ImageNet (original) (raw)

News and updates

March 11 2021: ImageNet website update and a new paper on privacy preservation.
October 10, 2019: The ILSVRC 2012 classification and localization test set has been updated. The Kaggle challenge and our download page both now contain the updated data.
September 21, 2019: ImageNet 10th Birthday Party
September 17, 2019: Research update on filtering and balancing the ImageNet person subtree
June 2019: ImageNet wins the PAMI Longuet-Higgins Prize (Retrospective Most Impactful Paper from CVPR 2009)
July 2017: ImageNet: Where have we been? Where are we going? at a CVPR 2017 workshop.
July 2017: ImageNet is covered by Quartz.
June 2, 2015: Follow-up update regarding status of the server
May 19, 2015: Annoucement regarding the submission server

Overview

Welcome to the ImageNet project! ImageNet is an ongoing research effort to provide researchers around the world with image data for training large-scale object recognition models.

What is ImageNet?

ImageNet is an image dataset organized according to the WordNet hierarchy. Each meaningful concept in WordNet, possibly described by multiple words or word phrases, is called a "synonym set" or "synset". There are more than 100,000 synsets in WordNet; the majority of them are nouns (80,000+). In ImageNet, we aim to provide on average 1000 images to illustrate each synset. Images of each concept are quality-controlled and human-annotated. In its completion, we hope ImageNet will offer tens of millions of cleanly labeled and sorted images for most of the concepts in the WordNet hierarchy.

Why ImageNet?

The ImageNet project was inspired by two important needs in computer vision research. The first was the need to establish a clear North Star problem in computer vision. While the field enjoyed an abundance of important tasks to work on, from stereo vision to image retrieval, from 3D reconstruction to image segmentation, object categorization was recognized to be one of the most fundamental capabilities of both human and machine vision. Hence there was a growing demand for a high quality object categorization benchmark with clearly established evaluation metrics. Second, there was a critical need for more data to enable more generalizable machine learning methods. Ever since the birth of the digital era and the availability of web-scale data exchanges, researchers in these fields have been working hard to design more and more sophisticated algorithms to index, retrieve, organize and annotate multimedia data. But good research requires good resources. To tackle this problem at scale (think of your growing personal collection of digital images, or videos, or a commercial web search engine’s database), it was critical to provide researchers with a large-scale image database for both training and testing. The convergence of these two intellectual reasons motivated us to build ImageNet.

Does ImageNet own the images? Can I download the images?

No, ImageNet does not own the copyright of the images. ImageNet only compiles an accurate list of web images for each synset of WordNet. For researchers and educators who wish to use the images for non-commercial research and/or educational purposes, we can provide access through our site under certain conditions and terms. For details click here.

Research Team

Senior Research Team

Prof. Li Fei-Fei, PI, Stanford University
Prof. Jia Deng, Princeton University
Prof. Olga Russakovsky, Princeton University
Prof. Alex Berg, UNC Chapel Hill, Facebook, Shopagon
Prof. Kai Li, Princeton University For students, advisors, and other contributors to the project please see the list of publications below.

Citations and publications

Publications

Kaiyu Yang, Klint Qinami, Li Fei-Fei, Jia Deng and Olga Russakovsky. Towards Fairer Datasets: Filtering and Balancing the Distribution of the People Subtree in the ImageNet Hierarchy. Conference on Fairness, Accountabiility and Transparency (FAT*), 2020.paper |bibtex |project |blog
Olga Russakovsky*, Jia Deng*, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg and Li Fei-Fei. (* = equal contribution) ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision, 2015.paper |bibtex |paper content on arxiv |attribute annotations
J. Deng, O. Russakovsky, J. Krause, M. Bernstein, A. Berg, L. Fei-Fei. Scalable multi-label annotation. ACM conference on human factors in computing (CHI), 2014.pdf |bibtex |slides
O. Russakovsky, J. Deng, Z. Huang, A. Berg and L. Fei-Fei, Detecting avocados to zucchinis: what have we done, and where are we going?,Proceedings of the International Conference of Computer Vision (ICCV). 2013. pdf |supplement |website |BibTex
J. Deng, A. Berg, K. Li and L. Fei-Fei, What does classifying more than 10,000 image categories tell us? Proceedings of the 12th European Conference of Computer Vision (ECCV). 2010. pdf | BibTex
O. Russakovsky and L. Fei-Fei, Attribute Learning in Large-scale Datasets. Proceedings of the 12th European Conference of Computer Vision (ECCV), 1st International Workshop on Parts and Attributes. 2010. pdf | Bibtex | slides | data
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li and L. Fei-Fei, ImageNet: A Large-Scale Hierarchical Image Database. IEEE Computer Vision and Pattern Recognition (CVPR), 2009. pdf | BibTex
J. Deng, K. Li, M. Do, H. Su, L. Fei-Fei, Construction and Analysis of a Large Scale Image Ontology. In Vision Sciences Society (VSS), 2009. pdf | BibTex

Presentation and Slides

L. Fei-Fei and J. Deng. ImageNet: Where have we been? Where are we going?, CVPR Beyond ImageNet Large Scale Visual Recognition Challenge workshop, 2017, pdf
L. Fei-Fei and O. Russakovsky, Analysis of Large-Scale Visual Recognition, Bay Area Vision Meeting, October, 2013, pptx | pdf
L. Fei-Fei, ImageNet: crowdsourcing, benchmarking & other cool things, CMU VASC Seminar, March, 2010, ppt | pdf