---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': '0'
          '1': '1'
          '2': '2'
          '3': '3'
          '4': '4'
          '5': '5'
          '6': '6'
          '7': '7'
          '8': '8'
          '9': '9'
  splits:
  - name: train
    num_bytes: 2194749.625
    num_examples: 7291
  - name: test
    num_bytes: 609594.125
    num_examples: 2007
  download_size: 2559509
  dataset_size: 2804343.75
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
license: unknown
task_categories:
- image-classification
size_categories:
- 1K<n<10K
---

# 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](https://flower.ai/docs/datasets/) (flwr-datasets) for the dataset download and partitioning and [Flower](https://flower.ai/docs/framework/) (flwr) for conducting FL experiments.

To partition the dataset, do the following. 
1. Install the package.
```bash
pip install flwr-datasets[vision]
```
2. Use the HF Dataset under the hood in Flower Datasets.
```python
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](https://flower.ai/).