19,997 machine learning datasets
19,997 dataset results
The Cityscapes-Motion dataset is a supplement to the semantic annotations provided by the Cityscapes dataset, containing 2975 training images and 500 validation images. The dataset creators provide manually annotated motion labels for the category of cars. The images are of resolution 2048×1024 pixels. The task to learn is not just semantic segmentation but also the motion status of the objects.
The KITTI-Motion dataset contains pixel-wise semantic class labels and moving object annotations for 255 images taken from the KITTI Raw dataset. The images are of resolution 1280×384 pixels and contain scenes of freeways, residential areas and inner-cities. The task is not just to semantically segment objects but also to identify their motion status.
Freiburg Lighting Adaptable Map Tracking is a dataset for camera trajectory estimation. The dataset consists of two subdatasets, each consisting of a Lighting Adaptable Map and three camera trajectories recorded under varying lighting conditions. The map meshes are stored in PLY format with custom properties and elements. The trajectories contain synchronized RGB-D images, exposure times and gains, ground-truth light settings and camera poses, as well as the camera tracking results presented in the paper.
The Freiburg Poking dataset is a dataset for learning intuitive physics from physical interaction. It consists of 40K of interaction data with a KUKA LBR iiwa manipulator and a fixed Azure Kinect RGB-D camera. The dataset creators built an arena of styrofoam with walls for preventing objects from falling down. At any given time there were 3-7 objects randomly chosen from a set of 34 distinct objects present on the arena. The objects differed from each other in shape, appearance, material, mass and friction.
The Parzival dataset consists of 47 pages by three writers. These pages were taken from a medieval German manuscript from the 13th century that contains the epic poem Parzival by Wolfram von Eschenbach. The image size is 2000 x 3000 pixels. 24 pages are selected as training set; 14 pages are selected as test set; 2 pages are selected as validation set.
The RGB-D Scenes Dataset v2 consists of 14 scenes containing furniture (chair, coffee table, sofa, table) and a subset of the objects in the RGB-D Object Dataset (bowls, caps, cereal boxes, coffee mugs, and soda cans). Each scene is a point cloud created by aligning a set of video frames using Patch Volumes Mapping.
The RGB-D Scenes Dataset contains 8 scenes annotated with objects that belong to the Washington RGB-D Object Dataset. Each scene is a single video sequence consisting of multiple RGB-D frames.
The Freiburg RGB-D People dataset contains 3000+ RGB-D frames acquired in a university hall from three vertically mounted Kinect sensors. The data contains mostly upright walking and standing persons seen from different orientations and with different levels of occlusions.
PAVIS RGB-D is a dataset for person re-identification using depth information. The main motivation is that techniques such as SDALF fail when the individuals change their clothing, therefore they cannot be used for long-term video surveillance. Depth information is the solution to deal with this problem because it stays constant for a longer period of time. The dataset is composed by four different groups of data collected using the Kinect. The first group of data has been obtained by recording 79 people with a frontal view, walking slowly, avoiding occlusions and with stretched arms ("Collaborative"). This happened in an indoor scenario, where the people were at least 2 meters away from the camera. The second ("Walking1") and third ("Walking2") groups of data are composed by frontal recordings of the same 79 people walking normally while entering the lab where they normally work. The fourth group ("Backwards") is a back view recording of the people walking away from the lab. The data
The Couples Therapy corpus contains audio, video recordings and manual transcriptions of conversations between 134 real-life couples attending marital therapy. In each session, one person selected a topic that was discussed over 10 minutes with the spouse. At the end of the session, both speakers were rated separately on 33 “behavior codes” by multiple annotators based on the Couples Interaction and Social Support Rating Systems. Each behavior was rated on a Likert scale from 1, indicating absence, to 9, indicating strong presence. A session-level rating was obtained for each speaker by averaging the annotator ratings. This process was repeated for the spouse, resulting in 2 sessions per couple at a time. The total number of sessions per couple varied between 2 and 6.
Drug side effects and drug-drug interactions were mined from publicly available data. Offsides is a database of drug side-effects that were found, but are not listed on the official FDA label. Twosides is the only comprehensive database drug-drug-effect relationships. Over 3,300 drugs and 63,000 combinations connected to millions of potential adverse reactions.
The DUC 2005 data set is a dataset for summarization which consists of 50 document collections of 25 documents each; each document collection includes a human-written query. Each document collection additionally has five human-written “reference” summaries (250 words long, each) that serve as the gold standard
The TAU Spatial Sound Events 2019 - Ambisonic dataset contains recordings from a scene (along with the Microphone Array sister dataset). It provides four-channel First-Order Ambisonic (FOA) recordings. The recordings consist of stationary point sources from multiple sound classes each associated with a temporal onset and offset time, and DOA coordinate represented using azimuth and elevation angle. The development set consists of 400, one minute long recordings sampled at 48000 Hz, and divided into four cross-validation splits of 100 recordings each. These recordings were synthesized using spatial room impulse response (IRs) collected from five indoor locations, at 504 unique combinations of azimuth-elevation-distance. Furthermore, in order to synthesize the recordings, the collected IRs were convolved with isolated sound events dataset from DCASE 2016 task 2. Finally, to create a realistic sound scene recording, natural ambient noise collected in the IR recording locations was added t
The TAU Spatial Sound Events 2019 – Microphone Array dataset contains recordings from a scene (along with the Ambisonic sister dataset). It provides four-channel directional microphone recordings from a tetrahedral array configuration. The recordings consist of stationary point sources from multiple sound classes each associated with a temporal onset and offset time, and DOA coordinate represented using azimuth and elevation angle. The development set consists of 400, one minute long recordings sampled at 48000 Hz, and divided into four cross-validation splits of 100 recordings each. These recordings were synthesized using spatial room impulse response (IRs) collected from five indoor locations, at 504 unique combinations of azimuth-elevation-distance. Furthermore, in order to synthesize the recordings, the collected IRs were convolved with isolated sound events dataset from DCASE 2016 task 2. Finally, to create a realistic sound scene recording, natural ambient noise collected in the
KTI Multiview Football I is a dataset of football players with annotated joints that can be used for multi-view reconstruction. The dataset includes 771 images of football players, images taken from 3 views at 257 time instances, and 14 annotated body joints.
Sugar Beets 2016 is a robot dataset for plant classification as well as localization and mapping that covers the relevant stages for robotic intervention and weed control. It contains around 5TB of data recorded from a robot with a 4-channel multi-spectral camera and a RGB-D sensor to capture detailed information about the plantation.
USYD CAMPUS is a driving dataset collected by Zhou et al at the University of Sydney (USyd) campus and surroundings. This USYD Campus Dataset contains more than 60 weeks of drives and is continuously updated. It includes multiple sensor modalities (camera, lidar, GPS, IMU, wheel encoder, steering angle, etc.) and covers various environmental conditions as well as diverse changes to illumination, scene structure, and pedestrian/vehicle traffic volumes.
Bach chorales is a univariate time series based on chorales, where the task is to learn generative grammar. The dataset consists of single-line melodies of 100 Bach chorales (originally 4 voices). The melody line can be studied independently of other voices. The grand challenge is to learn a generative grammar for stylistically valid chorales.
Robbie Williams is a dataset of 65 songs by Robbie Williams. It consists of chords, keys and beats. The dataset does not include audio.
ISMIR2004 is an audio dataset consisting of 6 genres with 729 excerpts of 30 seconds. It is a dataset used for musical genre classification. The training set consists of 320 classical music samples, 115 electronic music samples, 26 jazz blues samples, 45 metal/punk samples, 101 rock/pop samples and 122 world samples.