19,997 machine learning datasets
19,997 dataset results
This dataset is meant to be used to develop models for next-day fire hazard forecasting in Greece. It contains data from 2009 to 2020 at a 1km x 1km x 1 daily grid.
CLUES (Constrained Language Understanding Evaluation Standard) is a benchmark for evaluating the few-shot learning capabilities of NLU models.
The provided dataset consists of high-quality realistic head models and combined EEG/MEG data which can be used for state-of-the-art methods in brain research, such as modern finite element methods (FEM) to compute the EEG/MEG forward problems using the software toolbox DUNEuro (http://duneuro.org).
VSLID stands for Very Small Lego Image Dataset. It has a bit over 1800 images of piles of LEGO bricks of 85 different types. There are between 1 and 10 bricks per image. Backgrounds and lighting conditions vary. All images are annotated with a list of the visible bricks. The images can have two resolutions, so rescaling them is recommended before usage.
The RIKEN Microstructural Imaging Metadatabase is a semantic web-based imaging database in which image metadata are described using the Resource Description Framework (RDF) and detailed biological properties observed in the images can be represented as Linked Open Data. The metadata are used to develop a large-scale imaging viewer that provides a straightforward graphical user interface to visualise a large microstructural tiling image at the gigabyte level.
This is a dataset of 306,006 galaxies whose coordinates are taken from the Sloan Digital Sky Survey Data Release 7 and a modified catalogue from Brinchmann+2003 and Wilman+2010. This volume complete sample has an r-band absolute magnitude limit of $M_r\leq-20$ and a redshift limit of $z\leq0.08$. See Arora+2019 for details.
A synthetic dataset containing 447 typefaces with only one font variation for each typeface, created for visual font recognition.
A synthetic dataset containing word images of 447 typefaces with font variations for each typeface, created for visual font recognition.
BPCIS is collection of 364 bacterial phase contrast images and corresponding label matrices for instance segmentation. Labels were made according to fluorescence channels where possible. Prior to manual annotation, images were automatically cropped into microcolonies and tiled into ensemble images to reduce the empty (non-cell) image regions for training and testing. Subsequent to annotation, we performed non-rigid registration of phase contrast to cell masks.
Audio files that supplement "Treatise on Hearing: The Temporal Auditory Imaging Theory Inspired by Optics and Communication".
The BCSS dataset contains over 20,000 segmentation annotations of tissue regions from breast cancer images from The Cancer Genome Atlas (TCGA). This large-scale dataset was annotated through the collaborative effort of pathologists, pathology residents, and medical students using the Digital Slide Archive. It enables the generation of highly accurate machine-learning models for tissue segmentation.
This record serves as an index to the other dataset releases that are part of the paper "Next2You: Robust Copresence Detection Based on Channel State Information" by Mikhail Fomichev, Luis F. Abanto-Leon, Max Stiegler, Alejandro Molina, Jakob Link, Matthias Hollick, in ACM Transactions on Internet of Things (2021).
Natural Adversarial Objects (NAO) is a new dataset to evaluate the robustness of object detection models. NAO contains 7,934 images and 9,943 objects that are unmodified and representative of real-world scenarios, but cause state-of-the-art detection models to misclassify with high confidence.
A large-scale dataset, namely Distorted Surveillance Video Database (DSurVD), which can be downloaded from the link: https://sites.google.com/site/sorsyuanyuan/home/dsurvd
A collection of long-running (80+ episodes) science fiction TV show synopses, scraped from Fandom.com wikis. Collected Nov 2017. Each episode is considered a "story".
This is a detailed description of the dataset, a data sheet for the dataset as proposed by Gebru et al.
Official dataset of Decrypting Cryptic Crosswords: Semantically Complex Wordplay Puzzles as a Target for NLP.
The first annotated corpus for multilingual analysis of potentially unfair clauses in online Terms of Service. The data set comprises a total of 100 contracts, obtained from 25 documents annotated in four different languages: English, German, Italian, and Polish. For each contract, potentially unfair clauses for the consumer are annotated, for nine different unfairness categories.
Follow the instructions provided in the companion repo to automatically download and decompress the archive. The following files are included:
There are three MONK's problems. The domains for all MONK's problems are the same (described below). One of the MONK's problems has noise added. For each problem, the domain has been partitioned into a train and test set.