19,997 machine learning datasets
19,997 dataset results
GeoQuestions1089 is a crowdsourced geospatial question-answering dataset that targets the Knowledge Graph YAGO2geo. It contains 1089 triples of geospatial questions, their answers, and the respective SPARQL/GeoSPARQL queries. It has been used to benchmark two state of the art Question Answering engines, GeoQA2 and the engine of Hamzei et al.
This dataset collection provides trajectory datasets of - synthetic trajectories with variants of different noise and novelty levels - brightkite with labels for evaluation - amazon drivers with labels for evaluation - Deutsche Bahn DBCargo shipcontainer trajectories with labels for evaluation
A dataset of gas dispersion simulations in complex 3D environments.
Efficiently rescaling a large dataset by adapting statistical computation to validation results of a pre-trained network. A unified collection of the sensitive images and those in their confused classes would form a challenging tiny set. An application for rescaling two large datasets: ImageNet and Places365 to obtain their rescaled subsets. Experimental results for image classification have validated the raised challenge of the rescaled subsets. Verifying models on these helps researchers save the computational cost and the necessary time for the early network drafts. It can be conducted that a network draft will obtain a good rate on large datasets if it is good on the rescaled subsets, correspondingly.
ESA Anomaly Dataset is the first large-scale, real-life satellite telemetry dataset with curated anomaly annotations originated from three ESA missions. We hope that this unique dataset will allow researchers and scientists from academia, research institutes, national and international space agencies, and industry to benchmark models and approaches on a common baseline as well as research and develop novel, computational-efficient approaches for anomaly detection in satellite telemetry data.
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Dataset Description High-level explanation of dataset characteristics: This dataset includes electromyographic (EMG) signals captured using the BiTalino device. EMG signals were recorded during three conditions: rest, additional weight exercise, and squat activity. Data were collected from four young subjects aged between 20 and 24 years, including both athletes and non-athletes.
LAVIB comprises a large collection of high-resolution videos sourced from the web. Metrics are computed for each video's motion magnitudes, luminance conditions, frame sharpness, and contrast. In total, LAVIB includes 283K clips from 17K ultra-HD videos, covering 77.6 hours. Benchmark train, val, and test sets maintain similar video metric distributions. Further splits are also created for out-of-distribution (OOD) challenges, with train and test splits including videos of dissimilar attributes.
VasTexture is a free giant repository of textures and PBR materials extracted from real-world images. The repository contains 500,000 highly diverse textures and PBR materials. All assets are free to download and use. The PBR materials and textures were extracted from natural images using an unsupervised approach (no human intervention). As a result, the textures and PBR materials are significantly more diverse but also significantly less refined compared to assets made using manual and AI approaches.
In doctor-patient conversations, identifying medically relevant information is crucial, posing the need for conversation summarization. In this work, we propose the first deployable real-time speech summarization system for real-world applications in industry, which generates a local summary after every N speech utterances within a conversation and a global summary after the end of a conversation. Our system could enhance user experience from a business standpoint, while also reducing computational costs from a technical perspective. Secondly, we present VietMed-Sum which, to our knowledge, is the first speech summarization dataset for medical conversations. Thirdly, we are the first to utilize LLM and human annotators collaboratively to create gold standard and synthetic summaries for medical conversation summarization.
This dataset is based on the MVTec and VisA datasets. The evaluation score is calculated by calculating the mean of the pooled class scores across the MVTec and VIsA datasets. In other works, an algorithm is ran and tested on each class of the MVTec and VisA datasets, these values are summed, and then divided by 27 (total number of classes). This dataset allows a thorough benchmarking of industrial anomaly detection, by taking into account both the MVTec and VIsA classes. The dataset is used in the Transactions of Machine Learning Research paper: VisionAD, a software package of performant anomaly detection algorithms, and Proportion Localised, an interpretable metric. The dataset is used to undertake a thorough benchmarking of the available anomaly detection algorithms.
This is a dataset of robot motions based on physics simulations.
WildAvatar is web-scale in-the-wild human avatar creation dataset extracted from YouTube, with 10,000+ different human subjects and scenes. WildAvatar is at least 10× richer than previous datasets for 3D human avatar creation.
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The dataset contains Tweet IDs along with the location and tweet timestamp. The tweets are labeled based on motivating/demotivating status, stance towards the COVID-19 vaccine, and topic in the tweet text. To comply with Twitter guidelines, we removed the tweet texts and author information.
Since the inception of our collection, we have actively maintained and updated our GitHub repository on a weekly basis. We have published over 123 million tweets, with over 60% of the tweets in English. This paper also presents basic statistics that show that Twitter activity responds and reacts to COVID-19-related events.
The dataset consists of two versions: $X_1$ with $P_3$ and $X_1$ without $P_3$, where $P_3$ represents a set of random unchanged functions from vulnerability fixing commits. This dataset is designed for finetuning large language models to detect vulnerabilities in code. It can be used for training and evaluating models in automated vulnerability detection tasks.
This is a C++ vulnerability detection dataset following realistic settings. For details, please check our study Revisiting the Performance of Deep Learning-Based Vulnerability Detection on Realistic Datasets (Partha et al., 2024)
This dataset provides harmful queries and their corresponding safe and harmful responses.
We introduce OpenDebateEvidence, a comprehensive dataset for argument mining and summarization sourced from the American Competitive Debate community. This dataset includes over 3.5 million documents with rich metadata, making it one of the most extensive collections of debate evidence. OpenDebateEvidence captures the complexity of arguments in high school and college debates, pro- viding valuable resources for training and evaluation. Our extensive experiments demonstrate the efficacy of fine-tuning state-of-the-art large language models for argumentative abstractive summarization across various methods, models, and datasets. By providing this comprehensive resource, we aim to advance com- putational argumentation and support practical applications for debaters, edu- cators, and researchers. OpenDebateEvidence is publicly available to support further research and innovation in computational argumentation. Access it here: https://huggingface.co/datasets/Yusuf5/OpenCaselist