README.md

---
license: cc-by-4.0
dataset_info:
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': airplane
          '1': automobile
          '2': bird
          '3': cat
          '4': deer
          '5': dog
          '6': frog
          '7': horse
          '8': ship
          '9': truck
  splits:
  - name: train
    num_bytes: 178662714
    num_examples: 90000
  - name: validation
    num_bytes: 180126542
    num_examples: 90000
  - name: test
    num_bytes: 178913694
    num_examples: 90000
  download_size: 771149160
  dataset_size: 537702950
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
task_categories:
- image-classification
size_categories:
- 100K<n<1M
---
# Dataset Card for CINIC-10

CINIC-10 has a total of 270,000 images equally split amongst three subsets: train, validate, and test. This means that CINIC-10 has 4.5 times as many samples than CIFAR-10. 

## Dataset Details

In each subset (90,000 images), there are ten classes (identical to [CIFAR-10](https://www.cs.toronto.edu/~kriz/cifar.html) classes). There are 9000 images per class per subset. Using the suggested data split (an equal three-way split), CINIC-10 has 1.8 times as many training samples as in CIFAR-10. CINIC-10 is designed to be directly swappable with CIFAR-10. 
To understand the motivation behind the dataset creation please visit the [GitHub repository](https://github.com/BayesWatch/cinic-10 ).

### Dataset Sources

- **Repository:** https://github.com/BayesWatch/cinic-10 
- **Paper:** https://arxiv.org/abs/1810.03505
- **Dataset:** http://dx.doi.org/10.7488/ds/2448
- **Benchmarking, Papers with code:** https://paperswithcode.com/sota/image-classification-on-cinic-10

## 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/cinic10",
    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.PngImagePlugin.PngImageFile image mode=RGB size=32x32>,
  'label': 0
}
```
### Data Split

```
DatasetDict({
    train: Dataset({
        features: ['image', 'label'],
        num_rows: 90000
    })
    validation: Dataset({
        features: ['image', 'label'],
        num_rows: 90000
    })
    test: Dataset({
        features: ['image', 'label'],
        num_rows: 90000
    })
})
```

## Citation

When working with the CINIC-10 dataset, please cite the original paper. 
If you're using this dataset with Flower Datasets and Flower, cite Flower.

**BibTeX:**

Original paper:
```
@misc{darlow2018cinic10imagenetcifar10,
      title={CINIC-10 is not ImageNet or CIFAR-10}, 
      author={Luke N. Darlow and Elliot J. Crowley and Antreas Antoniou and Amos J. Storkey},
      year={2018},
      eprint={1810.03505},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/1810.03505}, 
}
````

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/).