데이터셋 탐색

SNAP is a collection of large network datasets. It includes graphs representing social networks, citation networks, web graphs, online communities, online reviews and more. Social networks : online social networks, edges represent interactions between people Networks with ground-truth communities : ground-truth network communities in social and information networks Communication networks : email communication networks with edges representing communication Citation networks : nodes represent papers, edges represent citations Collaboration networks : nodes represent scientists, edges represent collaborations (co-authoring a paper) Web graphs : nodes represent webpages and edges are hyperlinks Amazon networks : nodes represent products and edges link commonly co-purchased products Internet networks : nodes represent computers and edges communication Road networks : nodes represent intersections and edges roads connecting the intersections Autonomous systems : graphs of the internet Signed networks : networks with positive and negative edges (friend/foe, trust/distrust) Location-based online social networks : social networks with geographic check-ins Wikipedia networks, articles, and metadata : talk, editing, voting, and article data from Wikipedia Temporal networks : networks where edges have timestamps Twitter and Memetracker : memetracker phrases, links and 467 million Tweets Online communities : data from online communities such as Reddit and Flickr Online reviews : data from online review systems such as BeerAdvocate and Amazon User actions : actions of users on social platforms. Face-to-face communication networks : networks of face-to-face (non-online) interactions Graph classification datasets : disjoint graphs from different classes Image Source: https://snap.stanford.edu/data/

Birdsnap is a large bird dataset consisting of 49,829 images from 500 bird species with 47,386 images used for training and 2,443 images used for testing. Source: Fine-Grained Classification via Mixture of Deep Convolutional Neural Networks Image Source: http://thomasberg.org/

Enables detailed human body model reconstruction in clothing from a single monocular RGB video without requiring a pre scanned template or manually clicked points. Source: Video Based Reconstruction of 3D People Models

The World Ocean Database (WOD) is world's largest collection of uniformly formatted, quality controlled, publicly available ocean profile data. This dataset is a snapshot of the XBT observations which have been preprocessed for use in a machine learning pipeline. The data is organised by year in CSV files, covering 1966-2015. This dataset does not include the actual temperature and depth profiles, as this dataset was focused on a project to improve the metadata. Links World Ocean Database