UCF101 is an action recognition data set of realistic action videos, collected from YouTube, having 101 action categories.

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.… See the full description on the dataset page: https://huggingface.co/datasets/flwrlabs/ucf101.

A 101-label video classification dataset.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('ucf101', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

This dataset repository contains a subset of the UCF-101 dataset [1]. The subset archive was obtained using the code from this guide.

  References

[1] UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild, https://arxiv.org/abs/1212.0402.

Existing benchmark datasets in real-world distribution shifts are generally synthetically generated via augmentations to simulate real-world shifts such as weather and camera rotation. The UCF101-DS dataset consists of real-world distribution shifts from user-generated videos without synthetic augmentation. It has videos for 47 UCF-101 classes with 63 different distribution shifts that can be categorized into 15 categories. A total of 536 unique videos split into a total of 4,708 clips. Each clip ranges from 7 to 10 seconds long.

Classify video clips with natural scenes of actions performed by people visible in the videos.

See the UCF101 Dataset web page: https://www.crcv.ucf.edu/data/UCF101.php#Results_on_UCF101

This example datasets consists of the 10 most numerous video from the UCF101 dataset. For the top 5 version, see: https://doi.org/10.5281/zenodo.7924745 .

Based on this code: https://keras.io/examples/vision/video_classification/ (needs to be updated, if has not yet been already; see the issue: https://github.com/keras-team/keras-io/issues/1342).

Testing if data can be downloaded from figshare with wget, see: https://github.com/mojaveazure/angsd-wrapper/issues/10

For generating the subset, see this notebook: https://colab.research.google.com/github/sayakpaul/Action-Recognition-in-TensorFlow/blob/main/Data_Preparation_UCF101.ipynb -- however, it also needs to be adjusted (if has not yet been already - then I will post a link to the notebook here or elsewhere, e.g., in the corrected notebook with Keras example).

I would like to thank Sayak Paul for contacting me about his example at Keras documentation being out of date.

Cite this dataset as:

Soomro, K., Zamir, A. R., & Shah, M. (2012). UCF101: A dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:1212.0402. https://doi.org/10.48550/arXiv.1212.0402

To download the dataset via the command line, please use:

wget -q https://zenodo.org/record/7882861/files/ucf101_top10.tar.gz -O ucf101_top10.tar.gz tar xf ucf101_top10.tar.gz

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UCF101 dataset

guyuchao/UCF101 dataset hosted on Hugging Face and contributed by the HF Datasets community

UCF101

## Overview

UCF101 is a dataset for classification tasks - it contains Actions annotations for 3,923 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

Dataset

This dataset was created by Shreya Sajal

Contents

The VIriors Action Recognition Challenge uses a subset of the UCF101 action recognition dataset:

Train set: ~4.8K clips. Validation set: ~4.7K clips. Test set: ~3.8K clips.

Dataset

This dataset was created by dhuh137

Contents

Dataset

This dataset was created by Sinan DÖNMEZ

Contents

VLM2Vec/UCF101 dataset hosted on Hugging Face and contributed by the HF Datasets community

Dataset

This dataset was created by Degior

Contents

With 13320 videos from 101 action categories, UCF101 gives the largest diversity in terms of actions and with the presence of large variations in camera motion, object appearance and pose, object scale, viewpoint, cluttered background, illumination conditions, etc, it is the most challenging data set to date. As most of the available action recognition data sets are not realistic and are staged by actors, UCF101 aims to encourage further research into action recognition by learning and exploring new realistic action categories. The videos in 101 action categories are grouped into 25 groups, where each group can consist of 4-7 videos of an action. The videos from the same group may share some common features, such as similar background, similar viewpoint, etc.

A neuromorphic vision sensing (NVS) device represents visual information as sequences of asynchronous discrete events (a.k.a., “spikes”) in response to changes in scene reflectance.

UCF101 dataset

For personal research. Original dataset link is located here. Yes there's missing metadata because I'm only doing video compression tasks.

  license: mit

language: - en - zh

Dataset

This dataset was created by DumbMachine

Released under CC0: Public Domain

Contents