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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?
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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);
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height of over 6'2" inches refers to height_in_inch > '6''2"'; born in Sweden refers to nation = 'Sweden' ;
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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;
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names of the players refers to PlayerName; team Avangard Omsk refers to TEAM = 'Avangard Omsk'; 2000-2001 season refers to SEASON = '2000-2001';
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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';
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heigh in inches refers to height_in_inch;
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committed the highest rule violations or penalty minutes refers to MAX(PIM); 2000-2001 season refers to SEASON = '2000-2001'
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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;
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weigh more than 90 kg refers to weight_in_kg > 90;
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weight in kilograms refers to weight_in_kg; highest number of goal differential of all time refers to MAX(PLUSMINUS);
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youngest player refers to MAX(birthdate); 1997-1998 season refers to SEASON = '1997-1998'; OHL league refers to LEAGUE = 'OHL';
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Identify the players who weigh 120 kg.
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players refers to PlayerName; weigh 120 kg refers to weight_in_kg = 120;
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born in 1980 refers to birthyear = 1980; weigh 185 in pounds refers to weight_in_lbs = 185;
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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';
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height of over 6'2" inches refers to height_in_inch > '6''2"'; born in Sweden refers to nation = 'Sweden' ;
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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';
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weight in kilograms refers to weight_in_kg; highest number of goal differential of all time refers to MAX(PLUSMINUS);
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most valuable player refers to MAX(P); 2000-2001 season refers to SEASON = '2000-2001'; International league refers to LEAGUE = 'International';
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average = AVG(height_in_cm); players refers to PlayerName; position of defense refers to position_info = 'D' ;
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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';
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heigh in inches refers to height_in_inch;
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tallest refers to MAX(height_in_cm);
player refers to PlayerName; team USA U20 refers to TEAM = 'USA U20';
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List out the name of players who have a height of 5'8".
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name of players refers to PlayerName; height of 5'8" refers to height_in_inch = '5''8"';
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weigh in kilograms refers to weight_in_kg;
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right-shooted refers to shoots = 'R'; weigh over 90 kg refers to weight_in_kg > 90;
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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;
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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';
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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';
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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';
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heigh in inches refers to height_in_inch;
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name of the player refers to PlayerName; position of the player refers to position_info; committed the most rule violations refers to MAX(PIM);
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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);
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average = AVG(height_in_cm); players refers to PlayerName; position of defense refers to position_info = 'D' ;
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Who is the tallest player in team USA U20?
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tallest refers to MAX(height_in_cm);
player refers to PlayerName; team USA U20 refers to TEAM = 'USA U20';
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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';
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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;
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right-shooted players refers to shoots = 'R'; height of 5'7'' refers to height_in_inch = '5''7"';
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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);
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USA refers to nation = 'USA' ; players refers to PlayerName; lightest weight refers to MIN(weight_in_lbs);
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weight in kilograms refers to weight_in_kg; highest number of goal differential of all time refers to MAX(PLUSMINUS);
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most valuable player refers to MAX(P); 2000-2001 season refers to SEASON = '2000-2001'; International league refers to LEAGUE = 'International';
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born in 1982 refers to birthyear = 1982; height above 182cm refers to height_in_cm > 182 ;
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names of the players refers to PlayerName; team Avangard Omsk refers to TEAM = 'Avangard Omsk'; 2000-2001 season refers to SEASON = '2000-2001';
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type of game refers to GAMETYPE;
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What is the average height in centimeters of all the players in the position of defense?
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average = AVG(height_in_cm); players refers to PlayerName; position of defense refers to position_info = 'D' ;
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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';
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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';
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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);
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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;
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name of players refers to PlayerName; height of 5'8" refers to height_in_inch = '5''8"';
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right-shooted players refers to shoots = 'R'; height of 5'7'' refers to height_in_inch = '5''7"';
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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);
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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';
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playoffs refers to GAMETYPE = 'Playoffs';
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name of the player refers to PlayerName; most NHL points in draft year refers to MAX(P);
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What is the percentage of Russian players who have a height of under 200 inch?
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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;
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right-shooted refers to shoots = 'R'; weigh over 90 kg refers to weight_in_kg > 90;
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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';
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born in 1980 refers to birthyear = 1980; weigh 185 in pounds refers to weight_in_lbs = 185;
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committed the highest rule violations or penalty minutes refers to MAX(PIM); 2000-2001 season refers to SEASON = '2000-2001'
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height of over 6'2" inches refers to height_in_inch > '6''2"'; born in Sweden refers to nation = 'Sweden' ;
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tallest refers to MAX(height_in_cm);
player refers to PlayerName; team USA U20 refers to TEAM = 'USA U20';
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heaviest player refers to MAX(weight_in_lb); drafted by Arizona Coyotes refers to overallby = 'Arizona Coyotes';
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height in inches refers to height_in_inch; players refers to PlayerName; team Oshawa Generals refers to TEAM = 'Oshawa Generals';
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players refers to PlayerName; weigh 120 kg refers to weight_in_kg = 120;
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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';
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How many players who were born in 1980 weigh 185 in pounds?
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born in 1980 refers to birthyear = 1980; weigh 185 in pounds refers to weight_in_lbs = 185;
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weigh more than 90 kg refers to weight_in_kg > 90;
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heaviest player refers to MAX(weight_in_lb); drafted by Arizona Coyotes refers to overallby = 'Arizona Coyotes';
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FALSE;
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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);
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committed the highest rule violations or penalty minutes refers to MAX(PIM); 2000-2001 season refers to SEASON = '2000-2001'
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right-shooted players refers to shoots = 'R'; height of 5'7'' refers to height_in_inch = '5''7"';
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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);
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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';
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most valuable player refers to MAX(P); 2000-2001 season refers to SEASON = '2000-2001'; International league refers to LEAGUE = 'International';
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heigh in inches refers to height_in_inch;
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How many players, who were drafted by Anaheim Ducks in 2008, have played for U.S. National U18 Team?
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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';
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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;
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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';
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born in 1982 refers to birthyear = 1982; height above 182cm refers to height_in_cm > 182 ;
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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';
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tallest refers to MAX(height_in_cm);
player refers to PlayerName; team USA U20 refers to TEAM = 'USA U20';
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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';
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youngest player refers to MAX(birthdate); 1997-1998 season refers to SEASON = '1997-1998'; OHL league refers to LEAGUE = 'OHL';
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name of the player refers to PlayerName; most NHL points in draft year refers to MAX(P);
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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;
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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;
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How many playoffs did Per Mars participate in?
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playoffs refers to GAMETYPE = 'Playoffs';
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played the most game plays refers to MAX(GP); 2000-2001 season refers to SEASON = '2000-2001'; International league refers to LEAGUE = 'International';
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youngest player refers to MAX(birthdate); 1997-1998 season refers to SEASON = '1997-1998'; OHL league refers to LEAGUE = 'OHL';
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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';
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FALSE;
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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;
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heigh in inches refers to height_in_inch;
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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';
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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';
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tallest player refers to MAX(height_in_cm);
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average = AVG(height_in_cm); players refers to PlayerName; position of defense refers to position_info = 'D' ;
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Mention the type of game that Matthias Trattnig played.
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type of game refers to GAMETYPE;
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name of the player refers to PlayerName; position of the player refers to position_info; committed the most rule violations refers to MAX(PIM);
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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';
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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';
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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';
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USA refers to nation = 'USA' ; players refers to PlayerName; lightest weight refers to MIN(weight_in_lbs);
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committed the highest rule violations or penalty minutes refers to MAX(PIM); 2000-2001 season refers to SEASON = '2000-2001'
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playoffs refers to GAMETYPE = 'Playoffs';
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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;
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weight in kilograms refers to weight_in_kg; highest number of goal differential of all time refers to MAX(PLUSMINUS);
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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';
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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?
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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';
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played the most game plays refers to MAX(GP); 2000-2001 season refers to SEASON = '2000-2001'; International league refers to LEAGUE = 'International';
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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';
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weigh in kilograms refers to weight_in_kg;
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height in inches refers to height_in_inch; players refers to PlayerName; team Oshawa Generals refers to TEAM = 'Oshawa Generals';
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born in 1980 refers to birthyear = 1980; weigh 185 in pounds refers to weight_in_lbs = 185;
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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';
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names of the players refers to PlayerName; team Avangard Omsk refers to TEAM = 'Avangard Omsk'; 2000-2001 season refers to SEASON = '2000-2001';
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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';
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FALSE;
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weight in kilograms refers to weight_in_kg; highest number of goal differential of all time refers to MAX(PLUSMINUS);
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Among all goals scored by Calgary Hitmen in the 2007-2008 season, identify the percentage scored by Ian Schultz.
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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';
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names of the players refers to PlayerName; team Avangard Omsk refers to TEAM = 'Avangard Omsk'; 2000-2001 season refers to SEASON = '2000-2001';
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tallest refers to MAX(height_in_cm);
player refers to PlayerName; team USA U20 refers to TEAM = 'USA U20';
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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';
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average = AVG(height_in_cm); players refers to PlayerName; position of defense refers to position_info = 'D' ;
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most valuable player refers to MAX(P); 2000-2001 season refers to SEASON = '2000-2001'; International league refers to LEAGUE = 'International';
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FALSE;
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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;
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youngest player refers to MAX(birthdate); 1997-1998 season refers to SEASON = '1997-1998'; OHL league refers to LEAGUE = 'OHL';
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players refers to PlayerName; weigh 120 kg refers to weight_in_kg = 120;
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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;
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Name the player who had the most goals for team Rimouski Oceanic in playoff.
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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';
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heaviest player refers to MAX(weight_in_lb); drafted by Arizona Coyotes refers to overallby = 'Arizona Coyotes';
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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';
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name of the player refers to PlayerName; most NHL points in draft year refers to MAX(P);
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weight in kilograms refers to weight_in_kg; highest number of goal differential of all time refers to MAX(PLUSMINUS);
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name of the player refers to PlayerName; position of the player refers to position_info; committed the most rule violations refers to MAX(PIM);
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playoffs refers to GAMETYPE = 'Playoffs';
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weigh in kilograms refers to weight_in_kg;
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born in 1980 refers to birthyear = 1980; weigh 185 in pounds refers to weight_in_lbs = 185;
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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';
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name of players refers to PlayerName; height of 5'8" refers to height_in_inch = '5''8"';
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Indicate the height of all players from team Oshawa Generals in inches.
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height in inches refers to height_in_inch; players refers to PlayerName; team Oshawa Generals refers to TEAM = 'Oshawa Generals';
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weight in kilograms refers to weight_in_kg; highest number of goal differential of all time refers to MAX(PLUSMINUS);
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tallest player refers to MAX(height_in_cm);
|
playoffs refers to GAMETYPE = 'Playoffs';
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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;
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name of the player refers to PlayerName; position of the player refers to position_info; committed the most rule violations refers to MAX(PIM);
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right-shooted refers to shoots = 'R'; weigh over 90 kg refers to weight_in_kg > 90;
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USA refers to nation = 'USA' ; players refers to PlayerName; lightest weight refers to MIN(weight_in_lbs);
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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;
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players refers to PlayerName; weigh 120 kg refers to weight_in_kg = 120;
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tallest refers to MAX(height_in_cm);
player refers to PlayerName; team USA U20 refers to TEAM = 'USA U20';
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What is the height of David Bornhammar in inches?
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heigh in inches refers to height_in_inch;
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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';
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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';
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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?
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1997-1998 season refers to SEASON = '1997-1998';
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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
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onion category refers to OBJ_CLASS = 'onion';
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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'
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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;
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images refer to IMG_ID; "keyboard" as object class refers to OBJ_CLASS = 'keyboard';
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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
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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
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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';
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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
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attribute class of "horse" refers to ATT_CLASS = 'horse'; object class of "fur" refers to OBJ_CLASS = 'fur';
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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';
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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;
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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'
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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;
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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
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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
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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';
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Which object classes belong to the onion category?
|
onion category refers to OBJ_CLASS = 'onion';
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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';
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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;
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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
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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
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unique id number identifying refers to OBJ_CLASS_ID; onion object class refers to OBJ_CLASS = 'onion'
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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
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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
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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'
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On image no. 20, identify the attribute ID that is composed of the highest number of objects.
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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))
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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;
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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;
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attribute classes of image ID 22 refer to ATT_CLASS where MG_ID = 22;
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samples of "bed" object refer to OBJ_SAMPLE_ID where OBJ_CLASS = 'bed'; image No.1098 refers to IMG_ID = 1098;
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has' predicate classes refers to PRED_CLASS = 'has'; image ID 107 refers to IMG_ID = 107;
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ID of all images refer to IMG_ID; attribute class of "horse" refers to ATT_CLASS = 'horse';
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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;
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ids of the images refers to IMG_ID; self-relations refers to OBJ1_SAMPLE_ID = OBJ2_SAMPLE_ID
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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;
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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'
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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
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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;
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images refer to IMG_ID; "vegetables" and "fruits" as object classes refer to OBJ_CLASS = 'vegetables' and OBJ_CLASS = 'fruits';
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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;
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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';
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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
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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'
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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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