19,997 machine learning datasets
19,997 dataset results
JigsawPlan contains room layouts and floorplans for 98,780 single-story houses/apartments from a production pipeline, designed for the Extreme Structure from Motion (E-SfM) problem.
Allergen30 is created with the goal of building a robust detection model that can assist people in avoiding possible allergic reactions.
STVD-FC is the largest public dataset on the political content analysis and fact-checking tasks. It consists of more than 1,200 fact-checked claims that have been scraped from a fact-checking service with associated metadata. For the video counterpart, the dataset contains nearly 6,730 TV programs, having a total duration of 6,540 hours, with metadata. These programs have been collected during the 2022 French presidential election with a dedicated workstation and protocol. The dataset is delivered as different parts for accessibility of the 2 TB of data and proper indexes. More information about the STVD-FC dataset can be found into the publication [1].
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MapAI: Precision in Building Segmentation Dataset The dataset comprises 7500 training images and 1500 validation images from Denmark. The test dataset is split into two tasks, where the first task (1368 images) is to segment the buildings only using aerial images. In contrast, the second task (978 images) allows using aerial images and lidar data. All data samples have a resolution of 500x500. The aerial images are RGB images, while the lidar data are rasterized. The ground truth masks have two classes, building, and background.
We present the Gracenote Multi-Crop (GNMC) dataset, to further research in algorithms for aesthetic image cropping. The dataset consists of a diverse collection of 10K images, each cropped in five different aspect ratios by experienced editors. GNMC is larger than existing datasets commonly used to benchmark image cropping approaches such as FCDB (1743 images) and FLMS (500 images). This dataset can enable aesthetic cropping algorithms as described in "An Experience-Based Direct Generation Approach to Automatic Image Cropping" by Christensen and Vartakavi.
This dataset focuses only on the robbery category, presenting a new weakly labelled dataset that contains 486 new real–world robbery surveillance videos acquired from public sources.
Article: A novel hierarchical model based on different emotion induction modalities for EEG emotion recognition
A fundamental component of human vision is our ability to parse complex visual scenes and judge the relations between their constituent objects. AI benchmarks for visual reasoning have driven rapid progress in recent years with state-of-the-art systems now reaching human accuracy on some of these benchmarks. Yet, there remains a major gap between humans and AI systems in terms of the sample efficiency with which they learn new visual reasoning tasks. Humans' remarkable efficiency at learning has been at least partially attributed to their ability to harness compositionality -- allowing them to efficiently take advantage of previously gained knowledge when learning new tasks. Here, we introduce a novel visual reasoning benchmark, Compositional Visual Relations (CVR), to drive progress towards the development of more data-efficient learning algorithms. We take inspiration from fluidic intelligence and non-verbal reasoning tests and describe a novel method for creating compositions of abs
TICNN dataset
We introduce the KAIST multi-spectral dataset, which covers a greater range of drivable regions, from urban to residential, for autonomous systems. Our dataset provides different perspectives of the world captured in coarse time slots (day and night) in addition to fine time slots (sunrise, morning, afternoon, sunset, night and dawn). For all-day perception of autonomous systems, we propose the use of a different spectral sensor, i.e., a thermal imaging camera. Toward this goal, we develop a multi-sensor platform, which supports the use of a co-aligned RGB/Thermal camera, RGB stereo, 3D LiDAR and inertial sensors (GPS/IMU) and a related calibration technique. We design a wide range of visual perception tasks including the object detection, drivable region detection, localization, image enhancement, depth estimation and colorization using a single/multi-spectral approach. In this paper, we provide a description of our benchmark with the recording platform, data format, development toolk
Alex Motor Imagery dataset. Dataset summary Motor imagery dataset from the PhD dissertation of A. Barachant.
BNCI 2014-001 Motor Imagery dataset Dataset IIa from BCI Competition 4 [1].
Dataset description
Dataset Description
Physionet MI dataset: https://physionet.org/pn4/eegmmidb/ This data set consists of over 1500 one- and two-minute EEG recordings, obtained from 109 volunteers [2]_.
Data Acquisition EEG and NIRS data was collected in an ordinary bright room. EEG data was recorded by a multichannel BrainAmp EEG amplifier with thirty active electrodes (Brain Products GmbH, Gilching, Germany) with linked mastoids reference at 1000 Hz sampling rate. The EEG amplifier was also used to measure the electrooculogram (EOG), electrocardiogram (ECG) and respiration with a piezo based breathing belt. Thirty EEG electrodes were placed on a custom-made stretchy fabric cap (EASYCAP GmbH, Herrsching am Ammersee, Germany) and placed according to the international 10-5 system (AFp1, AFp2, AFF1h, AFF2h, AFF5h, AFF6h, F3, F4, F7, F8, FCC3h, FCC4h, FCC5h, FCC6h, T7, T8, Cz, CCP3h, CCP4h, CCP5h, CCP6h, Pz, P3, P4, P7, P8, PPO1h, PPO2h, POO1, POO2 and Fz for ground electrode).
Data Acquisition
Dataset from the article Evaluation of EEG oscillatory patterns and cognitive process during simple and compound limb motor imagery [1]_.
Dataset from the article A Fully Automated Trial Selection Method for Optimization of Motor Imagery Based Brain-Computer Interface [1]_. This dataset contains data recorded on 4 subjects performing 3 type of motor imagery: left hand, right hand and feet.