List of datasets in computer vision and image processing

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This is a list of datasets for machine learning research. It is part of the list of datasets for machine-learning research. These datasets consist primarily of images or videos for tasks such as object detection, facial recognition, and multi-label classification.

Object detection and recognition

Dataset NameBrief descriptionPreprocessingInstancesFormatDefault TaskCreated (updated)ReferenceCreator
80 Million Tiny Images 80 million 32×32 images labelled with 75,062 non-abstract nouns.80,000,000image, label2008 [1] Torralba et al.
JFT-300MDataset internal to Google Research. 300M images with 375M labels in 18291 categories300,000,000image, label2017 [2] Google Research
Places 10+ million images in 400+ scene classes, with 5000 to 30,000 images per class.10,000,000image, label2018 [3] Zhou et al
Ego 4DA massive-scale, egocentric dataset and benchmark suite collected across 74 worldwide locations and 9 countries, with over 3,670 hours of daily-life activity video.Object bounding boxes, transcriptions, labeling.3,670 video hoursvideo, audio, transcriptionsMultimodal first-person task2022 [4] K. Grauman et al.
Wikipedia-based Image Text Dataset37.5 million image-text examples with 11.5 million unique images across 108 Wikipedia languages.11,500,000image, captionPretraining, image captioning2021 [5] Srinivasan e al, Google Research
Visual GenomeImages and their description108,000images, textImage captioning2016 [6] R. Krishna et al.
Berkeley 3-D Object Dataset849 images taken in 75 different scenes. About 50 different object classes are labeled.Object bounding boxes and labeling.849labeled images, textObject recognition2014 [7] [8] 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.500Segmented imagesContour detection and hierarchical image segmentation2011 [9] 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.2,500,000Labeled images, textObject recognition2015 [10] [11] [12] T. Lin et al.
SUN DatabaseVery large scene and object recognition database.Places and objects are labeled. Objects are segmented.131,067Images, textObject recognition, scene recognition2014 [13] [14] 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 features14,197,122Images, textObject recognition, scene recognition2009 (2014) [15] [16] [17] J. Deng et al.
LSUNLarge-scale Scene UNderstanding. 10 scene categories (bedroom, etc) and 20 object categories (airplane, etc)Images and labels.~60 millionImages, textObject recognition, scene recognition2015 [18] [19] [20] Yu et al.
Open ImagesA 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 boxes9,178,275Images, textClassification, Object recognition2017

(V7 : 2022)

 [21]
TV News Channel Commercial Detection DatasetTV commercials and news broadcasts.Audio and video features extracted from still images.129,685TextClustering, classification2015 [22] [23] P. Guha et al.
Statlog (Image Segmentation) DatasetThe instances were drawn randomly from a database of 7 outdoor images and hand-segmented to create a classification for every pixel.Many features calculated.2310TextClassification1990 [24] University of Massachusetts
Caltech 101 Pictures of objects.Detailed object outlines marked.9146ImagesClassification, object recognition2003 [25] [26] F. Li et al.
Caltech-256Large dataset of images for object classification.Images categorized and hand-sorted.30,607Images, TextClassification, object detection2007 [27] [28] G. Griffin et al.
COYO-700MImage–text-pair dataset10 billion pairs of alt-text and image sources in HTML documents in CommonCrawl746,972,269Images, TextClassification, Image-Language2022 [29]
SIFT10M Dataset SIFT features of Caltech-256 dataset.Extensive SIFT feature extraction.11,164,866TextClassification, object detection2016 [30] X. Fu et al.
LabelMe Annotated pictures of scenes.Objects outlined.187,240Images, textClassification, object detection2005 [31] MIT Computer Science and Artificial Intelligence Laboratory
PASCAL VOC DatasetLarge number of images for classification tasks.Labeling, bounding box included500,000Images, textClassification, object detection2010 [32] [33] M. Everingham et al.
CIFAR-10 DatasetMany small, low-resolution, images of 10 classes of objects.Classes labelled, training set splits created.60,000ImagesClassification2009 [16] [34] A. Krizhevsky et al.
CIFAR-100 DatasetLike CIFAR-10, above, but 100 classes of objects are given.Classes labelled, training set splits created.60,000ImagesClassification2009 [16] [34] A. Krizhevsky et al.
CINIC-10 DatasetA 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.270,000ImagesClassification2018 [35] Luke N. Darlow, Elliot J. Crowley, Antreas Antoniou, Amos J. Storkey
Fashion-MNIST A MNIST-like fashion product databaseClasses labelled, training set splits created.60,000ImagesClassification2017 [36] Zalando SE
notMNISTSome 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.500,000ImagesClassification2011 [37] Yaroslav Bulatov
Linnaeus 5 datasetImages of 5 classes of objects.Classes labelled, training set splits created.8000ImagesClassification2017 [38] Chaladze & Kalatozishvili
11K Hands11,076 hand images (1600 x 1200 pixels) of 190 subjects, of varying ages between 18 – 75 years old, for gender recognition and biometric identification.None11,076 hand imagesImages and (.mat, .txt, and .csv) label filesGender recognition and biometric identification2017 [39] M Afifi
CORe50Specifically 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 imagesimages (.png or .pkl)

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

Classification, Object recognition2017 [40] V. Lomonaco and D. Maltoni
OpenLORIS-ObjectLifelong/Continual Robotic Vision dataset (OpenLORIS-Object) collected by real robots mounted with multiple high-resolution sensors, includes a collection of 121 object instances (1st version of dataset, 40 categories daily necessities objects under 20 scenes). The dataset has rigorously considered 4 environment factors under different scenes, including illumination, occlusion, object pixel size and clutter, and defines the difficulty levels of each factor explicitly.Classes labelled, training/validation/testing set splits created by benchmark scripts.1,106,424 RBG-D imagesimages (.png and .pkl)

and (.pkl) label files

Classification, Lifelong object recognition, Robotic Vision2019 [41] Q. She et al.
THz and thermal video data setThis 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).AP2JExperiments with hidden object detection2019 [42] [43] Alexei A. Morozov and Olga S. Sushkova

Object detection and recognition for autonomous vehicles

Dataset NameBrief descriptionPreprocessingInstancesFormatDefault TaskCreated (updated)ReferenceCreator
Cityscapes DatasetStereo video sequences recorded in street scenes, with pixel-level annotations. Metadata also included.Pixel-level segmentation and labeling25,000Images, textClassification, object detection2016 [44] Daimler AG et al.
German Traffic Sign Detection Benchmark DatasetImages 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 labeled900ImagesClassification2013 [45] [46] S. Houben et al.
KITTI Vision Benchmark DatasetAutonomous 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 dataImages, textClassification, object detection2012 [47] [48] [49] A. Geiger et al.
FieldSAFEMulti-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 dataImages and 3D point cloudsClassification, object detection, object localization2017 [50] M. Kragh et al.
Daimler Monocular Pedestrian Detection datasetIt is a dataset of pedestrians in urban environments.Pedestrians are box-wise labeled.Labeled part contains 15560 samples with pedestrians and 6744 samples without. Test set contains 21790 images without labels.ImagesObject recognition and classification2006 [51] [52] [53] Daimler AG
CamVidThe Cambridge-driving Labeled Video Database (CamVid) is a collection of videos.The dataset is labeled with semantic labels for 32 semantic classes.over 700 imagesImagesObject recognition and classification2008 [54] [55] [56] Gabriel J. Brostow, Jamie Shotton, Julien Fauqueur, Roberto Cipolla
RailSem19RailSem19 is a dataset for understanding scenes for vision systems on railways.The dataset is labeled semanticly and box-wise.8500ImagesObject recognition and classification, scene recognition2019 [57] [58] Oliver Zendel, Markus Murschitz, Marcel Zeilinger, Daniel Steininger, Sara Abbasi, Csaba Beleznai
BOREASBOREAS is a multi-season autonomous driving dataset. It includes data from includes a Velodyne Alpha-Prime (128-beam) lidar, a FLIR Blackfly S camera, a Navtech CIR304-H radar, and an Applanix POS LV GNSS-INS.The data is annotated by 3D bounding boxes.350 km of driving dataImages, Lidar and Radar dataObject recognition and classification, scene recognition2023 [59] [60] Keenan Burnett, David J. Yoon, Yuchen Wu, Andrew Zou Li, Haowei Zhang, Shichen Lu, Jingxing Qian, Wei-Kang Tseng, Andrew Lambert, Keith Y.K. Leung, Angela P. Schoellig, Timothy D. Barfoot
Bosch Small Traffic Lights DatasetIt is a dataset of traffic lights.The labeling include bounding boxes of traffic lights together with their state (active light).5000 images for training and a video sequence of 8334 frames for evaluationImagesTraffic light recognition2017 [61] [62] Karsten Behrendt, Libor Novak, Rami Botros
FRSignIt is a dataset of French railway signals.The labeling include bounding boxes of railway signals together with their state (active light).more than 100000ImagesRailway signal recognition2020 [63] [64] Jeanine Harb, Nicolas Rébéna, Raphaël Chosidow, Grégoire Roblin, Roman Potarusov, Hatem Hajri
GERALDIt is a dataset of German railway signals.The labeling include bounding boxes of railway signals together with their state (active light).5000ImagesRailway signal recognition2023 [65] [66] Philipp Leibner, Fabian Hampel, Christian Schindler
Multi-cue pedestrianMulti-cue onboard pedestrian detection dataset is a dataset for detection of pedestrians.The databaset is labeled box-wise.1092 image pairs with 1776 boxes for pedestriansImagesObject recognition and classification2009 [67] Christian Wojek, Stefan Walk, Bernt Schiele
RAWPEDRAWPED is a dataset for detection of pedestrians in the context of railways.The dataset is labeled box-wise.26000ImagesObject recognition and classification2020 [68] [69] Tugce Toprak, Burak Belenlioglu, Burak Aydın, Cuneyt Guzelis, M. Alper Selver
OSDaR23OSDaR23 is a multi-sensory dataset for detection of objects in the context of railways.The databaset is labeled box-wise.16874 framesImages, Lidar, Radar and InfraredObject recognition and classification2023 [70] [71] DZSF, Digitale Schiene Deutschland, and FusionSystems
AgroverseArgoverse is a multi-sensory dataset for detection of objects in the context of roads.The dataset is annotated box-wise.320 hours of recordingData from 7 cameras and LiDARObject recognition and classification, object tracking2022 [72] [73] Argo AI, Carnegie Mellon University, Georgia Institute of Technology

Facial recognition

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.

Dataset nameBrief descriptionPreprocessingInstancesFormatDefault taskCreated (updated)ReferenceCreator
Aff-Wild298 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)the detected faces, facial landmarks and valence-arousal annotations~1,250,000 manually annotated imagesvideo (visual + audio modalities)affect recognition (valence-arousal estimation)2017CVPR [74]

IJCV [75]

D. Kollias et al.
Aff-Wild2558 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 = 1030x630)the detected faces, detected and aligned faces and annotations~2,800,000 manually annotated imagesvideo (visual + audio modalities)affect recognition (valence-arousal estimation, basic expression classification, action unit detection)2019BMVC [76]

FG [77]

D. Kollias et al.
FERET (facial recognition technology) 11338 images of 1199 individuals in different positions and at different times.None.11,338ImagesClassification, face recognition2003 [78] [79] United States Department of Defense
Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS)7,356 video and audio recordings of 24 professional actors. 8 emotions each at two intensities.Files labelled with expression. Perceptual validation ratings provided by 319 raters.7,356Video, sound filesClassification, face recognition, voice recognition2018 [80] [81] S.R. Livingstone and F.A. Russo
SCFaceColor images of faces at various angles.Location of facial features extracted. Coordinates of features given.4,160Images, text Classification, face recognition2011 [82] [83] M. Grgic et al.
Yale Face DatabaseFaces of 15 individuals in 11 different expressions.Labels of expressions.165ImagesFace recognition1997 [84] [85] J. Yang et al.
Cohn-Kanade AU-Coded Expression DatabaseLarge database of images with labels for expressions.Tracking of certain facial features.500+ sequencesImages, textFacial expression analysis2000 [86] [87] T. Kanade et al.
JAFFE Facial Expression Database213 images of 7 facial expressions (6 basic facial expressions + 1 neutral) posed by 10 Japanese female models.Images are cropped to the facial region. Includes semantic ratings data on emotion labels.213Images, textFacial expression cognition1998 [88] [89] Lyons, Kamachi, Gyoba
FaceScrubImages of public figures scrubbed from image searching.Name and m/f annotation.107,818Images, textFace recognition2014 [90] [91] H. Ng et al.
BioID Face DatabaseImages of faces with eye positions marked.Manually set eye positions.1521Images, textFace recognition2001 [92] [93] BioID
Skin Segmentation DatasetRandomly sampled color values from face images.B, G, R, values extracted.245,057TextSegmentation, classification2012 [94] [95] R. Bhatt.
Bosphorus3D Face image database.34 action units and 6 expressions labeled; 24 facial landmarks labeled.4652

Images, text

Face recognition, classification2008 [96] [97] A Savran et al.
UOY 3D-Faceneutral face, 5 expressions: anger, happiness, sadness, eyes closed, eyebrows raised.labeling.5250

Images, text

Face recognition, classification2004 [98] [99] University of York
CASIA 3D Face DatabaseExpressions: Anger, smile, laugh, surprise, closed eyes.None.4624

Images, text

Face recognition, classification2007 [100] [101] Institute of Automation, Chinese Academy of Sciences
CASIA NIRExpressions: Anger Disgust Fear Happiness Sadness SurpriseNone.480Annotated Visible Spectrum and Near Infrared Video captures at 25 frames per secondFace recognition, classification2011 [102] Zhao, G. et al.
BU-3DFEneutral face, and 6 expressions: anger, happiness, sadness, surprise, disgust, fear (4 levels). 3D images extracted.None.2500Images, textFacial expression recognition, classification2006 [103] Binghamton University
Face Recognition Grand Challenge DatasetUp to 22 samples for each subject. Expressions: anger, happiness, sadness, surprise, disgust, puffy. 3D Data.None.4007Images, textFace recognition, classification2004 [104] [105] National Institute of Standards and Technology
GavabdbUp to 61 samples for each subject. Expressions neutral face, smile, frontal accentuated laugh, frontal random gesture. 3D images.None.549Images, textFace recognition, classification2008 [106] [107] King Juan Carlos University
3D-RMAUp to 100 subjects, expressions mostly neutral. Several poses as well.None.9971Images, textFace recognition, classification2004 [108] [109] Royal Military Academy (Belgium)
SoF112 persons (66 males and 46 females) wear glasses under different illumination conditions.A set of synthetic filters (blur, occlusions, noise, and posterization ) with different level of difficulty.42,592 (2,662 original image × 16 synthetic image)Images, Mat fileGender classification, face detection, face recognition, age estimation, and glasses detection2017 [110] [111] Afifi, M. et al.
IMDb-WIKIIMDb and Wikipedia face images with gender and age labels.None523,051ImagesGender classification, face detection, face recognition, age estimation2015 [112] R. Rothe, R. Timofte, L. V. Gool

Action recognition

Dataset nameBrief descriptionPreprocessingInstancesFormatDefault TaskCreated (updated)ReferenceCreator
AVA-Kinetics Localized Human Actions VideoAnnotated 80 action classes from keyframes from videos from Kinetics-700.1.6 million annotations. 238,906 video clips, 624,430 keyframes.Annotations, videos.Action prediction2020 [113] [114] Li et al from Perception Team of Google AI.
TV Human Interaction DatasetVideos from 20 different TV shows for prediction social actions: handshake, high five, hug, kiss and none.None.6,766 video clipsvideo clipsAction prediction2013 [115] Patron-Perez, A. et al.
Berkeley Multimodal Human Action Database (MHAD)Recordings of a single person performing 12 actionsMoCap pre-processing660 action samples8 PhaseSpace Motion Capture, 2 Stereo Cameras, 4 Quad Cameras, 6 accelerometers, 4 microphonesAction classification2013 [116] Ofli, F. et al.
THUMOS DatasetLarge video dataset for action classification.Actions classified and labeled.45M frames of videoVideo, images, textClassification, action detection2013 [117] [118] Y. Jiang et al.
MEXAction2Video dataset for action localization and spottingActions classified and labeled.1000VideoAction detection2014 [119] Stoian et al.

Handwriting and character recognition

Dataset nameBrief descriptionPreprocessingInstancesFormatDefault TaskCreated (updated)ReferenceCreator
Artificial Characters DatasetArtificially generated data describing the structure of 10 capital English letters.Coordinates of lines drawn given as integers. Various other features.6000Text Handwriting recognition, classification1992 [120] H. Guvenir et al.
Letter DatasetUpper-case printed letters.17 features are extracted from all images.20,000TextOCR, classification1991 [121] [122] D. Slate et al.
CASIA-HWDBOffline handwritten Chinese character database. 3755 classes in the GB 2312 character set.Gray-scaled images with background pixels labeled as 255.1,172,907Images, TextHandwriting recognition, classification2009 [123] CASIA
CASIA-OLHWDBOnline handwritten Chinese character database, collected using Anoto pen on paper. 3755 classes in the GB 2312 character set.Provides the sequences of coordinates of strokes.1,174,364Images, TextHandwriting recognition, classification2009 [124] [123] CASIA
Character Trajectories DatasetLabeled samples of pen tip trajectories for people writing simple characters.3-dimensional pen tip velocity trajectory matrix for each sample2858TextHandwriting recognition, classification2008 [125] [126] B. Williams
Chars74K DatasetCharacter recognition in natural images of symbols used in both English and Kannada 74,107Character recognition, handwriting recognition, OCR, classification2009 [127] T. de Campos
EMNIST datasetHandwritten characters from 3600 contributorsDerived from NIST Special Database 19. Converted to 28x28 pixel images, matching the MNIST dataset. [128] 800,000Imagescharacter recognition, classification, handwriting recognition2016EMNIST dataset [129]

Documentation [130]

Gregory Cohen, et al.
UJI Pen Characters DatasetIsolated handwritten charactersCoordinates of pen position as characters were written given.11,640TextHandwriting recognition, classification2009 [131] [132] F. Prat et al.
Gisette DatasetHandwriting samples from the often-confused 4 and 9 characters.Features extracted from images, split into train/test, handwriting images size-normalized.13,500Images, textHandwriting recognition, classification2003 [133] Yann LeCun et al.
Omniglot dataset1623 different handwritten characters from 50 different alphabets.Hand-labeled.38,300Images, text, strokesClassification, one-shot learning 2015 [134] [135] American Association for the Advancement of Science
MNIST database Database of handwritten digits.Hand-labeled.60,000Images, textClassification1994 [136] [137] National Institute of Standards and Technology
Optical Recognition of Handwritten Digits DatasetNormalized bitmaps of handwritten data.Size normalized and mapped to bitmaps.5620Images, textHandwriting recognition, classification1998 [138] E. Alpaydin et al.
Pen-Based Recognition of Handwritten Digits DatasetHandwritten digits on electronic pen-tablet.Feature vectors extracted to be uniformly spaced.10,992Images, textHandwriting recognition, classification1998 [139] [140] E. Alpaydin et al.
Semeion Handwritten Digit DatasetHandwritten digits from 80 people.All handwritten digits have been normalized for size and mapped to the same grid.1593Images, textHandwriting recognition, classification2008 [141] T. Srl
HASYv2Handwritten mathematical symbolsAll symbols are centered and of size 32px x 32px.168233Images, textClassification2017 [142] Martin Thoma
Noisy Handwritten Bangla DatasetIncludes Handwritten Numeral Dataset (10 classes) and Basic Character Dataset (50 classes), each dataset has three types of noise: white gaussian, motion blur, and reduced contrast.All images are centered and of size 32x32.Numeral Dataset:

23330,

Character Dataset:

76000

Images,

text

Handwriting recognition,

classification

2017 [143] [144] M. Karki et al.

Aerial images

Dataset nameBrief descriptionPreprocessingInstancesFormatDefault TaskCreated (updated)ReferenceCreator
iSAID: Instance Segmentation in Aerial Images DatasetPrecise instance-level annotatio carried out by professional annotators, cross-checked and validated by expert annotators complying with well-defined guidelines.655,451 (15 classes)Images, jpg, jsonAerial Classification, Object Detection, Instance Segmentation2019 [145] [146] Syed Waqas Zamir,

Aditya Arora,

Akshita Gupta,

Salman Khan,

Guolei Sun,

Fahad Shahbaz Khan, Fan Zhu,

Ling Shao, Gui-Song Xia, Xiang Bai

Aerial Image Segmentation Dataset80 high-resolution aerial images with spatial resolution ranging from 0.3 to 1.0.Images manually segmented.80ImagesAerial Classification, object detection2013 [147] [148] J. Yuan et al.
KIT AIS Data SetMultiple labeled training and evaluation datasets of aerial images of crowds.Images manually labeled to show paths of individuals through crowds.~ 150Images with pathsPeople tracking, aerial tracking2012 [149] [150] M. Butenuth et al.
Wilt DatasetRemote sensing data of diseased trees and other land cover.Various features extracted.4899ImagesClassification, aerial object detection2014 [151] [152] B. Johnson
MASATI datasetMaritime scenes of optical aerial images from the visible spectrum. It contains color images in dynamic marine environments, each image may contain one or multiple targets in different weather and illumination conditions.Object bounding boxes and labeling.7389ImagesClassification, aerial object detection2018 [153] [154] A.-J. Gallego et al.
Forest Type Mapping DatasetSatellite imagery of forests in Japan.Image wavelength bands extracted.326TextClassification2015 [155] [156] B. Johnson
Overhead Imagery Research Data Set Annotated overhead imagery. Images with multiple objects.Over 30 annotations and over 60 statistics that describe the target within the context of the image.1000Images, textClassification2009 [157] [158] F. Tanner et al.
SpaceNetSpaceNet is a corpus of commercial satellite imagery and labeled training data.GeoTiff and GeoJSON files containing building footprints.>17533ImagesClassification, Object Identification2017 [159] [160] [161] DigitalGlobe, Inc.
UC Merced Land Use DatasetThese images were manually extracted from large images from the USGS National Map Urban Area Imagery collection for various urban areas around the US.This is a 21 class land use image dataset meant for research purposes. There are 100 images for each class.2,100Image chips of 256x256, 30 cm (1 foot) GSDLand cover classification2010 [162] Yi Yang and Shawn Newsam
SAT-4 Airborne DatasetImages were extracted from the National Agriculture Imagery Program (NAIP) dataset.SAT-4 has four broad land cover classes, includes barren land, trees, grassland and a class that consists of all land cover classes other than the above three. 500,000ImagesClassification2015 [163] [164] S. Basu et al.
SAT-6 Airborne DatasetImages were extracted from the National Agriculture Imagery Program (NAIP) dataset.SAT-6 has six broad land cover classes, includes barren land, trees, grassland, roads, buildings and water bodies.405,000ImagesClassification2015 [163] [164] S. Basu et al.

Underwater images

Dataset nameBrief descriptionPreprocessingInstancesFormatDefault TaskCreated (updated)ReferenceCreator
SUIM DatasetThe images have been rigorously collected during oceanic explorations and human-robot collaborative experiments, and annotated by human participants.Images with pixel annotations for eight object categories: fish (vertebrates), reefs (invertebrates), aquatic plants, wrecks/ruins, human divers, robots, and sea-floor.1,635ImagesSegmentation2020 [165] Md Jahidul Islam et al.
LIACI DatasetImages have been collected during underwater ship inspections and annotated by human domain experts.Images with pixel annotations for ten object categories: defects, corrosion, paint peel, marine growth, sea chest gratings, overboard valves, propeller, anodes, bilge keel and ship hull.1,893ImagesSegmentation2022 [166] Waszak et al.

Other images

Dataset nameBrief descriptionPreprocessingInstancesFormatDefault TaskCreated (updated)ReferenceCreator
NRC-GAMMAA novel benchmark gas meter image datasetNone28,883Image, LabelClassification2021 [167] [168] A. Ebadi, P. Paul, S. Auer, & S. Tremblay
The SUPATLANTIQUE datasetImages of scanned official and Wikipedia documentsNone4908TIFF/pdfSource device identification, forgery detection, Classification,..2020 [169] C. Ben Rabah et al.
Density functional theory quantum simulations of grapheneLabelled images of raw input to a simulation of grapheneRaw data (in HDF5 format) and output labels from density functional theory quantum simulation60744 test and 501473 training filesLabeled imagesRegression2019 [170] K. Mills & I. Tamblyn
Quantum simulations of an electron in a two dimensional potential wellLabelled images of raw input to a simulation of 2d Quantum mechanicsRaw data (in HDF5 format) and output labels from quantum simulation1.3 million imagesLabeled imagesRegression2017 [171] K. Mills, M.A. Spanner, & I. Tamblyn
MPII Cooking Activities DatasetVideos and images of various cooking activities.Activity paths and directions, labels, fine-grained motion labeling, activity class, still image extraction and labeling.881,755 framesLabeled video, images, textClassification2012 [172] [173] M. Rohrbach et al.
FAMOS Dataset5,000 unique microstructures, all samples have been acquired 3 times with two different cameras.Original PNG files, sorted per camera and then per acquisition. MATLAB datafiles with one 16384 times 5000 matrix per camera per acquisition.30,000Images and .mat filesAuthentication2012 [174] S. Voloshynovskiy, et al.
PharmaPack Dataset1,000 unique classes with 54 images per class.Class labeling, many local descriptors, like SIFT and aKaZE, and local feature agreators, like Fisher Vector (FV).54,000Images and .mat filesFine-grain classification2017 [175] O. Taran and S. Rezaeifar, et al.
Stanford Dogs DatasetImages of 120 breeds of dogs from around the world.Train/test splits and ImageNet annotations provided.20,580Images, textFine-grain classification2011 [176] [177] A. Khosla et al.
StanfordExtra Dataset2D keypoints and segmentations for the Stanford Dogs Dataset.2D keypoints and segmentations provided.12,035Labelled images3D reconstruction/pose estimation2020 [178] B. Biggs et al.
The Oxford-IIIT Pet Dataset37 categories of pets with roughly 200 images of each.Breed labeled, tight bounding box, foreground-background segmentation.~ 7,400Images, textClassification, object detection2012 [177] [179] O. Parkhi et al.
Corel Image Features Data SetDatabase of images with features extracted.Many features including color histogram, co-occurrence texture, and colormoments,68,040TextClassification, object detection1999 [180] [181] M. Ortega-Bindenberger et al.
Online Video Characteristics and Transcoding Time Dataset.Transcoding times for various different videos and video properties.Video features given.168,286TextRegression2015 [182] T. Deneke et al.
Microsoft Sequential Image Narrative Dataset (SIND)Dataset for sequential vision-to-languageDescriptive caption and storytelling given for each photo, and photos are arranged in sequences81,743Images, textVisual storytelling2016 [183] Microsoft Research
Caltech-UCSD Birds-200-2011 DatasetLarge dataset of images of birds.Part locations for birds, bounding boxes, 312 binary attributes given11,788Images, textClassification2011 [184] [185] C. Wah et al.
YouTube-8MLarge and diverse labeled video datasetYouTube video IDs and associated labels from a diverse vocabulary of 4800 visual entities8 millionVideo, textVideo classification2016 [186] [187] S. Abu-El-Haija et al.
YFCC100MLarge and diverse labeled image and video datasetFlickr Videos and Images and associated description, titles, tags, and other metadata (such as EXIF and geotags)100 millionVideo, Image, TextVideo and Image classification2016 [188] [189] B. Thomee et al.
Discrete LIRIS-ACCEDEShort videos annotated for valence and arousal.Valence and arousal labels.9800VideoVideo emotion elicitation detection2015 [190] Y. Baveye et al.
Continuous LIRIS-ACCEDELong videos annotated for valence and arousal while also collecting Galvanic Skin Response.Valence and arousal labels.30VideoVideo emotion elicitation detection2015 [191] Y. Baveye et al.
MediaEval LIRIS-ACCEDEExtension of Discrete LIRIS-ACCEDE including annotations for violence levels of the films.Violence, valence and arousal labels.10900VideoVideo emotion elicitation detection2015 [192] Y. Baveye et al.
Leeds Sports PoseArticulated human pose annotations in 2000 natural sports images from Flickr.Rough crop around single person of interest with 14 joint labels2000Images plus .mat file labelsHuman pose estimation2010 [193] S. Johnson and M. Everingham
Leeds Sports Pose Extended TrainingArticulated human pose annotations in 10,000 natural sports images from Flickr.14 joint labels via crowdsourcing10000Images plus .mat file labelsHuman pose estimation2011 [194] S. Johnson and M. Everingham
MCQ Dataset6 different real multiple choice-based exams (735 answer sheets and 33,540 answer boxes) to evaluate computer vision techniques and systems developed for multiple choice test assessment systems.None735 answer sheets and 33,540 answer boxesImages and .mat file labelsDevelopment of multiple choice test assessment systems2017 [195] [196] Afifi, M. et al.
Surveillance VideosReal surveillance videos cover a large surveillance time (7 days with 24 hours each).None19 surveillance videos (7 days with 24 hours each).VideosData compression2016 [197] Taj-Eddin, I. A. T. F. et al.
LILA BCLabeled Information Library of Alexandria: Biology and Conservation. Labeled images that support machine learning research around ecology and environmental science.None~10M imagesImagesClassification2019 [198] LILA working group
Can We See Photosynthesis?32 videos for eight live and eight dead leaves recorded under both DC and AC lighting conditions.None32 videosVideosLiveness detection of plants2017 [199] Taj-Eddin, I. A. T. F. et al.
Mathematical Mathematics MemesCollection of 10,000 memes on mathematics.None~10,000ImagesVisual storytelling, object detection.2021 [200] Mathematical Mathematics Memes
Flickr-Faces-HQ DatasetCollection of images containing a face each, crawled from FlickrPruned with "various automatic filters", cropped and aligned to faces, and had images of statues, paintings, or photos of photos removed via crowdsourcing70,000ImagesFace Generation2019 [201] Karras et al.
Fruits-360 datasetDatabase with images of 131 fruits, vegetables and nuts.100x100 pixels, white background.90483Images (jpg)Classification2017–2024 [202] Mihai Oltean

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