metadata

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 (flwr-datasets) for the dataset download and partitioning and Flower (flwr) for conducting FL experiments.

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 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.