Datasets:
image
imagewidth (px) 28
28
| writer_id
stringlengths 8
8
| hsf_id
int64 0
7
| character
class label 62
classes |
---|---|---|---|
f0000_14 | 0 | 00
|
|
f0000_14 | 0 | 53r
|
|
f0000_14 | 0 | 28S
|
|
f0000_14 | 0 | 28S
|
|
f0000_14 | 0 | 22M
|
|
f0000_14 | 0 | 58w
|
|
f0000_14 | 0 | 47l
|
|
f0000_14 | 0 | 23N
|
|
f0000_14 | 0 | 11
|
|
f0000_14 | 0 | 53r
|
|
f0000_14 | 0 | 55t
|
|
f0000_14 | 0 | 55
|
|
f0000_14 | 0 | 38c
|
|
f0000_14 | 0 | 77
|
|
f0000_14 | 0 | 18I
|
|
f0000_14 | 0 | 88
|
|
f0000_14 | 0 | 48m
|
|
f0000_14 | 0 | 40e
|
|
f0000_14 | 0 | 12C
|
|
f0000_14 | 0 | 22
|
|
f0000_14 | 0 | 23N
|
|
f0000_14 | 0 | 25P
|
|
f0000_14 | 0 | 53r
|
|
f0000_14 | 0 | 23N
|
|
f0000_14 | 0 | 66
|
|
f0000_14 | 0 | 29T
|
|
f0000_14 | 0 | 24O
|
|
f0000_14 | 0 | 27R
|
|
f0000_14 | 0 | 00
|
|
f0000_14 | 0 | 47l
|
|
f0000_14 | 0 | 10A
|
|
f0000_14 | 0 | 28S
|
|
f0000_14 | 0 | 27R
|
|
f0000_14 | 0 | 77
|
|
f0000_14 | 0 | 12C
|
|
f0000_14 | 0 | 00
|
|
f0000_14 | 0 | 99
|
|
f0000_14 | 0 | 22
|
|
f0000_14 | 0 | 47l
|
|
f0000_14 | 0 | 51p
|
|
f0000_14 | 0 | 22
|
|
f0000_14 | 0 | 30U
|
|
f0000_14 | 0 | 24O
|
|
f0000_14 | 0 | 35Z
|
|
f0000_14 | 0 | 30U
|
|
f0000_14 | 0 | 24O
|
|
f0000_14 | 0 | 39d
|
|
f0000_14 | 0 | 44
|
|
f0000_14 | 0 | 55t
|
|
f0000_14 | 0 | 39d
|
|
f0000_14 | 0 | 44
|
|
f0000_14 | 0 | 00
|
|
f0000_14 | 0 | 55t
|
|
f0000_14 | 0 | 53r
|
|
f0000_14 | 0 | 47l
|
|
f0000_14 | 0 | 40e
|
|
f0000_14 | 0 | 27R
|
|
f0000_14 | 0 | 33
|
|
f0000_14 | 0 | 47l
|
|
f0000_14 | 0 | 10A
|
|
f0000_14 | 0 | 22
|
|
f0000_14 | 0 | 44
|
|
f0000_14 | 0 | 24O
|
|
f0000_14 | 0 | 99
|
|
f0000_14 | 0 | 11
|
|
f0000_14 | 0 | 22
|
|
f0000_14 | 0 | 00
|
|
f0000_14 | 0 | 24O
|
|
f0000_14 | 0 | 22
|
|
f0000_14 | 0 | 30U
|
|
f0000_14 | 0 | 33
|
|
f0000_14 | 0 | 43h
|
|
f0000_14 | 0 | 12C
|
|
f0000_14 | 0 | 00
|
|
f0000_14 | 0 | 47l
|
|
f0000_14 | 0 | 55t
|
|
f0000_14 | 0 | 33
|
|
f0000_14 | 0 | 28S
|
|
f0000_14 | 0 | 15F
|
|
f0000_14 | 0 | 24O
|
|
f0000_14 | 0 | 40e
|
|
f0000_14 | 0 | 40e
|
|
f0000_14 | 0 | 55t
|
|
f0000_14 | 0 | 47l
|
|
f0000_14 | 0 | 12C
|
|
f0000_14 | 0 | 55
|
|
f0000_14 | 0 | 11
|
|
f0000_14 | 0 | 24O
|
|
f0000_14 | 0 | 33
|
|
f0000_14 | 0 | 11
|
|
f0000_14 | 0 | 24O
|
|
f0000_14 | 0 | 55t
|
|
f0000_14 | 0 | 24O
|
|
f0000_14 | 0 | 31V
|
|
f0000_14 | 0 | 00
|
|
f0000_14 | 0 | 28S
|
|
f0000_14 | 0 | 32W
|
|
f0000_14 | 0 | 28S
|
|
f0000_14 | 0 | 43h
|
|
f0000_14 | 0 | 40e
|
Dataset Card for FEMNIST
The FEMNIST dataset is a part of the LEAF benchmark. It represents image classification of handwritten digits, lower and uppercase letters, giving 62 unique labels.
Dataset Details
Dataset Description
Each sample is comprised of a (28x28) grayscale image, writer_id, hsf_id, and character.
- Curated by: LEAF
- License: BSD 2-Clause License
Dataset Sources
The FEMNIST is a preprocessed (in a way that resembles preprocessing for MNIST) version of NIST SD 19.
Uses
This dataset is intended to be used in Federated Learning settings.
Direct Use
We recommend using Flower Dataset (flwr-datasets) and Flower (flwr).
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 NaturalIdPartitioner
fds = FederatedDataset(
dataset="flwrlabs/femnist",
partitioners={"train": NaturalIdPartitioner(partition_by="writer_id")}
)
partition = fds.load_partition(partition_id=0)
Dataset Structure
The whole dataset is kept in the train split. If you want to leave out some part of the dataset for centralized evaluation, use Resplitter. (The full example is coming soon here) Dataset fields:
- image: grayscale of size (28, 28), PIL Image,
- writer_id: string, unique value per each writer,
- hsf_id: string, corresponds to the way that the data was collected (see more details here,
- character: ClassLabel (it means it's int if you access it in the dataset, but you can convert it to the original value by
femnist["train"].features["character"].int2str(value)
.
Dataset Creation
Curation Rationale
This dataset was created as a part of the LEAF benchmark. We make it available in the HuggingFace Hub to facilitate its seamless use in FlowerDatasets.
Source Data
Data Collection and Processing
For the preprocessing details, please refer to the original paper, the source code and NIST SD 19
Who are the source data producers?
For the preprocessing details, please refer to the original paper, the source code and NIST SD 19
Citation
When working on the LEAF benchmark, please cite the original paper. If you're using this dataset with Flower Datasets, you can cite Flower.
BibTeX:
@article{DBLP:journals/corr/abs-1812-01097,
author = {Sebastian Caldas and
Peter Wu and
Tian Li and
Jakub Kone{\v{c}}n{\'y} and
H. Brendan McMahan and
Virginia Smith and
Ameet Talwalkar},
title = {{LEAF:} {A} Benchmark for Federated Settings},
journal = {CoRR},
volume = {abs/1812.01097},
year = {2018},
url = {http://arxiv.org/abs/1812.01097},
eprinttype = {arXiv},
eprint = {1812.01097},
timestamp = {Wed, 23 Dec 2020 09:35:18 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-1812-01097.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
@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, please contact Flower Labs.
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