19,997 machine learning datasets
19,997 dataset results
Please see paper for questions. These are the answers to the surveys, processed and included in the paper via knitr
This dataset is a ground truth dataset that is used to identify bots. Each account in this dataset is rated by at least 3 raters with a high interrater agreement.
ISBNet is a dataset of images of recyclables. It is hand collected by our group at the International School of Beijing. The trash in these images was gathered from trash bins around the school. ISBNet totals 889 images distributed across 5 classes: cans (74), landfill (410), paper (182), plastic (122), and tetra pak (101). The data acquisition process involved using a piece of black poster paper as a background; this would create enough contrast for trash belonging to the paper category. These pictures were taken with an iPhone 8 and an iPhone XS. We recorded the trash bin in which the piece of trash originated from and any trash generating landmarks nearby. Please refer to the paper (ThanosNet: A Novel Trash Classification Method Using Metadata) for more about the format of the metadata.
Neural network model files and Madgraph event generator outputs used as inputs to the results presented in the paper "Learning to discover: expressive Gaussian mixture models for multi-dimensional simulation and parameter inference in the physical sciences" arXiv:2108.11481; 2022 Mach. Learn.: Sci. Technol. 3 015021 Code and model files can be found at: https://github.com/darrendavidprice/science-discovery/tree/master/expressive_gaussian_mixture_models
Electromagnetic (EM) showers simulated dataset. The data contains 16,577 showers. The data includes information about the tracklets: position coordinates, direction and shower id, and about the showers: shower id, initial particle position and direction, shower energy.
AlgorithmComparison: Comparison of algorithms on benchmark test cases. Details on included in the paper. 10 cases for each algorithm / benchmark test. Optimum.txt file includes the history of best optimum and SamplePointsResults.txt file contains results for all the black-box function evaluations. Last colum represents the objective value. GlobalOptimum.txt represents the global optimum for that specific test case.
The Super-resolution of Multi-Dimensional Diffusion MRI (Super MUDI) dataset contains the data of four healthyhuman subjects with ages range between 19 and 46 years. For each subject 1,344 MRI volumes are provided. Theimaging device was clinical 3T Philips Achieva Scanner (Best, Netherlands) with a 32-channel adult head coil. The Super MUDI Challenge comprises two tasks: isotropic, and anisotropic super-resolution. The names of these tasks were derived from the acquisition strategies of the low-resolution MRI data. The objective of using two down-sampling strategies is to compare the combinations of the down-sampling methods and the super-resolution approaches that can best to be used in a clinical scheme to obtain simulated high-quality and high-fidelity MRI images while reducing the acquisition time. In the anisotropic subsampling the volume has high in-plane resolution (2.5mm ×2.5mm), but thick axial slice (5mm), while in the isotropic subsampling the volume has low resolution (5mm
Abstract Meaning Representation (AMR) Annotation Release 3.0 was developed by the Linguistic Data Consortium (LDC), SDL/Language Weaver, Inc., the University of Colorado's Computational Language and Educational Research group and the Information Sciences Institute at the University of Southern California. It contains a sembank (semantic treebank) of over 59,255 English natural language sentences from broadcast conversations, newswire, weblogs, web discussion forums, fiction and web text.
NR-HCPI (Non-redundant Human CPI dataset)
The MedVidCL dataset contains a collection of 6, 617 videos annotated into ‘medical instructional’, ‘medical non-instructional' and ‘non-medical’ classes. A two-step approach is used to construct the MedVidCL dataset. In the first step, the videos annotated by health informatics experts are used to train a machine learning model that predicts the given video to one of the three aforementioned classes. In the second step, only the high-confidence videos are used and health informatics experts assess the model’s predicted video category and update the category wherever needed.
The IEEE Computational Intelligence Society ran a competition from July to November 2021 for predicting and optimizing based on renewable energy data.
LARa is the first freely accessible logistics-dataset for human activity recognition. In the ’Innovationlab Hybrid Services in Logistics’ at TU Dortmund University, two picking and one packing scenarios with 14 subjects were recorded using OMoCap, IMUs, and an RGB camera. 758 minutes of recordings were labeled by 12 annotators in 474 person-hours. The subsequent revision was carried out by 4 revisers in 143 person-hours. All the given data have been labeled and categorised into 8 activity classes and 19 binary coarse-semantic descriptions, also called attributes.
A prevalent use case of topic models is that of topic discovery. However, most of the topic model evaluation methods rely on abstract metrics such as perplexity or topic coherence. The topic coverage approach is to measure the models' performance by matching model-generated topics to topics discovered by humans. This way, the models are evaluated in the context of their use, by essentially simulating topic modeling in a fixed setting defined by a text collection and a set of reference topics.
CAT is a specialized dataset for co-saliency detection. This dataset is intended for both helping to assess the performance of vision algorithms and supporting research that aims to exploit large volumes of annotated data, e.g., for training deep neural networks.
Tool Database for image-set clustering This database was generated to evaluate a robotic application dealing with image-set clustering. The goal is to sort and store tools on an table in an unsupervised way, from pixel inputs. Pictures contains objects that can be found in a shop-floor. Each picture contains only one object. There are five different conditions, for each condition, lighting conditions and background are changed. For each condition, four picture of each object are taken under different orientations.
EmoFilm is a multilingual emotional speech corpus comprising 1115 audio instances produced in English, Italian, and Spanish languages. The audio clips (with a mean length of 3.5 sec. and std 1.2 sec.) were extracted in wave format (uncompressed, mono, 48 kHz sample rate and 16-bit) from 43 films (original in English and their over-dubbed Italian and Spanish versions). Genres including comedy, drama, horror, and thriller were considered; anger, contempt, happiness, fear, and sadness emotional states were taken into account. EmoFilm has been presented at Interspeech 2018: Emilia Parada-Cabaleiro, Giovanni Costantini, Anton Batliner, Alice Baird, and Björn Schuller (2018), Categorical vs Dimensional Perception of Italian Emotional Speech, in Proc. of Interspeech, Hyderabad, India, pp. 3638-3642.
Currently, an essential point in speech synthesis is the addressing of the variability of human speech. One of the main sources of this diversity is the emotional state of the speaker. Most of the recent work in this area has been focused on the prosodic aspects of speech and on rule-based formant synthesis experiments. Even when adopting an improved voice source, we cannot achieve a smiling happy voice or the menacing quality of cold anger. For this reason, we have performed two experiments aimed at developing a concatenative emotional synthesiser, a synthesiser that can copy the quality of an emotional voice without an explicit mathematical model.
The development of ecologically valid procedures for collecting reliable and unbiased emotional data towards computer interfaces with social and affective intelligence targeting patients with mental disorders. Following its development, presented with, the Athens Emotional States Inventory (AESI) proposes the design, recording and validation of an audiovisual database for five emotional states: anger, fear, joy, sadness and neutral. The items of the AESI consist of sentences each having content indicative of the corresponding emotion. Emotional content was assessed through a survey of 40 young participants with a questionnaire following the Latin square design. The emotional sentences that were correctly identified by 85% of the participants were recorded in a soundproof room with microphones and cameras. A preliminary validation of AESI is performed through automatic emotion recognition experiments from speech. The resulting database contains 696 recorded utterances in Greek language
Data for the paper entitled Quantifying yeast colony morphologies with feature engineering from time-lapse photography by A. Goldschmidt et al. (https://arxiv.org/abs/2201.05259)
BDD100K-weather is a dataset which is inherited from BDD100K using image attribute labels for Out-of-Distribution object detection. All images in BDD100K are categorized into six domains, including clear, overcast, foggy, partly cloudy, rainy and snowy. Clear and overcast are used for training while the rest is used for testing, moreover, per training domain is sampled 1.5k images at most while per testing domain is sampled 0.5k images at most. Thus, we have BDD100K-weather (paper is under review).