Список наборів даних для досліджень з машинного навчання

Набори даних використовується для досліджень в області машинного навчання, посилання на них використовуються в наукових академічних статтях. Набори даних орієнтовані, здебільшого, на вирішення задач класифікації та розпізнавання і містять оцифровані зображення, відео, тексти, сигнали, звуки тощо.

Зображення

Розпізнавання осіб

Лицьові зображення широко використовуються для розробки систем машинного зору та розпізнавання осіб та пов'язаних з класифікацією зображень задачах.

Назва Опис Обробка Розмір Формат Задачі Створений Посилання Джерело
Face Recognition Technology (FERET) 11338 images of 1199 individuals in different positions and at different times. None. 11,338 Images Classification, face recognition 2003 [1][2] United States Department of Defense
CMU Pose, Illumination, and Expression (PIE) 41,368 color images of 68 people in 13 different poses. Images labeled with expressions. 41,368 Images, text Classification, face recognition 2000 [3][4] R. Gross et al.
SCFace Color images of faces at various angles. Location of facial features extracted. Coordinates of features given. 4,160 Images, text Classification, face recognition 2011 [5][6] M. Grgic et al.
YouTube Faces DB Videos of 1,595 different people gathered from YouTube. Each clip is between 48 and 6,070 frames. Identity of those appearing in videos and descriptors. 3,425 videos Video, text Video classification, face recognition 2011 [7][8] L. Wolf et al.
300 videos in-the-Wild 114 videos annotated for facial landmark tracking. The 68 landmark mark-up is applied to every frame. None 114 videos, 218,000 frames. Video, annotation file. Facial landmark tracking. 2015 [9] Shen, Jie et al.
Grammatical Facial Expressions Dataset Grammatical Facial Expressions from Brazilian Sign Language. Microsoft Kinect features extracted. 27,965 Text Facial gesture recognition 2014 [10] F. Freitas et al.
CMU Face Images Dataset Images of faces. Each person is photographed multiple times to capture different expressions. Labels and features. 640 Images, Text Face recognition 1999 [11][12] T. Mitchell
Yale Face Database Faces of 15 individuals in 11 different expressions. Labels of expressions. 165 Images Face recognition 1997 [13][14] J. Yang et al.
Cohn-Kanade AU-Coded Expression Database Large database of images with labels for expressions. Tracking of certain facial features. 500+ sequences Images, text Facial expression analysis 2000 [15][16] T. Kanade et al.
FaceScrub Images of public figures scrubbed from image searching. Name and m/f annotation. 107,818 Images, text Face recognition 2014 [17][18] H. Ng et al.
BioID Face Database Images of faces with eye positions marked. Manually set eye positions. 1521 Images, text Face recognition 2001 [19][20] BioID
Skin Segmentation Dataset Randomly sampled color values from face images. B, G, R, values extracted. 245,057 Text Segmentation, classification 2012 [21][22] R. Bhatt.
Bosphorus 3D Face image database. 34 action units and 6 expressions labeled; 24 facial landmarks labeled. 4652

Images, text

 Face recognition, classification 2008 [23][24] A Savran et al.
UOY 3D-Face neutral face, 5 expressions: anger, happiness, sadness, eyes closed, eyebrows raised. labeling. 5250

Images, text

 Face recognition, classification 2004 [25][26] University of York
CASIA Expressions: Anger, smile, laugh, surprise, closed eyes. None. 4624

Images, text

 Face recognition, classification 2007 [27][28] Institute of Automation, Chinese Academy of Sciences
CASIA Expressions: Anger Disgust Fear Happiness Sadness Surprise None. 480 Annotated Visible Spectrum and Near Infrared Video captures at 25 frames per second Face recognition, classification 2011 [29] Zhao, G. et al.
BU-3DFE neutral face, and 6 expressions: anger, happiness, sadness, surprise, disgust, fear (4 levels). 3D images extracted. None. 2500 Images, text Facial expression recognition, classification 2006 [30] Binghamton University
Face Recognition Grand Challenge Dataset Up to 22 samples for each subject. Expressions: anger, happiness, sadness, surprise, disgust, puffy. 3D Data. None. 4007 Images, text Face recognition, classification 2004 [31][32] National Institute of Standards and Technology
Gavabdb Up to 61 samples for each subject. Expressions neutral face, smile, frontal accentuated laugh, frontal random gesture. 3D images. None. 549 Images, text Face recognition, classification 2008 [33][34] King Juan Carlos University
3D-RMA Up to 100 subjects, expressions mostly neutral. Several poses as well. None. 9971 Images, text Face recognition, classification 2004 [35][36] Royal Military Academy (Belgium)

Виявлення та розпізнавання об'єктів
Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
Visual Genome Images and their description 108,000 images, text Image captioning 2016 [37] R. Krishna et al.
DAVIS: Densely Annotated VIdeo Segmentation 2017 150 video sequences containing 10459 frames with a total of 376 objects annotated. Dataset released for the 2017 DAVIS Challenge with a dedicated workshop co-located with CVPR 2017. The videos contain several types of objects and humans with a high quality segmentation annotation.In each video sequence multiple instances are annotated. 10,459 Frames annotated Video object segmentation 2017 [38] Pont-Tuset, J. et al.
DAVIS: Densely Annotated VIdeo Segmentation 2016 50 video sequences containing 3455 frames with a total of 50 objects annotated. Dataset released with the CVPR 2016 paper. The videos contain several types of objects and humans with a high quality segmentation annotation. In each video sequence a single instance is annotated. 3,455 Frames annotated Video object segmentation 2016 [39] Perazzi, F. et al.
T-LESS: An RGB-D Dataset for 6D Pose Estimation of Texture-less Objects 30 industry-relevant objects. 39K training and 10K test images from each of three sensors. Two types of 3D models for each object. 6D poses for all modeled objects in all images. Per-pixel labelling can be obtained by rendering of the object models at the ground truth poses. 49,000 RGB-D images, 3D object models 6D object pose estimation, object detection 2017 [40] T. Hodan et al.
Berkeley 3-D Object Dataset 849 images taken in 75 different scenes. About 50 different object classes are labeled. Object bounding boxes and labeling. 849 labeled images, text Object recognition 2014 [41][42] A. Janoch et al.
Berkeley Segmentation Data Set and Benchmarks 500 (BSDS500) 500 natural images, explicitly separated into disjoint train, validation and test subsets + benchmarking code. Based on BSDS300. Each image segmented by five different subjects on average. 500 Segmented images Contour detection and hierarchical image segmentation 2011 [43] University of California, Berkeley
Microsoft Common Objects in Context (COCO) complex everyday scenes of common objects in their natural context. Object highlighting, labeling, and classification into 91 object types. 2,500,000 Labeled images, text Object recognition 2015 [44][45][46] T. Lin et al.
SUN Database Very large scene and object recognition database. Places and objects are labeled. Objects are segmented. 131,067 Images, text Object recognition, scene recognition 2014 [47][48] J. Xiao et al.
ImageNet Labeled object image database, used in the ImageNet Large Scale Visual Recognition Challenge Labeled objects, bounding boxes, descriptive words, SIFT features 14,197,122 Images, text Object recognition, scene recognition 2009 (2014) [49][50][51] J. Deng et al.
Open Images A Large set of images listed as having CC BY 2.0 license with image-level labels and bounding boxes spanning thousands of classes. Image-level labels, Bounding boxes 9,178,275 Images, text Classification, Object recognition 2017 [52]
TV News Channel Commercial Detection Dataset TV commercials and news broadcasts. Audio and video features extracted from still images. 129,685 Text Clustering, classification 2015 [53][54] P. Guha et al.
Statlog (Image Segmentation) Dataset The instances were drawn randomly from a database of 7 outdoor images and hand-segmented to create a classification for every pixel. Many features calculated. 2310 Text Classification 1990 [55] University of Massachusetts
Caltech 101 Pictures of objects. Detailed object outlines marked. 9146 Images Classification, object recognition. 2003 [56][57] F. Li et al.
Caltech-256 Large dataset of images for object classification. Images categorized and hand-sorted. 30,607 Images, Text Classification, object detection 2007 [58][59] G. Griffin et al.
SIFT10M Dataset SIFT features of Caltech-256 dataset. Extensive SIFT feature extraction. 11,164,866 Text Classification, object detection 2016 [60] X. Fu et al.
LabelMe Annotated pictures of scenes. Objects outlined. 187,240 Images, text Classification, object detection 2005 [61] MIT Computer Science and Artificial Intelligence Laboratory
Cityscapes Dataset Stereo video sequences recorded in street scenes, with pixel-level annotations. Metadata also included. Pixel-level segmentation and labeling 25,000 Images, text Classification, object detection 2016 [62] Daimler AG et al.
PASCAL VOC Dataset Large number of images for classification tasks. Labeling, bounding box included 500,000 Images, text Classification, object detection 2010 [63][64] M. Everingham et al.
CIFAR-10 Dataset Many small, low-resolution, images of 10 classes of objects. Classes labelled, training set splits created. 60,000 Images Classification 2009 [50][65] A. Krizhevsky et al.
CIFAR-100 Dataset Like CIFAR-10, above, but 100 classes of objects are given. Classes labelled, training set splits created. 60,000 Images Classification 2009 [50][65] A. Krizhevsky et al.
CINIC-10 Dataset A unified contribution of CIFAR-10 and Imagenet with 10 classes, and 3 splits. Larger than CIFAR-10. Classes labelled, training, validation, test set splits created. 270,000 Images Classification 2018 [66] Luke N. Darlow, Elliot J. Crowley, Antreas Antoniou, Amos J. Storkey
Fashion-MNIST A MNIST-like fashion product database Classes labelled, training set splits created. 60,000 Images Classification 2017 [67] Zalando SE
notMNIST Some publicly available fonts and extracted glyphs from them to make a dataset similar to MNIST. There are 10 classes, with letters A-J taken from different fonts. Classes labelled, training set splits created. 500,000 Images Classification 2011 [68] Yaroslav Bulatov
German Traffic Sign Detection Benchmark Dataset Images from vehicles of traffic signs on German roads. These signs comply with UN standards and therefore are the same as in other countries. Signs manually labeled 900 Images Classification 2013 [69][70] S Houben et al.
KITTI Vision Benchmark Dataset Autonomous vehicles driving through a mid-size city captured images of various areas using cameras and laser scanners. Many benchmarks extracted from data. >100 GB of data Images, text Classification, object detection 2012 [71][72][73] A Geiger et al.
Linnaeus 5 dataset Images of 5 classes of objects. Classes labelled, training set splits created. 8000 Images Classification 2017 [74] Chaladze & Kalatozishvili
FieldSAFE Multi-modal dataset for obstacle detection in agriculture including stereo camera, thermal camera, web camera, 360-degree camera, lidar, radar, and precise localization. Classes labelled geographically. >400 GB of data Images and 3D point clouds Classification, object detection, object localization 2017 [75] M. Kragh et al.
11K Hands 11,076 hand images (1600 x 1200 pixels) of 190 subjects, of varying ages between 18 – 75 years old, for gender recognition and biometric identification. None 11,076 hand images Images and (.mat, .txt, and .csv) label files Gender recognition and biometric identification 2017 [76] M Afifi
CORe50 Specifically designed for Continuous/Lifelong Learning and Object Recognition, is a collection of more than 500 videos (30fps) of 50 domestic objects belonging to 10 different categories. Classes labelled, training set splits created based on a 3-way, multi-runs benchmark. 164,866 RBG-D images images (.png or .pkl)

and (.pkl, .txt, .tsv) label files

 Classification, Object recognition 2017 [77] V. Lomonaco and D. Maltoni
THz and thermal video data set This multispectral data set includes terahertz, thermal, visual, near infrared, and three-dimensional videos of objects hidden under people's clothes. 3D lookup tables are provided that allow you to project images onto 3D point clouds. More than 20 videos. The duration of each video is about 85 seconds (about 345 frames). AP2J Experiments with hidden object detection 2019 [78][79] Alexei A. Morozov and Olga S. Sushkova

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