Update README.md

README.md

@@ -86,3 +86,111 @@ configs:
   - split: train
     path: data/train-*
 ---
+# 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. The implementation is based on the shared dataset and 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/).