = 'Ak Bars Kazan'; percentage = MULTIPLY(DIVIDE(SUM(PIM WHERE PlayerName = 'Yevgeni Muratov'), SUM(PIM)), 100.0); 1999-2000 season refers to SEASON = '1999-2000'; | born in 1980 refers to birthyear = 1980; weigh 185 in pounds refers to weight_in_lbs = 185; | name of players refers to PlayerName; height of 5'8" refers to height_in_inch = '5''8"'; | drafted by the Toronto Maple Leafs refers to overallby = 'Toronto Maple Leafs'; played over 300 games in their first 7 years of the NHL career refers to sum_7yr_GP > 300; | difference = SUBTRACT(SUM(G WHERE GAMETYPE = 'Regular Season'), SUM(G WHERE GAMETYPE = 'Playoffs') WHERE SEASON = '1998-1999'); number of goals scored refers to G; regular season refers to GAMETYPE = 'Regular Season'; playoffs refers to GAMETYPE = 'Playoffs'; 1998-1999 season refers to SEASON = '1998-1999'; | name of the player refers to PlayerName; playoffs refers to GAMETYPE = 'Playoffs'; highest points refers to MAX(P); 2006-2007 season refers to SEASON = '2006-2007'; SuperElit league refers to LEAGUE = 'SuperElit'; | total NHL games played in their first 7 years of NHL career is no less than 500 refers to sum_7yr_GP > 500; name of the player refers to PlayerName; committed the most rule violations refers to MAX(PIM); |
What team did Niklas Eckerblom play in the 1997-1998 season? | 1997-1998 season refers to SEASON = '1997-1998'; | players refers to PlayerName; drafted by the Toronto Maple Leafs refers to overallby = 'Toronto Maple Leafs'; percentage = MULTIPLY(DIVIDE(SUM(nation = 'Eastern Europe'), COUNT(ELITEID) WHERE overallby = 'Toronto Maple Leafs'), 100); from Eastern Europe refers to nation in ('Belarus', 'Bulgaria', 'Czech Republic', 'Hungary', 'Moldova', 'Poland', 'Romania', 'Slovakia', 'Ukraine'); countries in a continent can be identified by referring to https://worldpopulationreview.com/country-rankings/list-of-countries-by-continent; | penalty minutes refers to PIM; Ak Bars Kazan refers to TEAM = 'Ak Bars Kazan'; percentage = MULTIPLY(DIVIDE(SUM(PIM WHERE PlayerName = 'Yevgeni Muratov'), SUM(PIM)), 100.0); 1999-2000 season refers to SEASON = '1999-2000'; | playoffs refers to GAMETYPE = 'Playoffs'; | height in inches refers to height_in_inch; players refers to PlayerName; team Oshawa Generals refers to TEAM = 'Oshawa Generals'; | height of over 6'2" inches refers to height_in_inch > '6''2"'; born in Sweden refers to nation = 'Sweden' ; | players refers to PlayerName; weigh 120 kg refers to weight_in_kg = 120; | played the most game plays refers to MAX(GP); 2000-2001 season refers to SEASON = '2000-2001'; International league refers to LEAGUE = 'International'; | tallest player refers to MAX(height_in_cm); | committed the highest rule violations or penalty minutes refers to MAX(PIM); 2000-2001 season refers to SEASON = '2000-2001' | drafted by the Toronto Maple Leafs refers to overallby = 'Toronto Maple Leafs'; played over 300 games in their first 7 years of the NHL career refers to sum_7yr_GP > 300; |
Who is the most valuable player who played in the 2000-2001 season of the International league? | most valuable player refers to MAX(P); 2000-2001 season refers to SEASON = '2000-2001'; International league refers to LEAGUE = 'International'; | committed the highest rule violations or penalty minutes refers to MAX(PIM); 2000-2001 season refers to SEASON = '2000-2001' | penalty minutes refers to PIM; Ak Bars Kazan refers to TEAM = 'Ak Bars Kazan'; percentage = MULTIPLY(DIVIDE(SUM(PIM WHERE PlayerName = 'Yevgeni Muratov'), SUM(PIM)), 100.0); 1999-2000 season refers to SEASON = '1999-2000'; | type of game refers to GAMETYPE; | name of the player refers to PlayerName; most NHL points in draft year refers to MAX(P); | heaviest player refers to MAX(weight_in_lb); drafted by Arizona Coyotes refers to overallby = 'Arizona Coyotes'; | FALSE; | born in 1980 refers to birthyear = 1980; weigh 185 in pounds refers to weight_in_lbs = 185; | height in inches refers to height_in_inch; players refers to PlayerName; team Oshawa Generals refers to TEAM = 'Oshawa Generals'; | youngest player refers to MAX(birthdate); 1997-1998 season refers to SEASON = '1997-1998'; OHL league refers to LEAGUE = 'OHL'; | OHL league refers to LEAGUE = 'OHL'; who refers to PlayerName; regular season refers to GAMETYPE = 'Regular Season'; most number of assist refers to MAX(A); 2007-2008 season refers to SEASON = '2007-2008'; |
Name the player and his team who made the playoffs in the 2006-2007 season of SuperElit league with the highest points. | name of the player refers to PlayerName; playoffs refers to GAMETYPE = 'Playoffs'; highest points refers to MAX(P); 2006-2007 season refers to SEASON = '2006-2007'; SuperElit league refers to LEAGUE = 'SuperElit'; | committed the highest rule violations or penalty minutes refers to MAX(PIM); 2000-2001 season refers to SEASON = '2000-2001' | youngest player refers to MAX(birthdate); 1997-1998 season refers to SEASON = '1997-1998'; OHL league refers to LEAGUE = 'OHL'; | oldest player refers to MIN(birthdate); Avangard Omsk refers to TEAM = 'Avangard Omsk'; regular season refers to GAMETYPE = 'Regular Season'; 2000-2001 season refers to SEASON = '2000-2001';
| weigh more than 90 kg refers to weight_in_kg > 90; | difference = SUBTRACT(SUM(G WHERE GAMETYPE = 'Regular Season'), SUM(G WHERE GAMETYPE = 'Playoffs') WHERE SEASON = '1998-1999'); number of goals scored refers to G; regular season refers to GAMETYPE = 'Regular Season'; playoffs refers to GAMETYPE = 'Playoffs'; 1998-1999 season refers to SEASON = '1998-1999'; | name of players refers to PlayerName; height of 5'8" refers to height_in_inch = '5''8"'; | who refers to PlayerName; drafted by Arizona Coyotes refers to overallby = 'Arizona Coyotes'; committed the highest rule violations refers to MAX(PIM); in 2000 refers to draftyear = 2000; | names of the players refers to PlayerName; team Avangard Omsk refers to TEAM = 'Avangard Omsk'; 2000-2001 season refers to SEASON = '2000-2001'; | right-shooted players refers to shoots = 'R'; height of 5'7'' refers to height_in_inch = '5''7"'; | penalty minutes refers to PIM; Ak Bars Kazan refers to TEAM = 'Ak Bars Kazan'; percentage = MULTIPLY(DIVIDE(SUM(PIM WHERE PlayerName = 'Yevgeni Muratov'), SUM(PIM)), 100.0); 1999-2000 season refers to SEASON = '1999-2000'; |
How many players weigh more than 90 kg? | weigh more than 90 kg refers to weight_in_kg > 90; | height of over 6'2" inches refers to height_in_inch > '6''2"'; born in Sweden refers to nation = 'Sweden' ; | name of the player refers to PlayerName; playoffs refers to GAMETYPE = 'Playoffs'; highest points refers to MAX(P); 2006-2007 season refers to SEASON = '2006-2007'; SuperElit league refers to LEAGUE = 'SuperElit'; | committed the highest rule violations or penalty minutes refers to MAX(PIM); 2000-2001 season refers to SEASON = '2000-2001' | name of the player refers to PlayerName; position of the player refers to position_info; committed the most rule violations refers to MAX(PIM); | weight in kilograms refers to weight_in_kg; longest time on ice in the player's first 7 years of NHL career refers to MAX(sum_7yr_TOI); | height in inches refers to height_in_inch; players refers to PlayerName; team Oshawa Generals refers to TEAM = 'Oshawa Generals'; | percentage = MULTIPLY(DIVIDE(SUM(nation = 'Sweden'), COUNT(ELITEID) WHERE SEASON = '1997-2000'), 100); Swedish refers to nation = 'Sweden'; players refers to PlayerName; playoffs games refers to GAMETYPE = 'Playoffs'; 1997-2000 season refers to 3 consecutive SEASONs : '1997-1998', '1998-1999', '1999-2000'; | born in 1980 refers to birthyear = 1980; weigh 185 in pounds refers to weight_in_lbs = 185; | name of the player refers to PlayerName; most goals refers to MAX(G); team Rimouski Oceanic refers to TEAM = 'Rimouski Oceanic'; playoff refers to GAMETYPE = 'Playoffs'; | goals scored refers to G; Calgary Hitmen refers to TEAM = 'Calgary Hitmen'; percentage = MULTIPLY(DIVIDE(SUM(G WHERE PlayerName = 'Ian Schultz'), SUM(G)), 100); 2007-2008 season refers to SEASON = '2007-2008'; |
Among the players with a height of over 6'2" inches, how many of them were born in Sweden? | height of over 6'2" inches refers to height_in_inch > '6''2"'; born in Sweden refers to nation = 'Sweden' ; | name of the player refers to PlayerName; most goals refers to MAX(G); team Rimouski Oceanic refers to TEAM = 'Rimouski Oceanic'; playoff refers to GAMETYPE = 'Playoffs'; | born in 1980 refers to birthyear = 1980; weigh 185 in pounds refers to weight_in_lbs = 185; | drafted by the Toronto Maple Leafs refers to overallby = 'Toronto Maple Leafs'; played over 300 games in their first 7 years of the NHL career refers to sum_7yr_GP > 300; | difference = SUBTRACT(SUM(G WHERE GAMETYPE = 'Regular Season'), SUM(G WHERE GAMETYPE = 'Playoffs') WHERE SEASON = '1998-1999'); number of goals scored refers to G; regular season refers to GAMETYPE = 'Regular Season'; playoffs refers to GAMETYPE = 'Playoffs'; 1998-1999 season refers to SEASON = '1998-1999'; | right-shooted refers to shoots = 'R'; weigh over 90 kg refers to weight_in_kg > 90; | tallest player refers to MAX(height_in_cm); | names of the players refers to PlayerName; team Avangard Omsk refers to TEAM = 'Avangard Omsk'; 2000-2001 season refers to SEASON = '2000-2001'; | heigh in inches refers to height_in_inch; | FALSE; | oldest player refers to MIN(birthdate); Avangard Omsk refers to TEAM = 'Avangard Omsk'; regular season refers to GAMETYPE = 'Regular Season'; 2000-2001 season refers to SEASON = '2000-2001';
|
How many right-shooted players have a height of 5'7''? | right-shooted players refers to shoots = 'R'; height of 5'7'' refers to height_in_inch = '5''7"'; | name of the player refers to PlayerName; most goals refers to MAX(G); team Rimouski Oceanic refers to TEAM = 'Rimouski Oceanic'; playoff refers to GAMETYPE = 'Playoffs'; | playoffs refers to GAMETYPE = 'Playoffs'; | average weight in pounds = AVG(weight_in_lbs); weight in pounds refers to weight_in_lbs; players refers to PlayerName; drafted by Arizona Coyotes refers to overallby = 'Arizona Coyotes'; | name of the player refers to PlayerName; playoffs refers to GAMETYPE = 'Playoffs'; highest points refers to MAX(P); 2006-2007 season refers to SEASON = '2006-2007'; SuperElit league refers to LEAGUE = 'SuperElit'; | goals scored refers to G; Calgary Hitmen refers to TEAM = 'Calgary Hitmen'; percentage = MULTIPLY(DIVIDE(SUM(G WHERE PlayerName = 'Ian Schultz'), SUM(G)), 100); 2007-2008 season refers to SEASON = '2007-2008'; | weight in kilograms refers to weight_in_kg; highest number of goal differential of all time refers to MAX(PLUSMINUS); | OHL league refers to LEAGUE = 'OHL'; who refers to PlayerName; regular season refers to GAMETYPE = 'Regular Season'; most number of assist refers to MAX(A); 2007-2008 season refers to SEASON = '2007-2008'; | difference = SUBTRACT(SUM(G WHERE GAMETYPE = 'Regular Season'), SUM(G WHERE GAMETYPE = 'Playoffs') WHERE SEASON = '1998-1999'); number of goals scored refers to G; regular season refers to GAMETYPE = 'Regular Season'; playoffs refers to GAMETYPE = 'Playoffs'; 1998-1999 season refers to SEASON = '1998-1999'; | percentage = MULTIPLY(DIVIDE(SUM(nation = 'Russia' WHERE height_in_cm < 200), COUNT(ELITEID)), 100); Russian refers to nation = 'Russia'; players refers to PlayerName; height of under 200 inch refers to height_in_cm < 200; | most valuable player refers to MAX(P); 2000-2001 season refers to SEASON = '2000-2001'; International league refers to LEAGUE = 'International'; |
How many images have objects with the attributes of polka dot? | attributes of polka dot refer to ATT_CLASS = 'polka dot'; images refer to IMG_ID; | bounding boxes of the object samples refers to (x, y, W, H); predicted relation class of "by" refers to PRED_CLASS = 'by'; image no.1 refers to IMG_ID = 1 | blue' attribute classes on image ID 2355735 refer to ATT_CLASS = 'blue' where IMG_ID = 2355735; | object in image 5 refers to OBJ_SAMPLE_ID where IMG_ID = 5; coordinates of (634, 468) refer to X and Y coordinates of the bounding box in which X = 634 and Y = 468; | images refers to IMG_ID; have at least 5 "black" classes refers to count(ATT_CLASS_ID) where ATT_CLASS = 'black' > = 5 | images have more than 20 object samples refer to IMG_ID where COUNT(OBJ_SAMPLE_ID) > 20; | onion category refers to OBJ_CLASS = 'onion'; | classes for attributes refers to ATT_CLASS; image id 8 refers to IMG_ID = 8 | prediction relationship class id refers to PRED_CLASS_ID; tallest image refers to max(H) | image with a bounding (422, 63, 77, 363) refers to OBJ_CLASS_ID where X = 422 and Y = 63 and W = 77 and H = 363; | DIVIDE(SUM(OBJ_SAMPLE_ID where OBJ_CLASS = 'airplane'), COUNT(OBJ_CLASS)) as percentage; |
What are the width and height of the bounding box of the object with "keyboard" as their object class and (5, 647) as their coordinate? | The bounding box's W and H abbreviations stand for the object's width and height respectively; "keyboard" as object class refers to OBJ_CLASS = 'keyboard'; (5, 647) as coordinate refers to X and Y coordinates of the bounding box where X = 5 and Y = 647; | ID of all images refer to IMG_ID; if two objects (OBJ1_SAMPLE_ID,
OBJ2_SAMPLE_ID) has
multiple PRED_CLASS_ID, it
means these two objects
have multiple relations; | samples of "wall" refers to OBJ_SAMPLE_ID and OBJ_CLASS = 'wall' ; image no.2353079 refers to IMG_ID = 2353079 | predicted relation class refers to PRED_CLASS; self-relations refers to OBJ1_SAMPLE_ID = OBJ2_SAMPLE_ID; image no.5 refers to IMG_ID = 5 | relation refers to PRED_CLASS; object sample no.8 and object sample no.4 refers to OBJ1_SAMPLE_ID = 8 AND OBJ2_SAMPLE_ID = 4; image no.1 refers to IMG_ID = 1 | samples of "bed" object refer to OBJ_SAMPLE_ID where OBJ_CLASS = 'bed'; image No.1098 refers to IMG_ID = 1098; | object class of the image refers to OBJ_CLASS; bounding box of 0, 0, 135, 212 refers to X = 0 AND Y = 0 AND W = 135 AND H = 212 | bounding box of the object sample refers to (x, y, W, H); image no.5 refers to IMG_ID = 5; has a self-relation refers to OBJ1_SAMPLE_ID = OBJ2_SAMPLE_ID | bounding box refers to X, Y, W, H from IMG_OBJ; lowest relates to the height of the bounding box which refers to MIN(H); | object samples refers to OBJ_SAMPLE_ID; image no.1 refers to IMG_ID = 1 | attributes of polka dot refer to ATT_CLASS = 'polka dot'; images refer to IMG_ID; |
State the object class of sample no.10 of image no.2320341. | object class refers to OBJ_CLASS; sample no.10 refers to OBJ_SAMPLE_ID = 10; image no.2320341 refers to IMG_ID = 2320341 | bounding boxes of the object samples refers to (x, y, W, H); predicted relation class of "by" refers to PRED_CLASS = 'by'; image no.1 refers to IMG_ID = 1 | images refers to IMG_ID; have at least 25 attributes refers to count(ATT_CLASS_ID) > = 25 | has' predicate classes refers to PRED_CLASS = 'has'; image ID 107 refers to IMG_ID = 107; | object samples refers to OBJ_SAMPLE_ID; image no.1 refers to IMG_ID = 1 | pairs of object samples refers to OBJ1_SAMPLE_ID and OBJ2_SAMPLE_ID; image no.1 refers to IMG_ID = 1; relation of "parked on" refers to PRED_CLASS = 'parked on' | attribute classes of the image ID "15" refer to ATT_CLASS where IMG_ID = 15; | image with a bounding (422, 63, 77, 363) refers to OBJ_CLASS_ID where X = 422 and Y = 63 and W = 77 and H = 363; | AVG(IMG_ID) where OBJ_CLASS = 'keyboard'; | attribute classes refer to ATT_CLASS; (5,5) coordinate refers to X and Y coordinates of the bounding box where X = 5 and Y = 5; | images have more than 20 object samples refer to IMG_ID where COUNT(OBJ_SAMPLE_ID) > 20; |
Calculate the percentage of "airplane" object class in the table. | DIVIDE(SUM(OBJ_SAMPLE_ID where OBJ_CLASS = 'airplane'), COUNT(OBJ_CLASS)) as percentage; | object has the highest attribute classes refers to OBJ_SAMPLE_ID where MAX(COUNT(OBJ_SAMPLE_ID)); | dress' object classes refer to OBJ_CLASS = 'dress'; image ID 1764 refers to IMG_ID = 1764; X and Y refer to coordinates of the bounding box; | object number of the sample refers to OBJ1_SAMPLE_ID; object sample no.1 from image no.2345524 refers to OBJ2_SAMPLE_ID = 1 and IMG_ID = 2345524 | image no. 20 refers to IMG_ID = 20; attribute ID refers to ATT_CLASS_ID; highest number of objects refers to max(count(ATT_CLASS_ID)) | relationship refers to PRED_CLASS; "feathers" and "onion" in image no.2345528 refers to IMG_ID = 2345528 and OBJ_CLASS = 'feathers' and OBJ_CLASS = 'onion' | object in image 5 refers to OBJ_SAMPLE_ID where IMG_ID = 5; coordinates of (634, 468) refer to X and Y coordinates of the bounding box in which X = 634 and Y = 468; | caption for the prediction class id 12 refers to PRED_CLASS where PRED_CLASS_ID = 12; | have at least one object sample in the class of "man" refers to count(IMG_ID where OBJ_CLASS = 'man') > = 1 | bounding box refers to X, Y, W, H from IMG_OBJ; lowest relates to the height of the bounding box which refers to MIN(H); | colour refers to ATT_CLASS; van refers to OBJ_CLASS = 'van'; image no. 1 refers to IMG_ID = 1 |
How many white objects are there in image no.2347915? | white objects refers to ATT_CLASS = 'white'; image no.2347915 refers to IMG_ID = 2347915 | image with a bounding (422, 63, 77, 363) refers to OBJ_CLASS_ID where X = 422 and Y = 63 and W = 77 and H = 363; | AVG(IMG_ID) where OBJ_CLASS = 'keyboard'; | blue' attribute classes on image ID 2355735 refer to ATT_CLASS = 'blue' where IMG_ID = 2355735; | relationship refers to PRED_CLASS; "feathers" and "onion" in image no.2345528 refers to IMG_ID = 2345528 and OBJ_CLASS = 'feathers' and OBJ_CLASS = 'onion' | ID of all images refer to IMG_ID; if two objects (OBJ1_SAMPLE_ID,
OBJ2_SAMPLE_ID) has
multiple PRED_CLASS_ID, it
means these two objects
have multiple relations; | predicted relation class refers to PRED_CLASS; self-relations refers to OBJ1_SAMPLE_ID = OBJ2_SAMPLE_ID; image no.5 refers to IMG_ID = 5 | dimensions of the bounding box refers to (W, H); keyboard refers to OBJ_CLASS = 'keyboard'; image no. 3 refers to IMG_ID = 3 | samples of "wall" refers to OBJ_SAMPLE_ID and OBJ_CLASS = 'wall' ; image no.2353079 refers to IMG_ID = 2353079 | object samples refers to OBJ_SAMPLE_ID; image no.1 refers to IMG_ID = 1 | bounding box of the object refers to (X, Y, W, H); sample no.7 on image no.42 refers to IMG_ID = 42 and OBJ_SAMPLE_ID = 7 |
How many pairs of object samples in image no.1 have the relation of "parked on"? | pairs of object samples refers to OBJ1_SAMPLE_ID and OBJ2_SAMPLE_ID; image no.1 refers to IMG_ID = 1; relation of "parked on" refers to PRED_CLASS = 'parked on' | explanations about distinct object classes refers to OBJ_CLASS; images refers to IMG_ID; x and y coordinate of 0 refers to X = 0 AND Y = 0 | AVG(IMG_ID) where OBJ_CLASS = 'keyboard'; | ID of all images refer to IMG_ID; attribute class of "horse" refers to ATT_CLASS = 'horse'; | self-relations refers to OBJ1_SAMPLE_ID = OBJ2_SAMPLE_ID; image no.5 refers to IMG_ID = 5 | object has the highest attribute classes refers to OBJ_SAMPLE_ID where MAX(COUNT(OBJ_SAMPLE_ID)); | DIVIDE(COUNT(OBJ_SAMPLE_ID), COUNT(IMG_ID)); | How many images have at least one pair of object samples with the relation "parked on" refers to count(IMG_ID) where OBJ1_SAMPLE_ID ! = OBJ2_SAMPLE_ID and PRED_CLASS = 'parked on' | bounding box of the object sample refers to (x, y, W, H); image no.5 refers to IMG_ID = 5; has a self-relation refers to OBJ1_SAMPLE_ID = OBJ2_SAMPLE_ID | object samples refers to OBJ_SAMPLE_ID; image no.1 refers to IMG_ID = 1 | ids of the images refers to IMG_ID; self-relations refers to OBJ1_SAMPLE_ID = OBJ2_SAMPLE_ID |
What is the relationship between object sample no.12 and no.8 of image no.2345511? | relationship refers to PRED_CLASS; object sample no.12 and no.8 of image no.2345511 refers to IMG_ID = 2345511 AND OBJ1_SAMPLE_ID = 12 AND OBJ2_SAMPLE_ID = 8 | object has the highest attribute classes refers to OBJ_SAMPLE_ID where MAX(COUNT(OBJ_SAMPLE_ID)); | X and Y refer to coordinates of the bounding box; image ID 23 refers to IMG_ID = 23; 'cast' attribute class refers to ATT_CLASS = 'cast'; | ids of the images refers to IMG_ID; self-relations refers to OBJ1_SAMPLE_ID = OBJ2_SAMPLE_ID | images refers to IMG_ID; have at least 25 attributes refers to count(ATT_CLASS_ID) > = 25 | has' predicate classes refers to PRED_CLASS = 'has'; image ID 107 refers to IMG_ID = 107; | number of images refers to IMG_ID; object sample of "suit" refers to OBJ_CLASS = 'suit' | samples of "bed" object refer to OBJ_SAMPLE_ID where OBJ_CLASS = 'bed'; image No.1098 refers to IMG_ID = 1098; | onion category refers to OBJ_CLASS = 'onion'; | DIVIDE(SUM(OBJ_CLASS_ID where OBJ_CLASS = 'surface'), COUNT(OBJ_CLASS_ID)) as percentage where IMG_ID = 2654; | DIVIDE(SUM(OBJ_SAMPLE_ID where OBJ_CLASS = 'airplane'), COUNT(OBJ_CLASS)) as percentage; |
Provide the dimensions of the bounding box that contains the keyboard that was spotted in image no. 3. | dimensions of the bounding box refers to (W, H); keyboard refers to OBJ_CLASS = 'keyboard'; image no. 3 refers to IMG_ID = 3 | object number of the sample refers to OBJ1_SAMPLE_ID; object sample no.1 from image no.2345524 refers to OBJ2_SAMPLE_ID = 1 and IMG_ID = 2345524 | images have more than 20 object samples refer to IMG_ID where COUNT(OBJ_SAMPLE_ID) > 20; | How many object elements refers to OBJ_CLASS_ID; image no. 31 refers to IMG_ID = 31 | self-relations refers to OBJ1_SAMPLE_ID = OBJ2_SAMPLE_ID; image no.5 refers to IMG_ID = 5 | X and Y refer to coordinates of the bounding box; image ID 23 refers to IMG_ID = 23; 'cast' attribute class refers to ATT_CLASS = 'cast'; | predicate class of image ID 68 refers to PRED_CLASS where IMG_ID = 68; | "picture" as attribute class refers to ATT_CLASS = 'picture'; "bear" as object class refers to OBJ_CLASS = 'bear'; images refer to IMG_ID; | bounding boxes of the object samples refers to (x, y, W, H); predicted relation class of "by" refers to PRED_CLASS = 'by'; image no.1 refers to IMG_ID = 1 | object samples refers to OBJ_CLASS_ID; image no.1 refers to IMG_ID = 1; in the class of "man" refers to OBJ_CLASS = 'man' | images refer to IMG_ID; less than 15 object samples refer to COUNT(OBJ_SAMPLE_ID) < 15; |
List the ID of all images with objects that have multiple relations. | ID of all images refer to IMG_ID; if two objects (OBJ1_SAMPLE_ID,
OBJ2_SAMPLE_ID) has
multiple PRED_CLASS_ID, it
means these two objects
have multiple relations; | X and Y refer to coordinates of the bounding box; image ID 23 refers to IMG_ID = 23; 'cast' attribute class refers to ATT_CLASS = 'cast'; | caption for the prediction class id 12 refers to PRED_CLASS where PRED_CLASS_ID = 12; | have at least one object sample in the class of "man" refers to count(IMG_ID where OBJ_CLASS = 'man') > = 1 | classes for attributes refers to ATT_CLASS; image id 8 refers to IMG_ID = 8 | predicate class of image ID 68 refers to PRED_CLASS where IMG_ID = 68; | bounding box of the object sample refers to (x, y, W, H); image no.5 refers to IMG_ID = 5; has a self-relation refers to OBJ1_SAMPLE_ID = OBJ2_SAMPLE_ID | image no. 20 refers to IMG_ID = 20; attribute ID refers to ATT_CLASS_ID; highest number of objects refers to max(count(ATT_CLASS_ID)) | Name the object element refers to OBJ_CLASS; scattered refers to ATT_CLASS = 'scattered'; image no. 10 refers to IMG_ID = 10 | predicted relation class refers to PRED_CLASS; self-relations refers to OBJ1_SAMPLE_ID = OBJ2_SAMPLE_ID; image no.5 refers to IMG_ID = 5 | self-relations refers to OBJ1_SAMPLE_ID = OBJ2_SAMPLE_ID; image no.5 refers to IMG_ID = 5 |
Name the object class of the image with a bounding (422, 63, 77, 363). | image with a bounding (422, 63, 77, 363) refers to OBJ_CLASS_ID where X = 422 and Y = 63 and W = 77 and H = 363; | images have more than 20 object samples refer to IMG_ID where COUNT(OBJ_SAMPLE_ID) > 20; | X and Y refer to coordinates of the bounding box where X = 5 and Y = 5; images refer to IMG_ID; | images refer to IMG_ID; "keyboard" as object class refers to OBJ_CLASS = 'keyboard'; | image numbers that contain the "paint" object refer to IMG_ID where OBJ_CLASS = 'paint'; | object elements refers to OBJ_CLASS_ID; average = divide(count(OBJ_CLASS_ID), count(IMG_ID)) | object class refers to OBJ_CLASS; sample no.10 refers to OBJ_SAMPLE_ID = 10; image no.2320341 refers to IMG_ID = 2320341 | DIVIDE(SUM(OBJ_CLASS_ID where OBJ_CLASS = 'surface'), COUNT(OBJ_CLASS_ID)) as percentage where IMG_ID = 2654; | bounding box refers to X, Y, W, H from IMG_OBJ; lowest relates to the height of the bounding box which refers to MIN(H); | image no. 99 refers to IMG_ID = 99; described as white refers to ATT_CLASS = 'white'; percentage = divide(count(OBJ_SAMPLE_ID) where ATT_CLASS = 'white', count(OBJ_SAMPLE_ID)) as percentage | classes of all the object samples refers to OBJ_CLASS; image no.1 refers to IMG_ID = 1 |
Count the image numbers that contain the "paint" object. | image numbers that contain the "paint" object refer to IMG_ID where OBJ_CLASS = 'paint'; | prediction classes with "has" captions refers to PRED_CLASS = 'has'; image id 3050 refers to IMG_ID = 3050 | objects that have multiple relations refers to OBJ1_SAMPLE_ID ! = OBJ2_SAMPLE_ID; captions for the prediction class ids are "on" refers to PRED_CLASS = 'on' | attribute class of "horse" refers to ATT_CLASS = 'horse'; object class of "fur" refers to OBJ_CLASS = 'fur'; | colour refers to ATT_CLASS; van refers to OBJ_CLASS = 'van'; image no. 1 refers to IMG_ID = 1 | prediction relationship class id refers to PRED_CLASS_ID; tallest image refers to max(H) | bounding boxes of the object samples refers to (x, y, W, H); predicted relation class of "by" refers to PRED_CLASS = 'by'; image no.1 refers to IMG_ID = 1 | AVG(IMG_ID) where OBJ_CLASS = 'keyboard'; | attribute classes of image ID 22 refer to ATT_CLASS where MG_ID = 22; | DIVIDE(SUM(OBJ_CLASS_ID where OBJ_CLASS = 'surface'), COUNT(OBJ_CLASS_ID)) as percentage where IMG_ID = 2654; | object class refers to OBJ_CLASS; sample no.10 refers to OBJ_SAMPLE_ID = 10; image no.2320341 refers to IMG_ID = 2320341 |
List all the attribute classes of the images that have a (5,5) coordinate. | attribute classes refer to ATT_CLASS; (5,5) coordinate refers to X and Y coordinates of the bounding box where X = 5 and Y = 5; | bounding box of the object refers to (X, Y, W, H); sample no.7 on image no.42 refers to IMG_ID = 42 and OBJ_SAMPLE_ID = 7 | images refer to IMG_ID; "keyboard" as object class refers to OBJ_CLASS = 'keyboard'; | ID of all images refer to IMG_ID; if two objects (OBJ1_SAMPLE_ID,
OBJ2_SAMPLE_ID) has
multiple PRED_CLASS_ID, it
means these two objects
have multiple relations; | predicted relation class refers to PRED_CLASS; self-relations refers to OBJ1_SAMPLE_ID = OBJ2_SAMPLE_ID; image no.5 refers to IMG_ID = 5 | unique id number identifying refers to OBJ_CLASS_ID; onion object class refers to OBJ_CLASS = 'onion' | images refer to IMG_ID; "vegetables" and "fruits" as object classes refer to OBJ_CLASS = 'vegetables' and OBJ_CLASS = 'fruits'; | relation refers to PRED_CLASS; object sample no.8 and object sample no.4 refers to OBJ1_SAMPLE_ID = 8 AND OBJ2_SAMPLE_ID = 4; image no.1 refers to IMG_ID = 1 | How many attributes refers to ATT_CLASS_ID; object sample no. 7 on image no. 4 refers to IMG_ID = 4 and OBJ_SAMPLE_ID = 7 | dress' object classes refer to OBJ_CLASS = 'dress'; image ID 1764 refers to IMG_ID = 1764; X and Y refer to coordinates of the bounding box; | bounding box of the object refers to (x, y, W, H); image id refers to IMG_ID; prediction relationship class id of 144 refers to PRED_CLASS_ID = 144 |
Name the object class of the image with lowest bounding box. | bounding box refers to X, Y, W, H from IMG_OBJ; lowest relates to the height of the bounding box which refers to MIN(H); | pairs of object samples refers to OBJ1_SAMPLE_ID and OBJ2_SAMPLE_ID; image no.1 refers to IMG_ID = 1; relation of "parked on" refers to PRED_CLASS = 'parked on' | caption for the prediction class id 12 refers to PRED_CLASS where PRED_CLASS_ID = 12; | blue' attribute classes on image ID 2355735 refer to ATT_CLASS = 'blue' where IMG_ID = 2355735; | ID of all images refer to IMG_ID; attribute class of "horse" refers to ATT_CLASS = 'horse'; | relationship refers to PRED_CLASS; object sample no.12 and no.8 of image no.2345511 refers to IMG_ID = 2345511 AND OBJ1_SAMPLE_ID = 12 AND OBJ2_SAMPLE_ID = 8 | dress' object classes refer to OBJ_CLASS = 'dress'; image ID 1764 refers to IMG_ID = 1764; X and Y refer to coordinates of the bounding box; | samples of clouds refer to IMG_ID where OBJ_CLASS = 'cloud'; image no.2315533 refers to IMG_ID = 2315533; | Name the object element refers to OBJ_CLASS; scattered refers to ATT_CLASS = 'scattered'; image no. 10 refers to IMG_ID = 10 | The bounding box's W and H abbreviations stand for the object's width and height respectively; "keyboard" as object class refers to OBJ_CLASS = 'keyboard'; (5, 647) as coordinate refers to X and Y coordinates of the bounding box where X = 5 and Y = 647; | images have more than 20 object samples refer to IMG_ID where COUNT(OBJ_SAMPLE_ID) > 20; |
How many attributes are related to the object sample no. 7 on image no. 4? | How many attributes refers to ATT_CLASS_ID; object sample no. 7 on image no. 4 refers to IMG_ID = 4 and OBJ_SAMPLE_ID = 7 | images refers to IMG_ID; have at least 5 "black" classes refers to count(ATT_CLASS_ID) where ATT_CLASS = 'black' > = 5 | self-relations refers to OBJ1_SAMPLE_ID = OBJ2_SAMPLE_ID; image no.5 refers to IMG_ID = 5 | object samples refers to OBJ_SAMPLE_ID; image no.1 refers to IMG_ID = 1 | bounding boxes of the object samples refers to (x, y, W, H); predicted relation class of "by" refers to PRED_CLASS = 'by'; image no.1 refers to IMG_ID = 1 | explanations about distinct object classes refers to OBJ_CLASS; images refers to IMG_ID; x and y coordinate of 0 refers to X = 0 AND Y = 0 | bounding box refers to X, Y, W, H from IMG_OBJ; lowest relates to the height of the bounding box which refers to MIN(H); | DIVIDE(SUM(OBJ_CLASS_ID where OBJ_CLASS = 'surface'), COUNT(OBJ_CLASS_ID)) as percentage where IMG_ID = 2654; | white objects refers to ATT_CLASS = 'white'; image no.2347915 refers to IMG_ID = 2347915 | samples of food object refers to OBJ_CLASS = 'food'; image no.6 refers to IMG_ID = 6 | attribute classes of the image ID "15" refer to ATT_CLASS where IMG_ID = 15; |
What is the caption for the prediction class id 12? | caption for the prediction class id 12 refers to PRED_CLASS where PRED_CLASS_ID = 12; | unique id number identifying refers to OBJ_CLASS_ID; onion object class refers to OBJ_CLASS = 'onion' | white objects refers to ATT_CLASS = 'white'; image no.2347915 refers to IMG_ID = 2347915 | image with a bounding (422, 63, 77, 363) refers to OBJ_CLASS_ID where X = 422 and Y = 63 and W = 77 and H = 363; | classes of all the object samples refers to OBJ_CLASS; image no.1 refers to IMG_ID = 1 | image numbers that contain the "paint" object refer to IMG_ID where OBJ_CLASS = 'paint'; | AVG(IMG_ID) where OBJ_CLASS = 'keyboard'; | explanations about distinct object classes refers to OBJ_CLASS; images refers to IMG_ID; x and y coordinate of 0 refers to X = 0 AND Y = 0 | predicate class of image ID 68 refers to PRED_CLASS where IMG_ID = 68; | DIVIDE(SUM(OBJ_CLASS_ID where OBJ_CLASS = 'surface'), COUNT(OBJ_CLASS_ID)) as percentage where IMG_ID = 2654; | dress' object classes refer to OBJ_CLASS = 'dress'; image ID 1764 refers to IMG_ID = 1764; X and Y refer to coordinates of the bounding box; |
How many images have less than 15 object samples? | images refer to IMG_ID; less than 15 object samples refer to COUNT(OBJ_SAMPLE_ID) < 15; | explanations about distinct object classes refers to OBJ_CLASS; images refers to IMG_ID; x and y coordinate of 0 refers to X = 0 AND Y = 0 | dress' object classes refer to OBJ_CLASS = 'dress'; image ID 1764 refers to IMG_ID = 1764; X and Y refer to coordinates of the bounding box; | bounding box of the object refers to (x, y, W, H); image id refers to IMG_ID; prediction relationship class id of 144 refers to PRED_CLASS_ID = 144 | bounding box of the object sample refers to (x, y, W, H); image no.5 refers to IMG_ID = 5; has a self-relation refers to OBJ1_SAMPLE_ID = OBJ2_SAMPLE_ID | object class refers to OBJ_CLASS; sample no.10 refers to OBJ_SAMPLE_ID = 10; image no.2320341 refers to IMG_ID = 2320341 | have at least one object sample in the class of "man" refers to count(IMG_ID where OBJ_CLASS = 'man') > = 1 | bounding boxes of the object samples refers to (x, y, W, H); predicted relation class of "by" refers to PRED_CLASS = 'by'; image no.1 refers to IMG_ID = 1 | X and Y refer to coordinates of the bounding box; image ID 23 refers to IMG_ID = 23; 'cast' attribute class refers to ATT_CLASS = 'cast'; | caption for the prediction class id 12 refers to PRED_CLASS where PRED_CLASS_ID = 12; | coordinates for the object refer to X, Y, W and H coordinates of the bounding box; object class "pizza" refers to OBJ_CLASS = 'pizza'; |
How many samples of clouds are there in the image no.2315533? | samples of clouds refer to IMG_ID where OBJ_CLASS = 'cloud'; image no.2315533 refers to IMG_ID = 2315533; | unique id number identifying refers to OBJ_CLASS_ID; onion object class refers to OBJ_CLASS = 'onion' | samples of "wall" refers to OBJ_SAMPLE_ID and OBJ_CLASS = 'wall' ; image no.2353079 refers to IMG_ID = 2353079 | has' predicate classes refers to PRED_CLASS = 'has'; image ID 107 refers to IMG_ID = 107; | colour refers to ATT_CLASS; van refers to OBJ_CLASS = 'van'; image no. 1 refers to IMG_ID = 1 | prediction classes with "has" captions refers to PRED_CLASS = 'has'; image id 3050 refers to IMG_ID = 3050 | attribute classes of image ID 22 refer to ATT_CLASS where MG_ID = 22; | images have more than 20 object samples refer to IMG_ID where COUNT(OBJ_SAMPLE_ID) > 20; | X and Y refer to coordinates of the bounding box where X = 5 and Y = 5; images refer to IMG_ID; | samples of food object refers to OBJ_CLASS = 'food'; image no.6 refers to IMG_ID = 6 | attribute classes refer to ATT_CLASS; (5,5) coordinate refers to X and Y coordinates of the bounding box where X = 5 and Y = 5; |
List the object sample IDs of image ID 17 with coordinates (0,0). | object sample ID refers to OBJ_SAMPLE_ID; image ID 17 refers to IMG_ID = 17; coordinates (0,0) refer to X and Y coordinates of the bounding box where X = 0 and Y = 0; | images refer to IMG_ID; "vegetables" and "fruits" as object classes refer to OBJ_CLASS = 'vegetables' and OBJ_CLASS = 'fruits'; | explanations about distinct object classes refers to OBJ_CLASS; images refers to IMG_ID; x and y coordinate of 0 refers to X = 0 AND Y = 0 | ID of all images refer to IMG_ID; if two objects (OBJ1_SAMPLE_ID,
OBJ2_SAMPLE_ID) has
multiple PRED_CLASS_ID, it
means these two objects
have multiple relations; | IDs of images refer to IMG_ID; objects with the attributes of 'wired' refer to ATT_CLASS = 'wired'; | DIVIDE(COUNT(IMG_ID where OBJ_CLASS = 'man'), COUNT(IMG_ID where OBJ_CLASS = 'person')); | images refers to IMG_ID; have at least 25 attributes refers to count(ATT_CLASS_ID) > = 25 | predicate class of image ID 68 refers to PRED_CLASS where IMG_ID = 68; | object samples refers to OBJ_CLASS_ID; image no.1 refers to IMG_ID = 1; in the class of "man" refers to OBJ_CLASS = 'man' | widest relates to the width of the bounding
box of the object which refers to MAX(W); object in image 8 refers to OBJ_SAMPLE_ID where IMG_ID = 8; | bounding box refers to X, Y, W, H from IMG_OBJ; lowest relates to the height of the bounding box which refers to MIN(H); |
Which images have more than 20 object samples? | images have more than 20 object samples refer to IMG_ID where COUNT(OBJ_SAMPLE_ID) > 20; | images refer to IMG_ID; total of 10 attribute classes refers to COUNT(OBJ_CLASS_ID) = 10; | dress' object classes refer to OBJ_CLASS = 'dress'; image ID 1764 refers to IMG_ID = 1764; X and Y refer to coordinates of the bounding box; | Y coordinate many are 0 refers to Y coordinates of the bounding box where Y = 0; image ID 12 refers to IMG_ID = 12; | ID of all images refer to IMG_ID; if two objects (OBJ1_SAMPLE_ID,
OBJ2_SAMPLE_ID) has
multiple PRED_CLASS_ID, it
means these two objects
have multiple relations; | How many object elements refers to OBJ_CLASS_ID; image no. 31 refers to IMG_ID = 31 | object in image 5 refers to OBJ_SAMPLE_ID where IMG_ID = 5; coordinates of (634, 468) refer to X and Y coordinates of the bounding box in which X = 634 and Y = 468; | images refers to IMG_ID; have at least 25 attributes refers to count(ATT_CLASS_ID) > = 25 | image numbers that contain the "paint" object refer to IMG_ID where OBJ_CLASS = 'paint'; | IDs of images refer to IMG_ID; objects with the attributes of 'wired' refer to ATT_CLASS = 'wired'; | prediction classes with "has" captions refers to PRED_CLASS = 'has'; image id 3050 refers to IMG_ID = 3050 |
What are the corresponding classes for the "very large bike" attribute? | attribute refers to ATT_CLASS | prediction relationship class id refers to PRED_CLASS_ID; tallest image refers to max(H) | How many images have at least one pair of object samples with the relation "parked on" refers to count(IMG_ID) where OBJ1_SAMPLE_ID ! = OBJ2_SAMPLE_ID and PRED_CLASS = 'parked on' | attribute class of "horse" refers to ATT_CLASS = 'horse'; object class of "fur" refers to OBJ_CLASS = 'fur'; | images refer to IMG_ID; "vegetables" and "fruits" as object classes refer to OBJ_CLASS = 'vegetables' and OBJ_CLASS = 'fruits'; | explanations about distinct object classes refers to OBJ_CLASS; images refers to IMG_ID; x and y coordinate of 0 refers to X = 0 AND Y = 0 | images refer to IMG_ID; "keyboard" as object class refers to OBJ_CLASS = 'keyboard'; | DIVIDE(SUM(OBJ_CLASS_ID where OBJ_CLASS = 'surface'), COUNT(OBJ_CLASS_ID)) as percentage where IMG_ID = 2654; | The bounding box's W and H abbreviations stand for the object's width and height respectively; "keyboard" as object class refers to OBJ_CLASS = 'keyboard'; (5, 647) as coordinate refers to X and Y coordinates of the bounding box where X = 5 and Y = 647; | have at least one object sample in the class of "man" refers to count(IMG_ID where OBJ_CLASS = 'man') > = 1 | pairs of object samples refers to OBJ1_SAMPLE_ID and OBJ2_SAMPLE_ID; image no.1 refers to IMG_ID = 1; relation of "parked on" refers to PRED_CLASS = 'parked on' |
How many images have an x-coordinate of 5 and y-coordinate of 5? | X and Y refer to coordinates of the bounding box where X = 5 and Y = 5; images refer to IMG_ID; | AVG(IMG_ID) where OBJ_CLASS = 'keyboard'; | unique id number identifying refers to OBJ_CLASS_ID; onion object class refers to OBJ_CLASS = 'onion' | images refer to IMG_ID; less than 15 object samples refer to COUNT(OBJ_SAMPLE_ID) < 15; | The bounding box's W and H abbreviations stand for the object's width and height respectively; "keyboard" as object class refers to OBJ_CLASS = 'keyboard'; (5, 647) as coordinate refers to X and Y coordinates of the bounding box where X = 5 and Y = 647; | widest relates to the width of the bounding
box of the object which refers to MAX(W); object in image 8 refers to OBJ_SAMPLE_ID where IMG_ID = 8; | "picture" as attribute class refers to ATT_CLASS = 'picture'; "bear" as object class refers to OBJ_CLASS = 'bear'; images refer to IMG_ID; | coordinates for the object refer to X, Y, W and H coordinates of the bounding box; object class "pizza" refers to OBJ_CLASS = 'pizza'; | classes of all the object samples refers to OBJ_CLASS; image no.1 refers to IMG_ID = 1 | object class refers to OBJ_CLASS; sample no.10 refers to OBJ_SAMPLE_ID = 10; image no.2320341 refers to IMG_ID = 2320341 | images refer to IMG_ID; "vegetables" and "fruits" as object classes refer to OBJ_CLASS = 'vegetables' and OBJ_CLASS = 'fruits'; |
List all the ids of the images that have a self-relation relationship. | ids of the images refers to IMG_ID; self-relations refers to OBJ1_SAMPLE_ID = OBJ2_SAMPLE_ID | The bounding box's W and H abbreviations stand for the object's width and height respectively; "keyboard" as object class refers to OBJ_CLASS = 'keyboard'; (5, 647) as coordinate refers to X and Y coordinates of the bounding box where X = 5 and Y = 647; | Y coordinate many are 0 refers to Y coordinates of the bounding box where Y = 0; image ID 12 refers to IMG_ID = 12; | samples of food object refers to OBJ_CLASS = 'food'; image no.6 refers to IMG_ID = 6 | prediction classes with "has" captions refers to PRED_CLASS = 'has'; image id 3050 refers to IMG_ID = 3050 | attributes of polka dot refer to ATT_CLASS = 'polka dot'; images refer to IMG_ID; | DIVIDE(SUM(OBJ_CLASS_ID where OBJ_CLASS = 'surface'), COUNT(OBJ_CLASS_ID)) as percentage where IMG_ID = 2654; | samples of "bed" object refer to OBJ_SAMPLE_ID where OBJ_CLASS = 'bed'; image No.1098 refers to IMG_ID = 1098; | object class of the image refers to OBJ_CLASS; bounding box of 0, 0, 135, 212 refers to X = 0 AND Y = 0 AND W = 135 AND H = 212 | Name the object element refers to OBJ_CLASS; scattered refers to ATT_CLASS = 'scattered'; image no. 10 refers to IMG_ID = 10 | caption for the prediction class id 12 refers to PRED_CLASS where PRED_CLASS_ID = 12; |
What is the percentage of the object samples in the class of "man" in image no.1? | object samples refers to OBJ_SAMPLE_ID; class of "man" refers to OBJ_CLASS = 'man'; image no.1 refers to IMG_ID = 1; percentage = divide(count(OBJ_SAMPLE_ID)when OBJ_CLASS = 'man', count(OBJ_SAMPLE_ID)) as percentage | attribute class of "horse" refers to ATT_CLASS = 'horse'; object class of "fur" refers to OBJ_CLASS = 'fur'; | samples of "bed" object refer to OBJ_SAMPLE_ID where OBJ_CLASS = 'bed'; image No.1098 refers to IMG_ID = 1098; | object samples refers to OBJ_CLASS_ID; image no.1 refers to IMG_ID = 1; in the class of "man" refers to OBJ_CLASS = 'man' | bounding box of the object sample refers to (x, y, W, H); image no.5 refers to IMG_ID = 5; has a self-relation refers to OBJ1_SAMPLE_ID = OBJ2_SAMPLE_ID | How many object elements refers to OBJ_CLASS_ID; image no. 31 refers to IMG_ID = 31 | object elements refers to OBJ_CLASS_ID; average = divide(count(OBJ_CLASS_ID), count(IMG_ID)) | object in image 5 refers to OBJ_SAMPLE_ID where IMG_ID = 5; coordinates of (634, 468) refer to X and Y coordinates of the bounding box in which X = 634 and Y = 468; | images refers to IMG_ID; have at least 5 "black" classes refers to count(ATT_CLASS_ID) where ATT_CLASS = 'black' > = 5 | X and Y refer to coordinates of the bounding box; image ID 23 refers to IMG_ID = 23; 'cast' attribute class refers to ATT_CLASS = 'cast'; | DIVIDE(COUNT(IMG_ID where OBJ_CLASS = 'man'), COUNT(IMG_ID where OBJ_CLASS = 'person')); |
Give the number of images containing the object sample of "suit". | number of images refers to IMG_ID; object sample of "suit" refers to OBJ_CLASS = 'suit' | relationship refers to PRED_CLASS; object sample no.12 and no.8 of image no.2345511 refers to IMG_ID = 2345511 AND OBJ1_SAMPLE_ID = 12 AND OBJ2_SAMPLE_ID = 8 | predicted relation class refers to PRED_CLASS; self-relations refers to OBJ1_SAMPLE_ID = OBJ2_SAMPLE_ID; image no.5 refers to IMG_ID = 5 | attributes of polka dot refer to ATT_CLASS = 'polka dot'; images refer to IMG_ID; | How many images have at least one pair of object samples with the relation "parked on" refers to count(IMG_ID) where OBJ1_SAMPLE_ID ! = OBJ2_SAMPLE_ID and PRED_CLASS = 'parked on' | dimensions of the bounding box refers to (W, H); keyboard refers to OBJ_CLASS = 'keyboard'; image no. 3 refers to IMG_ID = 3 | bounding box of the object refers to (x, y, W, H); image id refers to IMG_ID; prediction relationship class id of 144 refers to PRED_CLASS_ID = 144 | unique id number identifying refers to OBJ_CLASS_ID; onion object class refers to OBJ_CLASS = 'onion' | How many attributes refers to ATT_CLASS_ID; object sample no. 7 on image no. 4 refers to IMG_ID = 4 and OBJ_SAMPLE_ID = 7 | object number of the sample refers to OBJ1_SAMPLE_ID; object sample no.1 from image no.2345524 refers to OBJ2_SAMPLE_ID = 1 and IMG_ID = 2345524 | ID of all images refer to IMG_ID; attribute class of "horse" refers to ATT_CLASS = 'horse'; |
What is the prediction relationship class id of the tallest image? | prediction relationship class id refers to PRED_CLASS_ID; tallest image refers to max(H) | object number of the sample refers to OBJ1_SAMPLE_ID; object sample no.1 from image no.2345524 refers to OBJ2_SAMPLE_ID = 1 and IMG_ID = 2345524 | pairs of object samples refers to OBJ1_SAMPLE_ID and OBJ2_SAMPLE_ID; image no.1 refers to IMG_ID = 1; relation of "parked on" refers to PRED_CLASS = 'parked on' | images refer to IMG_ID; total of 10 attribute classes refers to COUNT(OBJ_CLASS_ID) = 10; | has' predicate classes refers to PRED_CLASS = 'has'; image ID 107 refers to IMG_ID = 107; | Name the object element refers to OBJ_CLASS; scattered refers to ATT_CLASS = 'scattered'; image no. 10 refers to IMG_ID = 10 | widest relates to the width of the bounding
box of the object which refers to MAX(W); object in image 8 refers to OBJ_SAMPLE_ID where IMG_ID = 8; | attribute classes of image ID 22 refer to ATT_CLASS where MG_ID = 22; | object class refers to OBJ_CLASS; sample no.10 refers to OBJ_SAMPLE_ID = 10; image no.2320341 refers to IMG_ID = 2320341 | images refers to IMG_ID; have at least 25 attributes refers to count(ATT_CLASS_ID) > = 25 | unique id number identifying refers to OBJ_CLASS_ID; onion object class refers to OBJ_CLASS = 'onion' |
In the Y coordinate of image ID 12, how many are 0? | Y coordinate many are 0 refers to Y coordinates of the bounding box where Y = 0; image ID 12 refers to IMG_ID = 12; | samples of clouds refer to IMG_ID where OBJ_CLASS = 'cloud'; image no.2315533 refers to IMG_ID = 2315533; | explanations about distinct object classes refers to OBJ_CLASS; images refers to IMG_ID; x and y coordinate of 0 refers to X = 0 AND Y = 0 | attribute class of "horse" refers to ATT_CLASS = 'horse'; object class of "fur" refers to OBJ_CLASS = 'fur'; | The bounding box's W and H abbreviations stand for the object's width and height respectively; "keyboard" as object class refers to OBJ_CLASS = 'keyboard'; (5, 647) as coordinate refers to X and Y coordinates of the bounding box where X = 5 and Y = 647; | coordinates for the object refer to X, Y, W and H coordinates of the bounding box; object class "pizza" refers to OBJ_CLASS = 'pizza'; | classes of all the object samples refers to OBJ_CLASS; image no.1 refers to IMG_ID = 1 | object has the highest attribute classes refers to OBJ_SAMPLE_ID where MAX(COUNT(OBJ_SAMPLE_ID)); | predicted relation classes refers to PRED_CLASS; object sample no.14 in image no.1 refers to OBJ1_SAMPLE_ID = 14 AND OBJ2_SAMPLE_ID = 14 and IMG_ID = 1 | widest relates to the width of the bounding
box of the object which refers to MAX(W); object in image 8 refers to OBJ_SAMPLE_ID where IMG_ID = 8; | image numbers that contain the "paint" object refer to IMG_ID where OBJ_CLASS = 'paint'; |
How many images have at least one pair of object samples with the relation "parked on"? | How many images have at least one pair of object samples with the relation "parked on" refers to count(IMG_ID) where OBJ1_SAMPLE_ID ! = OBJ2_SAMPLE_ID and PRED_CLASS = 'parked on' | Y coordinate many are 0 refers to Y coordinates of the bounding box where Y = 0; image ID 12 refers to IMG_ID = 12; | unique id number identifying refers to OBJ_CLASS_ID; onion object class refers to OBJ_CLASS = 'onion' | IDs of images refer to IMG_ID; objects with the attributes of 'wired' refer to ATT_CLASS = 'wired'; | relationship refers to PRED_CLASS; object sample no.12 and no.8 of image no.2345511 refers to IMG_ID = 2345511 AND OBJ1_SAMPLE_ID = 12 AND OBJ2_SAMPLE_ID = 8 | attribute classes of the image ID "15" refer to ATT_CLASS where IMG_ID = 15; | pairs of object samples refers to OBJ1_SAMPLE_ID and OBJ2_SAMPLE_ID; image no.1 refers to IMG_ID = 1; relation of "parked on" refers to PRED_CLASS = 'parked on' | bounding boxes refers to (x, y, W, H); image 2222 refers to IMG_ID = 2222; object classes are feathers refers to OBJ_CLASS = 'feathers' | onion category refers to OBJ_CLASS = 'onion'; | How many object elements refers to OBJ_CLASS_ID; image no. 31 refers to IMG_ID = 31 | predicate class of image ID 68 refers to PRED_CLASS where IMG_ID = 68; |
How many samples of food object are there in image no.6? | samples of food object refers to OBJ_CLASS = 'food'; image no.6 refers to IMG_ID = 6 | object samples refers to OBJ_SAMPLE_ID; image no.1 refers to IMG_ID = 1 | predicate class of image ID 68 refers to PRED_CLASS where IMG_ID = 68; | images refer to IMG_ID; less than 15 object samples refer to COUNT(OBJ_SAMPLE_ID) < 15; | attributes of polka dot refer to ATT_CLASS = 'polka dot'; images refer to IMG_ID; | self-relations refers to OBJ1_SAMPLE_ID = OBJ2_SAMPLE_ID; image no.5 refers to IMG_ID = 5 | predicted relation class refers to PRED_CLASS; self-relations refers to OBJ1_SAMPLE_ID = OBJ2_SAMPLE_ID; image no.5 refers to IMG_ID = 5 | bounding boxes refers to (x, y, W, H); image 2222 refers to IMG_ID = 2222; object classes are feathers refers to OBJ_CLASS = 'feathers' | object has the highest attribute classes refers to OBJ_SAMPLE_ID where MAX(COUNT(OBJ_SAMPLE_ID)); | ID of all images refer to IMG_ID; if two objects (OBJ1_SAMPLE_ID,
OBJ2_SAMPLE_ID) has
multiple PRED_CLASS_ID, it
means these two objects
have multiple relations; | coordinates for the object refer to X, Y, W and H coordinates of the bounding box; object class "pizza" refers to OBJ_CLASS = 'pizza'; |
Give the X and Y coordinates of the sample object of image ID 23 that has the 'cast' attribute class. | X and Y refer to coordinates of the bounding box; image ID 23 refers to IMG_ID = 23; 'cast' attribute class refers to ATT_CLASS = 'cast'; | has' predicate classes refers to PRED_CLASS = 'has'; image ID 107 refers to IMG_ID = 107; | bounding box of the object refers to (X, Y, W, H); sample no.7 on image no.42 refers to IMG_ID = 42 and OBJ_SAMPLE_ID = 7 | onion category refers to OBJ_CLASS = 'onion'; | relationship refers to PRED_CLASS; "feathers" and "onion" in image no.2345528 refers to IMG_ID = 2345528 and OBJ_CLASS = 'feathers' and OBJ_CLASS = 'onion' | samples of "bed" object refer to OBJ_SAMPLE_ID where OBJ_CLASS = 'bed'; image No.1098 refers to IMG_ID = 1098; | images have more than 20 object samples refer to IMG_ID where COUNT(OBJ_SAMPLE_ID) > 20; | images refer to IMG_ID; "keyboard" as object class refers to OBJ_CLASS = 'keyboard'; | bounding boxes of the object samples refers to (x, y, W, H); predicted relation class of "by" refers to PRED_CLASS = 'by'; image no.1 refers to IMG_ID = 1 | objects that have multiple relations refers to OBJ1_SAMPLE_ID ! = OBJ2_SAMPLE_ID; captions for the prediction class ids are "on" refers to PRED_CLASS = 'on' | classes of all the object samples refers to OBJ_CLASS; image no.1 refers to IMG_ID = 1 |
To which predicted relation class does the self-relation of the object sample in image no.5 belong? | predicted relation class refers to PRED_CLASS; self-relations refers to OBJ1_SAMPLE_ID = OBJ2_SAMPLE_ID; image no.5 refers to IMG_ID = 5 | dress' object classes refer to OBJ_CLASS = 'dress'; image ID 1764 refers to IMG_ID = 1764; X and Y refer to coordinates of the bounding box; | How many object elements refers to OBJ_CLASS_ID; image no. 31 refers to IMG_ID = 31 | objects that have multiple relations refers to OBJ1_SAMPLE_ID ! = OBJ2_SAMPLE_ID; captions for the prediction class ids are "on" refers to PRED_CLASS = 'on' | colour refers to ATT_CLASS; van refers to OBJ_CLASS = 'van'; image no. 1 refers to IMG_ID = 1 | relationship refers to PRED_CLASS; "feathers" and "onion" in image no.2345528 refers to IMG_ID = 2345528 and OBJ_CLASS = 'feathers' and OBJ_CLASS = 'onion' | bounding box of the object sample refers to (x, y, W, H); image no.5 refers to IMG_ID = 5; has a self-relation refers to OBJ1_SAMPLE_ID = OBJ2_SAMPLE_ID | predicted relation classes refers to PRED_CLASS; object sample no.14 in image no.1 refers to OBJ1_SAMPLE_ID = 14 AND OBJ2_SAMPLE_ID = 14 and IMG_ID = 1 | image numbers that contain the "paint" object refer to IMG_ID where OBJ_CLASS = 'paint'; | object samples refers to OBJ_SAMPLE_ID; class of "man" refers to OBJ_CLASS = 'man'; image no.1 refers to IMG_ID = 1; percentage = divide(count(OBJ_SAMPLE_ID)when OBJ_CLASS = 'man', count(OBJ_SAMPLE_ID)) as percentage | widest relates to the width of the bounding
box of the object which refers to MAX(W); object in image 8 refers to OBJ_SAMPLE_ID where IMG_ID = 8; |
How many images have "keyboard" as their object class? | images refer to IMG_ID; "keyboard" as object class refers to OBJ_CLASS = 'keyboard'; | object class refers to OBJ_CLASS; sample no.10 refers to OBJ_SAMPLE_ID = 10; image no.2320341 refers to IMG_ID = 2320341 | attribute class of "horse" refers to ATT_CLASS = 'horse'; object class of "fur" refers to OBJ_CLASS = 'fur'; | object samples refers to OBJ_CLASS_ID; image no.1 refers to IMG_ID = 1; in the class of "man" refers to OBJ_CLASS = 'man' | How many attributes refers to ATT_CLASS_ID; object sample no. 7 on image no. 4 refers to IMG_ID = 4 and OBJ_SAMPLE_ID = 7 | DIVIDE(COUNT(OBJ_SAMPLE_ID), COUNT(IMG_ID)); | attribute classes of image ID 22 refer to ATT_CLASS where MG_ID = 22; | blue' attribute classes on image ID 2355735 refer to ATT_CLASS = 'blue' where IMG_ID = 2355735; | samples of "bed" object refer to OBJ_SAMPLE_ID where OBJ_CLASS = 'bed'; image No.1098 refers to IMG_ID = 1098; | image with a bounding (422, 63, 77, 363) refers to OBJ_CLASS_ID where X = 422 and Y = 63 and W = 77 and H = 363; | bounding boxes refers to (x, y, W, H); image 2222 refers to IMG_ID = 2222; object classes are feathers refers to OBJ_CLASS = 'feathers' |
Calculate the average number of images with an attribute class of "keyboard". | AVG(IMG_ID) where OBJ_CLASS = 'keyboard'; | samples of "bed" object refer to OBJ_SAMPLE_ID where OBJ_CLASS = 'bed'; image No.1098 refers to IMG_ID = 1098; | DIVIDE(SUM(OBJ_CLASS_ID where OBJ_CLASS = 'surface'), COUNT(OBJ_CLASS_ID)) as percentage where IMG_ID = 2654; | images refer to IMG_ID; "vegetables" and "fruits" as object classes refer to OBJ_CLASS = 'vegetables' and OBJ_CLASS = 'fruits'; | predicate class of image ID 68 refers to PRED_CLASS where IMG_ID = 68; | object class of the image refers to OBJ_CLASS; bounding box of 0, 0, 135, 212 refers to X = 0 AND Y = 0 AND W = 135 AND H = 212 | attribute classes of image ID 22 refer to ATT_CLASS where MG_ID = 22; | number of images refers to IMG_ID; object sample of "suit" refers to OBJ_CLASS = 'suit' | object class refers to OBJ_CLASS; sample no.10 refers to OBJ_SAMPLE_ID = 10; image no.2320341 refers to IMG_ID = 2320341 | IDs of images refer to IMG_ID; objects with the attributes of 'wired' refer to ATT_CLASS = 'wired'; | samples of food object refers to OBJ_CLASS = 'food'; image no.6 refers to IMG_ID = 6 |
List all the ID of the images that have an attribute class of "horse". | ID of all images refer to IMG_ID; attribute class of "horse" refers to ATT_CLASS = 'horse'; | images refer to IMG_ID; "vegetables" and "fruits" as object classes refer to OBJ_CLASS = 'vegetables' and OBJ_CLASS = 'fruits'; | X and Y refer to coordinates of the bounding box where X = 5 and Y = 5; images refer to IMG_ID; | bounding boxes of the object samples refers to (x, y, W, H); predicted relation class of "by" refers to PRED_CLASS = 'by'; image no.1 refers to IMG_ID = 1 | How many images have at least one pair of object samples with the relation "parked on" refers to count(IMG_ID) where OBJ1_SAMPLE_ID ! = OBJ2_SAMPLE_ID and PRED_CLASS = 'parked on' | DIVIDE(SUM(OBJ_SAMPLE_ID where OBJ_CLASS = 'airplane'), COUNT(OBJ_CLASS)) as percentage; | samples of "wall" refers to OBJ_SAMPLE_ID and OBJ_CLASS = 'wall' ; image no.2353079 refers to IMG_ID = 2353079 | Y coordinate many are 0 refers to Y coordinates of the bounding box where Y = 0; image ID 12 refers to IMG_ID = 12; | object number of the sample refers to OBJ1_SAMPLE_ID; object sample no.1 from image no.2345524 refers to OBJ2_SAMPLE_ID = 1 and IMG_ID = 2345524 | image with a bounding (422, 63, 77, 363) refers to OBJ_CLASS_ID where X = 422 and Y = 63 and W = 77 and H = 363; | object elements refers to OBJ_CLASS_ID; average = divide(count(OBJ_CLASS_ID), count(IMG_ID)) |
How many images have over 20 object samples? | over 20 object samples refers to COUNT(OBJ_SAMPLE_ID) > 20 | has' predicate classes refers to PRED_CLASS = 'has'; image ID 107 refers to IMG_ID = 107; | object class refers to OBJ_CLASS; sample no.10 refers to OBJ_SAMPLE_ID = 10; image no.2320341 refers to IMG_ID = 2320341 | X and Y refer to coordinates of the bounding box where X = 5 and Y = 5; images refer to IMG_ID; | classes of all the object samples refers to OBJ_CLASS; image no.1 refers to IMG_ID = 1 | dress' object classes refer to OBJ_CLASS = 'dress'; image ID 1764 refers to IMG_ID = 1764; X and Y refer to coordinates of the bounding box; | object samples refers to OBJ_CLASS_ID; image no.1 refers to IMG_ID = 1; in the class of "man" refers to OBJ_CLASS = 'man' | How many images have at least one pair of object samples with the relation "parked on" refers to count(IMG_ID) where OBJ1_SAMPLE_ID ! = OBJ2_SAMPLE_ID and PRED_CLASS = 'parked on' | object in image 5 refers to OBJ_SAMPLE_ID where IMG_ID = 5; coordinates of (634, 468) refer to X and Y coordinates of the bounding box in which X = 634 and Y = 468; | predicted relation class refers to PRED_CLASS; self-relations refers to OBJ1_SAMPLE_ID = OBJ2_SAMPLE_ID; image no.5 refers to IMG_ID = 5 | explanations about distinct object classes refers to OBJ_CLASS; images refers to IMG_ID; x and y coordinate of 0 refers to X = 0 AND Y = 0 |
Find the object in image 5 where the object with the coordinate of (634, 468). | object in image 5 refers to OBJ_SAMPLE_ID where IMG_ID = 5; coordinates of (634, 468) refer to X and Y coordinates of the bounding box in which X = 634 and Y = 468; | ID of all images refer to IMG_ID; attribute class of "horse" refers to ATT_CLASS = 'horse'; | object class refers to OBJ_CLASS; sample no.10 refers to OBJ_SAMPLE_ID = 10; image no.2320341 refers to IMG_ID = 2320341 | caption for the prediction class id 12 refers to PRED_CLASS where PRED_CLASS_ID = 12; | The bounding box's W and H abbreviations stand for the object's width and height respectively; "keyboard" as object class refers to OBJ_CLASS = 'keyboard'; (5, 647) as coordinate refers to X and Y coordinates of the bounding box where X = 5 and Y = 647; | images refer to IMG_ID; total of 10 attribute classes refers to COUNT(OBJ_CLASS_ID) = 10; | DIVIDE(SUM(OBJ_CLASS_ID where OBJ_CLASS = 'surface'), COUNT(OBJ_CLASS_ID)) as percentage where IMG_ID = 2654; | explanations about distinct object classes refers to OBJ_CLASS; images refers to IMG_ID; x and y coordinate of 0 refers to X = 0 AND Y = 0 | colour refers to ATT_CLASS; van refers to OBJ_CLASS = 'van'; image no. 1 refers to IMG_ID = 1 | bounding box of the object refers to (X, Y, W, H); sample no.7 on image no.42 refers to IMG_ID = 42 and OBJ_SAMPLE_ID = 7 | IDs of images refer to IMG_ID; objects with the attributes of 'wired' refer to ATT_CLASS = 'wired'; |
Which object classes belong to the onion category? | onion category refers to OBJ_CLASS = 'onion'; | Y coordinate many are 0 refers to Y coordinates of the bounding box where Y = 0; image ID 12 refers to IMG_ID = 12; | attributes of polka dot refer to ATT_CLASS = 'polka dot'; images refer to IMG_ID; | IDs of images refer to IMG_ID; objects with the attributes of 'wired' refer to ATT_CLASS = 'wired'; | image with a bounding (422, 63, 77, 363) refers to OBJ_CLASS_ID where X = 422 and Y = 63 and W = 77 and H = 363; | predicted relation class refers to PRED_CLASS; self-relations refers to OBJ1_SAMPLE_ID = OBJ2_SAMPLE_ID; image no.5 refers to IMG_ID = 5 | relation refers to PRED_CLASS; object sample no.8 and object sample no.4 refers to OBJ1_SAMPLE_ID = 8 AND OBJ2_SAMPLE_ID = 4; image no.1 refers to IMG_ID = 1 | unique id number identifying refers to OBJ_CLASS_ID; onion object class refers to OBJ_CLASS = 'onion' | bounding box of the object refers to (x, y, W, H); image id refers to IMG_ID; prediction relationship class id of 144 refers to PRED_CLASS_ID = 144 | How many attributes refers to ATT_CLASS_ID; object sample no. 7 on image no. 4 refers to IMG_ID = 4 and OBJ_SAMPLE_ID = 7 | How many images have at least one pair of object samples with the relation "parked on" refers to count(IMG_ID) where OBJ1_SAMPLE_ID ! = OBJ2_SAMPLE_ID and PRED_CLASS = 'parked on' |
On image no. 20, identify the attribute ID that is composed of the highest number of objects. | image no. 20 refers to IMG_ID = 20; attribute ID refers to ATT_CLASS_ID; highest number of objects refers to max(count(ATT_CLASS_ID)) | object sample ID refers to OBJ_SAMPLE_ID; image ID 17 refers to IMG_ID = 17; coordinates (0,0) refer to X and Y coordinates of the bounding box where X = 0 and Y = 0; | have at least one object sample in the class of "man" refers to count(IMG_ID where OBJ_CLASS = 'man') > = 1 | bounding box of the object refers to (x, y, W, H); image id refers to IMG_ID; prediction relationship class id of 144 refers to PRED_CLASS_ID = 144 | attribute classes refer to ATT_CLASS; (5,5) coordinate refers to X and Y coordinates of the bounding box where X = 5 and Y = 5; | attribute classes of image ID 22 refer to ATT_CLASS where MG_ID = 22; | samples of "bed" object refer to OBJ_SAMPLE_ID where OBJ_CLASS = 'bed'; image No.1098 refers to IMG_ID = 1098; | has' predicate classes refers to PRED_CLASS = 'has'; image ID 107 refers to IMG_ID = 107; | ID of all images refer to IMG_ID; attribute class of "horse" refers to ATT_CLASS = 'horse'; | image no. 99 refers to IMG_ID = 99; described as white refers to ATT_CLASS = 'white'; percentage = divide(count(OBJ_SAMPLE_ID) where ATT_CLASS = 'white', count(OBJ_SAMPLE_ID)) as percentage | samples of "wall" refers to OBJ_SAMPLE_ID and OBJ_CLASS = 'wall' ; image no.2353079 refers to IMG_ID = 2353079 |
What is the unique id number identifying the onion object class? | unique id number identifying refers to OBJ_CLASS_ID; onion object class refers to OBJ_CLASS = 'onion' | bounding box of the object refers to (x, y, W, H); image id refers to IMG_ID; prediction relationship class id of 144 refers to PRED_CLASS_ID = 144 | Y coordinate many are 0 refers to Y coordinates of the bounding box where Y = 0; image ID 12 refers to IMG_ID = 12; | ids of the images refers to IMG_ID; self-relations refers to OBJ1_SAMPLE_ID = OBJ2_SAMPLE_ID | images refer to IMG_ID; total of 10 attribute classes refers to COUNT(OBJ_CLASS_ID) = 10; | The bounding box's W and H abbreviations stand for the object's width and height respectively; "keyboard" as object class refers to OBJ_CLASS = 'keyboard'; (5, 647) as coordinate refers to X and Y coordinates of the bounding box where X = 5 and Y = 647; | images refers to IMG_ID; have at least 5 "black" classes refers to count(ATT_CLASS_ID) where ATT_CLASS = 'black' > = 5 | relationship refers to PRED_CLASS; "feathers" and "onion" in image no.2345528 refers to IMG_ID = 2345528 and OBJ_CLASS = 'feathers' and OBJ_CLASS = 'onion' | image no. 20 refers to IMG_ID = 20; attribute ID refers to ATT_CLASS_ID; highest number of objects refers to max(count(ATT_CLASS_ID)) | samples of food object refers to OBJ_CLASS = 'food'; image no.6 refers to IMG_ID = 6 | images refer to IMG_ID; "vegetables" and "fruits" as object classes refer to OBJ_CLASS = 'vegetables' and OBJ_CLASS = 'fruits'; |
Name number of samples of "bed" object are there in the image No.1098? | samples of "bed" object refer to OBJ_SAMPLE_ID where OBJ_CLASS = 'bed'; image No.1098 refers to IMG_ID = 1098; | images refer to IMG_ID; "vegetables" and "fruits" as object classes refer to OBJ_CLASS = 'vegetables' and OBJ_CLASS = 'fruits'; | object sample ID refers to OBJ_SAMPLE_ID; image ID 17 refers to IMG_ID = 17; coordinates (0,0) refer to X and Y coordinates of the bounding box where X = 0 and Y = 0; | attribute classes of the image ID "15" refer to ATT_CLASS where IMG_ID = 15; | object class of the image refers to OBJ_CLASS; bounding box of 0, 0, 135, 212 refers to X = 0 AND Y = 0 AND W = 135 AND H = 212 | dimensions of the bounding box refers to (W, H); keyboard refers to OBJ_CLASS = 'keyboard'; image no. 3 refers to IMG_ID = 3 | bounding box of the object refers to (x, y, W, H); image id refers to IMG_ID; prediction relationship class id of 144 refers to PRED_CLASS_ID = 144 | relationship refers to PRED_CLASS; "feathers" and "onion" in image no.2345528 refers to IMG_ID = 2345528 and OBJ_CLASS = 'feathers' and OBJ_CLASS = 'onion' | images refer to IMG_ID; less than 15 object samples refer to COUNT(OBJ_SAMPLE_ID) < 15; | Name the object element refers to OBJ_CLASS; scattered refers to ATT_CLASS = 'scattered'; image no. 10 refers to IMG_ID = 10 | ID of all images refer to IMG_ID; attribute class of "horse" refers to ATT_CLASS = 'horse'; |
What is the predicate class of image ID 68? | predicate class of image ID 68 refers to PRED_CLASS where IMG_ID = 68; | attribute class of "horse" refers to ATT_CLASS = 'horse'; object class of "fur" refers to OBJ_CLASS = 'fur'; | samples of food object refers to OBJ_CLASS = 'food'; image no.6 refers to IMG_ID = 6 | classes of all the object samples refers to OBJ_CLASS; image no.1 refers to IMG_ID = 1 | images refer to IMG_ID; total of 10 attribute classes refers to COUNT(OBJ_CLASS_ID) = 10; | object in image 5 refers to OBJ_SAMPLE_ID where IMG_ID = 5; coordinates of (634, 468) refer to X and Y coordinates of the bounding box in which X = 634 and Y = 468; | attribute refers to ATT_CLASS | dress' object classes refer to OBJ_CLASS = 'dress'; image ID 1764 refers to IMG_ID = 1764; X and Y refer to coordinates of the bounding box; | attribute classes refer to ATT_CLASS; (5,5) coordinate refers to X and Y coordinates of the bounding box where X = 5 and Y = 5; | Name the object element refers to OBJ_CLASS; scattered refers to ATT_CLASS = 'scattered'; image no. 10 refers to IMG_ID = 10 | dimensions of the bounding box refers to (W, H); keyboard refers to OBJ_CLASS = 'keyboard'; image no. 3 refers to IMG_ID = 3 |
Which object in image 8 is the widest? State its object sample ID. | widest relates to the width of the bounding
box of the object which refers to MAX(W); object in image 8 refers to OBJ_SAMPLE_ID where IMG_ID = 8; | AVG(IMG_ID) where OBJ_CLASS = 'keyboard'; | ID of all images refer to IMG_ID; attribute class of "horse" refers to ATT_CLASS = 'horse'; | attribute classes of the image ID "15" refer to ATT_CLASS where IMG_ID = 15; | images refer to IMG_ID; "keyboard" as object class refers to OBJ_CLASS = 'keyboard'; | caption for the prediction class id 12 refers to PRED_CLASS where PRED_CLASS_ID = 12; | How many object elements refers to OBJ_CLASS_ID; image no. 31 refers to IMG_ID = 31 | images refer to IMG_ID; less than 15 object samples refer to COUNT(OBJ_SAMPLE_ID) < 15; | samples of food object refers to OBJ_CLASS = 'food'; image no.6 refers to IMG_ID = 6 | How many attributes refers to ATT_CLASS_ID; object sample no. 7 on image no. 4 refers to IMG_ID = 4 and OBJ_SAMPLE_ID = 7 | images refers to IMG_ID; have at least 25 attributes refers to count(ATT_CLASS_ID) > = 25 |
What is the relationship between "feathers" and "onion" in image no.2345528? | relationship refers to PRED_CLASS; "feathers" and "onion" in image no.2345528 refers to IMG_ID = 2345528 and OBJ_CLASS = 'feathers' and OBJ_CLASS = 'onion' | X and Y refer to coordinates of the bounding box where X = 5 and Y = 5; images refer to IMG_ID; | attribute refers to ATT_CLASS | attributes of polka dot refer to ATT_CLASS = 'polka dot'; images refer to IMG_ID; | coordinates for the object refer to X, Y, W and H coordinates of the bounding box; object class "pizza" refers to OBJ_CLASS = 'pizza'; | images refer to IMG_ID; total of 10 attribute classes refers to COUNT(OBJ_CLASS_ID) = 10; | pairs of object samples refers to OBJ1_SAMPLE_ID and OBJ2_SAMPLE_ID; image no.1 refers to IMG_ID = 1; relation of "parked on" refers to PRED_CLASS = 'parked on' | object samples refers to OBJ_SAMPLE_ID; image no.1 refers to IMG_ID = 1 | samples of "wall" refers to OBJ_SAMPLE_ID and OBJ_CLASS = 'wall' ; image no.2353079 refers to IMG_ID = 2353079 | images have more than 20 object samples refer to IMG_ID where COUNT(OBJ_SAMPLE_ID) > 20; | colour refers to ATT_CLASS; van refers to OBJ_CLASS = 'van'; image no. 1 refers to IMG_ID = 1 |
What are the bounding boxes of the object samples with a predicted relation class of "by" in image no.1? | bounding boxes of the object samples refers to (x, y, W, H); predicted relation class of "by" refers to PRED_CLASS = 'by'; image no.1 refers to IMG_ID = 1 | image with a bounding (422, 63, 77, 363) refers to OBJ_CLASS_ID where X = 422 and Y = 63 and W = 77 and H = 363; | number of images refers to IMG_ID; object sample of "suit" refers to OBJ_CLASS = 'suit' | caption for the prediction class id 12 refers to PRED_CLASS where PRED_CLASS_ID = 12; | bounding box of the object sample refers to (x, y, W, H); image no.5 refers to IMG_ID = 5; has a self-relation refers to OBJ1_SAMPLE_ID = OBJ2_SAMPLE_ID | DIVIDE(COUNT(IMG_ID where OBJ_CLASS = 'man'), COUNT(IMG_ID where OBJ_CLASS = 'person')); | predicate class of image ID 68 refers to PRED_CLASS where IMG_ID = 68; | image no. 99 refers to IMG_ID = 99; described as white refers to ATT_CLASS = 'white'; percentage = divide(count(OBJ_SAMPLE_ID) where ATT_CLASS = 'white', count(OBJ_SAMPLE_ID)) as percentage | images refer to IMG_ID; "vegetables" and "fruits" as object classes refer to OBJ_CLASS = 'vegetables' and OBJ_CLASS = 'fruits'; | coordinates for the object refer to X, Y, W and H coordinates of the bounding box; object class "pizza" refers to OBJ_CLASS = 'pizza'; | colour refers to ATT_CLASS; van refers to OBJ_CLASS = 'van'; image no. 1 refers to IMG_ID = 1 |
How many self-relations are there between the object samples in image no.5? | self-relations refers to OBJ1_SAMPLE_ID = OBJ2_SAMPLE_ID; image no.5 refers to IMG_ID = 5 | bounding box of the object sample refers to (x, y, W, H); image no.5 refers to IMG_ID = 5; has a self-relation refers to OBJ1_SAMPLE_ID = OBJ2_SAMPLE_ID | object elements refers to OBJ_CLASS_ID; average = divide(count(OBJ_CLASS_ID), count(IMG_ID)) | bounding box of the object refers to (X, Y, W, H); sample no.7 on image no.42 refers to IMG_ID = 42 and OBJ_SAMPLE_ID = 7 | images refer to IMG_ID; total of 10 attribute classes refers to COUNT(OBJ_CLASS_ID) = 10; | samples of "wall" refers to OBJ_SAMPLE_ID and OBJ_CLASS = 'wall' ; image no.2353079 refers to IMG_ID = 2353079 | prediction classes with "has" captions refers to PRED_CLASS = 'has'; image id 3050 refers to IMG_ID = 3050 | images have more than 20 object samples refer to IMG_ID where COUNT(OBJ_SAMPLE_ID) > 20; | attributes of polka dot refer to ATT_CLASS = 'polka dot'; images refer to IMG_ID; | DIVIDE(SUM(OBJ_CLASS_ID where OBJ_CLASS = 'surface'), COUNT(OBJ_CLASS_ID)) as percentage where IMG_ID = 2654; | images refer to IMG_ID; "vegetables" and "fruits" as object classes refer to OBJ_CLASS = 'vegetables' and OBJ_CLASS = 'fruits'; |
How many images have at least 5 "black" classes? | images refers to IMG_ID; have at least 5 "black" classes refers to count(ATT_CLASS_ID) where ATT_CLASS = 'black' > = 5 | object number of the sample refers to OBJ1_SAMPLE_ID; object sample no.1 from image no.2345524 refers to OBJ2_SAMPLE_ID = 1 and IMG_ID = 2345524 | bounding boxes refers to (x, y, W, H); image 2222 refers to IMG_ID = 2222; object classes are feathers refers to OBJ_CLASS = 'feathers' | dress' object classes refer to OBJ_CLASS = 'dress'; image ID 1764 refers to IMG_ID = 1764; X and Y refer to coordinates of the bounding box; | attributes of polka dot refer to ATT_CLASS = 'polka dot'; images refer to IMG_ID; | over 20 object samples refers to COUNT(OBJ_SAMPLE_ID) > 20 | prediction classes with "has" captions refers to PRED_CLASS = 'has'; image id 3050 refers to IMG_ID = 3050 | IDs of images refer to IMG_ID; objects with the attributes of 'wired' refer to ATT_CLASS = 'wired'; | predicate class of image ID 68 refers to PRED_CLASS where IMG_ID = 68; | object samples refers to OBJ_SAMPLE_ID; class of "man" refers to OBJ_CLASS = 'man'; image no.1 refers to IMG_ID = 1; percentage = divide(count(OBJ_SAMPLE_ID)when OBJ_CLASS = 'man', count(OBJ_SAMPLE_ID)) as percentage | bounding box of the object refers to (x, y, W, H); image id refers to IMG_ID; prediction relationship class id of 144 refers to PRED_CLASS_ID = 144 |
List all the attribute classes of the image ID "15". | attribute classes of the image ID "15" refer to ATT_CLASS where IMG_ID = 15; | bounding box of the object refers to (x, y, W, H); image id refers to IMG_ID; prediction relationship class id of 144 refers to PRED_CLASS_ID = 144 | objects that have multiple relations refers to OBJ1_SAMPLE_ID ! = OBJ2_SAMPLE_ID; captions for the prediction class ids are "on" refers to PRED_CLASS = 'on' | self-relations refers to OBJ1_SAMPLE_ID = OBJ2_SAMPLE_ID; image no.5 refers to IMG_ID = 5 | caption for the prediction class id 12 refers to PRED_CLASS where PRED_CLASS_ID = 12; | bounding box refers to X, Y, W, H from IMG_OBJ; lowest relates to the height of the bounding box which refers to MIN(H); | DIVIDE(COUNT(IMG_ID where OBJ_CLASS = 'man'), COUNT(IMG_ID where OBJ_CLASS = 'person')); | ID of all images refer to IMG_ID; if two objects (OBJ1_SAMPLE_ID,
OBJ2_SAMPLE_ID) has
multiple PRED_CLASS_ID, it
means these two objects
have multiple relations; | ID of all images refer to IMG_ID; attribute class of "horse" refers to ATT_CLASS = 'horse'; | relation refers to PRED_CLASS; object sample no.8 and object sample no.4 refers to OBJ1_SAMPLE_ID = 8 AND OBJ2_SAMPLE_ID = 4; image no.1 refers to IMG_ID = 1 | prediction classes with "has" captions refers to PRED_CLASS = 'has'; image id 3050 refers to IMG_ID = 3050 |
List all the IDs of images that have objects with the attributes of 'wired'. | IDs of images refer to IMG_ID; objects with the attributes of 'wired' refer to ATT_CLASS = 'wired'; | images have more than 20 object samples refer to IMG_ID where COUNT(OBJ_SAMPLE_ID) > 20; | object samples refers to OBJ_CLASS_ID; image no.1 refers to IMG_ID = 1; in the class of "man" refers to OBJ_CLASS = 'man' | object number of the sample refers to OBJ1_SAMPLE_ID; object sample no.1 from image no.2345524 refers to OBJ2_SAMPLE_ID = 1 and IMG_ID = 2345524 | samples of "bed" object refer to OBJ_SAMPLE_ID where OBJ_CLASS = 'bed'; image No.1098 refers to IMG_ID = 1098; | ID of all images refer to IMG_ID; attribute class of "horse" refers to ATT_CLASS = 'horse'; | How many attributes refers to ATT_CLASS_ID; object sample no. 7 on image no. 4 refers to IMG_ID = 4 and OBJ_SAMPLE_ID = 7 | How many object elements refers to OBJ_CLASS_ID; image no. 31 refers to IMG_ID = 31 | images refer to IMG_ID; "vegetables" and "fruits" as object classes refer to OBJ_CLASS = 'vegetables' and OBJ_CLASS = 'fruits'; | samples of clouds refer to IMG_ID where OBJ_CLASS = 'cloud'; image no.2315533 refers to IMG_ID = 2315533; | ID of all images refer to IMG_ID; if two objects (OBJ1_SAMPLE_ID,
OBJ2_SAMPLE_ID) has
multiple PRED_CLASS_ID, it
means these two objects
have multiple relations; |
Calculate the ratio of the total number of images with an object class of "man" and "person". | DIVIDE(COUNT(IMG_ID where OBJ_CLASS = 'man'), COUNT(IMG_ID where OBJ_CLASS = 'person')); | onion category refers to OBJ_CLASS = 'onion'; | DIVIDE(COUNT(OBJ_SAMPLE_ID), COUNT(IMG_ID)); | Name the object element refers to OBJ_CLASS; scattered refers to ATT_CLASS = 'scattered'; image no. 10 refers to IMG_ID = 10 | images refer to IMG_ID; "vegetables" and "fruits" as object classes refer to OBJ_CLASS = 'vegetables' and OBJ_CLASS = 'fruits'; | AVG(IMG_ID) where OBJ_CLASS = 'keyboard'; | object samples refers to OBJ_SAMPLE_ID; image no.1 refers to IMG_ID = 1 | samples of clouds refer to IMG_ID where OBJ_CLASS = 'cloud'; image no.2315533 refers to IMG_ID = 2315533; | relationship refers to PRED_CLASS; object sample no.12 and no.8 of image no.2345511 refers to IMG_ID = 2345511 AND OBJ1_SAMPLE_ID = 12 AND OBJ2_SAMPLE_ID = 8 | bounding boxes refers to (x, y, W, H); image 2222 refers to IMG_ID = 2222; object classes are feathers refers to OBJ_CLASS = 'feathers' | image numbers that contain the "paint" object refer to IMG_ID where OBJ_CLASS = 'paint'; |
How many images have at least 25 attributes? | images refers to IMG_ID; have at least 25 attributes refers to count(ATT_CLASS_ID) > = 25 | relationship refers to PRED_CLASS; "feathers" and "onion" in image no.2345528 refers to IMG_ID = 2345528 and OBJ_CLASS = 'feathers' and OBJ_CLASS = 'onion' | samples of clouds refer to IMG_ID where OBJ_CLASS = 'cloud'; image no.2315533 refers to IMG_ID = 2315533; | Name the object element refers to OBJ_CLASS; scattered refers to ATT_CLASS = 'scattered'; image no. 10 refers to IMG_ID = 10 | object class of the image refers to OBJ_CLASS; bounding box of 0, 0, 135, 212 refers to X = 0 AND Y = 0 AND W = 135 AND H = 212 | prediction relationship class id refers to PRED_CLASS_ID; tallest image refers to max(H) | images refers to IMG_ID; have at least 5 "black" classes refers to count(ATT_CLASS_ID) where ATT_CLASS = 'black' > = 5 | image no. 99 refers to IMG_ID = 99; described as white refers to ATT_CLASS = 'white'; percentage = divide(count(OBJ_SAMPLE_ID) where ATT_CLASS = 'white', count(OBJ_SAMPLE_ID)) as percentage | attribute classes of the image ID "15" refer to ATT_CLASS where IMG_ID = 15; | over 20 object samples refers to COUNT(OBJ_SAMPLE_ID) > 20 | self-relations refers to OBJ1_SAMPLE_ID = OBJ2_SAMPLE_ID; image no.5 refers to IMG_ID = 5 |
Calculate the average of object samples for the image. | DIVIDE(COUNT(OBJ_SAMPLE_ID), COUNT(IMG_ID)); | object class refers to OBJ_CLASS; sample no.10 refers to OBJ_SAMPLE_ID = 10; image no.2320341 refers to IMG_ID = 2320341 | widest relates to the width of the bounding
box of the object which refers to MAX(W); object in image 8 refers to OBJ_SAMPLE_ID where IMG_ID = 8; | IDs of images refer to IMG_ID; objects with the attributes of 'wired' refer to ATT_CLASS = 'wired'; | predicate class of image ID 68 refers to PRED_CLASS where IMG_ID = 68; | dimensions of the bounding box refers to (W, H); keyboard refers to OBJ_CLASS = 'keyboard'; image no. 3 refers to IMG_ID = 3 | explanations about distinct object classes refers to OBJ_CLASS; images refers to IMG_ID; x and y coordinate of 0 refers to X = 0 AND Y = 0 | "picture" as attribute class refers to ATT_CLASS = 'picture'; "bear" as object class refers to OBJ_CLASS = 'bear'; images refer to IMG_ID; | prediction relationship class id refers to PRED_CLASS_ID; tallest image refers to max(H) | ID of all images refer to IMG_ID; attribute class of "horse" refers to ATT_CLASS = 'horse'; | attributes of polka dot refer to ATT_CLASS = 'polka dot'; images refer to IMG_ID; |
How many object elements are there on average in each image? | object elements refers to OBJ_CLASS_ID; average = divide(count(OBJ_CLASS_ID), count(IMG_ID)) | widest relates to the width of the bounding
box of the object which refers to MAX(W); object in image 8 refers to OBJ_SAMPLE_ID where IMG_ID = 8; | images refer to IMG_ID; "keyboard" as object class refers to OBJ_CLASS = 'keyboard'; | object number of the sample refers to OBJ1_SAMPLE_ID; object sample no.1 from image no.2345524 refers to OBJ2_SAMPLE_ID = 1 and IMG_ID = 2345524 | white objects refers to ATT_CLASS = 'white'; image no.2347915 refers to IMG_ID = 2347915 | bounding box of the object refers to (X, Y, W, H); sample no.7 on image no.42 refers to IMG_ID = 42 and OBJ_SAMPLE_ID = 7 | DIVIDE(COUNT(IMG_ID where OBJ_CLASS = 'man'), COUNT(IMG_ID where OBJ_CLASS = 'person')); | "picture" as attribute class refers to ATT_CLASS = 'picture'; "bear" as object class refers to OBJ_CLASS = 'bear'; images refer to IMG_ID; | DIVIDE(SUM(OBJ_SAMPLE_ID where OBJ_CLASS = 'airplane'), COUNT(OBJ_CLASS)) as percentage; | has' predicate classes refers to PRED_CLASS = 'has'; image ID 107 refers to IMG_ID = 107; | How many object elements refers to OBJ_CLASS_ID; image no. 31 refers to IMG_ID = 31 |
Name the object element that is described as being scattered on image no. 10. | Name the object element refers to OBJ_CLASS; scattered refers to ATT_CLASS = 'scattered'; image no. 10 refers to IMG_ID = 10 | coordinates for the object refer to X, Y, W and H coordinates of the bounding box; object class "pizza" refers to OBJ_CLASS = 'pizza'; | object class refers to OBJ_CLASS; sample no.10 refers to OBJ_SAMPLE_ID = 10; image no.2320341 refers to IMG_ID = 2320341 | have at least one object sample in the class of "man" refers to count(IMG_ID where OBJ_CLASS = 'man') > = 1 | prediction relationship class id refers to PRED_CLASS_ID; tallest image refers to max(H) | has' predicate classes refers to PRED_CLASS = 'has'; image ID 107 refers to IMG_ID = 107; | samples of food object refers to OBJ_CLASS = 'food'; image no.6 refers to IMG_ID = 6 | bounding boxes refers to (x, y, W, H); image 2222 refers to IMG_ID = 2222; object classes are feathers refers to OBJ_CLASS = 'feathers' | DIVIDE(COUNT(IMG_ID where OBJ_CLASS = 'man'), COUNT(IMG_ID where OBJ_CLASS = 'person')); | explanations about distinct object classes refers to OBJ_CLASS; images refers to IMG_ID; x and y coordinate of 0 refers to X = 0 AND Y = 0 | predicted relation classes refers to PRED_CLASS; object sample no.14 in image no.1 refers to OBJ1_SAMPLE_ID = 14 AND OBJ2_SAMPLE_ID = 14 and IMG_ID = 1 |
What is the percentage of "surface" object samples in image No.2654? | DIVIDE(SUM(OBJ_CLASS_ID where OBJ_CLASS = 'surface'), COUNT(OBJ_CLASS_ID)) as percentage where IMG_ID = 2654; | explanations about distinct object classes refers to OBJ_CLASS; images refers to IMG_ID; x and y coordinate of 0 refers to X = 0 AND Y = 0 | coordinates for the object refer to X, Y, W and H coordinates of the bounding box; object class "pizza" refers to OBJ_CLASS = 'pizza'; | images have more than 20 object samples refer to IMG_ID where COUNT(OBJ_SAMPLE_ID) > 20; | image with a bounding (422, 63, 77, 363) refers to OBJ_CLASS_ID where X = 422 and Y = 63 and W = 77 and H = 363; | Name the object element refers to OBJ_CLASS; scattered refers to ATT_CLASS = 'scattered'; image no. 10 refers to IMG_ID = 10 | predicted relation classes refers to PRED_CLASS; object sample no.14 in image no.1 refers to OBJ1_SAMPLE_ID = 14 AND OBJ2_SAMPLE_ID = 14 and IMG_ID = 1 | white objects refers to ATT_CLASS = 'white'; image no.2347915 refers to IMG_ID = 2347915 | How many images have at least one pair of object samples with the relation "parked on" refers to count(IMG_ID) where OBJ1_SAMPLE_ID ! = OBJ2_SAMPLE_ID and PRED_CLASS = 'parked on' | object has the highest attribute classes refers to OBJ_SAMPLE_ID where MAX(COUNT(OBJ_SAMPLE_ID)); | caption for the prediction class id 12 refers to PRED_CLASS where PRED_CLASS_ID = 12; |
How many images have at least one object sample in the class of "man"? | have at least one object sample in the class of "man" refers to count(IMG_ID where OBJ_CLASS = 'man') > = 1 | attributes of polka dot refer to ATT_CLASS = 'polka dot'; images refer to IMG_ID; | attribute refers to ATT_CLASS | object samples refers to OBJ_SAMPLE_ID; class of "man" refers to OBJ_CLASS = 'man'; image no.1 refers to IMG_ID = 1; percentage = divide(count(OBJ_SAMPLE_ID)when OBJ_CLASS = 'man', count(OBJ_SAMPLE_ID)) as percentage | relation refers to PRED_CLASS; object sample no.8 and object sample no.4 refers to OBJ1_SAMPLE_ID = 8 AND OBJ2_SAMPLE_ID = 4; image no.1 refers to IMG_ID = 1 | The bounding box's W and H abbreviations stand for the object's width and height respectively; "keyboard" as object class refers to OBJ_CLASS = 'keyboard'; (5, 647) as coordinate refers to X and Y coordinates of the bounding box where X = 5 and Y = 647; | How many object elements refers to OBJ_CLASS_ID; image no. 31 refers to IMG_ID = 31 | ID of all images refer to IMG_ID; if two objects (OBJ1_SAMPLE_ID,
OBJ2_SAMPLE_ID) has
multiple PRED_CLASS_ID, it
means these two objects
have multiple relations; | relationship refers to PRED_CLASS; object sample no.12 and no.8 of image no.2345511 refers to IMG_ID = 2345511 AND OBJ1_SAMPLE_ID = 12 AND OBJ2_SAMPLE_ID = 8 | attribute classes of image ID 22 refer to ATT_CLASS where MG_ID = 22; | bounding box of the object refers to (x, y, W, H); image id refers to IMG_ID; prediction relationship class id of 144 refers to PRED_CLASS_ID = 144 |
Give the object number of the sample which has the relationship of "lying on" with object sample no.1 from image no.2345524. | object number of the sample refers to OBJ1_SAMPLE_ID; object sample no.1 from image no.2345524 refers to OBJ2_SAMPLE_ID = 1 and IMG_ID = 2345524 | samples of clouds refer to IMG_ID where OBJ_CLASS = 'cloud'; image no.2315533 refers to IMG_ID = 2315533; | Y coordinate many are 0 refers to Y coordinates of the bounding box where Y = 0; image ID 12 refers to IMG_ID = 12; | relationship refers to PRED_CLASS; "feathers" and "onion" in image no.2345528 refers to IMG_ID = 2345528 and OBJ_CLASS = 'feathers' and OBJ_CLASS = 'onion' | X and Y refer to coordinates of the bounding box; image ID 23 refers to IMG_ID = 23; 'cast' attribute class refers to ATT_CLASS = 'cast'; | classes of all the object samples refers to OBJ_CLASS; image no.1 refers to IMG_ID = 1 | blue' attribute classes on image ID 2355735 refer to ATT_CLASS = 'blue' where IMG_ID = 2355735; | dress' object classes refer to OBJ_CLASS = 'dress'; image ID 1764 refers to IMG_ID = 1764; X and Y refer to coordinates of the bounding box; | images refer to IMG_ID; "vegetables" and "fruits" as object classes refer to OBJ_CLASS = 'vegetables' and OBJ_CLASS = 'fruits'; | DIVIDE(SUM(OBJ_CLASS_ID where OBJ_CLASS = 'surface'), COUNT(OBJ_CLASS_ID)) as percentage where IMG_ID = 2654; | dimensions of the bounding box refers to (W, H); keyboard refers to OBJ_CLASS = 'keyboard'; image no. 3 refers to IMG_ID = 3 |
Provide the x-coordinate and y-coordinate of the image with an attribute class of ''horse" and an object class of "fur". | attribute class of "horse" refers to ATT_CLASS = 'horse'; object class of "fur" refers to OBJ_CLASS = 'fur'; | object elements refers to OBJ_CLASS_ID; average = divide(count(OBJ_CLASS_ID), count(IMG_ID)) | colour refers to ATT_CLASS; van refers to OBJ_CLASS = 'van'; image no. 1 refers to IMG_ID = 1 | explanations about distinct object classes refers to OBJ_CLASS; images refers to IMG_ID; x and y coordinate of 0 refers to X = 0 AND Y = 0 | ids of the images refers to IMG_ID; self-relations refers to OBJ1_SAMPLE_ID = OBJ2_SAMPLE_ID | attribute refers to ATT_CLASS | attribute classes of the image ID "15" refer to ATT_CLASS where IMG_ID = 15; | How many attributes refers to ATT_CLASS_ID; object sample no. 7 on image no. 4 refers to IMG_ID = 4 and OBJ_SAMPLE_ID = 7 | object has the highest attribute classes refers to OBJ_SAMPLE_ID where MAX(COUNT(OBJ_SAMPLE_ID)); | "picture" as attribute class refers to ATT_CLASS = 'picture'; "bear" as object class refers to OBJ_CLASS = 'bear'; images refer to IMG_ID; | ID of all images refer to IMG_ID; attribute class of "horse" refers to ATT_CLASS = 'horse'; |
How many object elements can be detected on image no. 31? | How many object elements refers to OBJ_CLASS_ID; image no. 31 refers to IMG_ID = 31 | white objects refers to ATT_CLASS = 'white'; image no.2347915 refers to IMG_ID = 2347915 | The bounding box's W and H abbreviations stand for the object's width and height respectively; "keyboard" as object class refers to OBJ_CLASS = 'keyboard'; (5, 647) as coordinate refers to X and Y coordinates of the bounding box where X = 5 and Y = 647; | number of images refers to IMG_ID; object sample of "suit" refers to OBJ_CLASS = 'suit' | object samples refers to OBJ_SAMPLE_ID; image no.1 refers to IMG_ID = 1 | image no. 20 refers to IMG_ID = 20; attribute ID refers to ATT_CLASS_ID; highest number of objects refers to max(count(ATT_CLASS_ID)) | IDs of images refer to IMG_ID; objects with the attributes of 'wired' refer to ATT_CLASS = 'wired'; | ids of the images refers to IMG_ID; self-relations refers to OBJ1_SAMPLE_ID = OBJ2_SAMPLE_ID | bounding boxes of the object samples refers to (x, y, W, H); predicted relation class of "by" refers to PRED_CLASS = 'by'; image no.1 refers to IMG_ID = 1 | attribute classes of the image ID "15" refer to ATT_CLASS where IMG_ID = 15; | images refers to IMG_ID; have at least 5 "black" classes refers to count(ATT_CLASS_ID) where ATT_CLASS = 'black' > = 5 |
Define the bounding box of the object sample no. 7 on image no. 42. | bounding box of the object refers to (X, Y, W, H); sample no.7 on image no.42 refers to IMG_ID = 42 and OBJ_SAMPLE_ID = 7 | images refer to IMG_ID; "keyboard" as object class refers to OBJ_CLASS = 'keyboard'; | ID of all images refer to IMG_ID; if two objects (OBJ1_SAMPLE_ID,
OBJ2_SAMPLE_ID) has
multiple PRED_CLASS_ID, it
means these two objects
have multiple relations; | predicted relation class refers to PRED_CLASS; self-relations refers to OBJ1_SAMPLE_ID = OBJ2_SAMPLE_ID; image no.5 refers to IMG_ID = 5 | attribute refers to ATT_CLASS | DIVIDE(COUNT(IMG_ID where OBJ_CLASS = 'man'), COUNT(IMG_ID where OBJ_CLASS = 'person')); | ids of the images refers to IMG_ID; self-relations refers to OBJ1_SAMPLE_ID = OBJ2_SAMPLE_ID | samples of clouds refer to IMG_ID where OBJ_CLASS = 'cloud'; image no.2315533 refers to IMG_ID = 2315533; | classes of all the object samples refers to OBJ_CLASS; image no.1 refers to IMG_ID = 1 | has' predicate classes refers to PRED_CLASS = 'has'; image ID 107 refers to IMG_ID = 107; | relationship refers to PRED_CLASS; "feathers" and "onion" in image no.2345528 refers to IMG_ID = 2345528 and OBJ_CLASS = 'feathers' and OBJ_CLASS = 'onion' |
Please list all the predicted relation classes of object sample no.14 in image no.1. | predicted relation classes refers to PRED_CLASS; object sample no.14 in image no.1 refers to OBJ1_SAMPLE_ID = 14 AND OBJ2_SAMPLE_ID = 14 and IMG_ID = 1 | images refers to IMG_ID; have at least 5 "black" classes refers to count(ATT_CLASS_ID) where ATT_CLASS = 'black' > = 5 | images refer to IMG_ID; less than 15 object samples refer to COUNT(OBJ_SAMPLE_ID) < 15; | ids of the images refers to IMG_ID; self-relations refers to OBJ1_SAMPLE_ID = OBJ2_SAMPLE_ID | object elements refers to OBJ_CLASS_ID; average = divide(count(OBJ_CLASS_ID), count(IMG_ID)) | DIVIDE(COUNT(OBJ_SAMPLE_ID), COUNT(IMG_ID)); | have at least one object sample in the class of "man" refers to count(IMG_ID where OBJ_CLASS = 'man') > = 1 | object has the highest attribute classes refers to OBJ_SAMPLE_ID where MAX(COUNT(OBJ_SAMPLE_ID)); | object sample ID refers to OBJ_SAMPLE_ID; image ID 17 refers to IMG_ID = 17; coordinates (0,0) refer to X and Y coordinates of the bounding box where X = 0 and Y = 0; | attributes of polka dot refer to ATT_CLASS = 'polka dot'; images refer to IMG_ID; | prediction classes with "has" captions refers to PRED_CLASS = 'has'; image id 3050 refers to IMG_ID = 3050 |
What is the bounding box of the object sample in image no.5 that has a self-relation? | bounding box of the object sample refers to (x, y, W, H); image no.5 refers to IMG_ID = 5; has a self-relation refers to OBJ1_SAMPLE_ID = OBJ2_SAMPLE_ID | attribute classes of image ID 22 refer to ATT_CLASS where MG_ID = 22; | AVG(IMG_ID) where OBJ_CLASS = 'keyboard'; | number of images refers to IMG_ID; object sample of "suit" refers to OBJ_CLASS = 'suit' | images refers to IMG_ID; have at least 25 attributes refers to count(ATT_CLASS_ID) > = 25 | bounding box of the object refers to (x, y, W, H); image id refers to IMG_ID; prediction relationship class id of 144 refers to PRED_CLASS_ID = 144 | colour refers to ATT_CLASS; van refers to OBJ_CLASS = 'van'; image no. 1 refers to IMG_ID = 1 | How many attributes refers to ATT_CLASS_ID; object sample no. 7 on image no. 4 refers to IMG_ID = 4 and OBJ_SAMPLE_ID = 7 | How many object elements refers to OBJ_CLASS_ID; image no. 31 refers to IMG_ID = 31 | classes for attributes refers to ATT_CLASS; image id 8 refers to IMG_ID = 8 | "picture" as attribute class refers to ATT_CLASS = 'picture'; "bear" as object class refers to OBJ_CLASS = 'bear'; images refer to IMG_ID; |
How many 'blue' attribute classes are there on image ID 2355735? | blue' attribute classes on image ID 2355735 refer to ATT_CLASS = 'blue' where IMG_ID = 2355735; | attribute refers to ATT_CLASS | image no. 99 refers to IMG_ID = 99; described as white refers to ATT_CLASS = 'white'; percentage = divide(count(OBJ_SAMPLE_ID) where ATT_CLASS = 'white', count(OBJ_SAMPLE_ID)) as percentage | dimensions of the bounding box refers to (W, H); keyboard refers to OBJ_CLASS = 'keyboard'; image no. 3 refers to IMG_ID = 3 | The bounding box's W and H abbreviations stand for the object's width and height respectively; "keyboard" as object class refers to OBJ_CLASS = 'keyboard'; (5, 647) as coordinate refers to X and Y coordinates of the bounding box where X = 5 and Y = 647; | object elements refers to OBJ_CLASS_ID; average = divide(count(OBJ_CLASS_ID), count(IMG_ID)) | Y coordinate many are 0 refers to Y coordinates of the bounding box where Y = 0; image ID 12 refers to IMG_ID = 12; | objects that have multiple relations refers to OBJ1_SAMPLE_ID ! = OBJ2_SAMPLE_ID; captions for the prediction class ids are "on" refers to PRED_CLASS = 'on' | AVG(IMG_ID) where OBJ_CLASS = 'keyboard'; | DIVIDE(SUM(OBJ_SAMPLE_ID where OBJ_CLASS = 'airplane'), COUNT(OBJ_CLASS)) as percentage; | DIVIDE(COUNT(OBJ_SAMPLE_ID), COUNT(IMG_ID)); |
What is the relation between object sample no.8 and object sample no.4 in image no.1? | relation refers to PRED_CLASS; object sample no.8 and object sample no.4 refers to OBJ1_SAMPLE_ID = 8 AND OBJ2_SAMPLE_ID = 4; image no.1 refers to IMG_ID = 1 | images refer to IMG_ID; "vegetables" and "fruits" as object classes refer to OBJ_CLASS = 'vegetables' and OBJ_CLASS = 'fruits'; | "picture" as attribute class refers to ATT_CLASS = 'picture'; "bear" as object class refers to OBJ_CLASS = 'bear'; images refer to IMG_ID; | IDs of images refer to IMG_ID; objects with the attributes of 'wired' refer to ATT_CLASS = 'wired'; | unique id number identifying refers to OBJ_CLASS_ID; onion object class refers to OBJ_CLASS = 'onion' | ID of all images refer to IMG_ID; attribute class of "horse" refers to ATT_CLASS = 'horse'; | over 20 object samples refers to COUNT(OBJ_SAMPLE_ID) > 20 | bounding box of the object sample refers to (x, y, W, H); image no.5 refers to IMG_ID = 5; has a self-relation refers to OBJ1_SAMPLE_ID = OBJ2_SAMPLE_ID | images have more than 20 object samples refer to IMG_ID where COUNT(OBJ_SAMPLE_ID) > 20; | prediction classes with "has" captions refers to PRED_CLASS = 'has'; image id 3050 refers to IMG_ID = 3050 | self-relations refers to OBJ1_SAMPLE_ID = OBJ2_SAMPLE_ID; image no.5 refers to IMG_ID = 5 |
What is the object class of the image with a bounding box of 0, 0, 135, 212? | object class of the image refers to OBJ_CLASS; bounding box of 0, 0, 135, 212 refers to X = 0 AND Y = 0 AND W = 135 AND H = 212 | attribute classes of the image ID "15" refer to ATT_CLASS where IMG_ID = 15; | self-relations refers to OBJ1_SAMPLE_ID = OBJ2_SAMPLE_ID; image no.5 refers to IMG_ID = 5 | images refer to IMG_ID; "keyboard" as object class refers to OBJ_CLASS = 'keyboard'; | bounding boxes refers to (x, y, W, H); image 2222 refers to IMG_ID = 2222; object classes are feathers refers to OBJ_CLASS = 'feathers' | The bounding box's W and H abbreviations stand for the object's width and height respectively; "keyboard" as object class refers to OBJ_CLASS = 'keyboard'; (5, 647) as coordinate refers to X and Y coordinates of the bounding box where X = 5 and Y = 647; | Y coordinate many are 0 refers to Y coordinates of the bounding box where Y = 0; image ID 12 refers to IMG_ID = 12; | "picture" as attribute class refers to ATT_CLASS = 'picture'; "bear" as object class refers to OBJ_CLASS = 'bear'; images refer to IMG_ID; | IDs of images refer to IMG_ID; objects with the attributes of 'wired' refer to ATT_CLASS = 'wired'; | dimensions of the bounding box refers to (W, H); keyboard refers to OBJ_CLASS = 'keyboard'; image no. 3 refers to IMG_ID = 3 | samples of food object refers to OBJ_CLASS = 'food'; image no.6 refers to IMG_ID = 6 |
How many 'has' predicate classes does image ID 107 have? | has' predicate classes refers to PRED_CLASS = 'has'; image ID 107 refers to IMG_ID = 107; | prediction relationship class id refers to PRED_CLASS_ID; tallest image refers to max(H) | object has the highest attribute classes refers to OBJ_SAMPLE_ID where MAX(COUNT(OBJ_SAMPLE_ID)); | DIVIDE(COUNT(OBJ_SAMPLE_ID), COUNT(IMG_ID)); | blue' attribute classes on image ID 2355735 refer to ATT_CLASS = 'blue' where IMG_ID = 2355735; | images have more than 20 object samples refer to IMG_ID where COUNT(OBJ_SAMPLE_ID) > 20; | ID of all images refer to IMG_ID; if two objects (OBJ1_SAMPLE_ID,
OBJ2_SAMPLE_ID) has
multiple PRED_CLASS_ID, it
means these two objects
have multiple relations; | How many images have at least one pair of object samples with the relation "parked on" refers to count(IMG_ID) where OBJ1_SAMPLE_ID ! = OBJ2_SAMPLE_ID and PRED_CLASS = 'parked on' | relation refers to PRED_CLASS; object sample no.8 and object sample no.4 refers to OBJ1_SAMPLE_ID = 8 AND OBJ2_SAMPLE_ID = 4; image no.1 refers to IMG_ID = 1 | samples of "wall" refers to OBJ_SAMPLE_ID and OBJ_CLASS = 'wall' ; image no.2353079 refers to IMG_ID = 2353079 | IDs of images refer to IMG_ID; objects with the attributes of 'wired' refer to ATT_CLASS = 'wired'; |
Which object has the highest attribute classes? | object has the highest attribute classes refers to OBJ_SAMPLE_ID where MAX(COUNT(OBJ_SAMPLE_ID)); | images refers to IMG_ID; have at least 5 "black" classes refers to count(ATT_CLASS_ID) where ATT_CLASS = 'black' > = 5 | X and Y refer to coordinates of the bounding box; image ID 23 refers to IMG_ID = 23; 'cast' attribute class refers to ATT_CLASS = 'cast'; | bounding boxes of the object samples refers to (x, y, W, H); predicted relation class of "by" refers to PRED_CLASS = 'by'; image no.1 refers to IMG_ID = 1 | image numbers that contain the "paint" object refer to IMG_ID where OBJ_CLASS = 'paint'; | images have more than 20 object samples refer to IMG_ID where COUNT(OBJ_SAMPLE_ID) > 20; | attribute class of "horse" refers to ATT_CLASS = 'horse'; object class of "fur" refers to OBJ_CLASS = 'fur'; | has' predicate classes refers to PRED_CLASS = 'has'; image ID 107 refers to IMG_ID = 107; | predicted relation class refers to PRED_CLASS; self-relations refers to OBJ1_SAMPLE_ID = OBJ2_SAMPLE_ID; image no.5 refers to IMG_ID = 5 | samples of food object refers to OBJ_CLASS = 'food'; image no.6 refers to IMG_ID = 6 | relation refers to PRED_CLASS; object sample no.8 and object sample no.4 refers to OBJ1_SAMPLE_ID = 8 AND OBJ2_SAMPLE_ID = 4; image no.1 refers to IMG_ID = 1 |
What colour is the van that can be spotted in image no. 1? | colour refers to ATT_CLASS; van refers to OBJ_CLASS = 'van'; image no. 1 refers to IMG_ID = 1 | X and Y refer to coordinates of the bounding box where X = 5 and Y = 5; images refer to IMG_ID; | The bounding box's W and H abbreviations stand for the object's width and height respectively; "keyboard" as object class refers to OBJ_CLASS = 'keyboard'; (5, 647) as coordinate refers to X and Y coordinates of the bounding box where X = 5 and Y = 647; | object samples refers to OBJ_SAMPLE_ID; class of "man" refers to OBJ_CLASS = 'man'; image no.1 refers to IMG_ID = 1; percentage = divide(count(OBJ_SAMPLE_ID)when OBJ_CLASS = 'man', count(OBJ_SAMPLE_ID)) as percentage | bounding boxes of the object samples refers to (x, y, W, H); predicted relation class of "by" refers to PRED_CLASS = 'by'; image no.1 refers to IMG_ID = 1 | onion category refers to OBJ_CLASS = 'onion'; | images refer to IMG_ID; less than 15 object samples refer to COUNT(OBJ_SAMPLE_ID) < 15; | number of images refers to IMG_ID; object sample of "suit" refers to OBJ_CLASS = 'suit' | object elements refers to OBJ_CLASS_ID; average = divide(count(OBJ_CLASS_ID), count(IMG_ID)) | image with a bounding (422, 63, 77, 363) refers to OBJ_CLASS_ID where X = 422 and Y = 63 and W = 77 and H = 363; | has' predicate classes refers to PRED_CLASS = 'has'; image ID 107 refers to IMG_ID = 107; |
Write 10 coordinates with the object class "pizza." | coordinates for the object refer to X, Y, W and H coordinates of the bounding box; object class "pizza" refers to OBJ_CLASS = 'pizza'; | relation refers to PRED_CLASS; object sample no.8 and object sample no.4 refers to OBJ1_SAMPLE_ID = 8 AND OBJ2_SAMPLE_ID = 4; image no.1 refers to IMG_ID = 1 | number of images refers to IMG_ID; object sample of "suit" refers to OBJ_CLASS = 'suit' | objects that have multiple relations refers to OBJ1_SAMPLE_ID ! = OBJ2_SAMPLE_ID; captions for the prediction class ids are "on" refers to PRED_CLASS = 'on' | AVG(IMG_ID) where OBJ_CLASS = 'keyboard'; | image no. 99 refers to IMG_ID = 99; described as white refers to ATT_CLASS = 'white'; percentage = divide(count(OBJ_SAMPLE_ID) where ATT_CLASS = 'white', count(OBJ_SAMPLE_ID)) as percentage | blue' attribute classes on image ID 2355735 refer to ATT_CLASS = 'blue' where IMG_ID = 2355735; | images refers to IMG_ID; have at least 25 attributes refers to count(ATT_CLASS_ID) > = 25 | samples of "bed" object refer to OBJ_SAMPLE_ID where OBJ_CLASS = 'bed'; image No.1098 refers to IMG_ID = 1098; | have at least one object sample in the class of "man" refers to count(IMG_ID where OBJ_CLASS = 'man') > = 1 | images refer to IMG_ID; less than 15 object samples refer to COUNT(OBJ_SAMPLE_ID) < 15; |
List all the explanations about object classes of all the images with an x and y coordinate of 0. | explanations about distinct object classes refers to OBJ_CLASS; images refers to IMG_ID; x and y coordinate of 0 refers to X = 0 AND Y = 0 | onion category refers to OBJ_CLASS = 'onion'; | widest relates to the width of the bounding
box of the object which refers to MAX(W); object in image 8 refers to OBJ_SAMPLE_ID where IMG_ID = 8; | bounding boxes refers to (x, y, W, H); image 2222 refers to IMG_ID = 2222; object classes are feathers refers to OBJ_CLASS = 'feathers' | image no. 20 refers to IMG_ID = 20; attribute ID refers to ATT_CLASS_ID; highest number of objects refers to max(count(ATT_CLASS_ID)) | attribute class of "horse" refers to ATT_CLASS = 'horse'; object class of "fur" refers to OBJ_CLASS = 'fur'; | prediction classes with "has" captions refers to PRED_CLASS = 'has'; image id 3050 refers to IMG_ID = 3050 | over 20 object samples refers to COUNT(OBJ_SAMPLE_ID) > 20 | blue' attribute classes on image ID 2355735 refer to ATT_CLASS = 'blue' where IMG_ID = 2355735; | image no. 99 refers to IMG_ID = 99; described as white refers to ATT_CLASS = 'white'; percentage = divide(count(OBJ_SAMPLE_ID) where ATT_CLASS = 'white', count(OBJ_SAMPLE_ID)) as percentage | classes of all the object samples refers to OBJ_CLASS; image no.1 refers to IMG_ID = 1 |
Please list the classes of all the object samples in image no.1. | classes of all the object samples refers to OBJ_CLASS; image no.1 refers to IMG_ID = 1 | self-relations refers to OBJ1_SAMPLE_ID = OBJ2_SAMPLE_ID; image no.5 refers to IMG_ID = 5 | images refer to IMG_ID; less than 15 object samples refer to COUNT(OBJ_SAMPLE_ID) < 15; | object elements refers to OBJ_CLASS_ID; average = divide(count(OBJ_CLASS_ID), count(IMG_ID)) | How many attributes refers to ATT_CLASS_ID; object sample no. 7 on image no. 4 refers to IMG_ID = 4 and OBJ_SAMPLE_ID = 7 | object has the highest attribute classes refers to OBJ_SAMPLE_ID where MAX(COUNT(OBJ_SAMPLE_ID)); | samples of "wall" refers to OBJ_SAMPLE_ID and OBJ_CLASS = 'wall' ; image no.2353079 refers to IMG_ID = 2353079 | AVG(IMG_ID) where OBJ_CLASS = 'keyboard'; | prediction classes with "has" captions refers to PRED_CLASS = 'has'; image id 3050 refers to IMG_ID = 3050 | predicate class of image ID 68 refers to PRED_CLASS where IMG_ID = 68; | predicted relation classes refers to PRED_CLASS; object sample no.14 in image no.1 refers to OBJ1_SAMPLE_ID = 14 AND OBJ2_SAMPLE_ID = 14 and IMG_ID = 1 |
Count the number of 'dress' object classes and include their X and Y coordinates in image ID 1764. | dress' object classes refer to OBJ_CLASS = 'dress'; image ID 1764 refers to IMG_ID = 1764; X and Y refer to coordinates of the bounding box; | predicted relation classes refers to PRED_CLASS; object sample no.14 in image no.1 refers to OBJ1_SAMPLE_ID = 14 AND OBJ2_SAMPLE_ID = 14 and IMG_ID = 1 | classes for attributes refers to ATT_CLASS; image id 8 refers to IMG_ID = 8 | object samples refers to OBJ_CLASS_ID; image no.1 refers to IMG_ID = 1; in the class of "man" refers to OBJ_CLASS = 'man' | image numbers that contain the "paint" object refer to IMG_ID where OBJ_CLASS = 'paint'; | object class refers to OBJ_CLASS; sample no.10 refers to OBJ_SAMPLE_ID = 10; image no.2320341 refers to IMG_ID = 2320341 | images refer to IMG_ID; total of 10 attribute classes refers to COUNT(OBJ_CLASS_ID) = 10; | attributes of polka dot refer to ATT_CLASS = 'polka dot'; images refer to IMG_ID; | coordinates for the object refer to X, Y, W and H coordinates of the bounding box; object class "pizza" refers to OBJ_CLASS = 'pizza'; | has' predicate classes refers to PRED_CLASS = 'has'; image ID 107 refers to IMG_ID = 107; | blue' attribute classes on image ID 2355735 refer to ATT_CLASS = 'blue' where IMG_ID = 2355735; |
How many images have "picture" as their attribute class and "bear" as their object class? | "picture" as attribute class refers to ATT_CLASS = 'picture'; "bear" as object class refers to OBJ_CLASS = 'bear'; images refer to IMG_ID; | caption for the prediction class id 12 refers to PRED_CLASS where PRED_CLASS_ID = 12; | predicate class of image ID 68 refers to PRED_CLASS where IMG_ID = 68; | image no. 99 refers to IMG_ID = 99; described as white refers to ATT_CLASS = 'white'; percentage = divide(count(OBJ_SAMPLE_ID) where ATT_CLASS = 'white', count(OBJ_SAMPLE_ID)) as percentage | prediction relationship class id refers to PRED_CLASS_ID; tallest image refers to max(H) | object in image 5 refers to OBJ_SAMPLE_ID where IMG_ID = 5; coordinates of (634, 468) refer to X and Y coordinates of the bounding box in which X = 634 and Y = 468; | object has the highest attribute classes refers to OBJ_SAMPLE_ID where MAX(COUNT(OBJ_SAMPLE_ID)); | attribute classes of the image ID "15" refer to ATT_CLASS where IMG_ID = 15; | blue' attribute classes on image ID 2355735 refer to ATT_CLASS = 'blue' where IMG_ID = 2355735; | The bounding box's W and H abbreviations stand for the object's width and height respectively; "keyboard" as object class refers to OBJ_CLASS = 'keyboard'; (5, 647) as coordinate refers to X and Y coordinates of the bounding box where X = 5 and Y = 647; | ids of the images refers to IMG_ID; self-relations refers to OBJ1_SAMPLE_ID = OBJ2_SAMPLE_ID |
How many object samples are there in image no.1? | object samples refers to OBJ_SAMPLE_ID; image no.1 refers to IMG_ID = 1 | How many images have at least one pair of object samples with the relation "parked on" refers to count(IMG_ID) where OBJ1_SAMPLE_ID ! = OBJ2_SAMPLE_ID and PRED_CLASS = 'parked on' | predicted relation classes refers to PRED_CLASS; object sample no.14 in image no.1 refers to OBJ1_SAMPLE_ID = 14 AND OBJ2_SAMPLE_ID = 14 and IMG_ID = 1 | IDs of images refer to IMG_ID; objects with the attributes of 'wired' refer to ATT_CLASS = 'wired'; | object has the highest attribute classes refers to OBJ_SAMPLE_ID where MAX(COUNT(OBJ_SAMPLE_ID)); | has' predicate classes refers to PRED_CLASS = 'has'; image ID 107 refers to IMG_ID = 107; | object sample ID refers to OBJ_SAMPLE_ID; image ID 17 refers to IMG_ID = 17; coordinates (0,0) refer to X and Y coordinates of the bounding box where X = 0 and Y = 0; | images have more than 20 object samples refer to IMG_ID where COUNT(OBJ_SAMPLE_ID) > 20; | samples of food object refers to OBJ_CLASS = 'food'; image no.6 refers to IMG_ID = 6 | object class refers to OBJ_CLASS; sample no.10 refers to OBJ_SAMPLE_ID = 10; image no.2320341 refers to IMG_ID = 2320341 | classes for attributes refers to ATT_CLASS; image id 8 refers to IMG_ID = 8 |
Give all the bounding boxes for image 2222 whose object classes are feathers. | bounding boxes refers to (x, y, W, H); image 2222 refers to IMG_ID = 2222; object classes are feathers refers to OBJ_CLASS = 'feathers' | X and Y refer to coordinates of the bounding box where X = 5 and Y = 5; images refer to IMG_ID; | prediction relationship class id refers to PRED_CLASS_ID; tallest image refers to max(H) | bounding box of the object refers to (x, y, W, H); image id refers to IMG_ID; prediction relationship class id of 144 refers to PRED_CLASS_ID = 144 | bounding box of the object sample refers to (x, y, W, H); image no.5 refers to IMG_ID = 5; has a self-relation refers to OBJ1_SAMPLE_ID = OBJ2_SAMPLE_ID | number of images refers to IMG_ID; object sample of "suit" refers to OBJ_CLASS = 'suit' | white objects refers to ATT_CLASS = 'white'; image no.2347915 refers to IMG_ID = 2347915 | relationship refers to PRED_CLASS; "feathers" and "onion" in image no.2345528 refers to IMG_ID = 2345528 and OBJ_CLASS = 'feathers' and OBJ_CLASS = 'onion' | images refer to IMG_ID; "keyboard" as object class refers to OBJ_CLASS = 'keyboard'; | dimensions of the bounding box refers to (W, H); keyboard refers to OBJ_CLASS = 'keyboard'; image no. 3 refers to IMG_ID = 3 | attribute refers to ATT_CLASS |
How many samples of "wall" are there in image no.2353079? | samples of "wall" refers to OBJ_SAMPLE_ID and OBJ_CLASS = 'wall' ; image no.2353079 refers to IMG_ID = 2353079 | object samples refers to OBJ_SAMPLE_ID; class of "man" refers to OBJ_CLASS = 'man'; image no.1 refers to IMG_ID = 1; percentage = divide(count(OBJ_SAMPLE_ID)when OBJ_CLASS = 'man', count(OBJ_SAMPLE_ID)) as percentage | explanations about distinct object classes refers to OBJ_CLASS; images refers to IMG_ID; x and y coordinate of 0 refers to X = 0 AND Y = 0 | dress' object classes refer to OBJ_CLASS = 'dress'; image ID 1764 refers to IMG_ID = 1764; X and Y refer to coordinates of the bounding box; | objects that have multiple relations refers to OBJ1_SAMPLE_ID ! = OBJ2_SAMPLE_ID; captions for the prediction class ids are "on" refers to PRED_CLASS = 'on' | object class refers to OBJ_CLASS; sample no.10 refers to OBJ_SAMPLE_ID = 10; image no.2320341 refers to IMG_ID = 2320341 | coordinates for the object refer to X, Y, W and H coordinates of the bounding box; object class "pizza" refers to OBJ_CLASS = 'pizza'; | images have more than 20 object samples refer to IMG_ID where COUNT(OBJ_SAMPLE_ID) > 20; | white objects refers to ATT_CLASS = 'white'; image no.2347915 refers to IMG_ID = 2347915 | relationship refers to PRED_CLASS; "feathers" and "onion" in image no.2345528 refers to IMG_ID = 2345528 and OBJ_CLASS = 'feathers' and OBJ_CLASS = 'onion' | pairs of object samples refers to OBJ1_SAMPLE_ID and OBJ2_SAMPLE_ID; image no.1 refers to IMG_ID = 1; relation of "parked on" refers to PRED_CLASS = 'parked on' |
List all the corresponding classes for attributes of image id 8. | classes for attributes refers to ATT_CLASS; image id 8 refers to IMG_ID = 8 | X and Y refer to coordinates of the bounding box; image ID 23 refers to IMG_ID = 23; 'cast' attribute class refers to ATT_CLASS = 'cast'; | "picture" as attribute class refers to ATT_CLASS = 'picture'; "bear" as object class refers to OBJ_CLASS = 'bear'; images refer to IMG_ID; | bounding box refers to X, Y, W, H from IMG_OBJ; lowest relates to the height of the bounding box which refers to MIN(H); | object sample ID refers to OBJ_SAMPLE_ID; image ID 17 refers to IMG_ID = 17; coordinates (0,0) refer to X and Y coordinates of the bounding box where X = 0 and Y = 0; | relation refers to PRED_CLASS; object sample no.8 and object sample no.4 refers to OBJ1_SAMPLE_ID = 8 AND OBJ2_SAMPLE_ID = 4; image no.1 refers to IMG_ID = 1 | DIVIDE(COUNT(OBJ_SAMPLE_ID), COUNT(IMG_ID)); | onion category refers to OBJ_CLASS = 'onion'; | dimensions of the bounding box refers to (W, H); keyboard refers to OBJ_CLASS = 'keyboard'; image no. 3 refers to IMG_ID = 3 | object elements refers to OBJ_CLASS_ID; average = divide(count(OBJ_CLASS_ID), count(IMG_ID)) | bounding box of the object refers to (x, y, W, H); image id refers to IMG_ID; prediction relationship class id of 144 refers to PRED_CLASS_ID = 144 |
How many object samples in image no.1 are in the class of "man"? | object samples refers to OBJ_CLASS_ID; image no.1 refers to IMG_ID = 1; in the class of "man" refers to OBJ_CLASS = 'man' | unique id number identifying refers to OBJ_CLASS_ID; onion object class refers to OBJ_CLASS = 'onion' | object in image 5 refers to OBJ_SAMPLE_ID where IMG_ID = 5; coordinates of (634, 468) refer to X and Y coordinates of the bounding box in which X = 634 and Y = 468; | images refer to IMG_ID; less than 15 object samples refer to COUNT(OBJ_SAMPLE_ID) < 15; | X and Y refer to coordinates of the bounding box; image ID 23 refers to IMG_ID = 23; 'cast' attribute class refers to ATT_CLASS = 'cast'; | classes for attributes refers to ATT_CLASS; image id 8 refers to IMG_ID = 8 | blue' attribute classes on image ID 2355735 refer to ATT_CLASS = 'blue' where IMG_ID = 2355735; | "picture" as attribute class refers to ATT_CLASS = 'picture'; "bear" as object class refers to OBJ_CLASS = 'bear'; images refer to IMG_ID; | bounding box of the object refers to (x, y, W, H); image id refers to IMG_ID; prediction relationship class id of 144 refers to PRED_CLASS_ID = 144 | samples of "bed" object refer to OBJ_SAMPLE_ID where OBJ_CLASS = 'bed'; image No.1098 refers to IMG_ID = 1098; | predicate class of image ID 68 refers to PRED_CLASS where IMG_ID = 68; |
How many images have "vegetable" and "fruits" as their object classes? | images refer to IMG_ID; "vegetables" and "fruits" as object classes refer to OBJ_CLASS = 'vegetables' and OBJ_CLASS = 'fruits'; | ID of all images refer to IMG_ID; if two objects (OBJ1_SAMPLE_ID,
OBJ2_SAMPLE_ID) has
multiple PRED_CLASS_ID, it
means these two objects
have multiple relations; | attribute classes of image ID 22 refer to ATT_CLASS where MG_ID = 22; | predicted relation class refers to PRED_CLASS; self-relations refers to OBJ1_SAMPLE_ID = OBJ2_SAMPLE_ID; image no.5 refers to IMG_ID = 5 | attribute classes refer to ATT_CLASS; (5,5) coordinate refers to X and Y coordinates of the bounding box where X = 5 and Y = 5; | DIVIDE(SUM(OBJ_SAMPLE_ID where OBJ_CLASS = 'airplane'), COUNT(OBJ_CLASS)) as percentage; | image with a bounding (422, 63, 77, 363) refers to OBJ_CLASS_ID where X = 422 and Y = 63 and W = 77 and H = 363; | object elements refers to OBJ_CLASS_ID; average = divide(count(OBJ_CLASS_ID), count(IMG_ID)) | predicate class of image ID 68 refers to PRED_CLASS where IMG_ID = 68; | bounding boxes refers to (x, y, W, H); image 2222 refers to IMG_ID = 2222; object classes are feathers refers to OBJ_CLASS = 'feathers' | number of images refers to IMG_ID; object sample of "suit" refers to OBJ_CLASS = 'suit' |
Among the objects that have multiple relations, how many images whose captions for the prediction class ids are "on"? | objects that have multiple relations refers to OBJ1_SAMPLE_ID ! = OBJ2_SAMPLE_ID; captions for the prediction class ids are "on" refers to PRED_CLASS = 'on' | relation refers to PRED_CLASS; object sample no.8 and object sample no.4 refers to OBJ1_SAMPLE_ID = 8 AND OBJ2_SAMPLE_ID = 4; image no.1 refers to IMG_ID = 1 | coordinates for the object refer to X, Y, W and H coordinates of the bounding box; object class "pizza" refers to OBJ_CLASS = 'pizza'; | images have more than 20 object samples refer to IMG_ID where COUNT(OBJ_SAMPLE_ID) > 20; | X and Y refer to coordinates of the bounding box where X = 5 and Y = 5; images refer to IMG_ID; | bounding boxes of the object samples refers to (x, y, W, H); predicted relation class of "by" refers to PRED_CLASS = 'by'; image no.1 refers to IMG_ID = 1 | relationship refers to PRED_CLASS; object sample no.12 and no.8 of image no.2345511 refers to IMG_ID = 2345511 AND OBJ1_SAMPLE_ID = 12 AND OBJ2_SAMPLE_ID = 8 | images refers to IMG_ID; have at least 5 "black" classes refers to count(ATT_CLASS_ID) where ATT_CLASS = 'black' > = 5 | object elements refers to OBJ_CLASS_ID; average = divide(count(OBJ_CLASS_ID), count(IMG_ID)) | pairs of object samples refers to OBJ1_SAMPLE_ID and OBJ2_SAMPLE_ID; image no.1 refers to IMG_ID = 1; relation of "parked on" refers to PRED_CLASS = 'parked on' | attributes of polka dot refer to ATT_CLASS = 'polka dot'; images refer to IMG_ID; |
On image no. 99 identify the percentage of objects that are described as white. | image no. 99 refers to IMG_ID = 99; described as white refers to ATT_CLASS = 'white'; percentage = divide(count(OBJ_SAMPLE_ID) where ATT_CLASS = 'white', count(OBJ_SAMPLE_ID)) as percentage | over 20 object samples refers to COUNT(OBJ_SAMPLE_ID) > 20 | DIVIDE(COUNT(OBJ_SAMPLE_ID), COUNT(IMG_ID)); | attribute refers to ATT_CLASS | Y coordinate many are 0 refers to Y coordinates of the bounding box where Y = 0; image ID 12 refers to IMG_ID = 12; | samples of "wall" refers to OBJ_SAMPLE_ID and OBJ_CLASS = 'wall' ; image no.2353079 refers to IMG_ID = 2353079 | attributes of polka dot refer to ATT_CLASS = 'polka dot'; images refer to IMG_ID; | images refers to IMG_ID; have at least 5 "black" classes refers to count(ATT_CLASS_ID) where ATT_CLASS = 'black' > = 5 | relationship refers to PRED_CLASS; "feathers" and "onion" in image no.2345528 refers to IMG_ID = 2345528 and OBJ_CLASS = 'feathers' and OBJ_CLASS = 'onion' | X and Y refer to coordinates of the bounding box; image ID 23 refers to IMG_ID = 23; 'cast' attribute class refers to ATT_CLASS = 'cast'; | DIVIDE(SUM(OBJ_CLASS_ID where OBJ_CLASS = 'surface'), COUNT(OBJ_CLASS_ID)) as percentage where IMG_ID = 2654; |
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