images
imagewidth (px) 32
32
| ord_labels
class label 10
classes | cl_labels
sequencelengths 3
3
|
|---|---|---|
6frog
| [
3,
9,
6
] |
|
9truck
| [
7,
5,
2
] |
|
9truck
| [
6,
0,
6
] |
|
4deer
| [
6,
1,
2
] |
|
1automobile
| [
2,
8,
7
] |
|
1automobile
| [
3,
7,
8
] |
|
2bird
| [
4,
3,
0
] |
|
7horse
| [
6,
6,
2
] |
|
8ship
| [
0,
7,
3
] |
|
3cat
| [
2,
8,
9
] |
|
4deer
| [
5,
2,
0
] |
|
7horse
| [
8,
1,
5
] |
|
7horse
| [
0,
2,
0
] |
|
2bird
| [
5,
3,
6
] |
|
9truck
| [
4,
1,
5
] |
|
9truck
| [
1,
2,
6
] |
|
9truck
| [
5,
1,
5
] |
|
3cat
| [
1,
7,
0
] |
|
2bird
| [
5,
4,
8
] |
|
6frog
| [
2,
0,
0
] |
|
4deer
| [
8,
0,
8
] |
|
3cat
| [
1,
7,
0
] |
|
6frog
| [
0,
1,
0
] |
|
6frog
| [
8,
7,
4
] |
|
2bird
| [
1,
9,
7
] |
|
6frog
| [
2,
4,
7
] |
|
3cat
| [
7,
2,
6
] |
|
5dog
| [
0,
2,
1
] |
|
4deer
| [
5,
0,
7
] |
|
0airplane
| [
5,
8,
4
] |
|
0airplane
| [
5,
6,
3
] |
|
9truck
| [
6,
2,
2
] |
|
1automobile
| [
3,
0,
2
] |
|
3cat
| [
9,
3,
8
] |
|
4deer
| [
8,
7,
6
] |
|
0airplane
| [
2,
7,
2
] |
|
3cat
| [
5,
3,
4
] |
|
7horse
| [
1,
6,
9
] |
|
3cat
| [
0,
4,
1
] |
|
3cat
| [
8,
9,
8
] |
|
5dog
| [
8,
0,
9
] |
|
2bird
| [
7,
7,
8
] |
|
2bird
| [
6,
8,
3
] |
|
7horse
| [
9,
4,
1
] |
|
1automobile
| [
0,
1,
2
] |
|
1automobile
| [
7,
1,
6
] |
|
1automobile
| [
4,
4,
8
] |
|
2bird
| [
2,
9,
7
] |
|
2bird
| [
9,
3,
5
] |
|
0airplane
| [
4,
6,
9
] |
|
9truck
| [
4,
4,
3
] |
|
5dog
| [
9,
8,
7
] |
|
7horse
| [
6,
1,
0
] |
|
9truck
| [
2,
7,
1
] |
|
2bird
| [
0,
9,
1
] |
|
2bird
| [
5,
6,
6
] |
|
5dog
| [
8,
1,
1
] |
|
2bird
| [
0,
8,
1
] |
|
4deer
| [
9,
0,
5
] |
|
3cat
| [
0,
8,
8
] |
|
1automobile
| [
0,
3,
2
] |
|
1automobile
| [
4,
3,
9
] |
|
8ship
| [
6,
7,
5
] |
|
2bird
| [
8,
0,
0
] |
|
1automobile
| [
5,
3,
5
] |
|
1automobile
| [
6,
1,
1
] |
|
4deer
| [
2,
6,
0
] |
|
9truck
| [
6,
4,
6
] |
|
7horse
| [
0,
2,
7
] |
|
8ship
| [
1,
2,
0
] |
|
5dog
| [
9,
3,
8
] |
|
9truck
| [
3,
5,
7
] |
|
6frog
| [
0,
7,
1
] |
|
7horse
| [
4,
1,
0
] |
|
3cat
| [
0,
2,
3
] |
|
1automobile
| [
5,
5,
2
] |
|
9truck
| [
6,
2,
3
] |
|
0airplane
| [
6,
3,
3
] |
|
3cat
| [
3,
2,
3
] |
|
1automobile
| [
7,
3,
5
] |
|
3cat
| [
4,
8,
7
] |
|
5dog
| [
5,
8,
8
] |
|
4deer
| [
8,
5,
4
] |
|
5dog
| [
8,
0,
4
] |
|
7horse
| [
6,
0,
5
] |
|
7horse
| [
1,
4,
1
] |
|
4deer
| [
0,
6,
8
] |
|
7horse
| [
2,
1,
4
] |
|
9truck
| [
4,
5,
8
] |
|
4deer
| [
8,
8,
8
] |
|
2bird
| [
7,
9,
7
] |
|
3cat
| [
1,
7,
7
] |
|
8ship
| [
8,
0,
6
] |
|
0airplane
| [
3,
2,
9
] |
|
1automobile
| [
9,
1,
8
] |
|
6frog
| [
9,
1,
9
] |
|
1automobile
| [
6,
9,
2
] |
|
1automobile
| [
0,
3,
5
] |
|
4deer
| [
9,
0,
5
] |
|
1automobile
| [
9,
0,
5
] |
Dataset Card for CLCIFAR10
This Complementary labeled CIFAR10 dataset contains 3 human-annotated complementary labels for all 50000 images in the training split of CIFAR10. 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 0x799538D3D5A0>,
'ord_labels': 6,
'cl_labels': [3, 9, 6]
}
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 9 as follows:0: airplane 1: automobile 2: bird 3: cat 4: deer 5: dog 6: frog 7: horse 8: ship 9: truck
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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