Update README.md

README.md

@@ -130,4 +130,111 @@ configs:
     path: data/train-*
   - split: test
     path: data/test-*
+license: unknown
 ---
+# Dataset Card for UCF101
+
+UCF101 is an action recognition data set of realistic action videos collected from YouTube, having 101 action categories. This version of the dataset does not contain images but images saved frame by frame. Train and test splits are generated based on the authors' first version train/test list.
+
+## Dataset Details
+
+The UCF101 includes 13320 videos from 101 action categories. For more details, visit the website and the publication specified below.
+
+### Dataset Sources
+
+- **Paper:** https://arxiv.org/abs/1212.0402
+- **Website** https://www.crcv.ucf.edu/data/UCF101.php
+
+## Uses
+
+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 NaturalIdPartitioner
+
+fds = FederatedDataset(
+    dataset="flwrlabs/ucf101",
+    partitioners={"train": NaturalIdPartitioner(partition_by="video_id")}
+)
+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=320x240>,
+ 'video_id': 'v_ApplyEyeMakeup_g08',
+ 'clip_id': 'v_ApplyEyeMakeup_g08_c01',
+ 'frame': 1,
+ 'label': 0}
+```
+### Data Split
+
+```
+DatasetDict({
+    train: Dataset({
+        features: ['image', 'video_id', 'clip_id', 'frame', 'label'],
+        num_rows: 1786096
+    })
+    test: Dataset({
+        features: ['image', 'video_id', 'clip_id', 'frame', 'label'],
+        num_rows: 697222
+    })
+})
+```
+
+## Citation
+
+When working with the Ambient Acoustic Context dataset, please cite the original paper. 
+If you're using this dataset with Flower Datasets and Flower, cite Flower.
+
+**BibTeX:**
+
+Original paper:
+```
+@misc{soomro2012ucf101dataset101human,
+      title={UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild}, 
+      author={Khurram Soomro and Amir Roshan Zamir and Mubarak Shah},
+      year={2012},
+      eprint={1212.0402},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/1212.0402}, 
+}
+````
+
+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
+
+In case of any doubts about the dataset preprocessing and preparation, please contact [Flower Labs](https://flower.ai/).