cifar100_n

Warning: Manual download required. See instructions below.

• Description:

A re-labeled version of CIFAR-100 with real human annotation errors. For every pair (image, label) in the original CIFAR-100 train set, it provides an additional label given by a real human annotator.

• Homepage: https://www.cs.toronto.edu/~kriz/cifar.html

• Source code: tfds.image_classification.cifar100_n.Cifar100N

• Versions:

  • 1.0.0: Initial release.
  • 1.0.1 (default): Fixed correspondence between annotations and images.

• Download size: 160.71 MiB

• Dataset size: 136.07 MiB

• Manual download instructions: This dataset requires you to download the source data manually into download_config.manual_dir (defaults to ~/tensorflow_datasets/downloads/manual/):
  Download 'side_info_cifar100N.csv', 'CIFAR-100_human_ordered.npy' and 'image_order_c100.npy' from https://github.com/UCSC-REAL/cifar-10-100n

Then convert 'CIFAR-100_human_ordered.npy' into a CSV file 'CIFAR-100_human_annotations.csv'. This can be done with the following code:

import numpy as np
from tensorflow_datasets.core.utils.lazy_imports_utils import pandas as pd
from tensorflow_datasets.core.utils.lazy_imports_utils import tensorflow as tf

human_labels_np_path = '<local_path>/CIFAR-100_human_ordered.npy'
human_labels_csv_path = '<local_path>/CIFAR-100_human_annotations.csv'

with tf.io.gfile.GFile(human_labels_np_path, "rb") as f:
  human_annotations = np.load(f, allow_pickle=True)

df = pd.DataFrame(human_annotations[()])

with tf.io.gfile.GFile(human_labels_csv_path, "w") as f:
  df.to_csv(f, index=False)

• Auto-cached (documentation): Yes

• Splits:

Split	Examples
'test'	10,000
'train'	50,000

• Feature structure:

FeaturesDict({
    'coarse_label': ClassLabel(shape=(), dtype=int64, num_classes=20),
    'id': Text(shape=(), dtype=string),
    'image': Image(shape=(32, 32, 3), dtype=uint8),
    'label': ClassLabel(shape=(), dtype=int64, num_classes=100),
    'noise_label': ClassLabel(shape=(), dtype=int64, num_classes=100),
    'worker_id': int64,
    'worker_time': float32,
})

• Feature documentation:

Feature	Class	Shape	Dtype	Description
	FeaturesDict
coarse_label	ClassLabel		int64
id	Text		string
image	Image	(32, 32, 3)	uint8
label	ClassLabel		int64
noise_label	ClassLabel		int64
worker_id	Tensor		int64
worker_time	Tensor		float32

• Supervised keys (See as_supervised doc): None

• Figure (tfds.show_examples):

• Examples (tfds.as_dataframe):

• 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}
}