11/29/2023 0 Comments Ny statistics raw data setRandomly sampled color values from face images.ģ4 action units and 6 expressions labeled 24 facial landmarks labeled. Images of faces with eye positions marked. Images of public figures scrubbed from image searching. Includes semantic ratings data on emotion labels. Large database of images with labels for expressions.Ģ13 images of 7 facial expressions (6 basic facial expressions + 1 neutral) posed by 10 Japanese female models. Coordinates of features given.įaces of 15 individuals in 11 different expressions. Perceptual validation ratings provided by 319 raters.Ĭlassification, face recognition, voice recognition 8 emotions each at two intensities.įiles labelled with expression. Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS)ħ,356 video and audio recordings of 24 professional actors. The detected faces, detected and aligned faces and annotationsĪffect recognition (valence-arousal estimation, basic expression classification, action unit detection)ġ1338 images of 1199 individuals in different positions and at different times. The detected faces, facial landmarks and valence-arousal annotationsĪffect recognition (valence-arousal estimation)ĥ58 videos of 458 individuals, ~2,800,000 manually annotated images: annotated in terms of i) categorical affect (7 basic expressions: neutral, happiness, sadness, surprise, fear, disgust, anger) ii) dimensional affect (valence-arousal) iii) action units (AUs 1,2,4,6,12,15,20,25) in-the-wild setting color database various resolutions (average = 1030圆30) In computer vision, face images have been used extensively to develop facial recognition systems, face detection, and many other projects that use images of faces.Ģ98 videos of 200 individuals, ~1,250,000 manually annotated images: annotated in terms of dimensional affect (valence-arousal) in-the-wild setting color database various resolutions (average = 640x360) These datasets consist primarily of images or videos for tasks such as object detection, facial recognition, and multi-label classification. It has been suggested that this section be split out into another article titled List of datasets in computer vision and image processing. List of portals suitable for a specific subtype of applications Global Open Data Index – Open Knowledge Foundation The data portal sometimes lists a wide variety of subtypes of datasets pertaining to many machine learning applications. The open source license based data portals are known as open data portals which are used by many government organizations and academic institutions. The data portal is classified based on its type of license. Verified, In-Preparation, Deactivated(or Deprecated) Last-Hour, Last-Day, Last-Week, Last-Month, Last-Year Tabular, Graph, Text, Image, Sound, VideoĬSV, JSON, XML, KML, GeoJSON, Shapefile, GMLĬreative-Commons, GPL, Other Non-Open data licenses Mandarin Chinese, Spanish, English, Arabic, Hindi, Bengali Supranational Union, National, Subnational, Municipality, Urban, Rural List of sorting used for datasets Typeįinance, Economics, Commerce, Societal, Health, Academy, Sports, Food, Agriculture, Travel, Geospatial, Political, Consumer, Transport, Logistics, Environmental, Real-Estate, Legal, Entertainment, Energy, Hospitality The datasets are made available as various sorted types and subtypes. They are made available for searching, depositing and accessing through interfaces like Open API. The datasets are ported on open data portals. The datasets from various governmental-bodies are presented in List of open government data sites. The datasets are classified, based on the licenses, as Open data and Non-Open data. Many organizations including governments publish and share their datasets. Although they do not need to be labeled, high-quality datasets for unsupervised learning can also be difficult and costly to produce. High-quality labeled training datasets for supervised and semi-supervised machine learning algorithms are usually difficult and expensive to produce because of the large amount of time needed to label the data. Major advances in this field can result from advances in learning algorithms (such as deep learning), computer hardware, and, less-intuitively, the availability of high-quality training datasets. Datasets are an integral part of the field of machine learning. These datasets are applied for machine learning (ML) research and have been cited in peer-reviewed academic journals.
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