19,997 machine learning datasets
19,997 dataset results
Includes 100K depth images under challenging scenarios.
Hyperpartisan News Detection was a dataset created for PAN @ SemEval 2019 Task 4. Given a news article text, decide whether it follows a hyperpartisan argumentation, i.e., whether it exhibits blind, prejudiced, or unreasoning allegiance to one party, faction, cause, or person.
The UAVA,<i>UAV-Assistant</i>, dataset is specifically designed for fostering applications which consider UAVs and humans as cooperative agents. We employ a real-world 3D scanned dataset (<a href="https://niessner.github.io/Matterport/">Matterport3D</a>), physically-based rendering, a gamified simulator for realistic drone navigation trajectory collection, to generate realistic multimodal data both from the user’s exocentric view of the drone, as well as the drone’s egocentric view.
The normal chest X-ray (left panel) depicts clear lungs without any areas of abnormal opacification in the image. Bacterial pneumonia (middle) typically exhibits a focal lobar consolidation, in this case in the right upper lobe (white arrows), whereas viral pneumonia (right) manifests with a more diffuse ‘‘interstitial’’ pattern in both lungs. http://www.cell.com/cell/fulltext/S0092-8674(18)30154-5
We provide two image stacks where each contains 20 sections from serial section Transmission Electron Microscopy (ssTEM) of the Drosophila melanogaster third instar larva ventral nerve cord. Both stacks measure approx. 4.7 x 4.7 x 1 microns with a resolution of 4.6 x 4.6 nm/pixel and section thickness of 45-50 nm.
The AND Dataset contains 13700 handwritten samples and 15 corresponding expert examined features for each sample. The dataset is released for public use and the methods can be extended to provide explanations on other verification tasks like face verification and bio-medical comparison. This dataset can serve as the basis and benchmark for future research in explanation based handwriting verification.
A new shared task of semantic retrieval from legal texts, in which a so-called contract discovery is to be performed, where legal clauses are extracted from documents, given a few examples of similar clauses from other legal acts.
The BIWI Walking Pedestrians dataset consists of walking pedestrians in busy scenarios from a birds eye view.
The Sheffield (previously UMIST) Face Database consists of 564 images of 20 individuals (mixed race/gender/appearance). Each individual is shown in a range of poses from profile to frontal views – each in a separate directory labelled 1a, 1b, … 1t and images are numbered consecutively as they were taken. The files are all in PGM format, approximately 220 x 220 pixels with 256-bit grey-scale.
Arxiv GR-QC (General Relativity and Quantum Cosmology) collaboration network is from the e-print arXiv and covers scientific collaborations between authors papers submitted to General Relativity and Quantum Cosmology category. If an author i co-authored a paper with author j, the graph contains a undirected edge from i to j. If the paper is co-authored by k authors this generates a completely connected (sub)graph on k nodes.
The Jamendo Corpus is a voice detection dataset consisting of 93 songs with Creative Commons license from the Jamendo free music sharing website. Segments of each song are annotated as “voice” (sung or spoken) or “no-voice”. The songs constitute a total of about 6 hours of music. The files are all from different artists and represent various genres from mainstream commercial music. The Jamendo audio files are coded in stereo Vorbis OGG 44.1kHz with 112KB/s bitrate. The original split contains 61, 16 and 16 songs in training, validation and testing set, respectively.
The Papers with Code Leaderboards dataset is a collection of over 5,000 results capturing performance of machine learning models. Each result is a tuple of form (task, dataset, metric name, metric value). The data was collected using the Papers with Code review interface.
The ICVL dataset is a hand pose estimation dataset that consists of 330K training frames and 2 testing sequences with each 800 frames. The dataset is collected from 10 different subjects with 16 hand joint annotations for each frame.
RobotPush is a dataset for object singulation – the task of separating cluttered objects through physical interaction. The dataset contains 3456 training images with labels and 1024 validation images with labels. It consists of simulated and real-world data collected from a PR2 robot that equipped with a Kinect 2 camera. The dataset also contains ground truth instance segmentation masks for 110 images in the test set.
The Specs on Faces (SoF) dataset, a collection of 42,592 (2,662×16) images for 112 persons (66 males and 46 females) who wear glasses under different illumination conditions. The dataset is FREE for reasonable academic fair use. The dataset presents a new challenge regarding face detection and recognition. It is focused on two challenges: harsh illumination environments and face occlusions, which highly affect face detection, recognition, and classification. The glasses are the common natural occlusion in all images of the dataset. However, there are two more synthetic occlusions (nose and mouth) added to each image. Moreover, three image filters, that may evade face detectors and facial recognition systems, were applied to each image. All generated images are categorized into three levels of difficulty (easy, medium, and hard). That enlarges the number of images to be 42,592 images (26,112 male images and 16,480 female images). There is metadata for each image that contains many infor
HDM05 is a MoCap (motion capture) dataset. It contains more than three hours of systematically recorded and well-documented motion capture data in the C3D as well as in the ASF/AMC data format. HDM05 contains almost 2337 sequences with 130 motion classes performed by 5 different actors.
The OpeReid dataset is a person re-identification dataset that consists of 7,413 images of 200 persons.
The LITIS-Rouen dataset is a dataset for audio scenes. It consists of 3026 examples of 19 scene categories. Each class is specific to a location such as a train station or an open market. The audio recordings have a duration of 30 seconds and a sampling rate of 22050 Hz. The dataset has a total duration of 1500 minutes.
The BirdVox-full-night dataset contains 6 audio recordings, each about ten hours in duration. These recordings come from ROBIN autonomous recording units, placed near Ithaca, NY, USA during the fall 2015. They were captured on the night of September 23rd, 2015, by six different sensors, originally numbered 1, 2, 3, 5, 7, and 10. Andrew Farnsworth used the Raven software to pinpoint every avian flight call in time and frequency. He found 35402 flight calls in total. He estimates that about 25 different species of passerines (thrushes, warblers, and sparrows) are present in this recording. Species are not labeled in BirdVox-full-night, but it is possible to tell apart thrushes from warblers and sparrrows by looking at the center frequencies of their calls. The annotation process took 102 hours.
DCASE2014 is an audio classification benchmark.