README.md

---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: video_id
    dtype: string
  - name: clip_id
    dtype: string
  - name: frame
    dtype: int64
  - name: label
    dtype:
      class_label:
        names:
          '0': ApplyEyeMakeup
          '1': ApplyLipstick
          '2': Archery
          '3': BabyCrawling
          '4': BalanceBeam
          '5': BandMarching
          '6': BaseballPitch
          '7': Basketball
          '8': BasketballDunk
          '9': BenchPress
          '10': Biking
          '11': Billiards
          '12': BlowDryHair
          '13': BlowingCandles
          '14': BodyWeightSquats
          '15': Bowling
          '16': BoxingPunchingBag
          '17': BoxingSpeedBag
          '18': BreastStroke
          '19': BrushingTeeth
          '20': CleanAndJerk
          '21': CliffDiving
          '22': CricketBowling
          '23': CricketShot
          '24': CuttingInKitchen
          '25': Diving
          '26': Drumming
          '27': Fencing
          '28': FieldHockeyPenalty
          '29': FloorGymnastics
          '30': FrisbeeCatch
          '31': FrontCrawl
          '32': GolfSwing
          '33': Haircut
          '34': HammerThrow
          '35': Hammering
          '36': HandstandPushups
          '37': HandstandWalking
          '38': HeadMassage
          '39': HighJump
          '40': HorseRace
          '41': HorseRiding
          '42': HulaHoop
          '43': IceDancing
          '44': JavelinThrow
          '45': JugglingBalls
          '46': JumpRope
          '47': JumpingJack
          '48': Kayaking
          '49': Knitting
          '50': LongJump
          '51': Lunges
          '52': MilitaryParade
          '53': Mixing
          '54': MoppingFloor
          '55': Nunchucks
          '56': ParallelBars
          '57': PizzaTossing
          '58': PlayingCello
          '59': PlayingDaf
          '60': PlayingDhol
          '61': PlayingFlute
          '62': PlayingGuitar
          '63': PlayingPiano
          '64': PlayingSitar
          '65': PlayingTabla
          '66': PlayingViolin
          '67': PoleVault
          '68': PommelHorse
          '69': PullUps
          '70': Punch
          '71': PushUps
          '72': Rafting
          '73': RockClimbingIndoor
          '74': RopeClimbing
          '75': Rowing
          '76': SalsaSpin
          '77': ShavingBeard
          '78': Shotput
          '79': SkateBoarding
          '80': Skiing
          '81': Skijet
          '82': SkyDiving
          '83': SoccerJuggling
          '84': SoccerPenalty
          '85': StillRings
          '86': SumoWrestling
          '87': Surfing
          '88': Swing
          '89': TableTennisShot
          '90': TaiChi
          '91': TennisSwing
          '92': ThrowDiscus
          '93': TrampolineJumping
          '94': Typing
          '95': UnevenBars
          '96': VolleyballSpiking
          '97': WalkingWithDog
          '98': WallPushups
          '99': WritingOnBoard
          '100': YoYo
  splits:
  - name: train
    num_bytes: 31974277609.664
    num_examples: 1786096
  - name: test
    num_bytes: 14764935697.708
    num_examples: 697222
  download_size: 54505499695
  dataset_size: 46739213307.372
configs:
- config_name: default
  data_files:
  - split: train
    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/).