fphool/usps

---
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/).