images imagewidth (px) 32 32 | ord_labels class label 20
classes | cl_labels sequencelengths 3 3 |
|---|---|---|
16small mammals | [
1,
4,
5
] | |
15reptiles | [
2,
14,
4
] | |
15reptiles | [
3,
19,
6
] | |
2flowers | [
13,
13,
15
] | |
14people | [
6,
19,
1
] | |
18transportation vehicles | [
4,
1,
11
] | |
4fruit, vegetables and mushrooms | [
17,
19,
14
] | |
2flowers | [
15,
11,
9
] | |
17trees | [
6,
6,
5
] | |
14people | [
3,
13,
6
] | |
19non-transportation vehicles | [
0,
8,
13
] | |
9large man-made outdoor things | [
4,
7,
11
] | |
19non-transportation vehicles | [
14,
19,
1
] | |
10large natural outdoor scenes | [
6,
13,
6
] | |
6household furniture | [
13,
14,
7
] | |
14people | [
17,
19,
15
] | |
5household electrical devices | [
6,
7,
17
] | |
0aquatic_mammals | [
8,
7,
6
] | |
14people | [
6,
8,
17
] | |
7insects | [
3,
6,
6
] | |
6household furniture | [
4,
11,
15
] | |
13non-insect invertebrates | [
3,
6,
5
] | |
2flowers | [
4,
0,
15
] | |
0aquatic_mammals | [
4,
5,
6
] | |
12medium-sized mammals | [
19,
7,
6
] | |
18transportation vehicles | [
13,
11,
2
] | |
2flowers | [
7,
15,
0
] | |
16small mammals | [
15,
9,
6
] | |
11large omnivores and herbivores | [
17,
5,
7
] | |
12medium-sized mammals | [
15,
17,
19
] | |
9large man-made outdoor things | [
7,
0,
2
] | |
5household electrical devices | [
2,
15,
1
] | |
8large carnivores and bear | [
6,
4,
5
] | |
16small mammals | [
1,
15,
6
] | |
19non-transportation vehicles | [
6,
13,
2
] | |
10large natural outdoor scenes | [
7,
16,
3
] | |
17trees | [
1,
13,
6
] | |
2flowers | [
1,
12,
11
] | |
5household electrical devices | [
19,
4,
5
] | |
12medium-sized mammals | [
5,
5,
17
] | |
12medium-sized mammals | [
5,
1,
18
] | |
2flowers | [
14,
8,
19
] | |
17trees | [
8,
8,
4
] | |
16small mammals | [
2,
3,
4
] | |
9large man-made outdoor things | [
4,
15,
14
] | |
0aquatic_mammals | [
6,
11,
17
] | |
13non-insect invertebrates | [
3,
2,
18
] | |
8large carnivores and bear | [
7,
6,
7
] | |
12medium-sized mammals | [
17,
8,
17
] | |
0aquatic_mammals | [
17,
19,
5
] | |
15reptiles | [
9,
3,
2
] | |
4fruit, vegetables and mushrooms | [
5,
8,
0
] | |
18transportation vehicles | [
7,
6,
15
] | |
0aquatic_mammals | [
1,
4,
13
] | |
13non-insect invertebrates | [
1,
6,
15
] | |
5household electrical devices | [
1,
13,
17
] | |
17trees | [
7,
15,
1
] | |
2flowers | [
1,
8,
5
] | |
5household electrical devices | [
4,
6,
7
] | |
10large natural outdoor scenes | [
6,
8,
6
] | |
18transportation vehicles | [
2,
4,
2
] | |
18transportation vehicles | [
13,
13,
13
] | |
18transportation vehicles | [
6,
0,
6
] | |
18transportation vehicles | [
0,
2,
7
] | |
0aquatic_mammals | [
6,
6,
19
] | |
17trees | [
3,
4,
0
] | |
2flowers | [
1,
15,
6
] | |
10large natural outdoor scenes | [
19,
17,
6
] | |
13non-insect invertebrates | [
9,
17,
6
] | |
13non-insect invertebrates | [
3,
11,
9
] | |
15reptiles | [
3,
19,
18
] | |
7insects | [
15,
1,
13
] | |
9large man-made outdoor things | [
11,
2,
2
] | |
9large man-made outdoor things | [
1,
13,
2
] | |
13non-insect invertebrates | [
18,
14,
2
] | |
3food_containers | [
1,
4,
17
] | |
12medium-sized mammals | [
17,
6,
18
] | |
19non-transportation vehicles | [
3,
14,
6
] | |
9large man-made outdoor things | [
7,
1,
7
] | |
16small mammals | [
2,
2,
5
] | |
14people | [
0,
2,
6
] | |
17trees | [
4,
8,
13
] | |
6household furniture | [
7,
4,
19
] | |
6household furniture | [
0,
16,
9
] | |
16small mammals | [
6,
5,
11
] | |
2flowers | [
8,
1,
3
] | |
15reptiles | [
2,
2,
17
] | |
10large natural outdoor scenes | [
15,
14,
6
] | |
15reptiles | [
2,
19,
19
] | |
17trees | [
0,
2,
8
] | |
1fish | [
18,
8,
0
] | |
12medium-sized mammals | [
1,
6,
2
] | |
0aquatic_mammals | [
4,
2,
1
] | |
19non-transportation vehicles | [
4,
15,
15
] | |
1fish | [
13,
17,
5
] | |
9large man-made outdoor things | [
14,
8,
13
] | |
16small mammals | [
13,
14,
3
] | |
11large omnivores and herbivores | [
0,
13,
13
] | |
7insects | [
18,
1,
7
] | |
17trees | [
9,
13,
7
] |
Dataset Card for ACLCIFAR20
This Complementary labeled CIFAR20 dataset contains auto-labeled complementary labels for all 50000 images in the training split of CIFAR20. We group 4-6 categories as a superclass and collect the complementary labels of these 20 superclasses.
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 0x7BFA04799E10>,
'ord_labels': 16,
'cl_labels': [1, 4, 5]
}
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.
Citing
If you find this dataset useful, please cite the following:
@InProceedings{aclimage2025,
author="Mai, Tan-Ha and Ye, Nai-Xuan and Kuan, Yu-Wei and Lu, Po-Yi and Lin, Hsuan-Tien",
title="The Unexplored Potential of Vision-Language Models for Generating Large-Scale Complementary-Label Learning Data",
booktitle="Pacific-Asia Conference on Knowledge Discovery and Data Mining",
year="2025",
pages="90--102"
}
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