19,997 machine learning datasets
19,997 dataset results
So2Sat LCZ42 consists of local climate zone (LCZ) labels of about half a million Sentinel-1 and Sentinel-2 image patches in 42 urban agglomerations (plus 10 additional smaller areas) across the globe. This dataset was labeled by 15 domain experts following a carefully designed labeling work flow and evaluation process over a period of six months.
TACO is a growing image dataset of waste in the wild. It contains images of litter taken under diverse environments: woods, roads and beaches. These images are manually labelled and segmented according to a hierarchical taxonomy to train and evaluate object detection algorithms. The annotations are provided in COCO format.
Talk The Walk is a large-scale dialogue dataset grounded in action and perception. The task involves two agents (a “guide” and a “tourist”) that communicate via natural language in order to achieve a common goal: having the tourist navigate to a given target location.
TaPaCo is a freely available paraphrase corpus for 73 languages extracted from the Tatoeba database.
AKCES-GEC is a new dataset on grammatical error correction for Czech.
The Breast Cancer Histopathological Image Classification (BreakHis) is composed of 9,109 microscopic images of breast tumor tissue collected from 82 patients using different magnifying factors (40X, 100X, 200X, and 400X). It contains 2,480 benign and 5,429 malignant samples (700X460 pixels, 3-channel RGB, 8-bit depth in each channel, PNG format). This database has been built in collaboration with the P&D Laboratory - Pathological Anatomy and Cytopathology, Parana, Brazil.
TRIPOD contains screenplays and plot synopses with turning point (TP) annotations for 99 movies. Each movie contains:
Synbols is a dataset generator designed for probing the behavior of learning algorithms. By defining the distribution over latent factors one can craft a dataset specifically tailored to answer specific questions about a given algorithm.
Five classic grayscale images commonly used for image quality assessment tasks.
Tasks Our shared task has three subtasks. Subtask 1 and 2 focus on evaluating machine learning models' performance with regard to two definitions of abstractness (Spreen and Schulz, 1966; Changizi, 2008), which we call imperceptibility and nonspecificity, respectively. Subtask 3 aims to provide some insights to their relationships.
BIKED is a dataset comprised of 4500 individually designed bicycle models sourced from hundreds of designers. BIKED enables a variety of data-driven design applications for bicycles and generally supports the development of data-driven design methods. The dataset is comprised of a variety of design information including assembly images, component images, numerical design parameters, and class labels.
ARCH is a computational pathology (CP) multiple instance captioning dataset to facilitate dense supervision of CP tasks. Existing CP datasets focus on narrow tasks; ARCH on the other hand contains dense diagnostic and morphological descriptions for a range of stains, tissue types and pathologies.
Diabetic retinopathy is the leading cause of blindness in the working-age population of the developed world. It is estimated to affect over 93 million people.
BL30K is a synthetic dataset rendered using Blender with ShapeNet's data. We break the dataset into six segments, each with approximately 5K videos. The videos are organized in a similar format as DAVIS and YouTubeVOS, so dataloaders for those datasets can be used directly. Each video is 160 frames long, and each frame has a resolution of 768*512. There are 3-5 objects per video, and each object has a random smooth trajectory -- we tried to optimize the trajectories in a greedy fashion to minimize object intersection (not guaranteed), with occlusions still possible (happen a lot in reality). See MiVOS for details.
PATS dataset consists of a diverse and large amount of aligned pose, audio and transcripts. With this dataset, we hope to provide a benchmark that would help develop technologies for virtual agents which generate natural and relevant gestures.
BSTC (Baidu Speech Translation Corpus) is a large-scale dataset for automatic simultaneous interpretation. BSTC version 1.0 contains 50 hours of real speeches, including three parts, the audio files, the transcripts, and the translations. The corpus can be used to build automatic simultaneous interpretation system. The corpus is collected from the Chinese mandarin talks and reports, including science, technology, culture, economy, etc.,. The utterances in talks and reports are carefully transcribed into Chinese text, and further translated into English text. The sentence boundary is determined by the English text instead of the Chinese text which is analogous to the previous related corpus (TED and Translation Augmented LibriSpeech Corpus).
StylePTB is a fine-grained text style transfer benchmark. It consists of paired sentences undergoing 21 fine-grained stylistic changes spanning atomic lexical, syntactic, semantic, and thematic transfers of text, as well as compositions of multiple transfers which allow modelling of fine-grained stylistic changes as building blocks for more complex, high-level transfers.
RGB-D-D is a large-scale dataset for depth map super-resolution (SR). It consists of real-world paired low-resolution (LR) and high-resolution (HR) depth maps. The paired LR and HR depth maps are captured from mobile phone and Lucid Helios respectively ranging from indoor scenes to challenging outdoor scenes.
The Sleep Heart Health Study (SHHS) is a multi-center cohort study implemented by the National Heart Lung & Blood Institute to determine the cardiovascular and other consequences of sleep-disordered breathing. It tests whether sleep-related breathing is associated with an increased risk of coronary heart disease, stroke, all cause mortality, and hypertension. In all, 6,441 men and women aged 40 years and older were enrolled between November 1, 1995 and January 31, 1998 to take part in SHHS Visit 1. During exam cycle 3 (January 2001- June 2003), a second polysomnogram (SHHS Visit 2) was obtained in 3,295 of the participants. CVD Outcomes data were monitored and adjudicated by parent cohorts between baseline and 2011. More than 130 manuscripts have been published investigating predictors and outcomes of sleep disorders.
The LandCover.ai (Land Cover from Aerial Imagery) dataset is a dataset for automatic mapping of buildings, woodlands, water and roads from aerial images.