CIFAR-100N

Overview

CIFAR-100N pairs the 50,000 CIFAR-100 training images with their clean fine and superclass labels and the human noisy labels from the CIFAR-N release. It also provides the 10,000 standard CIFAR-100 test images and labels. The test examples have None for noisy_label because CIFAR-N annotations cover only training images.

• Train: 50,000 images (500 per class)

• Test: 10,000 images (100 per class)

Data Structure

When accessing an example using ds[i], you will receive a dictionary with the following keys:

Key	Type	Description
image	PIL.Image.Image	32x32x3 RGB image
label	int	Clean fine-grained class label (0-99)
superclass	int	Clean superclass label (0-19)
noisy_label	int or None	Human annotation label (0-99); None on the test split

Usage Example

Basic Usage

from stable_datasets.images.cifar100_n import CIFAR100N

train = CIFAR100N(split="train")
test = CIFAR100N(split="test")
sample = train[0]

print(sample["label"], sample["superclass"], sample["noisy_label"])
print(test[0]["noisy_label"])  # None: no human annotations are provided for test images

Related Datasets

• CIFAR-10: CIFAR-10 with 10 classes

• CIFAR-100: CIFAR-100 with 100 fine-grained classes

• CIFAR-10-C: CIFAR-10-C corruption benchmark

• CIFAR-100-C: CIFAR-100-C corruption benchmark

• CIFAR-10N: CIFAR-10 with real-world human annotations

References

• CIFAR-N release: https://github.com/UCSC-REAL/cifar-10-100n

• License: CC BY-NC 4.0

• CIFAR-100 dataset: https://www.cs.toronto.edu/~kriz/cifar.html

• Wei et al., Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations, ICLR 2022: https://openreview.net/forum?id=TBWA6PLJZQm

Citation

@inproceedings{wei2022learning,
  title={Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations},
  author={Jiaheng Wei and Zhaowei Zhu and Hao Cheng and Tongliang Liu and Gang Niu and Yang Liu},
  booktitle={International Conference on Learning Representations},
  year={2022},
  url={https://openreview.net/forum?id=TBWA6PLJZQm}
}