Dataset Viewer
Auto-converted to Parquet
split
stringclasses
1 value
file_name
stringlengths
16
16
class_1
stringclasses
80 values
class_2
stringclasses
80 values
count_1
int64
1
28
count_2
int64
1
28
operation
stringclasses
4 values
answer
float64
-27
350
train
000000391895.jpg
person
motorcycle
2
1
multiply
2
train
000000391895.jpg
person
motorcycle
2
1
divide
2
train
000000391895.jpg
bicycle
person
1
2
divide
0.5
train
000000391895.jpg
bicycle
motorcycle
1
1
add
2
train
000000391895.jpg
motorcycle
bicycle
1
1
divide
1
train
000000391895.jpg
motorcycle
person
1
2
subtract
-1
train
000000391895.jpg
motorcycle
person
1
2
divide
0.5
train
000000391895.jpg
bicycle
person
1
2
divide
0.5
train
000000391895.jpg
person
motorcycle
2
1
multiply
2
train
000000391895.jpg
bicycle
person
1
2
divide
0.5
train
000000522418.jpg
cake
sink
1
1
divide
1
train
000000522418.jpg
sink
cake
1
1
multiply
1
train
000000522418.jpg
sink
knife
1
1
subtract
0
train
000000522418.jpg
person
cake
1
1
multiply
1
train
000000522418.jpg
person
cake
1
1
multiply
1
train
000000522418.jpg
knife
sink
1
1
divide
1
train
000000522418.jpg
knife
cake
1
1
multiply
1
train
000000522418.jpg
cake
knife
1
1
subtract
0
train
000000522418.jpg
knife
person
1
1
multiply
1
train
000000522418.jpg
sink
knife
1
1
add
2
train
000000184613.jpg
cow
person
9
14
divide
0.64
train
000000184613.jpg
person
umbrella
14
1
multiply
14
train
000000184613.jpg
person
umbrella
14
1
multiply
14
train
000000184613.jpg
umbrella
person
1
14
add
15
train
000000184613.jpg
cow
umbrella
9
1
subtract
8
train
000000184613.jpg
person
cow
14
9
add
23
train
000000184613.jpg
person
umbrella
14
1
multiply
14
train
000000184613.jpg
cow
umbrella
9
1
multiply
9
train
000000184613.jpg
umbrella
cow
1
9
subtract
-8
train
000000184613.jpg
umbrella
cow
1
9
subtract
-8
train
000000318219.jpg
keyboard
tv
2
3
subtract
-1
train
000000318219.jpg
person
tv
2
3
add
5
train
000000318219.jpg
tv
person
3
2
multiply
6
train
000000318219.jpg
tv
mouse
3
4
multiply
12
train
000000318219.jpg
keyboard
tv
2
3
multiply
6
train
000000318219.jpg
tv
mouse
3
4
divide
0.75
train
000000318219.jpg
keyboard
person
2
2
subtract
0
train
000000318219.jpg
mouse
tv
4
3
subtract
1
train
000000318219.jpg
person
keyboard
2
2
divide
1
train
000000318219.jpg
keyboard
mouse
2
4
add
6
train
000000554625.jpg
tv
keyboard
5
4
subtract
1
train
000000554625.jpg
tv
keyboard
5
4
divide
1.25
train
000000554625.jpg
mouse
person
5
5
divide
1
train
000000554625.jpg
tv
keyboard
5
4
divide
1.25
train
000000554625.jpg
mouse
tv
5
5
add
10
train
000000554625.jpg
keyboard
person
4
5
add
9
train
000000554625.jpg
keyboard
mouse
4
5
divide
0.8
train
000000554625.jpg
tv
person
5
5
multiply
25
train
000000554625.jpg
keyboard
mouse
4
5
add
9
train
000000554625.jpg
tv
person
5
5
divide
1
train
000000574769.jpg
sink
person
1
1
add
2
train
000000574769.jpg
clock
bottle
1
2
multiply
2
train
000000574769.jpg
bowl
refrigerator
1
1
multiply
1
train
000000574769.jpg
oven
spoon
1
1
subtract
0
train
000000574769.jpg
potted plant
oven
1
1
divide
1
train
000000574769.jpg
handbag
potted plant
1
1
subtract
0
train
000000574769.jpg
cat
person
1
1
divide
1
train
000000574769.jpg
clock
sink
1
1
subtract
0
train
000000574769.jpg
spoon
potted plant
1
1
divide
1
train
000000574769.jpg
oven
sink
1
1
subtract
0
train
000000060623.jpg
spoon
dining table
1
1
subtract
0
train
000000060623.jpg
dining table
spoon
1
1
subtract
0
train
000000060623.jpg
dining table
wine glass
1
1
multiply
1
train
000000060623.jpg
bowl
dining table
1
1
multiply
1
train
000000060623.jpg
wine glass
dining table
1
1
add
2
train
000000060623.jpg
spoon
person
1
3
multiply
3
train
000000060623.jpg
person
wine glass
3
1
multiply
3
train
000000060623.jpg
wine glass
spoon
1
1
divide
1
train
000000060623.jpg
wine glass
spoon
1
1
add
2
train
000000060623.jpg
bowl
person
1
3
multiply
3
train
000000309022.jpg
sink
bottle
1
4
multiply
4
train
000000309022.jpg
bowl
sink
1
1
multiply
1
train
000000309022.jpg
sink
bowl
1
1
subtract
0
train
000000309022.jpg
bowl
sink
1
1
divide
1
train
000000309022.jpg
bowl
sink
1
1
add
2
train
000000309022.jpg
oven
bottle
2
4
multiply
8
train
000000309022.jpg
sink
bottle
1
4
divide
0.25
train
000000309022.jpg
bowl
sink
1
1
multiply
1
train
000000309022.jpg
bottle
bowl
4
1
divide
4
train
000000309022.jpg
sink
bowl
1
1
subtract
0
train
000000005802.jpg
backpack
knife
1
4
multiply
4
train
000000005802.jpg
bowl
backpack
4
1
multiply
4
train
000000005802.jpg
person
bowl
2
4
divide
0.5
train
000000005802.jpg
cup
knife
8
4
multiply
32
train
000000005802.jpg
bottle
bowl
7
4
multiply
28
train
000000005802.jpg
backpack
person
1
2
add
3
train
000000005802.jpg
person
bowl
2
4
subtract
-2
train
000000005802.jpg
person
cup
2
8
multiply
16
train
000000005802.jpg
bowl
backpack
4
1
subtract
3
train
000000005802.jpg
cup
person
8
2
subtract
6
train
000000222564.jpg
bottle
oven
1
3
divide
0.33
train
000000222564.jpg
person
dining table
2
1
add
3
train
000000222564.jpg
person
microwave
2
1
subtract
1
train
000000222564.jpg
person
oven
2
3
divide
0.67
train
000000222564.jpg
oven
dining table
3
1
divide
3
train
000000222564.jpg
microwave
person
1
2
multiply
2
train
000000222564.jpg
person
dining table
2
1
subtract
1
train
000000222564.jpg
bottle
person
1
2
subtract
-1
train
000000222564.jpg
bottle
person
1
2
multiply
2
train
000000222564.jpg
microwave
oven
1
3
subtract
-2
End of preview. Expand in Data Studio
README.md exists but content is empty.
Downloads last month
0