19,997 machine learning datasets
19,997 dataset results
Data supporting: Improved Tangential Interpolation-based Multi-input Multi-output Modal Analysis of a Full Aircraft
The DRIFT dataset includes 25k image patches collected in five European countries sourced from aerial and nanosatellite image archives. Each image patch is associated with three target variables to predict:
Demonstration data for 4 FurnitureBench tasks collected with a SpaceMouse using a DiffIK Controller.
In everyday language processing, sentence context affects how readers and listeners process upcoming words. In experimental situations, it can be useful to identify words that are predicted to greater or lesser degrees by the preceding context. Here we report completion norms for 3085 English sentences, collected online using a written cloze procedure in which participants were asked to provide their best guess for the word completing a sentence. Sentences varied between 8–10 words in length. At least 100 unique participants contributed to each sentence. All responses were reviewed by human raters to mitigate the influence of mis-spellings and typographical errors. The responses provide a range of predictability values for 13,438 unique target words, 6,790 of which appear in more than one sentence context. We also provide entropy values based on the relative predictability of multiple responses. Finally, we provide the code used to collate and organize the responses to facilitate addit
Click to add a brief description of the dataset (Markdown and LaTeX enabled).
Two-person interaction dataset consisting of fullbody and hand motions. The dataset captures interactive, challenging two-person motions in two scenarios: the fast-paced swing style Lindy Hop dancing and the martial art technique of Ninjutsu. ∼275.7K frames (2.04 hours) of motion data at a frame rate of 50 fps for the Lindy Hop and 25 fps for the Ninjutsu.
This page contains ARINC 429 message data recorded from the hardware-in-a-loop simulator. These messages were recorded using a SIGLENT SDS2204X Plus oscilloscope sampling at 20 MHz. The intent of this data is to enable cybersecurity research and development for ARINC 429 by providing detailed message data from multiple hardware sources.
MVX incorporates realistic physical world simulation with a differentiable accurate ray tracing wireless simulation that includes multi-agent and multimodal datasets for AI-driven digital twin applications in vehicular communication systems.
Spectroscopic techniques are essential tools for determining the structure of molecules. Different spectroscopic techniques, such as Nuclear magnetic resonance (NMR), Infrared spectroscopy, and Mass Spectrometry, provide insight into the molecular structure, including the presence or absence of functional groups. Chemists leverage the complementary nature of the different methods to their advantage. However, the lack of a comprehensive multimodal dataset, containing spectra from a variety of spectroscopic techniques, has limited machine-learning approaches mostly to single-modality tasks for predicting molecular structures from spectra. Here we introduce a dataset comprising simulated 1H-NMR, 13C-NMR, HSQC-NMR, Infrared, and Mass spectra (positive and negative ion modes) for 790k molecules extracted from chemical reactions in patent data. This dataset enables the development of foundation models for integrating information from multiple spectroscopic modalities, emulating the approach
The SPOTS-10 dataset is an extensive collection of grayscale images showcasing diverse patterns found in ten animal species. Specifically, SPOTS-10 contains 50,000 32 x 32 grayscale images, divided into ten categories, with 5,000 images per category. The training set comprises 40,000 images, while the test set contains 10,000 images. The SPOTS-10 dataset is freely available on the project GitHub page: https://github.com/amotica/spots-10 by cloning the repository.
This dataset extends the Semantic Segmentation of Underwater Imagery: Dataset and Benchmark, adding an uncertainty evaluation component. To facilitate uncertainty analysis, the test set incorporates a comprehensive range of perturbations, inspired by Benchmarking Neural Network Robustness to Common Corruptions and Perturbations, applied at four intensity levels. These perturbations, which preserve the original ground truth labels, encompass variations in Brightness and Contrast (simulating diverse lighting and object coloration), Gaussian and Shot Noise (reflecting low-light and discrete light properties), and Impulse Noise (resulting from bit errors). Additionally, Defocus, Motion, and Zoom Blurs are included, along with Elastic Transformations, Pixelation from upscaling, and JPEG Compression artifacts. This enhanced dataset enables an in-depth evaluation of model robustness, providing valuable insights into performance under a wide range of challenging, real-world underwater condit
Click to add a brief description of the dataset (Markdown and LaTeX enabled).
https://huggingface.co/datasets/OpenDFM/MobA-MobBench
CausalChaos! is a dataset for causal video question answering. It is based on Tom and Jerry cartoons. It features longer causal chains embedded in dynamic visual scenes. It also features challenging incorrect options, especially, Causal Confusion set which contains causally confounding incorrect options. All these factors prove to be challenging for current VLMs and other traditional Video Question Answering models.
Raw antibody microarray data. The observed array signal intensity for each antibody is listed per sample.
Three target attributes like AD123, ABETA12, and AV45AB12, representing various stages ofAlzheimer’s disease and captured through DTI analysis for white matter integrity. 177 samples and 263 features.
The dataset is composed of Hematoxylin and eosin (H&E) stained breast histology microscopy and whole-slide images. Challenge participants should evaluate the performance of their method on either/both sets of images.
The datasets used in our WACV paper High-Fidelity Document Stain Removal via A Large-Scale Real-World Dataset and A Memory-Augmented Transformer. StainDoc is a newly created document stain dataset. StainDoc_mark and StainDoc_seal are synthetic datasets.
Click to add a brief description of the dataset (Markdown and LaTeX enabled).
The CICIoMT2024 dataset is a comprehensive dataset designed for cybersecurity research focused on the Internet of Medical Things (IoMT). Developed by the Canadian Institute for Cybersecurity, it simulates realistic IoMT network traffic, representing the diverse and evolving threats faced by connected healthcare devices. The dataset comprises network traffic data from various IoMT devices, with labeled instances for 18 distinct types of cyberattacks, alongside benign traffic data.