|
--- |
|
dataset_info: |
|
features: |
|
- name: image |
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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 |
|
|
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## 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( |
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dataset="flwrlabs/usps", |
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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/). |
