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Dataset Card for USPS
USPS is a digit dataset automatically scanned from envelopes by the U.S. Postal Service containing a total of 9,298 16×16 pixel grayscale samples.
Dataset Details
The images are centered and normalized. They show a broad range of font styles.
Dataset Sources
- Repository: train set https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass/usps.bz2, test set: https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass/usps.t.bz2
- Paper: https://ieeexplore.ieee.org/abstract/document/291440
Uses
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/usps",
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=L size=16x16 at 0x133B4BA90>,
'label': 6
}
Data Split
DatasetDict({
train: Dataset({
features: ['image', 'label'],
num_rows: 7291
})
test: Dataset({
features: ['image', 'label'],
num_rows: 2007
})
})
Citation
When working with the USPS dataset, please cite the original paper. If you're using this dataset with Flower Datasets and Flower, cite Flower.
BibTeX:
Original paper:
@article{hull1994database,
title={A database for handwritten text recognition research},
journal={IEEE Transactions on pattern analysis and machine intelligence},
volume={16},
number={5},
pages={550--554},
year={1994},
publisher={IEEE}
}
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
In case of any doubts about the dataset preprocessing and preparation, please contact Flower Labs.
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