19,997 machine learning datasets
19,997 dataset results
This dataset consists of 225 malicious tasks, which were integrated into ten distinct jailbreaking prompts. The malicious tasks were divided into five categories, namely,
Data for Score-Based Generative Models for PET Image Reconstruction. All simuations based on BrainWeb dataset. The image simulation either taken from Georg Schramm's BrainWeb simulation in 2D, or in 3D it was simulated using BrainWeb package. The 2D measurements were simulated using pyParallelProj and 3D measurements using SIRF (with STIR backend).
Data collected from two budget surveys (FY2021 in 2020 and FY2022 in 2021) in collaboration with the City of Austin budget department. Data contains preferences for each respondent and the day of their participation.
We provide all the expected data inputs to GUISS such as meshes, texture images, and blend files. Generated datasets used in our experiments along with the stereo depth estimations can be downloaded. We have defined seven dataset types: scene_reconstructions, texture_variation, gaea_texture_variation, generative_texture, terrain_variation, rocks, and generative_texture_snow. Each dataset type contains renderings with varying values of different parameters such as lighting angle, texture imgs, albedo, etc. Position each dataset type folder under data/dataset/.
The data set includes information about 120+ elections (configuration settings and descriptive statistics), projects and 125k+ anonymized voters and their budget preferences. Preferences were sollicited with different elicitation methods (K-approval, knapsack, K-ranking and K-token). For some elections, voters provided also preferences under a secondary elicitation method, resulting in vote pairs from the same voter on the same budgeting question but with a different elicitation method.
HIghly Heterogeneous KGs for entity alignment research.
A Large-Scale Chinese Image-Text Benchmark for Real-World Short Video Search Scenario
Super-CLEVR-3D is a visual question answering (VQA) dataset where the questions are about the explicit 3D configuration of the objects from images (i.e. 3D poses, parts, and occlusion). It consists of objects from 5 categories: aeroplanes, buses, bicycles, cars and motorbikes. The rendered objects are from CGParts dataset, with the same setting as Super-CLEVR dataset.
WorldFloods: a newly compiled dataset of 119 globally verified flooding events from disaster response organizations
We propose an efficient high-throughput scheme for the discovery of stable crystalline phases. Our approach is based on the transmutation of known compounds, through the substitution of atoms in the crystal structure with chemically similar ones. The concept of similarity is defined quantitatively using a measure of chemical replaceability, extracted by data-mining experimental databases. In this way we build 189,981 possible crystal phases, including 18,479 that are on the convex hull of stability. The resulting success rate of 9.72% is at least one order of magnitude better than the usual success rate of systematic high-throughput calculations for a specific family of materials, and comparable with speed-up factors of machine learning filtering procedures. As a characterization of the set of 18,479 stable compounds, we calculate their electronic band gaps, magnetic moments, and hardness. Our approach, that can be used as a filter on top of any high-throughput scheme, enables us to ef
Understanding comprehensive assembly knowledge from videos is critical for futuristic ultra-intelligent industry. To enable technological breakthrough, we present HA-ViD – an assembly video dataset that features representative industrial assembly scenarios, natural procedural knowledge acquisition process, and consistent human-robot shared annotations. Specifically, HA-ViD captures diverse collaboration patterns of real-world assembly, natural human behaviors and learning progression during assembly, and granulate action annotations to subject, action verb, manipulated object, target object, and tool. We provide 3222 multi-view and multi-modality videos, 1.5M frames, 96K temporal labels and 2M spatial labels. We benchmark four foundational video understanding tasks: action recognition, action segmentation, object detection and multi-object tracking. Importantly, we analyze their performance and the further reasoning steps for comprehending knowledge in assembly progress, process effici
This dataset is originally created for the Knowledge Graph Reasoning Challenge for Social Issues (KGRC4SI) Video data that simulates daily life actions in a virtual space from Scenario Data. Knowledge graphs, and transcriptions of the Video Data content ("who" did what "action" with what "object," when and where, and the resulting "state" or "position" of the object). Knowledge Graph Embedding Data are created for reasoning based on machine learning
Dataset information (e.g., google drive link) is attached in the GitHub repo: https://github.com/YY-GX/Annotated-Hands-Dataset
The Instance Segmentation task, an extension of the well-known Object Detection task, is of great help in many areas, such as precision agriculture: being able to automatically identify plant organs and the possible diseases associated with them, allows to effectively scale and automate crop monitoring and its diseases control.
The Vashantor dataset consists of 32,500 sentences from different regions, including Chittagong, Noakhali, Sylhet, Barishal, and Mymensingh. It is categorized into two language formats: "Bangla" and "Banglish." Each region and language combination has specified quantities for training, testing, and validation samples. The dataset details are as follows:
The ROAD dataset is made up of observations from the Low Frequency Array (LOFAR) telescope. LOFAR is comprised of 52 stations across Europe, where each station is an array of 96 dual polarisation low-band antennas (LBA) in the 10–90 MHz range and 48 or 96 dual polarisation high-band antenna antennas (HBA) in the 110–250 MHz range. The data are four dimensional, with the dimensions corresponding to time, frequency, polarisation, and station. dictate the array configuration (i.e. the number of stations used), the number of frequency channels (Nf), the time sampling, as well as the overall integration time (Nt) of the observing session. Furthermore, the dual-polarisation of the antennas results in a correlation product (Npol) of size 4. The ROAD dataset contains ten classes that describe various system-wide phenomena and anomalies from data obtained by the LOFAR telescope. These classes are categorised into four groups: data processing system failures, electronic anomalies, environmental
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Scene Text Recognition training data
We filter and match the landmarks in the Google Landmarks dataset with their OpenStreetMap polygons and filter for those located in the United States, resulting in 602 landmarks. Then, we obtain the latest high-resolution aerial images of the obtained polygons through the National Agriculture Imagery Program (NAIP) of the United States Department of Agriculture (USDA). Finally, we construct multiple-choice questions about the name of the landmark with incorrect answers from other landmarks in the same category.
EgoPW training dataset with scene annotations Since we want to generalize to data captured with a real head-mounted camera, we also extended the EgoPW training dataset. For this, we first reconstruct the scene geometry from the egocentric image sequences of the EgoPW training dataset with a Structure-from-Motion (SfM) algorithm. This step provides a dense reconstruction of the background scene. The global scale of the reconstruction is recovered from known objects present in the sequences, such as laptops and chairs. We further render the depth maps of the scene in the egocentric perspective based on the reconstructed geometry. Our EgoPW-Scene dataset contains 92 K frames in total, which are distributed in 30 sequences performed by 5 actors.