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• Title: Open Data for Deep Learning

Open Data for Deep Learning

Recent Additions

• Open Source Biometric Recognition Data
• 300 terabytes of high-quality data from the Large Hadron Collider (LHC) at CERN
• Data USA: The most comprehensive visualization of US public data
• EU Surveillance Atlas of Infectious Diseases
• EU Gender statistics database
• The Netherlands’ Nationaal Georegister (Dutch)
• United Nations Development Programme Projects

Symbolic Music Datasets

• Piano-midi.de: classical piano pieces
• Nottingham : over 1000 folk tunes
• MuseData: electronic library of classical music scores
• JSB Chorales: set of four-part harmonized chorales

Natural-Image Datasets

• MNIST: handwritten digits
• CIFAR10 / CIFAR100: 32×32 natural image dataset with 10/100 categories
• Caltech 101: pictures of objects belonging to 101 categories
• Caltech 256: pictures of objects belonging to 256 categories
• STL-10 dataset is an image recognition dataset for developing unsupervised feature learning, deep learning, self-taught learning algorithms. Like CIFAR-10 with some modifications.
• The Street View House Numbers (SVHN) Dataset
• NORB: binocular images of toy figurines under various illumination and pose
• Imagenet: image database organized according to the WordNethierarchy
• Pascal VOC: various object recognition challenges
• Labelme: A large dataset of annotated images
• COIL 20: different objects imaged at every angle in a 360 rotation
• COIL100: different objects imaged at every angle in a 360 rotation

Artificial Datasets

• Arcade Universe - An artificial dataset generator with images containing arcade games sprites such as tetris pentomino/tetromino objects. This generator is based on the O. Breleux’s bugland dataset generator.
• A collection of datasets inspired by the ideas from BabyAISchool:
• BabyAIShapesDatasets: distinguishing between 3 simple shapes
• BabyAIImageAndQuestionDatasets: a question-image-answer dataset
• Datasets generated for the purpose of an empirical evaluation of deep architectures (DeepVsShallowComparisonICML2007):
• MnistVariations: introducing controlled variations in MNIST
• RectanglesData: discriminating between wide and tall rectangles
• ConvexNonConvex: discriminating between convex and nonconvex shapes
• BackgroundCorrelation: controling the degree of correlation in noisy MNIST backgrounds.

Facial Datasets

• Labelled Faces in the Wild: 13,000 images of faces collected from the web, labeled with the name of the person pictured.
• Olivetti: a few images of several different people
• Multi-Pie: The CMU Multi-PIE Face Database
• Face-in-Action
• JACFEE: Japanese and Caucasian Facial Expressions of Emotion
• FERET: The Facial Recognition Technology Database
• mmifacedb: MMI Facial Expression Database
• IndianFaceDatabase
• The Yale Face Database and The Yale Face Database B).

Text Datasets

• 20 newsgroups: classification task, mapping word occurences to newsgroup ID
• Reuters (RCV*) Corpuses: text/topic prediction
• Penn Treebank : used for next word prediction or next character prediction
• Broadcast News: large text dataset, classically used for next word prediction
• Multidomain sentiment analysis dataset

Speech Datasets

• TIMIT Speech Corpus: phoneme classification
• MovieLens The first dataset has 100,000 ratings for 1682 movies by 943 users, subdivided into five disjoint subsets. The second dataset has about 1 million ratings for 3900 movies by 6040 users.
• Jester: 4.1 million continuous ratings (-10.00 to +10.00) of 100 jokes from 73,421 users.
• Netflix Prize: Netflix released an anonymized version of their movie rating dataset; it consists of 100 million ratings, done by 480,000 users who have rated between 1 and all of the 17,770 movies.
• Book-Crossing dataset: From the Book-Crossing community. Contains 278,858 users providing 1,149,780 ratings about 271,379 books.

Miscellaneous Datasets

• CMU Motion Capture Database
• Brodatz dataset: texture modeling
• Million Song dataset
• Merck Molecular Activity Challenge

Thanks to deeplearning.net for many of these links and dataset descriptions. Any suggestions of open data sets we should include for the Deeplearning4j community are welcome!