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imagewidth (px)
32
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class label
10 classes
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Dataset Card for CINIC-10

CINIC-10 has a total of 270,000 images equally split amongst three subsets: train, validate, and test. This means that CINIC-10 has 4.5 times as many samples than CIFAR-10.

Dataset Details

In each subset (90,000 images), there are ten classes (identical to CIFAR-10 classes). There are 9000 images per class per subset. Using the suggested data split (an equal three-way split), CINIC-10 has 1.8 times as many training samples as in CIFAR-10. CINIC-10 is designed to be directly swappable with CIFAR-10. To understand the motivation behind the dataset creation please visit the GitHub repository.

Dataset Sources

Use in FL

In order to prepare the dataset for the FL settings, we recommend using Flower Dataset (flwr-datasets) for the dataset download and partitioning and Flower (flwr) for conducting FL experiments.

To partition the dataset, do the following.

  1. Install the package.
pip install flwr-datasets[vision]
  1. Use the HF Dataset under the hood in Flower Datasets.
from flwr_datasets import FederatedDataset
from flwr_datasets.partitioner import IidPartitioner

fds = FederatedDataset(
    dataset="flwrlabs/cinic10",
    partitioners={"train": IidPartitioner(num_partitions=10)}
)
partition = fds.load_partition(partition_id=0)

Dataset Structure

Data Instances

The first instance of the train split is presented below:

{
  'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=32x32>,
  'label': 0
}

Data Split

DatasetDict({
    train: Dataset({
        features: ['image', 'label'],
        num_rows: 90000
    })
    validation: Dataset({
        features: ['image', 'label'],
        num_rows: 90000
    })
    test: Dataset({
        features: ['image', 'label'],
        num_rows: 90000
    })
})

Citation

When working with the CINIC-10 dataset, please cite the original paper. If you're using this dataset with Flower Datasets and Flower, cite Flower.

BibTeX:

Original paper:

@misc{darlow2018cinic10imagenetcifar10,
      title={CINIC-10 is not ImageNet or CIFAR-10}, 
      author={Luke N. Darlow and Elliot J. Crowley and Antreas Antoniou and Amos J. Storkey},
      year={2018},
      eprint={1810.03505},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/1810.03505}, 
}

Flower:

@article{DBLP:journals/corr/abs-2007-14390,
  author       = {Daniel J. Beutel and
                  Taner Topal and
                  Akhil Mathur and
                  Xinchi Qiu and
                  Titouan Parcollet and
                  Nicholas D. Lane},
  title        = {Flower: {A} Friendly Federated Learning Research Framework},
  journal      = {CoRR},
  volume       = {abs/2007.14390},
  year         = {2020},
  url          = {https://arxiv.org/abs/2007.14390},
  eprinttype    = {arXiv},
  eprint       = {2007.14390},
  timestamp    = {Mon, 03 Aug 2020 14:32:13 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2007-14390.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

Dataset Card Contact

If you have any questions about the dataset preprocessing and preparation, please contact Flower Labs.

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