Machine Learning Datasets Machine Learning Datasets
  • GitHub
  • Slack
  • Documentation
Get Started
Machine Learning Datasets Machine Learning Datasets
Get Started
Machine Learning Datasets
  • GitHub
  • Slack
  • Documentation

Docy

Machine Learning Datasets

  • Folder icon closed Folder open iconDatasets
    • MNIST
    • ImageNet Dataset
    • COCO Dataset
    • CIFAR 10 Dataset
    • CIFAR 100 Dataset
    • FFHQ Dataset
    • Places205 Dataset
    • GTZAN Genre Dataset
    • GTZAN Music Speech Dataset
    • The Street View House Numbers (SVHN) Dataset
    • Caltech 101 Dataset
    • LibriSpeech Dataset
    • dSprites Dataset
    • PUCPR Dataset
    • RAVDESS Dataset
    • GTSRB Dataset
    • CSSD Dataset
    • ATIS Dataset
    • Free Spoken Digit Dataset (FSDD)
    • not-MNIST Dataset
    • ECSSD Dataset
    • COCO-Text Dataset
    • CoQA Dataset
    • FGNET Dataset
    • ESC-50 Dataset
    • GlaS Dataset
    • UTZappos50k Dataset
    • Pascal VOC 2012 Dataset
    • Pascal VOC 2007 Dataset
    • Omniglot Dataset
    • HMDB51 Dataset
    • Chest X-Ray Image Dataset
    • NIH Chest X-ray Dataset
    • Fashionpedia Dataset
    • DRIVE Dataset
    • Kaggle Cats & Dogs Dataset
    • Lincolnbeet Dataset
    • Sentiment-140 Dataset
    • MURA Dataset
    • LIAR Dataset
    • Stanford Cars Dataset
    • SWAG Dataset
    • HASYv2 Dataset
    • WFLW Dataset
    • Visdrone Dataset
    • 11k Hands Dataset
    • QuAC Dataset
    • LFW Deep Funneled Dataset
    • LFW Funneled Dataset
    • Office-Home Dataset
    • LFW Dataset
    • PlantVillage Dataset
    • Optical Handwritten Digits Dataset
    • UCI Seeds Dataset
    • STN-PLAD Dataset
    • FER2013 Dataset
    • Adience Dataset
    • PPM-100 Dataset
    • CelebA Dataset
    • Fashion MNIST Dataset
    • Google Objectron Dataset
    • CARPK Dataset
    • CACD Dataset
    • Flickr30k Dataset
    • Kuzushiji-Kanji (KKanji) dataset
    • KMNIST
    • EMNIST Dataset
    • USPS Dataset
    • MARS Dataset
    • HICO Classification Dataset
    • NSynth Dataset
    • RESIDE dataset
    • Electricity Dataset
    • DRD Dataset
    • Caltech 256 Dataset
    • AFW Dataset
    • PACS Dataset
    • TIMIT Dataset
    • KTH Actions Dataset
    • WIDER Face Dataset
    • WISDOM Dataset
    • DAISEE Dataset
    • WIDER Dataset
    • LSP Dataset
    • UCF Sports Action Dataset
    • Wiki Art Dataset
    • FIGRIM Dataset
    • ANIMAL (ANIMAL10N) Dataset
    • OPA Dataset
    • DomainNet Dataset
    • HAM10000 Dataset
    • Tiny ImageNet Dataset
    • Speech Commands Dataset
    • 300w Dataset
    • Food 101 Dataset
    • VCTK Dataset
    • LOL Dataset
    • AQUA Dataset
    • LFPW Dataset
    • ARID Video Action dataset
    • NABirds Dataset
    • SQuAD Dataset
    • ICDAR 2013 Dataset
    • Animal Pose Dataset
  • Folder icon closed Folder open iconDeep Lake Docs Home
  • Folder icon closed Folder open iconDataset Visualization
  • API Basics
  • Storage & Credentials
  • Getting Started
  • Tutorials (w Colab)
  • Playbooks
  • Data Layout
  • Folder icon closed Folder open iconShuffling in ds.pytorch()
  • Folder icon closed Folder open iconStorage Synchronization
  • Folder icon closed Folder open iconTensor Relationships
  • Folder icon closed Folder open iconQuickstart
  • Folder icon closed Folder open iconHow to Contribute

Wiki Art Dataset

Estimated reading: 3 minutes

Visualization of wiki art dataset in the Deep Lake UI

Wiki Art dataset

What is Wiki Art Dataset?

The WikiArt dataset contains paintings from 195 different artists. The dataset has 42129 images for training and 10628 images for testing. Because WikiArt is available to the public, it has a well-developed structure, WikiArt is often used in the field of machine learning. Namely, it is used to train AI on WikiArt data to discover its ability to recognize, classify, and generate art.

Download Wiki Art Dataset in Python

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

Load Wiki Art Dataset Training Subset in Python

				
					import deeplake
ds = deeplake.load('hub://activeloop/wiki-art')
				
			

DRD Dataset Structure

Wiki Art Data Fields
  • images: tensor containing the image.
  • labels: tensor to represent various emotions.
Wiki Art Data Splits

The Wiki Art dataset training set is composed of 81433 images.

How to use Wiki Art Dataset with PyTorch and TensorFlow in Python

Train a model on Wiki Art 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 the Wiki Art dataset with TensorFlow in Python
				
					dataloader = ds.tensorflow()
				
			

Additional Information about Wiki Art Dataset

Wiki Art Dataset Description

  • Homepage: http://saifmohammad.com/WebPages/wikiartemotions.html
  • Paper: WikiArt Emotions: An Annotated Dataset of Emotions Evoked by Art. Saif M. Mohammad and Svetlana Kiritchenko. In Proceedings of the 11th Edition of the Language Resources and Evaluation Conference (LREC-2018), May 2018, Miyazaki, Japan.
  • Point of Contact: [email protected]
Wiki Art Dataset Curators

Art. Saif M. Mohammad and Svetlana Kiritchenko

Wiki Art 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!
Wiki Art Dataset Citation Information
				
					@inproceedings{danielczuk2019segmenting,
  title={Segmenting Unknown 3D Objects from Real Depth Images using Mask R-CNN Trained on Synthetic Data},
  author={Danielczuk, Michael and Matl, Matthew and Gupta, Saurabh and Li, Andrew and Lee, Andrew and Mahler, Jeffrey and Goldberg, Ken},
  booktitle={Proc. IEEE Int. Conf. Robotics and Automation (ICRA)},
  year={2019}
}
				
			

Wiki Art Dataset FAQs

What is the Wiki Art dataset for Python?

The WikiArt dataset is often used in the field of machine learning. Namely, it is used to train AI on WikiArt data to discover its ability to recognize, classify, and generate art. It contains painting from 195 different artists and the dataset has 42129 images for training and 10628 images for testing.

How can I use the Wiki Art dataset in PyTorch or TensorFlow?

You can stream the Wiki Art 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 the Wiki Art dataset with PyTorch in Python or train a model on the Wiki Art dataset with TensorFlow in Python.

Datasets - Previous UCF Sports Action Dataset Next - Datasets FIGRIM Dataset
Leaf Illustration

© 2022 All Rights Reserved by Snark AI, inc dba Activeloop