flwrlabs/office-home

---
dataset_info:
features:
- name: image
dtype: image
- name: domain
dtype: string
- name: label
dtype:
class_label:
names:
'0': Alarm_Clock
'1': Backpack
'2': Batteries
'3': Bed
'4': Bike
'5': Bottle
'6': Bucket
'7': Calculator
'8': Calendar
'9': Candles
'10': Chair
'11': Clipboards
'12': Computer
'13': Couch
'14': Curtains
'15': Desk_Lamp
'16': Drill
'17': Eraser
'18': Exit_Sign
'19': Fan
'20': File_Cabinet
'21': Flipflops
'22': Flowers
'23': Folder
'24': Fork
'25': Glasses
'26': Hammer
'27': Helmet
'28': Kettle
'29': Keyboard
'30': Knives
'31': Lamp_Shade
'32': Laptop
'33': Marker
'34': Monitor
'35': Mop
'36': Mouse
'37': Mug
'38': Notebook
'39': Oven
'40': Pan
'41': Paper_Clip
'42': Pen
'43': Pencil
'44': Postit_Notes
'45': Printer
'46': Push_Pin
'47': Radio
'48': Refrigerator
'49': Ruler
'50': Scissors
'51': Screwdriver
'52': Shelf
'53': Sink
'54': Sneakers
'55': Soda
'56': Speaker
'57': Spoon
'58': TV
'59': Table
'60': Telephone
'61': ToothBrush
'62': Toys
'63': Trash_Can
'64': Webcam
splits:
- name: train
num_bytes: 1300903275.02
num_examples: 15588
download_size: 1158984115
dataset_size: 1300903275.02
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: other
license_name: fair-use-notice
license_link: https://www.hemanthdv.org/officeHomeDataset.html#:~:text=Fair%20Use%20Notice,Christopher%20Thomas)
size_categories:
- 10K<n<100K
---
# Dataset Card for Office-Home

The Office-Home dataset has been created to evaluate domain adaptation algorithms for object recognition using deep learning. It consists of images from 4 different domains: Artistic images, Clip Art, Product images and Real-World images. For each domain, the dataset contains images of 65 object categories found typically in Office and Home settings.

## Dataset Details

The dataset information is based on the original dataset website: https://www.hemanthdv.org/officeHomeDataset.html.
This implementation is based on the shared data (images + a CSV file).

### Dataset Sources

- **Website:** https://www.hemanthdv.org/officeHomeDataset.html
- **Paper:** https://openaccess.thecvf.com/content_cvpr_2017/papers/Venkateswara_Deep_Hashing_Network_CVPR_2017_paper.pdf
- **Original Code:** https://github.com/hemanthdv/da-hash

## Use in FL

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/office-home",
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.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640>,
'domain': 'Real World',
'label': 0
}
```
### Data Split

```
DatasetDict({
train: Dataset({
features: ['image', 'domain', 'label'],
num_rows: 15588
})
})
```

## Implementation details
The CSV file from the original source contains paths to samples with a subfolder named "Clock";
however, such data does not exist. However, if counting this category, there would be 66 classes.
I believe this class was forgotten to be edited because there's a different class present in the
dataset named "Alarm-Clock". This state better reflects the number of samples specified in the paper.

## Citation

When working with the Office-Home dataset, please cite the original paper.
If you're using this dataset with Flower Datasets and Flower, cite Flower.

**BibTeX:**

Original paper:
```
@inproceedings{venkateswara2017deep,
title={Deep hashing network for unsupervised domain adaptation},
author={Venkateswara, Hemanth and Eusebio, Jose and Chakraborty, Shayok and Panchanathan, Sethuraman},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
pages={5018--5027},
year={2017}
}
````

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

If you have any questions about the dataset preprocessing and preparation, please contact [Flower Labs](https://flower.ai/).