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.

1. Install the package.

pip install flwr-datasets[vision]

2. 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.