19,997 machine learning datasets
19,997 dataset results
Mr. HiSum is a large-scale video highlight detection and summarization dataset, which contains 31,892 videos selected from YouTube-8M dataset and reliable frame importance score labels aggregated from 50,000+ users per video.
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This dataset contains synthetically generated discussions and annotations using exclusively Large Language Model (LLM) agents. Discussions are performed between randomly selected users, with a LLM moderator/facilitator following various facilitation strategies.
GroundCap is a novel grounded image captioning dataset derived from MovieNet, containing 52,350 movie frames with detailed grounded captions. The dataset uniquely features an ID-based system that maintains object identity throughout captions, enables tracking of object interactions, and grounds not only objects but also actions and locations in the scene.
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This dataset contains manipulated images and real images. The manipulated images are the faces which are created by various means. The source for this dataset was https://zenodo.org/record/5528418#.YpdlS2hBzDd this dataset was processed as our will to get maximum outcome out of these images. Each image is a 256 X 256 jpg image of human face either real or fake
MFW+ is a benchmark dataset for masked face recognition and an extended version of MFW. The original MFW, published as a benchmark for masked face recognition, is composed of 300 IDs and 3,000 images. However, with two duplicate IDs found in MFW, the dataset actually contains 298 unique IDs and 2,980 images. To evaluate models under various mask conditions and environments, we manually gathered additional data from the web. The refined and extended MFW, which we named MFW+, contains 606 IDs, 2,911 unmasked face images, and 2,838 masked face images. Paper: https://bmvc2022.mpi-inf.mpg.de/0723.pdf
Synthetic soccer players rendered on top of real world stadium images in 4K covering half a pitch each. Ground truth annotations in form of precise location of players on the pitch as well as 3D location of player pelvis and image bounding boxes.
Heat pumps are essential for decarbonizing residential heating but consume substantial electrical energy, impacting operational costs and grid demand. Many systems run inefficiently due to planning flaws, operational faults, or misconfigurations. While optimizing performance requires skilled professionals, labor shortages hinder large-scale interventions. However, digital tools and improved data availability create new service opportunities for energy efficiency, predictive maintenance, and demand-side management. To support research and practical solutions, we present an open-source dataset of electricity consumption from 1,408 households with heat pumps and smart electricity meters in the canton of Zurich, Switzerland, recorded at 15-minute and daily resolutions between 2018-11-03 and 2024-03-21. The dataset includes household metadata, weather data from 8 stations, and ground truth data from 410 field visit protocols collected by energy consultants during system optimizations. Addit
LLM Health Benchmarks Dataset The Health Benchmarks Dataset is a specialized resource for evaluating large language models (LLMs) in different medical specialties. It provides structured question-answer pairs designed to test the performance of AI models in understanding and generating domain-specific knowledge.
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Poly-FEVER is a multilingual fact verification benchmark designed to evaluate hallucination detection in large language models (LLMs). It extends three widely used fact-checking datasets—FEVER, Climate-FEVER, and SciFact—by translating claims into 11 languages, enabling cross-linguistic analysis of LLM performance.
A benchmark that focuses on the sampling dilemma in long-video tasks. The LSDBench dataset is designed to evaluate the sampling efficiency of long-video VLMs. It consists of multiple-choice question-answer pairs based on hour-long videos, focusing on dense and short-duration actions with high Necessary Sampling Density (NSD).
capturing features generated duringthe execution of packages and libraries in isolated environments.The dataset includes 9,461 package reports, of which 1,962 are identified as malicious, and encompasses both static and dynamic features such as files, sockets, commands, and DNS records. Each report is labeled with verified information and detailed sub-labels for attack types, facilitating the identification of malicious indicators when source code is unavailable. This dataset supports runtime detection, enhances detection model training, and enables efficient comparative analysis across ecosystems, contributing to the strengthening of supply chain security
The existing multi-modality image fusion dataset lacks comprehensive coverage of adverse weather scenarios. To address this, we introduce AWMM-100k, a benchmark dataset constructed by selecting samples from RoadScene, MSRS, M3FD, and LLVIP, followed by controlled degradation processing to simulate adverse weather conditions. Combined with real-world data captured using a DJI M30T drone equipped with high-resolution visible and thermal cameras, AWMM-100k comprises 187,699 images covering rain, haze, and snow, each categorized into heavy, medium, and light intensities. This dataset supports research on multi-modality image fusion under challenging weather conditions and is also applicable to image restoration tasks such as dehazing, deraining, and desnowing. We thank the original dataset for its contribution. In addition, we believe this dataset significantly expands the scope of multimodal image processing and computer vision research, facilitating advancements in both image fusion and
📥 The CipherSpectrum dataset has 4 ZIP files, each containing network traffic data for the same set of 40 domains as follows:
Overview
GeoJEPAD is a multimodal dataset combining OpenStreetMap (OSM) data (attributes and geometries) with high-resolution aerial imagery from diverse urban areas. Sourced from NAIP and OSM and then processed, tiled, and cropped. Geometries and relations represented as graphs with optional visibility edges.
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This dataset has been curated for a student research project at the Technische Hochschule Ingolstadt with Mi4Poeople and its Soil project (https://de.mi4people.org/soil-quality-evaluation-system).