KTH Actions Dataset

Name: KTH Actions
Creator: Roth, Peter M. and Mauthner, Thomas and Khan, Inayatullah and Bischof, Horst
License: https://creativecommons.org/licenses/by-nc-nd/3.0/

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KTH Actions dataset

What is KTH Actions Dataset?

The KTH Actions dataset contains six actions (walk, jog, run, box, hand-wave, and hand-clap). It is one of the most standard datasets. Each action in the dataset is performed by 25 different individuals, and the setting is systematically altered for each action per actor. Setting variations include outdoor, outdoor with scale variation, outdoor with different clothes, and indoor (s4). These variations test the ability of algorithms to identify actions independent of the background, the appearance of the actors, and the scale of the actors.

Download KTH Actions Dataset in Python

Instead of downloading the KTH Actions dataset in Python, you can effortlessly load it in Python via our Deep Lake open-source with just one line of code.

Load KTH Actions Dataset Training Subset in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/kth-actions")

KTH Actions Dataset Structure

KTH Actions Data Fields

videos: tensor containing the videos.
actions: tensor to represent the various actions.
scenerios: tensor to represent the different scenerios.

KTH Actions Data Splits

The KTH Actions dataset training set is composed of 599 images.

How to use KTH Actions Dataset with PyTorch and TensorFlow in Python

Train a model on KTH Actions dataset with PyTorch in Python

Let’s use Deep Lake built-in PyTorch one-line dataloader to connect the data to the compute:

				
					dataloader = ds.pytorch(num_workers=0, batch_size=4, shuffle=False)

Train a model on KTH Actions dataset with TensorFlow in Python

				
					dataloader = ds.tensorflow()

Additional Information about KTH Actions Dataset

KTH Actions Dataset Description

Homepage: https://www.csc.kth.se/cvap/actions/
Paper: Manu BN (2022). Human Action Recognition using KTH Dataset (https://www.mathworks.com/matlabcentral/fileexchange/55111-human-action-recognition-using-kth-dataset), MATLAB Central File Exchange. Retrieved March 28, 2022.
Point of Contact:
- Ivan Laptev <laptevnada.kth.se>
- Barbara Caputo

KTH Actions Dataset Curators

Roth, Peter M. and Mauthner, Thomas and Khan, Inayatullah and Bischof, Horst

KTH Actions Dataset Licensing Information

Deep Lake users may have access to a variety of publicly available datasets. We do not host or distribute these datasets, vouch for their quality or fairness, or claim that you have a license to use the datasets. It is your responsibility to determine whether you have permission to use the datasets under their license.

If you’re a dataset owner and do not want your dataset to be included in this library, please get in touch through a GitHub issue. Thank you for your contribution to the ML community!

KTH Actions Dataset Citation Information

				
					@inproceedings{inproceedings,
author = {Roth, Peter M. and Mauthner, Thomas and Khan, Inayatullah and Bischof, Horst},
year = {2009},
month = {11},
pages = {546 - 553},
title = {Efficient human action recognition by cascaded linear classifcation},
doi = {10.1109/ICCVW.2009.5457655}
}

KTH Actions Dataset FAQs

What is the KTH Actions dataset for Python?

The KTH dataset is one of the most standard datasets, which contains six activities: walk, run, run, box, hand-wave, and hand applaud. To represent execution subtlety, each activity is performed by 25 distinct people, and the setting is deliberately adjusted for each activity per entertainer. Setting varieties in the dataset include open-air, outside with scale variety, open-air with various garments, and indoor.

How can I use KTH Actions dataset in PyTorch or TensorFlow?

You can stream the KTH Actions dataset while training a model in PyTorch or TensorFlow with one line of code using the open-source package Activeloop Deep Lake in Python. See detailed instructions on how to train a model on KTH Actions dataset with PyTorch in Python or train a model on KTH Actions dataset with TensorFlow in Python.