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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
- Repository: https://github.com/BayesWatch/cinic-10
- Paper: https://arxiv.org/abs/1810.03505
- Dataset: http://dx.doi.org/10.7488/ds/2448
- Benchmarking, Papers with code: https://paperswithcode.com/sota/image-classification-on-cinic-10
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.
- Install the package.
pip install flwr-datasets[vision]
- 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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