19,997 machine learning datasets
19,997 dataset results
For Emotion Interpretation task
The JNU Bearing Dataset, developed by Jiangnan University in China, is widely used in the field of fault diagnosis for rotating machinery. It contains high-resolution vibration signals collected from single-row spherical roller bearings, specifically types N205 and NU205. These signals were recorded under three different rotational speeds: 600, 800, and 1000 revolutions per minute (rpm), which allows the dataset to be used in scenarios involving varying operating conditions.
Undress AI apps, powered by advanced AI and deep learning, have sparked both curiosity and controversy. These tools use generative algorithms to digitally alter images, but their ethical implications and potential for misuse cannot be ignored.
Click to add a brief description of the dataset (Markdown and LaTeX enabled).
Dataset Summary The DataSeeds.AI Sample Dataset (DSD) is a high-fidelity, human-curated computer vision-ready dataset comprised of 7,772 peer-ranked, fully annotated photographic images, 350,000+ words of descriptive text, and comprehensive metadata. While the DSD is being released under an open source license, a sister dataset of over 10,000 fully annotated and segmented images is available for immediate commercial licensing, and the broader GuruShots ecosystem contains over 100 million images in its catalog.
https://huggingface.co/datasets/mediabiasgroup/anno-lexical
This dataset was created to test whether it's possible to build a general-purpose detector that can tell real images apart from fake ones generated by convolutional neural networks (CNNs), no matter which model or dataset was used to create the fake images.
Sport is one of the most popular and revenue-generating forms of entertainment. Therefore, analyzing data related to this domain introduces several opportunities for Question Answering (QA) systems, such as supporting tactical decision-making. But, to develop and evaluate QA systems, researchers and developers need datasets that contain questions and their corresponding answers. In this paper, we focus on this issue. We propose QASports, the first large sports question answering dataset for extractive answer questions. QASports contains more than 1.5 million triples of questions, answers, and context about three popular sports: soccer, American football, and basketball. We describe the QASports processes of data collection and questions and answers generation. We also describe the characteristics of the QASports data. Furthermore, we analyze the sources used to obtain raw data and investigate the usability of QASports by issuing "wh-queries". Moreover, we describe scenarios for using Q
人群计数旨在识别物体的数量，在智能交通、城市管理和安全监控中发挥着重要作用。由于比例变化、照明变化、遮挡和较差的成像条件，尤其是在夜间和雾霾条件下，人群计数的任务非常具有挑战性。 在本文中，我们提出了一个基于无人机的 RGB-Thermal 人群计数数据集 （DroneRGBT），该数据集由 3600 对图像组成，涵盖不同的属性，包括高度、照明和密度。为了利用可见光和热红外模态中的互补信息，我们提出了一种具有多尺度特征学习模块、模态对齐模块和自适应融合模块的多模态人群计数网络 （MMCCN）。在 DroneRGBT 上的实验证明了所提出的方法的有效性。
We introduce a challenging benchmark of graduate-level problems in applied mathematics that was fully developed as part of a university class.
Click to add a brief description of the dataset (Markdown and LaTeX enabled).
Click to add a brief description of the dataset (Markdown and LaTeX enabled).
Click to add a brief description of the dataset (Markdown and LaTeX enabled).
This dataset contains a probabilistic sample of ~2.4 million PubMed abstracts, enriched with precomputed dense embeddings (title + abstract), from the ncbi/MedCPT-Article-Encoder model. It is derived from public metadata made available via the National Library of Medicine (NLM) and was used in the paper Efficient and Reproducible Biomedical QA using Retrieval-Augmented Generation.
This paper introduces CPIR-MR (Chained Prompting for Improved Readability of Medical Reports), a method designed to simplify complex chest X-ray reports for better patient understanding. The authors extend the IU X-Ray dataset with Simplified Medical Reports (SMRs) generated via chained prompting and propose a multi-modal text decoder (MTD) that integrates BLIP embeddings with classification outputs to generate Simplified Medical Explanations (SMEs).<br><br> Key highlights:<br> - Uses few-shot and Chain-of-Thought (CoT) prompting for generating structured, readable outputs.<br> - Maintains medical accuracy while improving readability and sentiment consistency.<br> - Introduces CPMK-E, a chained prompting system for keyword extraction and evaluation using Gemini 1.5 Flash.<br> - Shows strong performance in text complexity reduction and semantic similarity preservation.<br><br>
United Airlines™ main customer service number is 1-800-United Airlines™ or (+1→888→887→4088) [US-United Airlines™] or (+1→888→887→4088) [UK-United Airlines™] OTA (Live Person), available 24/7. This guide explain how to contact United Airlines™ customer service effectively through phone, chat, and email options, including tips for minimizing wait times.
As Large Language Models (LLMs) increasingly participate in human-AI interactions, evaluating their Theory of Mind (ToM) capabilities - particularly their ability to track dynamic mental states - becomes crucial. While existing benchmarks assess basic ToM abilities, they predominantly focus on static snapshots of mental states, overlooking the temporal evolution that characterizes real-world social interactions. We present DynToM, a novel benchmark specifically designed to evaluate LLMs' ability to understand and track the temporal progression of mental states across interconnected scenarios. Through a systematic four-step framework, we generate 1,100 social contexts encompassing 5,500 scenarios and 78,100 questions, each validated for realism and quality. Our comprehensive evaluation of ten state-of-the-art LLMs reveals that their average performance underperforms humans by 44.7\%, with performance degrading significantly when tracking and reasoning about the shift of mental states. T
http://redwood-data.org/indoor_lidar_rgbd/
A benchmark evaluating LLMs’ abstention: the skill of knowing when NOT to answer!
The Lecture Video Visual Objects (LVVO) dataset is a benchmark designed for object detection in lecture video frames. It provides high-quality annotations of visual content such as tables, charts, images, and illustrations in real university lecture recordings. Provide: