19,997 machine learning datasets
19,997 dataset results
Games dataset containing 100,000 Gameplay Images of 175 Video Games across 10 Sports Genres - AMERICAN FOOTBALL, BASKETBALL, BIKE RACING, CAR RACING, FIGHTING, HOCKEY, SOCCER, TABLE TENNIS, TENNIS.
Taiga is a corpus, where text sources and their meta-information are collected according to popular ML tasks.
Russian reading comprehension with Commonsense reasoning (RuCoS) is a large-scale reading comprehension dataset that requires commonsense reasoning. RuCoS consists of queries automatically generated from CNN/Daily Mail news articles; the answer to each query is a text span from a summarizing passage of the corresponding news. The goal of RuCoS is to evaluate a machine`s ability of commonsense reasoning in reading comprehension.
LiDiRus is a diagnostic dataset that covers a large volume of linguistic phenomena, while allowing you to evaluate information systems on a simple test of textual entailment recognition. See more details diagnostics.
LIVE Livestream is a database for Video Quality Assessment (VQA), specifically designed for live streaming VQA research. The dataset is called the Laboratory for Image and Video Engineering (LIVE) Live stream Database. The LIVE Livestream Database includes 315 videos of 45 contents impaired by 6 types of distortions.
It contains 19 HDF5 files that represent a data collection campaign run on the NI mmWave Transceiver System with four SiBeam 60 GHz radio heads and on two Pi-Radio digital 60 GHz radios.
IowaRain is a dataset of rainfall events for the state of Iowa (2016-2019) acquired from the National Weather Service Next Generation Weather Radar (NEXRAD) system and processed by a quantitative precipitation estimation system. The dataset presented in this study could be used for better disaster monitoring, response and recovery by paving the way for both predictive and prescriptive modeling
VinDr-RibCXR is a benchmark dataset for automatic segmentation and labeling of individual ribs from chest X-ray (CXR) scans. The VinDr-RibCXR contains 245 CXRs with corresponding ground truth annotations provided by human experts.
The Sims4Action Dataset: a videogame-based dataset for Synthetic→Real domain adaptation for human activity recognition.
NucMM is a dataset for segmenting 3D cell nuclei from microscopy image volumes that pushes the task forward to the sub-cubic millimeter scale. It consists of two fully annotated volumes: one electron microscopy (EM) volume containing nearly the entire zebrafish brain with around 170,000 nuclei; and one micro-CT (uCT) volume containing part of a mouse visual cortex with about 7,000 nuclei.
We created a dataset of clinical action items annotated over MIMIC-III. This dataset, which we call CLIP, is annotated by physicians and covers 718 discharge summaries, representing 107,494 sentences. Annotations were collected as character-level spans to discharge summaries after applying surrogate generation to fill in the anonymized templates from MIMIC-III text with faked data. We release these spans, their aggregation into sentence-level labels, and the sentence tokenizer used to aggregate the spans and label sentences. We also release the surrogate data generator, and the document IDs used for training, validation, and test splits, to enable reproduction. The spans are annotated with 0 or more labels of 7 different types, representing the different actions that may need to be taken: Appointment, Lab, Procedure, Medication, Imaging, Patient Instructions, and Other. We encourage the community to use this dataset to develop methods for automatically extracting clinical action items
The OLR 2021 dataset contains the data for the Oriental Language Recognition (OLR) 2021 Challenge, which intends to improve the performance of language recognition systems and speech recognition systems within multilingual scenarios.
mTVR is a large-scale multilingual video moment retrieval dataset, containing 218K English and Chinese queries from 21.8K TV show video clips. The dataset is collected by extending the popular TVR dataset (in English) with paired Chinese queries and subtitles. Compared to existing moment retrieval datasets, mTVR is multilingual, larger, and comes with diverse annotations.
VidOR (Video Object Relation) dataset contains 10,000 videos (98.6 hours) from YFCC100M collection together with a large amount of fine-grained annotations for relation understanding. In particular, 80 categories of objects are annotated with bounding-box trajectory to indicate their spatio-temporal location in the videos; and 50 categories of relation predicates are annotated among all pairs of annotated objects with starting and ending frame index. This results in around 50,000 object and 380,000 relation instances annotated. To use the dataset for model development, the dataset is split into 7,000 videos for training, 835 videos for validation, and 2,165 videos for testing.
The BEOID dataset includes object interactions ranging from preparing a coffee to operating a weight lifting machine and opening a door. The dataset is recorded at six locations: kitchen, workspace, laser printer, corridor with a locked door, cardiac gym, and weight-lifting machine. For the first four locations, sequences from five different operators were recorded (two sequences per operator), and from three operators for the last two locations (three sequences per operator). The wearable gaze tracker hardware (ASL Mobile Eye XG) was used to record the dataset. Synchronized wide-lens video data with calibrated 2D gaze fixations are available. Moreover, we release 3D information using a pre-built cloud point map and PTAM tracking. Three-dimensional information of the image and the gaze fixations are included.
Knowledge about software used in scientific investigations is important for several reasons, for instance, to enable an understanding of provenance and methods involved in data handling. However, software is usually not formally cited, but rather mentioned informally within the scholarly description of the investigation, raising the need for automatic information extraction and disambiguation. Given the lack of reliable ground truth data, we present SoMeSci - Software Mentions in Science - a gold standard knowledge graph of software mentions in scientific articles. It contains high quality annotations (IRR: κ = .82) of 3756 software mentions in 1367 PubMed Central articles. Besides the plain mention of the software, we also provide relation labels for additional information, such as the version, the developer, a URL or citations. Moreover, we distinguish between different types, such as application, plugin or programming environment, as well as different types of mentions, such as usag
MEDIC is a large social media image classification dataset for humanitarian response consisting of 71,198 images to address four different tasks in a multi-task learning setup. It consists data from several data sources such as CrisisMMD, data from AIDR and Damage Multimodal Dataset (DMD).
CICIDS2018 includes seven different attack scenarios: Brute-force, Heartbleed, Botnet, DoS, DDoS, Web attacks, and infiltration of the network from inside. The attacking infrastructure includes 50 machines and the victim organization has 5 departments and includes 420 machines and 30 servers. The dataset includes the captures network traffic and system logs of each machine, along with 80 features extracted from the captured traffic using CICFlowMeter-V3.
We present a novel approach to reference-based super-resolution (RefSR) with the focus on real-world dual-camera super-resolution (DCSR). This dataset currently consists of 143 pairs of telephoto and wide-angle images in 4K resolution captured by smartphone dual-cameras. See our paper for more details: Dual-Camera Super-Resolution with Aligned Attention Modules.
This is a dataset for a shot boundary detection task. The dataset contains 2 existing datasets and 19 manually marked up open source videos with a total length of more than 1200 minutes and 10000 scene transitions. The dataset includes different types of videos with different resolutions from 360×288 to 1920×1080 in MP4 and MKV formats. Videos include samples in RGB scale or in grayscale with FPS from 23 to 60.