images
imagewidth (px) 32
32
| ord_labels
class label 20
classes | cl_labels
sequencelengths 3
3
|
|---|---|---|
16small mammals
| [
4,
17,
17
] |
|
15reptiles
| [
4,
2,
19
] |
|
15reptiles
| [
14,
6,
3
] |
|
2flowers
| [
14,
1,
19
] |
|
14people
| [
0,
6,
10
] |
|
18transportation vehicles
| [
2,
14,
16
] |
|
4fruit, vegetables and mushrooms
| [
18,
7,
14
] |
|
2flowers
| [
14,
7,
14
] |
|
17trees
| [
12,
3,
15
] |
|
14people
| [
2,
1,
2
] |
|
19non-transportation vehicles
| [
4,
0,
3
] |
|
9large man-made outdoor things
| [
17,
14,
18
] |
|
19non-transportation vehicles
| [
18,
14,
19
] |
|
10large natural outdoor scenes
| [
4,
3,
2
] |
|
6household furniture
| [
3,
1,
10
] |
|
14people
| [
6,
8,
1
] |
|
5household electrical devices
| [
8,
19,
14
] |
|
0aquatic_mammals
| [
14,
14,
8
] |
|
14people
| [
4,
17,
18
] |
|
7insects
| [
18,
5,
6
] |
|
6household furniture
| [
7,
9,
3
] |
|
13non-insect invertebrates
| [
16,
5,
19
] |
|
2flowers
| [
4,
6,
3
] |
|
0aquatic_mammals
| [
2,
18,
3
] |
|
12medium-sized mammals
| [
5,
3,
17
] |
|
18transportation vehicles
| [
16,
4,
11
] |
|
2flowers
| [
1,
19,
14
] |
|
16small mammals
| [
2,
2,
15
] |
|
11large omnivores and herbivores
| [
4,
5,
7
] |
|
12medium-sized mammals
| [
18,
7,
5
] |
|
9large man-made outdoor things
| [
18,
3,
16
] |
|
5household electrical devices
| [
4,
7,
12
] |
|
8large carnivores and bear
| [
6,
14,
17
] |
|
16small mammals
| [
18,
5,
14
] |
|
19non-transportation vehicles
| [
14,
16,
4
] |
|
10large natural outdoor scenes
| [
1,
1,
1
] |
|
17trees
| [
14,
11,
8
] |
|
2flowers
| [
1,
18,
1
] |
|
5household electrical devices
| [
8,
0,
8
] |
|
12medium-sized mammals
| [
17,
18,
18
] |
|
12medium-sized mammals
| [
19,
15,
3
] |
|
2flowers
| [
19,
0,
18
] |
|
17trees
| [
11,
18,
18
] |
|
16small mammals
| [
2,
17,
18
] |
|
9large man-made outdoor things
| [
4,
7,
4
] |
|
0aquatic_mammals
| [
9,
14,
18
] |
|
13non-insect invertebrates
| [
5,
12,
4
] |
|
8large carnivores and bear
| [
19,
2,
18
] |
|
12medium-sized mammals
| [
5,
17,
4
] |
|
0aquatic_mammals
| [
2,
4,
4
] |
|
15reptiles
| [
9,
10,
5
] |
|
4fruit, vegetables and mushrooms
| [
15,
14,
13
] |
|
18transportation vehicles
| [
0,
3,
6
] |
|
0aquatic_mammals
| [
11,
3,
5
] |
|
13non-insect invertebrates
| [
7,
6,
5
] |
|
5household electrical devices
| [
3,
14,
4
] |
|
17trees
| [
13,
14,
6
] |
|
2flowers
| [
11,
14,
5
] |
|
5household electrical devices
| [
7,
1,
1
] |
|
10large natural outdoor scenes
| [
14,
2,
14
] |
|
18transportation vehicles
| [
3,
0,
13
] |
|
18transportation vehicles
| [
15,
4,
7
] |
|
18transportation vehicles
| [
1,
5,
11
] |
|
18transportation vehicles
| [
0,
10,
0
] |
|
0aquatic_mammals
| [
3,
14,
6
] |
|
17trees
| [
1,
5,
13
] |
|
2flowers
| [
1,
4,
6
] |
|
10large natural outdoor scenes
| [
19,
1,
4
] |
|
13non-insect invertebrates
| [
4,
15,
9
] |
|
13non-insect invertebrates
| [
17,
0,
4
] |
|
15reptiles
| [
17,
17,
18
] |
|
7insects
| [
13,
4,
6
] |
|
9large man-made outdoor things
| [
1,
17,
18
] |
|
9large man-made outdoor things
| [
15,
2,
4
] |
|
13non-insect invertebrates
| [
4,
3,
2
] |
|
3food_containers
| [
1,
7,
13
] |
|
12medium-sized mammals
| [
19,
9,
19
] |
|
19non-transportation vehicles
| [
1,
2,
1
] |
|
9large man-made outdoor things
| [
15,
3,
12
] |
|
16small mammals
| [
4,
17,
19
] |
|
14people
| [
11,
8,
11
] |
|
17trees
| [
1,
15,
4
] |
|
6household furniture
| [
11,
19,
1
] |
|
6household furniture
| [
10,
15,
12
] |
|
16small mammals
| [
5,
18,
5
] |
|
2flowers
| [
14,
6,
0
] |
|
15reptiles
| [
4,
5,
5
] |
|
10large natural outdoor scenes
| [
14,
19,
16
] |
|
15reptiles
| [
10,
17,
19
] |
|
17trees
| [
18,
6,
6
] |
|
1fish
| [
17,
19,
10
] |
|
12medium-sized mammals
| [
0,
18,
16
] |
|
0aquatic_mammals
| [
10,
5,
3
] |
|
19non-transportation vehicles
| [
11,
16,
2
] |
|
1fish
| [
14,
8,
19
] |
|
9large man-made outdoor things
| [
9,
7,
2
] |
|
16small mammals
| [
10,
2,
18
] |
|
11large omnivores and herbivores
| [
17,
1,
2
] |
|
7insects
| [
10,
19,
18
] |
|
17trees
| [
7,
2,
15
] |
Dataset Card for CLCIFAR20
This Complementary labeled CIFAR100 dataset contains 3 human annotated complementary labels for all 50000 images in the training split of CIFAR100. We group 4-6 categories as a superclass and collect the complementary labels of these 20 superclasses. The workers are from Amazon Mechanical Turk. We randomly sampled 4 different labels for 3 different annotators, so each image would have 3 (probably repeated) complementary labels.
For more details, please visit our github or paper.
Dataset Structure
Data Instances
A sample from the training set is provided below:
{
'images': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=32x32 at 0x79964C139F90>,
'ord_labels': 16,
'cl_labels': [4, 17, 17]
}
Data Fields
images: APIL.Image.Imageobject containing the 32x32 image.ord_labels: The ordinary labels of the images, and they are labeled from 0 to 19 as follows:0: aquatic_mammals 1: fish 2: flowers 3: food_containers 4: fruit, vegetables and mushrooms 5: household electrical devices 6: household furniture 7: insects 8: large carnivores and bear 9: large man-made outdoor things 10: large natural outdoor scenes 11: large omnivores and herbivores 12: medium-sized mammals 13: non-insect invertebrates 14: people 15: reptiles 16: small mammals 17: trees 18: transportation vehicles 19: non-transportation vehicles
cl_labels: Three complementary labels for each image from three different workers.
Annotation Task Design and Deployment on Amazon MTurk
To collect human-annotated labels, we used Amazon Mechanical Turk (MTurk) to deploy our annotation task. The layout and interface design for the MTurk task can be found in the file design-layout-mturk.html.
In each task, a single image was enlarged to 200 x 200 for clarity and presented alongside the question: Choose any one "incorrect" label for this image? Annotators were given four example labels to choose from (e.g., dog, cat, ship, bird), and were instructed to select the one that does not correctly describe the image.
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