19,997 machine learning datasets
19,997 dataset results
The dataset contains naive and stylized sketches for a chair category of the ShapeNetCore dataset. Each chair shape folder contains two subfolders: "naive" and "stylized", representing two rendering styles.
Mood ratings of 8 emotions gathered across 360 pop songs 166 raters from US, S.Korea and Brazil MIR features from Spotify
Puzzles dataset: comparison, knight&knaves, and zebra puzzles.
The Cooperative Driving dataset is a synthetic dataset generated using CARLA that contains lidar data from multiple vehicles navigating simultaneously through a diverse set of driving scenarios. This dataset was created to enable further research in multi-agent perception (cooperative perception) including cooperative 3D object detection, cooperative object tracking, multi-agent SLAM and point cloud registration. Towards that goal, all the frames have been labelled with ground-truth sensor pose and 3D object bounding boxes.
Dataset with raw outputs of experiments connected to the GitHub repository:
We present the Webis-STEREO-21 dataset, a massive collection of Scientific Text Reuse in Open-access publications. It contains more than 91 million cases of reused text passages found in 4.2 million unique open-access publications. Featuring a high coverage of scientific disciplines and varieties of reuse, as well as comprehensive metadata to contextualize each case, our dataset addresses the most salient shortcomings of previous ones on scientific writing. Webis-STEREO-21 allows for tackling a wide range of research questions from different scientific backgrounds, facilitating both qualitative and quantitative analysis of the phenomenon as well as a first-time grounding on the base rate of text reuse in scientific publications.
A new subset of the popular open source electroencephalogram (EEG) corpus – TUH EEG: - The Temple University Artifact Corpus (TUAR) consists of high yield artifact files annotated using a five-way classification system: 1. Chewing (CHEW): An artifact resulting from the tensing and relaxing of the jaw muscles. 2. Electrode (ELEC): An artifact that encompasses various electrode related phenomena. 3. Eye Movement (EYEM): A spike-like waveform created during patient eye movement. 4. Muscle (MUSC): A common artifact with high frequency, sharp waves corresponding to patient movement. 5. Shiver (SHIV): A specific and sustained sharp wave artifact that occurs when a patient shivers. - EEG artifacts are waveforms that are not of cerebral origin and may have been affected by several external and physiological factors. - These artifacts cause false alarms in seizure prediction machine learning systems. This corpus was developed to support research and evaluation of artifact suppression technology
Multimodal object recognition is still an emerging field. Thus, publicly available datasets are still rare and of small size. This dataset was developed to help fill this void and presents multimodal data for 63 objects with some visual and haptic ambiguity. The dataset contains visual, kinesthetic and tactile (audio/vibrations) data. To completely solve sensory ambiguity, sensory integration/fusion would be required. This report describes the creation and structure of the dataset. The first section explains the underlying approach used to capture the visual and haptic properties of the objects. The second section describes the technical aspects (experimental setup) needed for the collection of the data. The third section introduces the objects, while the final section describes the structure and content of the dataset.
We release various types of word embeddings for multiple Indian languages. Please note that for a majority of our work, we had transliterated the corpora to the Devanagiri script and the script is changed. Word Embedding models using FastText, ElMo, and cross-lingual models based on an orthogonal alignment of monolingual models for all pairs of these languages.
A dataset of illusions generated by the AI model EIGen.
A pretrained PredNet neural network, used in EIGen to generate grayscale illusions.
A pretrained PredNet neural network, used in EIGen to generate color illusions.
There has been increasing interest in smart factories powered by robotics systems to tackle repetitive, laborious tasks. One particular impactful yet challenging task in robotics-powered smart factory applications is robotic grasping: using robotic arms to grasp objects autonomously in different settings. Robotic grasping requires a variety of computer vision tasks such as object detection, segmentation, grasp prediction, pick planning, etc. While significant progress has been made in leveraging of machine learning for robotic grasping, particularly with deep learning, a big challenge remains in the need for large-scale, high-quality RGBD datasets that cover a wide diversity of scenarios and permutations.
There has been increasing interest in smart factories powered by robotics systems to tackle repetitive, laborious tasks. One particular impactful yet challenging task in robotics-powered smart factory applications is robotic grasping: using robotic arms to grasp objects autonomously in different settings. Robotic grasping requires a variety of computer vision tasks such as object detection, segmentation, grasp prediction, pick planning, etc. While significant progress has been made in leveraging of machine learning for robotic grasping, particularly with deep learning, a big challenge remains in the need for large-scale, high-quality RGBD datasets that cover a wide diversity of scenarios and permutations.
There has been increasing interest in smart factories powered by robotics systems to tackle repetitive, laborious tasks. One particular impactful yet challenging task in robotics-powered smart factory applications is robotic grasping: using robotic arms to grasp objects autonomously in different settings. Robotic grasping requires a variety of computer vision tasks such as object detection, segmentation, grasp prediction, pick planning, etc. While significant progress has been made in leveraging of machine learning for robotic grasping, particularly with deep learning, a big challenge remains in the need for large-scale, high-quality RGBD datasets that cover a wide diversity of scenarios and permutations.
There has been increasing interest in smart factories powered by robotics systems to tackle repetitive, laborious tasks. One particular impactful yet challenging task in robotics-powered smart factory applications is robotic grasping: using robotic arms to grasp objects autonomously in different settings. Robotic grasping requires a variety of computer vision tasks such as object detection, segmentation, grasp prediction, pick planning, etc. While significant progress has been made in leveraging of machine learning for robotic grasping, particularly with deep learning, a big challenge remains in the need for large-scale, high-quality RGBD datasets that cover a wide diversity of scenarios and permutations.
There has been increasing interest in smart factories powered by robotics systems to tackle repetitive, laborious tasks. One particular impactful yet challenging task in robotics-powered smart factory applications is robotic grasping: using robotic arms to grasp objects autonomously in different settings. Robotic grasping requires a variety of computer vision tasks such as object detection, segmentation, grasp prediction, pick planning, etc. While significant progress has been made in leveraging of machine learning for robotic grasping, particularly with deep learning, a big challenge remains in the need for large-scale, high-quality RGBD datasets that cover a wide diversity of scenarios and permutations.
Accompanying expert data and trained models for 2021 IROS paper on Multiview Manipulation.
CLIPS, ovvero Corpora e Lessici dell'Italiano Parlato e Scritto, è uno degli otto progetti (Progetto n. 2) del Cluster C18 "LINGUISTICA COMPUTAZIONALE: RICERCHE MONOLINGUI E MULTILINGUI" (Legge 488), finanziato dal Ministero dell'Istruzione, dell'Università e della Ricerca (MIUR).
We employ a nationwide phone call dataset from Jan. 2015 to Dec. 2016. The log interaction duration and log interaction frequency in each phase (intermediate results) are both provided. Currently, we upload the Results folder to Google Drive. (https://drive.google.com/drive/folders/1h4rHZvzzQO7niYMelbzToJZernOij1dv?usp=sharing)