cifar10_n

Warning: Manual download required. See instructions below.

• Description:

A re-labeled version of CIFAR-10 with real human annotation errors. For every pair (image, label) in the original CIFAR-10 train set, it provides several additional labels given by real human annotators.

• Homepage: https://ucsc-real.soe.ucsc.edu:1995/Home.html/

• Source code: tfds.image_classification.cifar10_n.Cifar10N

• Versions:

  • 1.0.0: Initial release.
  • 1.0.1: Fixed typo in worse_label key.
  • 1.0.2: Fixed correspondence between annotations and images.
  • 1.0.3: Fixed files in MANUAL_DIR.
  • 1.0.4 (default): Fixed loading of side information.

• Download size: 162.17 MiB

• Dataset size: 147.91 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_cifar10N.csv', 'CIFAR-10_human_ordered.npy' and 'image_order_c10.npy' from https://github.com/UCSC-REAL/cifar-10-100n

Then convert 'CIFAR-10_human_ordered.npy' into a CSV file 'CIFAR-10_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-10_human_ordered.npy'
human_labels_csv_path = '<local_path>/CIFAR-10_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({
    'aggre_label': ClassLabel(shape=(), dtype=int64, num_classes=10),
    'id': Text(shape=(), dtype=string),
    'image': Image(shape=(32, 32, 3), dtype=uint8),
    'label': ClassLabel(shape=(), dtype=int64, num_classes=10),
    'random_label1': ClassLabel(shape=(), dtype=int64, num_classes=10),
    'random_label2': ClassLabel(shape=(), dtype=int64, num_classes=10),
    'random_label3': ClassLabel(shape=(), dtype=int64, num_classes=10),
    'worker1_id': int64,
    'worker1_time': float32,
    'worker2_id': int64,
    'worker2_time': float32,
    'worker3_id': int64,
    'worker3_time': float32,
    'worse_label': ClassLabel(shape=(), dtype=int64, num_classes=10),
})

• Feature documentation:

Feature	Class	Shape	Dtype	Description
	FeaturesDict
aggre_label	ClassLabel		int64
id	Text		string
image	Image	(32, 32, 3)	uint8
label	ClassLabel		int64
random_label1	ClassLabel		int64
random_label2	ClassLabel		int64
random_label3	ClassLabel		int64
worker1_id	Tensor		int64
worker1_time	Tensor		float32
worker2_id	Tensor		int64
worker2_time	Tensor		float32
worker3_id	Tensor		int64
worker3_time	Tensor		float32
worse_label	ClassLabel		int64

• 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}
}