19,997 machine learning datasets
19,997 dataset results
The SWC is a corpus of aligned Spoken Wikipedia articles from the English, German, and Dutch Wikipedia. This corpus has several outstanding characteristics:
DensePose-Track is a dataset of videos where selected frames are annotated in the traditional DensePose manner.
Parkinson Speech Dataset is an audio dataset consisting of recordings of 20 Parkinson's Disease (PD) patients and 20 healthy subjects. From all subjects, multiple types of sound recordings (26) are taken. The goal is to classify which patients have Parkinson's.
Ciona17 is a semantic segmentation dataset with pixel-level annotations pertaining to invasive species in a marine environment. Diverse outdoor illumination, a range of object shapes, colour, and severe occlusion provide a significant real world challenge for the computer vision community.
The RWCP Sound Scene Database includes non-speech sounds recorded in an anechoic room, reconstructed signals in various rooms, impulse responses for a microphone array, speech data recorded with the same array, and recordings of background noises. It is intended for use when simulating sound scenes. It was developed by the Real Acoustic Environments Working Group of the Real World Computing Partnership (RWCP). The data was recorded from 1998 to 2000.
NAR is a dataset of audio recordings made with the humanoid robot Nao in real world conditions for sound recognition benchmarking. All the recordings were collected using the robot’s microphone and thus have the following characteristics: - recorded with low-quality sensors (300 Hz – 18 kHz bandpass) - suffering from typical fan noise from the robot’s internal hardware - recorded in mutiple real domestic environments (no special acoustic charateristics, reverberations, presence of multiple sound sources and unknown locations)
Indian Institute of Science VIdeo Naturalness Evaluation (IISc VINE) is a database consisting of 300 videos, obtained by applying different prediction models on different datasets, and accompanying human opinion scores.
MinNav is a synthetic dataset based on the sandbox game Minecraft. The dataset uses several plug-in program to generate rendered image sequences with time-aligned depth maps, surface normal maps and camera poses. Thanks for the large game's community, there is an extremely large number of 3D open-world environment, users can find suitable scenes for shooting and build data sets through it and they can also build scenes in-game.
ARVSU contains a vast body of image variations in visual scenes with an annotated utterance and a corresponding addressee for each scenario.
Minecraft Segmentation is a segmentation dataset for the Minecraft House that adds semantic segmentation labels for sub-components of the house. There are 2050 houses in total and 1038 distinct labels of subcomponents.
RGRS is a dataset for collaboratior recommendation on the ResearchGate academic social network. The data has been collected from Jan. 2019 to April 2019 and includes raw data of 3980 RG users.
Event-Stream Dataset is a robotic grasping dataset with 91 objects.
WIKIOG is a public collection which consists of over 1.75 million document-outline pairs for research on the OG task.
SemanticUSL is a dataset for domain adaptation for LiDAR point cloud semantic segmentation. The dataset has the same data format and ontology as SemanticKITTI.
TDW is a 3D virtual world simulation platform, utilizing state-of-the-art video game engine technology. A TDW simulation consists of two components: a) the Build, a compiled executable running on the Unity3D Engine, which is responsible for image rendering, audio synthesis and physics simulations; and b) the Controller, an external Python interface to communicate with the build.
WildestFaces is tailored to study cross-domain recognition under a variety of adverse conditions.
FAD is a dataset that have roughly 200,000 attribute labels for the above traits, for over 10,000 facial images.
HARRISON dataset is a benchmark on hashtag recommendation for real world images in social networks. The HARRISON dataset is a realistic dataset, composed of 57,383 photos from Instagram and an average of 4.5 associated hashtags for each photo.
MVB (Multi View Baggage) is a dataset for baggage ReID task which has some essential differences from person ReID. The features of MVB are three-fold. First, MVB is the first publicly released large-scale dataset that contains 4519 baggage identities and 22660 annotated baggage images as well as its surface material labels. Second, all baggage images are captured by specially-designed multi-view camera system to handle pose variation and occlusion, in order to obtain the 3D information of baggage surface as complete as possible. Third, MVB has remarkable inter-class similarity and intra-class dissimilarity, considering the fact that baggage might have very similar appearance while the data is collected in two real airport environments, where imaging factors varies significantly from each other.
SEmantic Salient Instance Video (SESIV) dataset is obtained by augmenting the DAVIS-2017 benchmark dataset by assigning semantic ground-truth for salient instance labels. The SESIV dataset consists of 84 high-quality video sequences with pixel-wisely per-frame ground-truth labels.