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Machine Learning Datasets Machine Learning Datasets
Get Started
Machine Learning Datasets
  • GitHub
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  • Datasets
    • 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
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    • Stanford Cars Dataset
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    • LFW Deep Funneled Dataset
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    • Optical Handwritten Digits Dataset
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Machine Learning Datasets

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    • MNIST
    • ImageNet Dataset
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    • 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
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Datasets

Omniglot Dataset

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Visualization of the Omniglot Dataset in the Deep Lake UI

Omniglot Dataset

What is Omniglot Dataset?

The Omniglot dataset is created with the goal of creating learning algorithms that are more human-like. It includes 1623 handwritten characters from 50 different alphabets. Each of the 1623 characters was created by 20 individuals using Amazon’s Mechanical Turk service. Each image is accompanied by stroke data, which consists of a series of [x,y,t] coordinates separated by time (t) in milliseconds. The dataset is split into a background set of 30 alphabets and an evaluation set of 20 alphabets.

Download Omniglot Dataset in Python

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

Load Omniglot Dataset Training Subset in Python

				
					import deeplake
ds = deeplake.load('hub://activeloop/omniglot-images-strokes-train')
				
			

Load Omniglot Dataset Validation Subset in Python

				
					import deeplake
ds = deeplake.load('hub://activeloop/omniglot-images-strokes-val')
				
			

Omniglot Dataset Structure

Omniglot Data Fields
  • image: tensor that contains the image of size 105×105.
  • alphabet: tensor that contains different alphabets.
  • character_in_alphabet: tensor that contains characters in the alphabet.
  • penstroke: tensor that contains stroke data, a sequence of [x,y,t] coordinates with time (t) in milliseconds beginning with “START” and Breaks between pen strokes are denoted as “BREAK” (indicating a pen up action).
Omniglot Data Splits
  • The Omniglot dataset training set is composed of 19280 samples.
  • The Omniglot dataset validation set was composed of 13180 samples.

How to use Omniglot Dataset with PyTorch and TensorFlow in Python

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

Additional Information about Omniglot Dataset

Omniglot Dataset Description

  • Homepage: https://github.com/brendenlake/omniglot​
  • Paper: https://www.cs.cmu.edu/~rsalakhu/papers/LakeEtAl2015Science.pdf
Omniglot Dataset Curators

Brenden M. Lake, Ruslan Salakhutdinov, Joshua B. Tenenbaum

Omniglot Dataset Licensing Information

MIT License

Omniglot Dataset Citation Information
				
					@article{lake2015human,
title={Human-level concept learning through probabilistic program induction},
author={Lake, Brenden M and Salakhutdinov, Ruslan and Tenenbaum, Joshua B},
journal={Science},
volume={350},
number={6266},
pages={1332--1338},
year={2015},
publisher={American Association for the Advancement of Science}
}
				
			
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