19,997 machine learning datasets
19,997 dataset results
Dataset originally conceived for multi-face tracking/detection for highly crowded scenarios. In these scenarios, the face is the only part that can be used to track the individuals.
Makeup216 contains a variety and representation of logo (captured from the real world) and is among the largest and most complex logo datasets in the field. It comprises of 216 logos and 157 brands, including 10,019 images and 37,018 annotated logo objects.
Wikidated 1.0 is a dataset of Wikidata's full revision history, which encodes changes between Wikidata revisions as sets of deletions and additions of RDF triples. It constitutes one of the first large datasets of an evolving knowledge graph, a recently emerging research subject in the Semantic Web community.
The data used for all results in this paper can be found here. This directory contains:
Extremely important: The ASLLVD video data are subject to Terms of Use: http://www.bu.edu/asllrp/signbank-terms.pdf. By downloading these video files, you are agreeing to respect these conditions. In particular, NO FURTHER REDISTRIBUTION OF THESE VIDEO FILES is allowed.
1、 Competition name:
A blackout poetry dataset constructed from publicly available short stories and large poems. The dataset consists of two variants: 8K and 16K examples of passages along with a poem generated from the passage and the indices of the words in the passage from which words in the poem have been selected. The dataset also contains perplexity scores for each of the poems indicating the language quality of the poems.
Stylianos ParaschiakosStylianos Paraschiakos, Beekman M. (Marian), Knobbe A. (Arno), Cachucho R. (Ricardo), Slagboom P. (Eline) Wearable sensor-based data of physical activities and indirect calorimetry for 35 (14 female, 21 male) healthy older individuals (over 60 years old). The data has been collected from different body locations and devices: 3x GeneActives accelerometers (ankle, wrist, and chest), 1x Equivital (chest) and COSMED (mask and belt on chest). The 35 individuals followed a protocol of 16 activities of daily living for approximately an hour and a half in a semi-lab environment. These include different types or paces of indoor and outdoor activities with low (lying down, sitting), mid (standing, household activities) and high (walking and cycling) levels of intensity. Additionally, some activities can be specified at different granularities. The study took place at LUMC, between February and May 2015.
The Human-to-Human-or-Object Interaction Dataset (H2O) dataset is a dataset for Human-Object Interaction (HOI) detection. It consists in determining and locating the list of triplets <subject,verb,target> which describe all the simultaneous interactions in an image.
ERD (Educational Resource Discovery) is a corpus of 39,728 manually labeled web resources and 659 queries from NLP, Computer Vision (CV), and Statistics (STATS) for educational resource discovery.
This is a large-scale RF fingerprinting dataset, collected from 25 different LoRa-enabled IoT transmitting devices using USRP B210 receivers. Our dataset consists of a large number of SigMF-compliant binary files representing the I/Q time-domain samples and their corresponding FFT-based files of LoRa transmissions.
Turath-150K is a database of images of the Arab world that reflect objects, activities, and scenarios commonly found there.
SFU-HW-Tracks is a dataset for Object Tracking on raw video sequences that contains object annotations with unique object identities (IDs) for the High Efficiency Video Coding (HEVC) v1 Common Test Conditions (CTC) sequences. It is the tracking extension of the dataset called SFU-HW-Objects-v1.
The Benchmark is a collection of datasets for Monocular Height Estimation. It consists of two datasets: GTAH and AHN.
Brown Pedestrian Odometry Dataset (BPOD) is a dataset for benchmarking visual odometry algorithms in head-mounted pedestrian settings. This dataset was captured using synchronized global and rolling shutter stereo cameras in 12 diverse indoor and outdoor locations on Brown University's campus. Compared to existing datasets, BPOD contains more image blur and self-rotation, which are common in pedestrian odometry but rare elsewhere. Ground-truth trajectories are generated from stick-on markers placed along the pedestrian’s path, and the pedestrian's position is documented using a third-person video.
This is a real-world industrial benchmark dataset from a major medical device manufacturer for the prediction of customer escalations. The dataset contains features derived from IoT (machine log) and enterprise data including labels for escalation from a fleet of thousands of customers of high-end medical devices.
Dataset consist of both real captures from Photoneo PhoXi structured light scanner devices annotated by hand and synthetic samples produced by custom generator. In comparison with existing datasets for 6D pose estimation, some notable differences include:
The results-A dataset is a dataset consisting of 22 infrared images commonly used for testing performance of Infrared Image Super-Resolution models.
The results-C dataset is a dataset consisting of 22 infrared images commonly used for testing performance of Infrared Image Super-Resolution models.
Data Collection We simulate real scenarios as much as possible during the UAV videos collection. Specifically, UAV videos are collected from different locations with distinct backgrounds and lighting conditions, e.g., including highways, urban road intersections, parking lots, etc. For vehicles at parking lots, we adopt various UAV sport modes such as cruising and rotating to record vehicles. This strategy introduces viewpoint and scale changes, as well as partial occlusions to images of the same vehicle. For moving vehicles, we use two UAVs to simultaneously shoot videos from different viewpoints and heights. This strategy introduces viewpoint, scale, and background changes. The flying height of UAVs ranges from 15 to 60 meters, leading to different scales of vehicle images. The vertical angle of UAV camera ranges from 40 to 80 degrees, which leads to different viewpoints of vehicle images. The videos are recorded at 30 frames per second (fps), with the resolution of 2704 × 15