19,997 machine learning datasets
19,997 dataset results
TLDR9+ is a large-scale summarization dataset containing over 9 million training instances extracted from Reddit discussion forum. This dataset is specifically gathered to perform extreme summarization (i.e., generating one-sentence summary in high compression and abstraction) and is more than twice larger than the previously proposed dataset. With the help of human annotations, a more fine-grained dataset is distilled by sampling High-Quality instances from TLDR9+ and call it TLDRHQ. dataset.
A procedurally generated jump'n'run game with control over level similarity.
An environment for RNA design given structure constraints with structures from different datasets to choose from.
Multirotor gym environment for learning control policies for various unmanned aerial vehicles.
Enables research on early detection of sexual predators in chats (eSPD). It is made from the sexual predator identification dataset from PAN12 and from the dataset ChatCoder2. It provides both full-length predator chats from PervertedJustice as well as short segments of non-predator chats. Together these can be used to evaluate eSPD systems.
Mila Simulated Floods Dataset is a 1.5 square km virtual world using the Unity3D game engine including urban, suburban and rural areas.
This dataset contains data which enables the evaluation of metamodels and approches for targeted test case selection without setting up test environments or performing test runs. The dataset is split into different scenarios: Each scenario comes with one or more tabular datasets containing the inputs and outputs of different test cases (concrete scenarios). A configuration file describes which of the columns are inputs and outputs and explains the different parameters. The config also contains verbal descriptions of the scenarios. Additionally, animations of the scenarios are available.
BEV Crowd-Counting dataset extended from CityUHK-X
EDFace-Celeb-1M is a public Ethnically Diverse Face dataset which is used to benchmark the task of face hallucination. The dataset includes 1.7 million photos that cover different countries, with balanced race composition.
QMAR is an RGB multi-view Quality of Human Movement Assessment dataset.
The dataset contains training and evaluation data for 12 languages: - Vietnamese - Romanian - Latvian - Czech - Polish - Slovak - Irish - Hungarian - French - Turkish - Spanish - Croatian
The airborne hyperspectral dataset was taken by Headwall Hyperspec-VNIR-C imaging sensor over agricultural and urban areas in Chikusei, Ibaraki, Japan, on July 29, 2014 between the times 9:56 to 10:53 UTC+9. The central point of the scene is located at coordinates: 36.294946N, 140.008380E. The hyperspectral dataset has 128 bands in the spectral range from 363 nm to 1018 nm. The scene consists of 2517x2335 pixels and the ground sampling distance was 2.5 m. Ground truth of 19 classes was collected via a field survey and visual inspection using high-resolution color images obtained by Canon EOS 5D Mark II together with the hyperspectral data. The hyperspectral data and ground truth were made available to the scientific community in the ENVI and MATLAB formats at http://park.itc.u-tokyo.ac.jp/sal/hyperdata. More details of the experiment are presented in the technical report given below.
The Model for Attended Awareness in Driving (MAAD) is a dataset of third-person estimates of a driver’s attended awareness. It consists of videos of a scene, as seen by a person performing a task in the scene, along with noisily registered ego-centric gaze sequences from that person.
Web services are often protected with a challenge that's supposed to be easy for people to solve, but difficult for computers. Such a challenge is often called a CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) or HIP (Human Interactive Proof). HIPs are used for many purposes, such as to reduce email and blog spam and prevent brute-force attacks on web site passwords.
The TAU-NIGENS Spatial Sound Events 2020 dataset contains multiple spatial sound-scene recordings, consisting of sound events of distinct categories integrated into a variety of acoustical spaces, and from multiple source directions and distances as seen from the recording position. The spatialization of all sound events is based on filtering through real spatial room impulse responses (RIRs), captured in multiple rooms of various shapes, sizes, and acoustical absorption properties. Furthermore, each scene recording is delivered in two spatial recording formats, a microphone array one (MIC), and first-order Ambisonics one (FOA). The sound events are spatialized as either stationary sound sources in the room, or moving sound sources, in which case time-variant RIRs are used. Each sound event in the sound scene is associated with a trajectory of its direction-of-arrival (DoA) to the recording point, and a temporal onset and offset time. The isolated sound event recordings used for the sy
Open Broadcast Media Audio from TV (OpenBMAT) is an open, annotated dataset for the task of music detection that contains over 27 hours of TV broadcast audio from 4 countries distributed over 1647 one-minute long excerpts. It is designed to encompass several essential features for any music detection dataset and is the first one to include annotations about the loudness of music in relation to other simultaneous non-music sounds. OpenBMAT has been cross-annotated by 3 annotators obtaining high inter-annotator agreement percentages, which validates the annotation methodology and ensures the annotations reliability.
Large scale machine reading comprehension dataset in Urdu language.
This dataset contains 200 famous songs in different genres (mostly in rock) and the beats and downbeat annotations are provided by T. de Clercq and D. Temperley [1].
This dataset includes music time information i.e. Beat, Bar, and meter annotations of the Indian Carnatic music dataset. The dataset is gathered by A. Srinivasamurthy and X. Serra [1].
This repository contains the SINGA:PURA dataset, a strongly-labelled polyphonic urban sound dataset with spatiotemporal context. The data were collected via a number of recording units deployed across Singapore as a part of a wireless acoustic sensor network. These recordings were made as part of a project to identify and mitigate noise sources in Singapore, but also possess a wider applicability to sound event detection, classification, and localization. The taxonomy we used for the labels in this dataset has been designed to be compatible with other existing datasets for urban sound tagging while also able to capture sound events unique to the Singaporean context. Please refer to our conference paper published in APSIPA 2021 (which is found in this repository as the file "APSIPA.pdf") or download the readme ("Readme.md") for more details regarding the data collection, annotation, and processing methodologies for the creation of the dataset.