19,997 machine learning datasets
19,997 dataset results
Deep neural networks (DNNs) have demonstrated superior performance when trained on well-illuminated environments, given that the images are captured through an Electro-Optical (EO) camera, which offers rich texture content. In critical applications such as aerial surveillance, maintaining consistent reliability of DNNs throughout all times of the day is paramount, including during low-light conditions where EO cameras often struggle to capture relevant details. Furthermore, UAV-based aerial object detection encounters significant scale variability stemming from varying altitudes and slant angles, introducing an additional layer of complexity. Existing approaches consider only illumination change/style variations as the domain shift, while in aerial surveillance, correlation shifts also acts as a hindrance to the performance of DNNs. In this paper we propose a multi-sensor (EO-IR) labelled object detection dataset consisting of 5276 images with 142991 instances covering multiple viewing
We present a new annotated corpus of written learner English, derived from essays submitted to the learning platform Write & Improve (W&I). Users of W&I are presented with automated scoring and feedback on grammatical errors, and are encouraged to act on their error feedback, submitting multiple versions of their essays for any given prompt. We build the corpus on this interplay between users and prompts, collecting sets of essays submitted by users for a selected list of 50 popular prompts. The prompts include 20 aimed at beginner learners of English, 20 aimed at intermediate learners, and 10 at advanced learners. This distribution reflects the greater use of W&I by beginner and intermediate learners of English. We ensured that the prompts were not likely to elicit personal information and covered a broad range of tasks and topics. This list of prompts enabled us to identify 5050 essay sets written by 766 users, forming the basis for the Write & Improve Corpus, which is being made ava
County-level and municipal-level data on private election administration grant receipt, census data, and election outcomes all in tabular form
This data set allows to systematically evaluate approaches for the task of identifying anomalies and extreme events in water cycle components by developing deep neural networks that detect anomalies and drivers of extremes in simulated data.
This dataset was collected by a collaboration of researchers from Children’s Wisconsin, Marquette University, Varian Medical Systems, Medical College of Wisconsin, and Stanford University as part of a project funded by the National Institute of Biomedical Imaging and Bioengineering (U01EB023822) to develop tools for rapid, patient-specific CT organ dose estimation. The collection consists of CT images in DICOM format of 359 pediatric chest-abdomen-pelvis or abdomen-pelvis exams acquired from three CT scanners. The datasets represent random pediatric cases based upon routine clinical indications. Each dataset contains expert contours of up to twenty-nine structures in DICOM RTSS format. Some datasets are missing structures that are not in the scan range or that, in younger patients, could not be reliably identified. Patient ages range from 5 days to 16 years, with a mean age of 7 and with a near equal distribution of male (180) and female (179) patients. The CT acquisition protocols an
Data used for calibrating Finnish Coastal nutrient load model (FICOS). See the Zenodo repository in the badge above for details.
Nutrient loadings and boundary conditions required by the FICOS model. See the Zenodo repository for details (link in the badge) above.
Multimodal Large Language Models (MLLMs) hallucinate, resulting in an emerging topic of visual hallucination evaluation (VHE). This paper contributes a ChatGPT-Prompted visual hallucination evaluation Dataset (PhD) for objective VHE at a large scale. The essence of VHE is to ask an MLLM questions about specific images to assess its susceptibility to hallucination. Depending on what to ask (objects, attributes, sentiment, etc.) and how the questions are asked, we structure PhD along two dimensions, i.e., task and mode. Five visual recognition tasks, ranging from low-level (object / attribute recognition) to middle-level (sentiment / position recognition and counting), are considered. Besides a normal visual QA mode, which we term PhD-base, PhD also asks questions with inaccurate context (PhD-iac) or with incorrect context (PhD-icc), or with AI-generated counter common sense images (PhD-ccs). We construct PhD by a ChatGPT-assisted semi-automated pipeline, encompassing four pivotal module
A large-scale benchmark dataset involving well-labelled datasets to employ the state-of-the-art machine intelligence technologies for map text annotation recognition, map scene classification, map super-resolution reconstruction, and map style transferring. Furthermore, these well-labelled datasets would facilitate map feature detection, map pattern recognition and map content retrieval. We hope our efforts would provide well-labelled data resources for advancing the ability to recognize and discover valuable map content.
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The dataset for masked autoencoder for X-ray fluorescence (XRF) is a following development after the dataset (Chao et al., 2022). Besides the published XRF spectra-target measurements (CaCO3 and TOC) pairs of data, we further upload the XRF spectra in that project but without alignments of the target measurements here. As the first XRF large dataset compiled in a ML friendly format, we expect to kickoff more ML studies in the field of XRF and geology, especially DL studies.
Repository for the question sets and resolution sets described produced by ForecastBench, a forecasting benchmark for LLMs. More info at https://www.forecastbench.org.
This dataset contains approximately 25,000 seismic events recorded by Observatorio Vulcanológico Andes Sur (OVDAS, SERNAGEOMIN) of four Chilean volcanoes: Nevados de Chillán Volcanic Complex (NVChVC), Villarrica (VCA), Laguna del Maule (LDM), and Puyehue-Cordón Caulle (CAU). Each event is stored as a NumPy array (.npy) of shape 14x8192. All arrays correspond to a 81.92-second (8192 samples @100Hz) window containing a single seismic event (or part of it, for longer events).
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Overview MILU (Multi-task Indic Language Understanding Benchmark) is a comprehensive evaluation dataset designed to assess the performance of Large Language Models (LLMs) across 11 Indic languages. It spans 8 domains and 42 subjects, reflecting both general and culturally specific knowledge from India.
This dataset was curated for Search Engine Optimization (SEO) analysis tasks, including categorization and spam detection. It covers 12 diverse topics: basketball, books, cats, gardening, history, movies, music, recipes, sports, technology, travel, and weather. Some topics have hierarchical relationships, such as sports and basketball, while others are closely related (e.g., movies and music) or unrelated (e.g., basketball and gardening), with varying degrees of overlap among them. For each topic, approximately 300 search queries were generated using large language models (LLMs) like GPT, Llama, and Claude. The top 10 URLs from the Google Search Console’s search engine results page (SERP) were retrieved for each query.
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Many research articles have explored the impact of surgical interventions on voice and speech evaluations, but advances are limited by the lack of publicly accessible datasets. To address this, a comprehensive corpus of 107 Spanish Castilian speakers was recorded, including control speakers and patients who underwent upper airway surgeries such as Tonsillectomy, Functional Endoscopic Sinus Surgery, and Septoplasty. The dataset contains 3,800 audio files, averaging 35.51 ± 5.91 recordings per patient. This resource enables systematic investigation of the effects of upper respiratory tract surgery on voice and speech. Previous studies using this corpus have shown no relevant changes in key acoustic parameters for sustained vowel phonation, consistent with initial hypotheses. However, the analysis of speech recordings, particularly nasalised segments, remains open for further research. Additionally, this dataset facilitates the study of the impact of upper airway surgery on speaker recogn
see detailed descriptions in readme.md
Overview PASSION derm is a pioneering initiative dedicated to closing the diversity gap in dermatology datasets. This project provides a unique dataset of skin condition images from Sub-Saharan Africa, with a focus on richly pigmented skin. The dataset is designed to emulate teledermatology settings and includes images of common pediatric skin conditions, such as eczema, fungal infections, scabies, and impetigo, in diverse quality and resolution. PASSION derm aims to improve access to dermatologic care in regions with limited healthcare resources.