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--- |
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dataset_info: |
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features: |
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- name: image |
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dtype: image |
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- name: domain |
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dtype: string |
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- name: label |
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dtype: |
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class_label: |
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names: |
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'0': dog |
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'1': elephant |
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'2': giraffe |
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'3': guitar |
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'4': horse |
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'5': house |
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'6': person |
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splits: |
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- name: train |
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num_bytes: 252282893.372 |
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num_examples: 9991 |
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download_size: 191395900 |
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dataset_size: 252282893.372 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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license: unknown |
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task_categories: |
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- image-classification |
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size_categories: |
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- 1K<n<10K |
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--- |
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# Dataset Card for PACS |
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PACS is an image dataset for domain generalization. It consists of four domains, namely Photo (1,670 images), Art Painting (2,048 images), Cartoon (2,344 images), and Sketch (3,929 images). Each domain contains seven categories (labels): Dog, Elephant, Giraffe, Guitar, Horse, and Person. The total number of sample is 9991. |
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## Dataset Details |
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PACS DG dataset is created by intersecting the classes found in Caltech256 (Photo), Sketchy (Photo, Sketch), TU-Berlin (Sketch) and Google Images(Art painting, Cartoon, Photo). |
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### Dataset Sources |
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- **Website:** https://sketchx.eecs.qmul.ac.uk/downloads/ |
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- **Paper:** https://arxiv.org/pdf/1710.03077 |
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- **Papers with code:** https://paperswithcode.com/dataset/pacs |
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## Use in FL |
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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. |
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To partition the dataset, do the following. |
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1. Install the package. |
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```bash |
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pip install flwr-datasets[vision] |
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``` |
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2. Use the HF Dataset under the hood in Flower Datasets. |
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```python |
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from flwr_datasets import FederatedDataset |
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from flwr_datasets.partitioner import IidPartitioner |
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|
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fds = FederatedDataset( |
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dataset="flwrlabs/pacs", |
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partitioners={"train": IidPartitioner(num_partitions=10)} |
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) |
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partition = fds.load_partition(partition_id=0) |
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``` |
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## Dataset Structure |
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### Data Instances |
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The first instance of the train split is presented below: |
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``` |
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{ |
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'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=227x227>, |
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'domain': 'art_painting', |
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'label': 0 |
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} |
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``` |
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### Data Split |
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|
|
``` |
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DatasetDict({ |
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train: Dataset({ |
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features: ['image', 'domain', 'label'], |
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num_rows: 9991 |
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}) |
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}) |
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``` |
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## Citation |
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When working with the PACS dataset, please cite the original paper. |
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If you're using this dataset with Flower Datasets and Flower, cite Flower. |
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**BibTeX:** |
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Original paper: |
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``` |
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@misc{li2017deeperbroaderartierdomain, |
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title={Deeper, Broader and Artier Domain Generalization}, |
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author={Da Li and Yongxin Yang and Yi-Zhe Song and Timothy M. Hospedales}, |
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year={2017}, |
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eprint={1710.03077}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV}, |
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url={https://arxiv.org/abs/1710.03077}, |
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} |
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```` |
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|
|
Flower: |
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|
|
``` |
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@article{DBLP:journals/corr/abs-2007-14390, |
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author = {Daniel J. Beutel and |
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Taner Topal and |
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Akhil Mathur and |
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Xinchi Qiu and |
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Titouan Parcollet and |
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Nicholas D. Lane}, |
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title = {Flower: {A} Friendly Federated Learning Research Framework}, |
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journal = {CoRR}, |
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volume = {abs/2007.14390}, |
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year = {2020}, |
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url = {https://arxiv.org/abs/2007.14390}, |
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eprinttype = {arXiv}, |
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eprint = {2007.14390}, |
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timestamp = {Mon, 03 Aug 2020 14:32:13 +0200}, |
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biburl = {https://dblp.org/rec/journals/corr/abs-2007-14390.bib}, |
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bibsource = {dblp computer science bibliography, https://dblp.org} |
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} |
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``` |
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## Dataset Card Contact |
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If you have any questions about the dataset preprocessing and preparation, please contact [Flower Labs](https://flower.ai/). |
