metadata
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
Uses
In order to prepare the dataset for the FL settings, we recommend using Flower Dataset (flwr-datasets) for the dataset download and partitioning and Flower (flwr) for conducting FL experiments.
To partition the dataset, do the following.
- Install the package.
pip install flwr-datasets[vision]
- Use the HF Dataset under the hood in Flower Datasets.
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.