Update README.md

README.md

@@ -1,42 +1,158 @@
----
-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: 178662714.0
-    num_examples: 90000
-  - name: validation
-    num_bytes: 180126542.0
-    num_examples: 90000
-  - name: test
-    num_bytes: 178913694.0
-    num_examples: 90000
-  download_size: 771149160
-  dataset_size: 537702950.0
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: validation
-    path: data/validation-*
-  - split: test
-    path: data/test-*
----
+---
+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/).