19,997 machine learning datasets
19,997 dataset results
nvBench is a large-scale NL2VIS (natural languagge to visualisations) benchmark, containing 25,750 (NL, VIS) pairs from 750 tables over 105 domains, synthesized from (NL, SQL) benchmarks to support cross-domain NLPVIS (Natural Language Query to Visualization) task.
The Met dataset is a large-scale dataset for Instance-Level Recognition (ILR) in the artwork domain. It relies on the open access collection from the Metropolitan Museum of Art (The Met) in New York to form the training set, which consists of about 400k images from more than 224k classes, with artworks of world-level geographic coverage and chronological periods dating back to the Paleolithic period. Each museum exhibit corresponds to a unique artwork, and defines its own class. The training set exhibits a long-tail distribution with more than half of the classes represented by a single image, making it a special case of few-shot learning.
A set of 19 ASC datasets (reviews of 19 products) producing a sequence of 19 tasks. Each dataset represents a task. The datasets are from 4 sources: (1) HL5Domains (Hu and Liu, 2004) with reviews of 5 products; (2) Liu3Domains (Liu et al., 2015) with reviews of 3 products; (3) Ding9Domains (Ding et al., 2008) with reviews of 9 products; and (4) SemEval14 with reviews of 2 products - SemEval 2014 Task 4 for laptop and restaurant. For (1), (2) and (3), we split about 10% of the original data as the validate data, another about 10% of the original data as the testing data. For (4), We use 150 examples from the training set for validation. To be consistent with existing research(Tang et al., 2016), examples belonging to the conflicting polarity (both positive and negative sentiments are expressed about an aspect term) are not used. Statistics and details of the 19 datasets are given on Page https://github.com/ZixuanKe/PyContinual.
This dataset has 20 classes and each class has about 1000 documents. The data split for train/validation/test is 1600/200/200. We created 10 tasks, 2 classes per task. Since this is topic-based text classification data, the classes are very different and have little shared knowledge. As mentioned above, this application (and dataset) is mainly used to show a CL model's ability to overcome forgetting. Detailed statistics please on page https://github.com/ZixuanKe/PyContinual
Over the past few years a number of research groups have made rapid advances in remote PPG methods for estimating heart rate from digital video and obtained impressive results. How these various methods compare in naturalistic conditions, where spontaneous behavior, facial expressions, and illumination changes are present, is relatively unknown. To enable comparisons among alternative methods, the Vision for Vitals dataset was introduced. It is a novel dataset containing high-resolution videos time-locked with varied physiological signals from a diverse population.
VinDr-CXR is an open large-scale dataset of chest X-rays with radiologist’s annotations. It's bult from more than 100,000 raw images in DICOM format that were retrospectively collected from the Hospital 108 and the Hanoi Medical University Hospital, two of the largest hospitals in Vietnam. The published dataset consists of 18,000 postero-anterior (PA) view CXR scans that come with both the localization of critical findings and the classification of common thoracic diseases. These images were annotated by a group of 17 radiologists with at least 8 years of experience for the presence of 22 critical findings (local labels) and 6 diagnoses (global labels); each finding is localized with a bounding box. The local and global labels correspond to the “Findings” and “Impressions” sections, respectively, of a standard radiology report.
P-DukeMTMC-reID is a modified version based on DukeMTMC-reID dataset. There are 12,927 images (665 identifies) in training set, 2,163 images (634 identities) for querying and 9,053 images in the gallery set.
A large-scale non-homogeneous remote sensing image dehazing dataset
Predicting forest cover type from cartographic variables only (no remotely sensed data). The actual forest cover type for a given observation (30 x 30 meter cell) was determined from US Forest Service (USFS) Region 2 Resource Information System (RIS) data. Independent variables were derived from data originally obtained from US Geological Survey (USGS) and USFS data. Data is in raw form (not scaled) and contains binary (0 or 1) columns of data for qualitative independent variables (wilderness areas and soil types).
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The SOMOS dataset is a large-scale mean opinion scores (MOS) dataset consisting of solely neural text-to-speech (TTS) samples. It can be employed to train automatic MOS prediction systems focused on the assessment of modern synthesizers, and can stimulate advancements in acoustic model evaluation. It consists of 20K synthetic utterances of the LJ Speech voice, a public domain speech dataset which is a common benchmark for building neural acoustic models and vocoders. Utterances are generated from 200 TTS systems including vanilla neural acoustic models as well as models which allow prosodic variations.
Kobest is a benchmark for Korean language reasoning. It consists of five Korean-language downstream tasks. Professional Korean linguists designed the tasks that require advanced Korean linguistic knowledge.
There are eight essay sets. Each of the sets of essays was generated from a single prompt. Selected essays range from an average length of 150 to 550 words per response. Some of the essays are dependent upon source information and others are not. All responses were written by students ranging in grade levels from Grade 7 to Grade 10. All essays were hand graded and were double-scored. Each of the eight data sets has its own unique characteristics. The variability is intended to test the limits of your scoring engine's capabilities.
BinaryCorp is built for binary similarity detection based on the ArchLinux official repositories and Arch User Repository. BinaryCorp contains tens of thousands of software, including editors, instant messenger, HTTP server, web browser, compiler, graphics library, cryptographic library, etc. The binary code similarity task requires a large number of labeled data, thus we use the infrastructures provided by ArchLinux to construct our dataset with different optimization levels (e.g O0, O1, O2, O3, Os).
JetClass is a new large-scale dataset to facilitate deep learning research in particle physics. It consists of 100M particle jets for training, 5M for validation and 20M for testing. The dataset contains 10 classes of jets, simulated with MadGraph + Pythia + Delphes. A detailed description of the JetClass dataset is presented in the paper Particle Transformer for Jet Tagging. An interface to use the dataset is provided here.
The ToughTables (2T) dataset was created for the SemTab challenge and includes 180 tables in total. The tables in this dataset can be categorized in two groups: the control (CTRL) group tables and tough (TOUGH) group tables.
4D-OR includes a total of 6734 scenes, recorded by six calibrated RGB-D Kinect sensors 1 mounted to the ceiling of the OR, with one frame-per-second, providing synchronized RGB and depth images. We provide fused point cloud sequences of entire scenes, automatically annotated human 6D poses and 3D bounding boxes for OR objects. Furthermore, we provide SSG annotations for each step of the surgery together with the clinical roles of all the humans in the scenes, e.g., nurse, head surgeon, anesthesiologist.
(1) provide financial news for each specific stock. (2) provide various stock technical factors and fundamental factors for each stock.
The StarCraft Multi-Agent Challenges+ requires agents to learn completion of multi-stage tasks and usage of environmental factors without precise reward functions. The previous challenges (SMAC) recognized as a standard benchmark of Multi-Agent Reinforcement Learning are mainly concerned with ensuring that all agents cooperatively eliminate approaching adversaries only through fine manipulation with obvious reward functions. This challenge, on the other hand, is interested in the exploration capability of MARL algorithms to efficiently learn implicit multi-stage tasks and environmental factors as well as micro-control. This study covers both offensive and defensive scenarios. In the offensive scenarios, agents must learn to first find opponents and then eliminate them. The defensive scenarios require agents to use topographic features. For example, agents need to position themselves behind protective structures to make it harder for enemies to attack.
Given 10 minimally contrastive (highly similar) images and a complex description for one of them, the task is to retrieve the correct image. The source of most images are videos and descriptions as well as retrievals come from human.