cifar10_n

  • 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)
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

Visualization

  • 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}
}