List of datasets in computer vision and image processing

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 Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference CreatorMNISTDatabase of grayscale handwritten digits.60,000image, labelclassification1994LeCun et al.Extended MNISTDatabase of grayscale handwritten digits and letters.810,000image, labelclassification2010NISTNYU Object Recognition Benchmark (NORB)Stereoscopic pairs of photos of toys in various orientations.Centering, perturbation.97,200 image pairs (50 uniform-colored toys under 36 angles, 9 azimuths, and 6 lighting conditions)ImagesObject recognition2004LeCun et al.80 Million Tiny Images80 million 32×32 images labelled with 75,062 non-abstract nouns.80,000,000image, label2008Torralba et al.Street View House Numbers (SVHN)630,420 digits with bounding boxes in house numbers captured in Google Street View.630,420image, label, bounding boxes2011Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, Andrew Y. Ng. "Reading Digits in Natural Images with Unsupervised Feature Learning" NIPS Workshop on Deep Learning and Unsupervised Feature Learning 2011Netzer et al.JFT-300MDataset internal to Google Research. 303M images with 375M labels in 18291 categories303,000,000image, label2017Google ResearchJFT-3BInternal to Google Research. 3 billion images, annotated with ~30k categories in a hierarchy.3,000,000,000image, label2021Google ResearchPlaces10+ million images in 400+ scene classes, with 5000 to 30,000 images per class.10,000,000image, label2018Zhou et alEgo 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 task2022K. 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 captioning2021Srinivasan e al, Google ResearchVisual GenomeImages and their description108,000images, textImage captioning2016R. 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 recognition2014Karayev, S., et al. "A category-level 3-D object dataset: putting the Kinect to work." Proceedings of the IEEE International Conference on Computer Vision Workshops. 2011.Tighe, Joseph, and Svetlana Lazebnik. "Superparsing: scalable nonparametric image parsing with superpixels ." Computer Vision–ECCV 2010. Springer Berlin Heidelberg, 2010. 352–365.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 segmentation2011University of California, BerkeleyMicrosoft Common Objects in Context (MS 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 recognition, image segmentation, keypointing, image captioning2015T. Lin et al.ImageNetLabeled object image database, used in the ImageNet Large Scale Visual Recognition ChallengeLabeled objects, bounding boxes, descriptive words, SIFT features 14,197,122Images, textObject recognition, scene recognition2009 (2014)Deng, Jia, et al. "Imagenet: A large-scale hierarchical image database."Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on. IEEE, 2009.J. Deng et al.SUN (Scene UNderstanding)Very large scene and object recognition database.Places and objects are labeled. Objects are segmented.131,067Images, textObject recognition, scene recognition2014J. Xiao et al.LSUN (Large SUN)10 scene categories (bedroom, etc) and 20 object categories (airplane, etc)Images and labels.~60 millionImages, textObject recognition, scene recognition2015Yu et al.LVIS (Large Vocabulary Instance Segmentation)segmentation masks for over 1000 entry-level object categories in images2.2 million segmentations, 164K imagesImages, segmentation masks.image segmentation masking2019Open 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)Ivan Krasin, Tom Duerig, Neil Alldrin, Andreas Veit, Sami Abu-El-Haija, Serge Belongie, David Cai, Zheyun Feng, Vittorio Ferrari, Victor Gomes, Abhinav Gupta, Dhyanesh Narayanan, Chen Sun, Gal Chechik, Kevin Murphy. "OpenImages: A public dataset for large-scale multi-label and multi-class image classification, 2017. Available from https://github.com/openimages."TV News Channel Commercial Detection DatasetTV commercials and news broadcasts.Audio and video features extracted from still images.129,685TextClustering, classification2015Vyas, Apoorv, et al. "Commercial Block Detection in Broadcast News Videos." Proceedings of the 2014 Indian Conference on Computer Vision Graphics and Image Processing. ACM, 2014.Hauptmann, Alexander G., and Michael J. Witbrock. "Story segmentation and detection of commercials in broadcast news video." Research and Technology Advances in Digital Libraries, 1998. ADL 98. Proceedings. IEEE International Forum on. IEEE, 1998.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.2310TextClassification1990Tung, Anthony KH, Xin Xu, and Beng Chin Ooi. "Curler: finding and visualizing nonlinear correlation clusters." Proceedings of the 2005 ACM SIGMOD international conference on Management of data. ACM, 2005.University of MassachusettsCaltech 101Pictures of objects.Detailed object outlines marked.9146ImagesClassification, object recognition2003Jarrett, Kevin, et al. "What is the best multi-stage architecture for object recognition?." Computer Vision, 2009 IEEE 12th International Conference on. IEEE, 2009.Lazebnik, Svetlana, Cordelia Schmid, and Jean Ponce. "Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories."Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on. Vol. 2. IEEE, 2006.F. Li et al.Caltech-256Large dataset of images for object classification.Images categorized and hand-sorted.30,607Images, TextClassification, object detection2007Griffin, G., A. Holub, and P. Perona. Caltech-256 object category dataset California Inst. Technol., Tech. Rep. 7694, 2007. Available: http://authors.library.caltech.edu/7694, 2007.Baeza-Yates, Ricardo, and Berthier Ribeiro-Neto. Modern information retrieval. Vol. 463. New York: ACM press, 1999.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-Language2022SIFT10M DatasetSIFT features of Caltech-256 dataset.Extensive SIFT feature extraction.11,164,866TextClassification, object detection2016Fu, Xiping, et al. "NOKMeans: Non-Orthogonal K-means Hashing." Computer Vision—ACCV 2014. Springer International Publishing, 2014. 162–177.X. Fu et al.LabelMeAnnotated pictures of scenes.Objects outlined.187,240Images, textClassification, object detection2005MIT Computer Science and Artificial Intelligence LaboratoryPASCAL VOC DatasetImages in 20 categories and localization bounding boxes.Labeling, bounding box included500,000Images, textClassification, object detection2010M. Everingham et al.CIFAR-10 DatasetMany small, low-resolution, images of 10 classes of objects.Classes labelled, training set splits created.60,000ImagesClassification2009Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep convolutional neural networks." Advances in neural information processing systems. 2012.Gong, Yunchao, and Svetlana Lazebnik. "Iterative quantization: A procrustean approach to learning binary codes." Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on. IEEE, 2011.A. Krizhevsky et al.CIFAR-100 DatasetLike CIFAR-10, above, but 100 classes of objects are given.Classes labelled, training set splits created.60,000ImagesClassification2009A. 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.270,000ImagesClassification2018Luke N. Darlow, Elliot J. Crowley, Antreas Antoniou, Amos J. StorkeyFashion-MNISTA MNIST-like fashion product databaseClasses labelled, training set splits created.60,000ImagesClassification2017Zalando SEnotMNISTSome 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.500,000ImagesClassification2011Yaroslav BulatovLinnaeus 5 datasetImages of 5 classes of objects.Classes labelled, training set splits created.8000ImagesClassification2017Chaladze, G., Kalatozishvili, L. (2017). Linnaeus 5 dataset. Chaladze.com. Retrieved 13 November 2017, from http://chaladze.com/l5/Chaladze & Kalatozishvili11K 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 identification2017M AfifiCORe50Specifically 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 filesClassification, Object recognition2017V. Lomonaco and D. MaltoniOpenLORIS-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 images images (.png and .pkl) and (.pkl) label filesClassification, Lifelong object recognition, Robotic Vision2019Q. 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.images and 3D point cloudsMore than 20 videos. The duration of each video is about 85 seconds (about 345 frames).AP2JExperiments with hidden object detection2019Alexei A. Morozov and Olga S. SushkovaTomatoMAPA large-scale, annotated RGB image dataset of tomato plants designed for fine-grained phenotyping.Labeling, bounding box included720,938imagesImage classification, object detection, semantic segmentation, instance segmentation2026Y. Zhang et al.

3D Objects