19,997 machine learning datasets
19,997 dataset results
This is a dataset for detection fake death hoaxes. It consists of of death reports collected from Twitter between 1st January, 2012 and 31st December, 2014. It was collected by tracking the keyword 'RIP', and matching those tweets in which a name is mentioned next to RIP. Matching names were identified by using Wikidata as a database of names.
The KACC benchmark consists of three subtasks that can be applied to knowledge graphs: knowledge abstraction, knowledge concretization and knowledge completion.
The AllMusic Mood Subset (AMS) is a dataset for mood classification from songs. It is created by matching a subset of the Million Song Dataset (MSD), totalling 67k tracks, with expert annotations of 188 different moods collected from AllMusic.
EDUVSUM contains educational videos with subtitles from three popular e-learning platforms: Edx,YouTube, and TIB AV-Portal that cover the following topics: crash course on history of science and engineering, computer science, python and web programming, machine learning and computer vision, Internet of things (IoT), and software engineering. In total, the current version of the dataset contains 98 videos with ground truth values annotated by a user with an academic background in computer science.
The proposed dataset includes 1,309 short text instances from Adobe Spark. The dataset is a collection of publicly available sample texts created by different designers. It covers a variety of topics found in posters, flyers, motivational quotes and advertisements.
The dataset contains both RGB and depth images, and the data from two accelerometers, together with ground truth calorie values from a calorimeter for calorie expenditure estimation in home environments.
This dataset is an audio dataset containing about 1500 audio clips recorded by multiple professional players.
This dataset consists of 18 movies with duration range between 10 and 104 minutes leveraged from the OVSD dataset (Rotman et al., 2016). For these videos, the summary length limit is set to be the minimum between 4 minutes and 10% of the video length.
PolarRR is a new dataset with more than 100 types of glass in which obtained transmission images are perfectly aligned with input mixed images.
PVDN is a dataset of vehicle detection at night, using light reflections caused by their headlamps. It contains 59,746 annotated grayscale images out of 346 different scenes in a rural environment at night. In these images, all oncoming vehicles, their corresponding light objects (e. g., headlamps), and their respective light reflections (e. g., light reflections on guardrails) are labeled. With this information, this dataset enables research into new methods of detecting oncoming vehicles based on the light reflections they cause, long before they are directly visible.
WordNet-feelings, is an affective dataset that identifies 3664 word senses as feelings, and associates each of these with one of the 9 categories of feeling. The 9 different categories are: Actions, Anger, Attention, Attraction, Hedonics, Other, Physiological, Social, Wellbeing.
Doc3DShade extends Doc3D with realistic lighting and shading. Follows a similar synthetic rendering procedure using captured document 3D shapes but final image generation step combines real shading of different types of paper materials under numerous illumination conditions.
TurkQA consists of a selection of sentences from English Wikipedia articles, with questions and answers crowdsourced from workers on Amazon Mechanical Turk.
Dialog-based Language Learning dataset is designed to measure how well models can perform at learning as a student given a teacher’s textual responses to the student’s answer (as well as potentially receiving an external real-valued reward signal).
To collect WikiSuggest, Google Suggest API is used to harvest natural language questions and submit them to Google Search. Whenever Google Search returns a box with a short answer from Wikipedia, an example from the question, answer, and the Wikipedia document are created. If the answer string is missing from the document this often implies a spurious question-answer pair, such as (‘what time is half time in rugby’, ‘80 minutes, 40 minutes’). Question-answer pairs without the exact answer string are pruned. Fifty examples after filtering are examined and 54% were found to be well-formed question-answer pairs where answers in the document can be grounded, 20% contained answers without textual evidence in the document (the answer string exists in an irreleveant context), and 26% contain incorrect QA pairs.
MessyTable features a large number of scenes with messy tables captured from multiple camera views. Each scene in this dataset is highly complex, containing multiple object instances that could be identical, stacked and occluded by other instances. The key challenge is to associate all instances given the RGB image of all views. The seemingly simple task surprisingly fails many popular methods or heuristics. The dataset challenges existing methods in mining subtle appearance differences, reasoning based on contexts, and fusing appearance with geometric cues for establishing an association.
The MSRA-B dataset is a dataset for salient object detection. It contains 5,000 images with a variety of image contents. Most of the images have a single salient object. There is a large variation among images including natural scenes, animals, indoor, outdoor, etc.
VOT2015 is a visual object tracking dataset. The dataset comprises 60 short sequences showing various objects in challenging backgrounds. The sequences were chosen from a large pool of sequences from different sources.
UCF50 is an action recognition data set with 50 action categories, consisting of realistic videos taken from youtube. This data set is an extension of YouTube Action data set (UCF11) which has 11 action categories.
Placepedia contains 240K places with 35M images from all over the world. Each place is associated with its district, city/town/village, state/province, country, continent, and a large amount of diverse photos. Both administrative areas and places have rich side information, e.g. discription, population, category, function. In addition, two cleaned subsets (Places-Coarse and Places-Fine) for experiments are provided.