19,997 machine learning datasets
19,997 dataset results
The AI-GA (Artificial Intelligence Generated Abstracts) dataset is a collection of abstracts and titles, with half of the abstracts being AI-generated and the other half being original. This dataset is designed to be used for research and experimentation in the field of natural language processing, particularly in the context of language generation and machine learning.
The most popular news portal's Facebook pages such as Prothom Alo, BBC Bangla, BD News 24, Bangla Tribune, Kaler Kantho, Daily Jugantor are picked to build the dataset. Following a manual collection of posts, a total of 130 posts for 11 news topics were obtained and converted into a CSV file. The dataset is annotated in Ekman's seven universal emotions and they are collected using a self-developed scraper algorithm.
This is the dataset for BirdSoundsDenoising including training, validation and test.
The primary data of the SaGA corpus are made up of 25 dialogs of interlocutors (50), who engage in a spatial communication task combining direction-giving and sight description. Six of those dialogues with data only from the direction giver are available including audio (.wav) and video (.mp4) data. The secondary data consists of annotations (*.eaf) of gestures and speech-gesture referents, which have been completely and systematically annotated based on an annotation grid (cf. the SaGA documentation). The corpus is comprised of of 9881 isolated words and 1764 isolated gestures. The stimulus is a model of a town presented in a Virtual Reality (VR) environment. Upon finishing a "bus ride" through the VR town along five landmarks, a router explained the route as well as the wayside landmarks to an unknown and naive follower. The SaGA Corpus was curated for CLARIN as part of the Curation Project "Editing and Integration of Multimodal Resources in CLARIN-D" by the CLARIN-D Working Group 6
The datasets are resulting from OPFLearn.jl, a Julia package for creating AC OPF datasets. The package was developed to provide researchers with a standardized way to efficiently create AC OPF datasets that are representative of more of the AC OPF feasible load space compared to typical dataset creation methods. The OPFLearn dataset creation method uses a relaxed AC OPF formulation to reduce the volume of the unclassified input space throughout the dataset creation process. The dataset contains load profiles and their respective optimal primal and dual solutions. Load samples are processed using AC OPF formulations from PowerModels.jl. More information on the dataset creation method can be found in our publication, "OPF-Learn: An Open-Source Framework for Creating Representative AC Optimal Power Flow Datasets" and in the package website: https://github.com/NREL/OPFLearn.jl.
UV6K is a high-resolution remote sensing urban vehicle segmentation dataset.
The paper presents a study of Clickbait PDFs, which are PDF documents leading to various attacks on the Web. Clickbait PDFs are different from the well-known "MalPDFs", usually found in phishing emails, as they do not contain malware.
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The “Mental Health” forum was used, a forum dedicated to people suffering from schizophrenia and different mental disorders. Relevant posts of active users, who regularly participate, were extrapolated providing a new method of obtaining low-bias content and without privacy issues. This corpus i then processed to offer a SQUAD (Standford Question Answering Dataset) version in order to train a ML QA model.
EPIC-STATES builds upon the raw data in the EPIC-KITCHENS dataset and consists of 10 object state categories: open, close, in-hand, out-of-hand, whole, cut, raw, cooked, peeled, unpeeled. EPIC-STATES consists of 14,346 object bounding boxes from the EPIC-KITCHENS dataset (2018 version), each labeled with 10 binary labels corresponding to the 10 state classes.
EPIC-ROI builds on top of the EPIC-KITCHENS dataset, and consists of 103 diverse images with pixel-level annotations for regions where human hands frequently touch in everyday interaction. Specifically, image regions that afford any of the most frequent actions: take, open, close, press, dry, turn, peel are considered as positives. We manually watched video for multiple participants to define a) object categories, and b) specific regions within each category where participants interacted while conducting any of the 7 selected actions. These 103 images were sampled from across 9 different kitchens (7 to 15 images with minimal overlap, from each kitchen). EPIC-ROI is only used for evaluation, and contains 32 val images and 71 test images. Images from the same kitchen are in the same split. The Regions-of-Interaction task is to score each pixel in the image with the probability of a hand interacting with it. Performance is measured using average precision.
Dataset with articles posted in the r/Liberal and r/Conservative subreddits. In total, we collected a corpus of 226,010 articles. We have collected news articles to understand political expression through the shared news articles.
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Dataset introduced by Xifeng Yan et al.
Dataset introduced by Xifeng Yan et al.
To facilitate research on asynchrony for collaborative perception, we simulate the first collaborative perception dataset with different temporal asynchronies based on CARLA, named IRregular V2V(IRV2V). We set 100ms as ideal sampling time interval and simulate various asynchronies in real-world scenarios from two main aspects: i) considering that agents are unsynchronized with the unified global clock, we uniformly sample a time shift $\delta_s\sim \mathcal{U}(-50,50)\text{ms}$ for each agent in the same scene, and ii) considering the trigger noise of the sensors, we uniformly sample a time turbulence $\delta_d\sim \mathcal{U}(-10,10)\text{ms}$ for each sampling timestamp. The final asynchronous time interval between adjacent timestamps is the summation of the time shift and time turbulence. In experiments, we also sample the frame intervals to achieve large-scale and diverse asynchrony. Each scene includes multiple collaborative agents ranging from 2 to 5. Each agent is equipped with
MMFace4D is a large-scale multi-modal 4D (3D sequence) face dataset consisting of 431 identities, 35,904 sequences, and 3.9 million frames MMFace4D has three appealing characteristics: 1) highly diversified subjects and corpus, 2) synchronized audio and 3D mesh sequence with high-resolution face details, and 3) low storage cost with a new efficient compression algorithm on 3D mesh sequences.
IMCPT-SparseGM dataset is a new visual graph matching benchmark addressing partial matching and graphs with larger sizes, based on the novel stereo benchmark Image Matching Challenge PhotoTourism (IMC-PT) 2020. This dataset is released in CVPR 2023 paper Deep Learning of Partial Graph Matching via Differentiable Top-K.
IMCPT-SparseGM dataset is a new visual graph matching benchmark addressing partial matching and graphs with larger sizes, based on the novel stereo benchmark Image Matching Challenge PhotoTourism (IMC-PT) 2020. This dataset is released in CVPR 2023 paper Deep Learning of Partial Graph Matching via Differentiable Top-K.