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Datasets

19,997 machine learning datasets

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19,997 dataset results

BrainInvaders2013a MOABB (P300 dataset BI2013a from a "Brain Invaders" experiment.)

1 papers9 benchmarks

BrainInvaders2014a MOABB (P300 dataset BI2014a from a "Brain Invaders" experiment.)

1 papers9 benchmarks

BrainInvaders2014b MOABB (P300 dataset BI2014b from a "Brain Invaders" experiment.)

1 papers9 benchmarks

BrainInvaders2015a MOABB (P300 dataset BI2015a from a "Brain Invaders" experiment.)

1 papers9 benchmarks

BrainInvaders2015b MOABB (P300 dataset BI2015b from a "Brain Invaders" experiment.)

1 papers9 benchmarks

BNCI2014-008 MOABB (BNCI 2014-008 P300 dataset.)

1 papers9 benchmarks

BNCI2014-009 MOABB (BNCI 2014-009 P300 dataset.)

1 papers9 benchmarks

BNCI2015-003 MOABB (BNCI 2015-003 P300 dataset.)

1 papers9 benchmarks

BNCI2015-004 MOABB (BNCI 2015-004 Motor Imagery dataset.)

1 papers12 benchmarks

Cattan2019-PHMD MOABB (Passive Head Mounted Display with Music Listening dataset.)

1 papers0 benchmarks

Cattan2019-VR MOABB (Dataset of an EEG-based BCI experiment in Virtual Reality using P300.)

1 papers9 benchmarks

PhysionetMotorImagery MOABB (Physionet Motor Imagery dataset.)

1 papers16 benchmarks

COCO-OOC

COCO-OOC goes beyond standard object detection to ask the question: Which objects are out-of-context (OOC)? Given an image with a set of objects, the goal of COCO-OOC is to determine if an object is inconsistent with the contextual relations, where it must detect the OOC object with a bounding box.

1 papers1 benchmarksImages

UIT-ViCoQA (Conversational machine reading comprehension in the Vietnamese language)

UIT-ViCoQA is a new corpus for conversational machine reading comprehension in the Vietnamese language. This corpus consists of 10,000 questions with answers over 2,000 conversations about health news articles.

1 papers0 benchmarks

ITD (Industrial Textile Dataset)

This dataset aims to provide a color dataset with real industrial fabric defect gathered in a visiting machine with several industrial cameras. It has been designed with the same nomenclature as MVTEC AD dataset

1 papers0 benchmarks

A Dependency Graph for 460,000 Papers and Their Software Mentions from the CZI Software Mentions Dataset

Using the CZI Software Mentions Dataset and ecosyste.ms we create a graph of papers, their mentioned software, and recursive dependencies of each piece of software across 466,000 papers and three software registries (PyPI, CRAN, and BioConductor).

1 papers0 benchmarks

CZ Software Mentions: A large dataset of software mentions in the biomedical literature

We describe the CZ Software Mentions dataset, a new dataset of software mentions in biomedical papers. Plain-text software mentions are extracted with a trained SciBERT model from several sources: the NIH PubMed Central collection and from papers provided by various publishers to the Chan Zuckerberg Initiative. The dataset provides sources, context and metadata, and, for a number of mentions, the disambiguated software entities and links. We extract 1.12 million unique string software mentions from 2.4 million papers in the NIH PMC-OA Commercial subset, 481k unique mentions from the NIH PMC-OA Non-Commercial subset (both gathered in October 2021) and 934k unique mentions from 4 million papers in the Publishers’ collection. There is variation in how software is mentioned in papers and extracted by the NER algorithm. We propose a clustering-based disambiguation algorithm to map plain-text software mentions into distinct software entities and apply it on the NIH PubMed Central Commercial

1 papers0 benchmarks

AVS Benchmark (Audio-Visual Synchrony Benchmark)

Provided in the linked paper.

1 papers0 benchmarksAudio, Videos

Simple_PS_Dataset

Photometric stereoscopic test data sets under six lights taken using laboratory equipment. Note that this dataset has no GT.

1 papers0 benchmarks

V2AIX (A Multi-Modal Real-World Dataset of ETSI ITS V2X Messages in Public Road Traffic)

Connectivity is a main driver for the ongoing megatrend of automated mobility: future Cooperative Intelligent Transport Systems (C-ITS) will connect road vehicles, traffic signals, roadside infrastructure, and even vulnerable road users, sharing data and compute for safer, more efficient, and more comfortable mobility. In terms of communication technology for realizing such vehicle-to-everything (V2X) communication, the WLAN-based peer-to-peer approach (IEEE 802.11p, ITS-G5 in Europe) competes with C-V2X based on cellular technologies (4G and beyond). Irrespective of the underlying communication standard, common message interfaces are crucial for a common understanding between vehicles, especially from different manufacturers. Targeting this issue, the European Telecommunications Standards Institute (ETSI) has been standardizing V2X message formats such as the Cooperative Awareness Message (CAM). In this work, we present V2AIX, a multi-modal real-world dataset of ETSI ITS messages gath

1 papers0 benchmarksImages, LiDAR, Point cloud
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