19,997 machine learning datasets
19,997 dataset results
Click to add a brief description of the dataset (Markdown and LaTeX enabled).
A diverse set of 21 relations, each covering a different set of subject-entities and a complete list of ground truth object-entities per subject-relation-pair. The total number of object-entities varies for a given subject-relation pair.
News article revision histories provide clues to narrative and factual evolution in news articles. To facilitate analysis of this evolution, we present the first publicly available dataset of news revision histories, NewsEdits. Our dataset is large-scale and multilingual; it contains 1.2 million articles with 4.6 million versions from over 22 English- and French-language newspaper sources based in three countries, spanning 15 years of coverage (2006-2021).
The SPKL dataset contains 1203 images of parking lots divided into 11 categories regarding vision conditions (including the 'winter' category absent in other datasets at the time of publishing).
Products for OCR and Information Extraction (POIE) dataset derives from camera images of various products in the real world. The images are carefully selected and manually annotated. Our labeling team consists of 8 experienced labelers. We first crop the nutrition tables from product images and adopt multiple commercial OCR engines (Azure and Baidu OCR) for pre-labeling. Then we use LabelMe to manually check the annotation of the location as well as transcription of every text box, and the values of entities for all the text in the images and repaired the OCR errors found. After discarding low-quality and blurred images, we obtain 3,000 images with 111,155 text instances.
ESP dataset (Evaluation for Styled Prompt dataset) is a benchmark for zero-shot domain-conditional caption generation. ESP is a new dataset focusing on providing multiple styled text targets for the same image. It comprises 4.8k captions from 1k images in the COCO Captions test set. We collect five text domains with everyday usage: blog, social media, instruction, story, and news.
Dataset used for the paper entitled "Towards a Fair Comparison and Realistic Evaluation Framework of Android Malware Detectors based on Static Analysis and Machine Learning".
Pre-rendered dataset used in Training and Predicting Visual Error for Real-Time Applications for the Sun Temple scene. Generated using the RT-Percept renderer and the RT-Percept scenes.
Pre-rendered dataset used in Training and Predicting Visual Error for Real-Time Applications for the Lumberyard Bistro scenes. Generated using the RT-Percept renderer and the RT-Percept scenes.
Pre-rendered dataset used in Training and Predicting Visual Error for Real-Time Applications for the Emerald Square scenes. Generated using the RT-Percept renderer and the RT-Percept scenes.
Pre-rendered dataset used in Training and Predicting Visual Error for Real-Time Applications for the Sibenik Cathedral scene. Generated using the RT-Percept renderer and the RT-Percept scenes.
The PaviaATN data consists of 62 4-channel fluorescence microscopy images of size 2720 × 2720. The four channels, in order, label Nuclei (first two), Actin and Tubulin. It was imaged in the Synthetic Physiology Laboratory(https://www.syntheticphysiologylab.com/) of the University of Pavia, and introduced in μSplit: image decomposition for fluorescence microscopy(https://arxiv.org/abs/2211.12872), published at ICCV 2023.
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 two types of audio recordings. The first set of audio recordings consists of MEMS microphone response to acoustic activities (e.g., 19 participants reading provided text in front of the Google Home Smart Assistant). The second set of audio recordings consists of MEMS microphone response to photo-acoustic activities (laser modulated--with audio recordings of 19 participants, firing at the MEMS microphone of Google Home Smart Assistant). A total of 19 students (10 male and 9 female) were enrolled for data collection. All participants were asked to read the following 5 sentences in the microphone, Hey Google, Open the garage door, Hey Google, Close the garage door, Hey Google, Turn the light on, Hey Google, Turn the light off, Hey Google, What is the weather today?. Each audio sample was injected into the microphone through a laser, and the response of the microphone was recorded. This method produced a total data set of 95 acoustic- and 95 laser-induced audio record
All possible intermediate result cardinalities for 3300 queries on IMDb.
EMBED contains 364,000 screening and diagnostic mammographic exams for 110,000 patients from four hospitals over an 8-year period. The EMBED AWS Open Data release represents 20% of the dataset divided into two equal cohorts at the patient level. This release of the dataset includes 2D and C-view images. Digital breast tomosynthesis, ultrasound, and MRI exams will be added at a later date.
The dataset is from google play store applications containing apps from different Google Play Store categories
We propose NurViD, a large video dataset with expert-level annotation for nursing procedure activity understanding. NurViD consists of over 1.5k videos totaling 144 hours, making it approximately four times longer than the existing largest nursing activity datasets. Notably, it encompasses 51 distinct nursing procedures and 177 action steps, providing a much more comprehensive coverage compared to existing datasets that primarily focus on limited procedures. To evaluate the efficacy of current deep learning methods on nursing activity understanding, we establish three benchmarks on NurViD: procedure recognition on untrimmed videos, procedure and action recognition on trimmed videos, and action detection.
Click to add a brief description of the dataset (Markdown and LaTeX enabled).