19,997 machine learning datasets
19,997 dataset results
Processed Twitter is a dataset that is used for Twitter topic recognition. It contains tweets from 6 different topics.
Robotic grasp dataset for multi-object multi-grasp evaluation with RGB-D data. This dataset is annotated using the same protocol as Cornell Dataset, and can be used as multi-object extension of Cornell Dataset.
The OFEQ-10k dataset contains 12,548 detailed questions with corresponding math headlines from MathOverflow.
The Jejueo Single Speaker Speech (JSS) dataset consists of 10k high-quality audio files recorded by a native Jejueo speaker and a transcript file.
needadvice is a dataset for advice classification extracted from Reddit. In this dataset, posts are annotated for whether they contain advice or not. It contains 6,148 samples for training, 816 for validation and 898 for testing.
The FB1.5M dataset is a benchmark for Knowledge Graph Completion. It is based on Freebase and it contains 30 relations with less than 500 triplets as low-resource relations.
Wiki-zh is an annotated Chinese dataset for domain detection extracted from Wikipedia. It includes texts from 7 different domains: “Business and Commerce” (BUS), “Government and Politics” (GOV), “Physical and Mental Health” (HEA), “Law and Order” (LAW), “Lifestyle” (LIF), “Military” (MIL), and “General Purpose” (GEN). It contains 26,280 documents split into training, validation and test.
The dataset consists of 90 000 grayscale videos that show two objects of equal shape and size in which one object approaches the other one. The object speed during the process of approaching is hereby modelled by a proportional-derivative controller. Overall, three different shapes (Rectangle, Triangle and Circle) are provided. Initial configuration of the objects such as position and color were randomly sampled. Different from the moving MNIST dataset, the samples comprise a goal-oriented task, namely one object has to fully cover the other object rather than randomly moving, making it better suitable for testing prediction capabilities of an ML model. For instance, one can use it as a toy dataset to investigate the capacity and output behavior of a deep neural network before testing it on real-world data.
The YFCC100M Fine-Grained Geolocation dataset is a subset of 100 a set of 36,146 YFCC100M images that had Flickr tags that could be identified as corresponding to one of the labels in the iNaturalist 2017 dataset. The 36,146 images that were selected so have the following characteristics: the image must have geolocation available, the image must have at most one iNaturalist label, at most ten examples were retained for each label.
AuxAI is a distantly supervised dataset for acronym identification.
The PART-OF dataset is a dataset of relations extracted from a medical ontology. The different entities in the ontology are parts of the human body. The dataset has 16,894 nodes with 19,436 edges between them.
IN2LAAMA is a set of lidar-inertial datasets collected with a Velodyne VLP-16 lidar and a Xsens MTi-3 IMU.
Spot the Difference Corpus is a corpus of task-oriented spontaneous dialogues which contains 54 interactions between pairs of subjects interacting to find differences in two very similar scenes. The corpus includes rich transcriptions, annotations, audio and video.
T2 Guiding is a dataset of 1000 images, each with six image labels. The images are from the Open Images Dataset (OID) and the dataset includes 2 sets of machine-generated labels for these images.
The dataset contains 26,645, 360 degree, street-level images collected via cycling with a GoPro Fusion camera, recorded Jan 14th -- 18th 2020. 10,106 advertisements were identified and classified as food (1335), alcohol (217), gambling (149) and other (8405) (e.g., cars and broadband).
The i3-video dataset contains "is-it-instructional" annotations for 6.4k videos from Youtube-8M. The videos are considered to be instructional if they focus on real-world human actions accompanied by procedural language that explains what’s happening on screen in reasonable details.
CLaRO is a new dataset of 234 Competency Questions that had been processed automatically into 106 patterns. The coverage of CLaRO, with its 93 main templates and 41 linguistic variants, is about 90% for unseen questions.
ImagiFilter focusses on photographic and/or natural images, a very common use-case in computer vision research. Annotations for coarse prediction are provided, i.e. photographic vs. non-photographic, and smaller fine-grained prediction tasks where the non-photographic class is broken down into five classes: maps, drawings, graphs, icons, and sketches.
Cross-Reference Omnidirectional Stitching IQA is a novel omnidirectional image dataset containing stitched images as well as dual-fisheye images captured from standard quarters of 0◦, 90◦ , 180◦ and 270◦. In this manner, when evaluating the quality of an image stitched from a pair of fisheye images (e.g., 0◦ and 180◦), the other pair of fisheye images (e.g., 90◦ and 270◦) can be used as the cross-reference to provide ground-truth observations of the stitching regions.
EPIC30M contains a subset of 26.2 millions tweets related to three general diseases, namely Ebola, Cholera and Swine Flu, and another subset of 4.7 millions tweets of six global epidemic outbreaks, including 2009 H1N1 Swine Flu, 2010 Haiti Cholera, 2012 Middle-East Respiratory Syndrome (MERS), 2013 West African Ebola, 2016 Yemen Cholera and 2018 Kivu Ebola.