---
license: mit
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
dataset_info:
  features:
  - name: images
    dtype: image
  - name: ord_labels
    dtype:
      class_label:
        names:
          '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
  - name: cl_labels
    sequence:
      class_label:
        names:
          '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
  splits:
  - name: train
    num_bytes: 113545106.0
    num_examples: 50000
  download_size: 116336858
  dataset_size: 113545106.0
---
## 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]([https://www.mturk.com](https://www.mturk.com/)). 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](https://github.com/ntucllab/CLImage_Dataset) or [paper](https://arxiv.org/abs/2305.08295).

### 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`: A `PIL.Image.Image` object 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.

## Citing

If you find this dataset useful, please cite the following:

```
@article{
  wang2024climage,
  title={{CLI}mage: Human-Annotated Datasets for Complementary-Label Learning},
  author={Hsiu-Hsuan Wang and Mai Tan Ha and Nai-Xuan Ye and Wei-I Lin and Hsuan-Tien Lin},
  journal={Transactions on Machine Learning Research},
  issn={2835-8856},
  year={2025}
}
```