ImageNet

Last updated

The ImageNet project is a large visual database designed for use in visual object recognition software research. More than 14 million [1] [2] images have been hand-annotated by the project to indicate what objects are pictured and in at least one million of the images, bounding boxes are also provided. [3] ImageNet contains more than 20,000 categories, [2] with a typical category, such as "balloon" or "strawberry", consisting of several hundred images. [4] The database of annotations of third-party image URLs is freely available directly from ImageNet, though the actual images are not owned by ImageNet. [5] Since 2010, the ImageNet project runs an annual software contest, the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), where software programs compete to correctly classify and detect objects and scenes. The challenge uses a "trimmed" list of one thousand non-overlapping classes. [6]

Contents

Significance for deep learning

On 30 September 2012, a convolutional neural network (CNN) called AlexNet [7] achieved a top-5 error of 15.3% in the ImageNet 2012 Challenge, more than 10.8 percentage points lower than that of the runner up. Using convolutional neural networks was feasible due to the use of graphics processing units (GPUs) during training, [7] an essential ingredient of the deep learning revolution. According to The Economist , "Suddenly people started to pay attention, not just within the AI community but across the technology industry as a whole." [4] [8] [9]

In 2015, AlexNet was outperformed by Microsoft's very deep CNN with over 100 layers, which won the ImageNet 2015 contest. [10]

History of the database

AI researcher Fei-Fei Li began working on the idea for ImageNet in 2006. At a time when most AI research focused on models and algorithms, Li wanted to expand and improve the data available to train AI algorithms. [11] In 2007, Li met with Princeton professor Christiane Fellbaum, one of the creators of WordNet, to discuss the project. As a result of this meeting, Li went on to build ImageNet starting from the word database of WordNet and using many of its features. [12]

As an assistant professor at Princeton, Li assembled a team of researchers to work on the ImageNet project. They used Amazon Mechanical Turk to help with the classification of images. [12]

They presented their database for the first time as a poster at the 2009 Conference on Computer Vision and Pattern Recognition (CVPR) in Florida. [12] [13] [14]

Dataset

ImageNet crowdsources its annotation process. Image-level annotations indicate the presence or absence of an object class in an image, such as "there are tigers in this image" or "there are no tigers in this image". Object-level annotations provide a bounding box around the (visible part of the) indicated object. ImageNet uses a variant of the broad WordNet schema to categorize objects, augmented with 120 categories of dog breeds to showcase fine-grained classification. [6] One downside of WordNet use is the categories may be more "elevated" than would be optimal for ImageNet: "Most people are more interested in Lady Gaga or the iPod Mini than in this rare kind of diplodocus."[ clarification needed ] In 2012 ImageNet was the world's largest academic user of Mechanical Turk. The average worker identified 50 images per minute. [2]

Subsets of the dataset

There are various subsets of the ImageNet dataset used in various context. One of the most highly used subset of ImageNet is the "ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2012-2017 image classification and localization dataset". This is also referred to in the research literature as ImageNet-1K or ILSVRC2017, reflecting the original ILSVRC challenge that involved 1,000 classes. ImageNet-1K contains 1,281,167 training images, 50,000 validation images and 100,000 test images. [15] The full original dataset is referred to as ImageNet-21K. ImageNet-21k contains 14,197,122 images divided into 21,841 classes. Some papers round this up and name it ImageNet-22k. [16]

History of the ImageNet challenge

Error rate history on ImageNet (showing best result per team and up to 10 entries per year) ImageNet error rate history (just systems).svg
Error rate history on ImageNet (showing best result per team and up to 10 entries per year)

The ILSVRC aims to "follow in the footsteps" of the smaller-scale PASCAL VOC challenge, established in 2005, which contained only about 20,000 images and twenty object classes. [6] To "democratize" ImageNet, Fei-Fei Li proposed to the PASCAL VOC team a collaboration, beginning in 2010, where research teams would evaluate their algorithms on the given data set, and compete to achieve higher accuracy on several visual recognition tasks. [12]

The resulting annual competition is now known as the ImageNet Large Scale Visual Recognition Challenge (ILSVRC). The ILSVRC uses a "trimmed" list of only 1000 image categories or "classes", including 90 of the 120 dog breeds classified by the full ImageNet schema. [6] The 2010s saw dramatic progress in image processing. Around 2011, a good ILSVRC classification top-5 error rate was 25%. In 2012, a deep convolutional neural net called AlexNet achieved 16%; in the next couple of years, top-5 error rates fell to a few percent. [17] While the 2012 breakthrough "combined pieces that were all there before", the dramatic quantitative improvement marked the start of an industry-wide artificial intelligence boom. [4] By 2015, researchers at Microsoft reported that their CNNs exceeded human ability at the narrow ILSVRC tasks. [10] [18] However, as one of the challenge's organizers, Olga Russakovsky, pointed out in 2015, the programs only have to identify images as belonging to one of a thousand categories; humans can recognize a larger number of categories, and also (unlike the programs) can judge the context of an image. [19]

By 2014, more than fifty institutions participated in the ILSVRC. [6] In 2017, 29 of 38 competing teams had greater than 95% accuracy. [20] In 2017 ImageNet stated it would roll out a new, much more difficult challenge in 2018 that involves classifying 3D objects using natural language. Because creating 3D data is more costly than annotating a pre-existing 2D image, the dataset is expected to be smaller. The applications of progress in this area would range from robotic navigation to augmented reality. [1]

Bias in ImageNet

A study of the history of the multiple layers (taxonomy, object classes and labeling) of ImageNet and WordNet in 2019 described how bias [ clarification needed ] is deeply embedded in most classification approaches for of all sorts of images. [21] [22] [23] [24] ImageNet is working to address various sources of bias. [25]

See also

Related Research Articles

Computer vision tasks include methods for acquiring, processing, analyzing and understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information, e.g. in the forms of decisions. Understanding in this context means the transformation of visual images into descriptions of the world that make sense to thought processes and can elicit appropriate action. This image understanding can be seen as the disentangling of symbolic information from image data using models constructed with the aid of geometry, physics, statistics, and learning theory.

<span class="mw-page-title-main">Automatic image annotation</span>

Automatic image annotation is the process by which a computer system automatically assigns metadata in the form of captioning or keywords to a digital image. This application of computer vision techniques is used in image retrieval systems to organize and locate images of interest from a database.

In computer vision, the bag-of-words model sometimes called bag-of-visual-words model can be applied to image classification or retrieval, by treating image features as words. In document classification, a bag of words is a sparse vector of occurrence counts of words; that is, a sparse histogram over the vocabulary. In computer vision, a bag of visual words is a vector of occurrence counts of a vocabulary of local image features.

Object recognition – technology in the field of computer vision for finding and identifying objects in an image or video sequence. Humans recognize a multitude of objects in images with little effort, despite the fact that the image of the objects may vary somewhat in different view points, in many different sizes and scales or even when they are translated or rotated. Objects can even be recognized when they are partially obstructed from view. This task is still a challenge for computer vision systems. Many approaches to the task have been implemented over multiple decades.

Caltech 101 is a data set of digital images created in September 2003 and compiled by Fei-Fei Li, Marco Andreetto, Marc 'Aurelio Ranzato and Pietro Perona at the California Institute of Technology. It is intended to facilitate Computer Vision research and techniques and is most applicable to techniques involving image recognition classification and categorization. Caltech 101 contains a total of 9,146 images, split between 101 distinct object categories and a background category. Provided with the images are a set of annotations describing the outlines of each image, along with a Matlab script for viewing.

In computer vision, the problem of object categorization from image search is the problem of training a classifier to recognize categories of objects, using only the images retrieved automatically with an Internet search engine. Ideally, automatic image collection would allow classifiers to be trained with nothing but the category names as input. This problem is closely related to that of content-based image retrieval (CBIR), where the goal is to return better image search results rather than training a classifier for image recognition.

<span class="mw-page-title-main">Object detection</span> Computer technology related to computer vision and image processing

Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class in digital images and videos. Well-researched domains of object detection include face detection and pedestrian detection. Object detection has applications in many areas of computer vision, including image retrieval and video surveillance.

<span class="mw-page-title-main">Deep learning</span> Branch of machine learning

Deep learning is the subset of machine learning methods based on artificial neural networks with representation learning. The adjective "deep" refers to the use of multiple layers in the network. Methods used can be either supervised, semi-supervised or unsupervised.

<span class="mw-page-title-main">WikiArt</span> User-generated website displaying artworks

WikiArt is a visual art wiki, active since 2010.

<span class="mw-page-title-main">MNIST database</span> Database of handwritten digits

The MNIST database is a large database of handwritten digits that is commonly used for training various image processing systems. The database is also widely used for training and testing in the field of machine learning. It was created by "re-mixing" the samples from NIST's original datasets. The creators felt that since NIST's training dataset was taken from American Census Bureau employees, while the testing dataset was taken from American high school students, it was not well-suited for machine learning experiments. Furthermore, the black and white images from NIST were normalized to fit into a 28x28 pixel bounding box and anti-aliased, which introduced grayscale levels.

Convolutional neural network (CNN) is a regularized type of feed-forward neural network that learns feature engineering by itself via filters optimization. Vanishing gradients and exploding gradients, seen during backpropagation in earlier neural networks, are prevented by using regularized weights over fewer connections. For example, for each neuron in the fully-connected layer 10,000 weights would be required for processing an image sized 100 × 100 pixels. However, applying cascaded convolution kernels, only 25 neurons are required to process 5x5-sized tiles. Higher-layer features are extracted from wider context windows, compared to lower-layer features.

<span class="mw-page-title-main">Fei-Fei Li</span> American computer scientist (born 1976)

Fei-Fei Li is a China-born American computer scientist, known for establishing ImageNet, the dataset that enabled rapid advances in computer vision in the 2010s. She is the Sequoia Capital Professor of Computer Science at Stanford University and former board director at Twitter. Li is a Co-Director of the Stanford Institute for Human-Centered Artificial Intelligence, and a Co-Director of the Stanford Vision and Learning Lab. She served as the director of the Stanford Artificial Intelligence Laboratory (SAIL) from 2013 to 2018.

<span class="mw-page-title-main">DeepDream</span> Software program

DeepDream is a computer vision program created by Google engineer Alexander Mordvintsev that uses a convolutional neural network to find and enhance patterns in images via algorithmic pareidolia, thus creating a dream-like appearance reminiscent of a psychedelic experience in the deliberately overprocessed images.

<span class="mw-page-title-main">AlexNet</span> Convolutional neural network

AlexNet is the name of a convolutional neural network (CNN) architecture, designed by Alex Krizhevsky in collaboration with Ilya Sutskever and Geoffrey Hinton, who was Krizhevsky's Ph.D. advisor at the University of Toronto.

<span class="mw-page-title-main">Neural style transfer</span> Type of software algorithm for image manipulation

Neural style transfer (NST) refers to a class of software algorithms that manipulate digital images, or videos, in order to adopt the appearance or visual style of another image. NST algorithms are characterized by their use of deep neural networks for the sake of image transformation. Common uses for NST are the creation of artificial artwork from photographs, for example by transferring the appearance of famous paintings to user-supplied photographs. Several notable mobile apps use NST techniques for this purpose, including DeepArt and Prisma. This method has been used by artists and designers around the globe to develop new artwork based on existent style(s).

Xu Li is a Chinese computer scientist and co-founder and current CEO of SenseTime, an artificial intelligence (AI) company. Xu has led SenseTime since the company's incorporation and helped it independently develop its proprietary deep learning platform.

Olga Russakovsky is an assistant professor of computer science at Princeton University. Her research investigates computer vision and machine learning. She was one of the leaders of the ImageNet Large Scale Visual Recognition challenge and has been recognised by MIT Technology Review as one of the world's top young innovators.

Self-supervised learning (SSL) is a paradigm in machine learning where a model is trained on a task using the data itself to generate supervisory signals, rather than relying on external labels provided by humans. In the context of neural networks, self-supervised learning aims to leverage inherent structures or relationships within the input data to create meaningful training signals. SSL tasks are designed so that solving it requires capturing essential features or relationships in the data. The input data is typically augmented or transformed in a way that creates pairs of related samples. One sample serves as the input, and the other is used to formulate the supervisory signal. This augmentation can involve introducing noise, cropping, rotation, or other transformations. Self-supervised learning more closely imitates the way humans learn to classify objects.

A vision transformer (ViT) is a transformer designed for computer vision. A ViT breaks down an input image into a series of patches, serialises each patch into a vector, and maps it to a smaller dimension with a single matrix multiplication. These vector embeddings are then processed by a transformer encoder as if they were token embeddings.

References

  1. 1 2 "New computer vision challenge wants to teach robots to see in 3D". New Scientist. 7 April 2017. Retrieved 3 February 2018.
  2. 1 2 3 Markoff, John (19 November 2012). "For Web Images, Creating New Technology to Seek and Find". The New York Times. Retrieved 3 February 2018.
  3. ↑ "ImageNet". 7 September 2020. Archived from the original on 7 September 2020. Retrieved 11 October 2022.
  4. 1 2 3 "From not working to neural networking". The Economist. 25 June 2016. Retrieved 3 February 2018.
  5. ↑ "ImageNet Overview". ImageNet. Retrieved 15 October 2022.
  6. 1 2 3 4 5 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. IJCV, 2015.
  7. 1 2 Krizhevsky, Alex; Sutskever, Ilya; Hinton, Geoffrey E. (June 2017). "ImageNet classification with deep convolutional neural networks" (PDF). Communications of the ACM. 60 (6): 84–90. doi: 10.1145/3065386 . ISSN   0001-0782. S2CID   195908774 . Retrieved 24 May 2017.
  8. ↑ "Machines 'beat humans' for a growing number of tasks". Financial Times. 30 November 2017. Retrieved 3 February 2018.
  9. ↑ Gershgorn, Dave (18 June 2018). "The inside story of how AI got good enough to dominate Silicon Valley". Quartz. Retrieved 10 December 2018.
  10. 1 2 He, Kaiming; Zhang, Xiangyu; Ren, Shaoqing; Sun, Jian (2016). "Deep Residual Learning for Image Recognition". 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 770–778. arXiv: 1512.03385 . doi:10.1109/CVPR.2016.90. ISBN   978-1-4673-8851-1. S2CID   206594692.
  11. ↑ Hempel, Jesse (13 November 2018). "Fei-Fei Li's Quest to Make AI Better for Humanity". Wired. Retrieved 5 May 2019. When Li, who had moved back to Princeton to take a job as an assistant professor in 2007, talked up her idea for ImageNet, she had a hard time getting faculty members to help out. Finally, a professor who specialized in computer architecture agreed to join her as a collaborator.
  12. 1 2 3 4 Gershgorn, Dave (26 July 2017). "The data that transformed AI research—and possibly the world". Quartz. Atlantic Media Co. Retrieved 26 July 2017. Having read about WordNet's approach, Li met with professor Christiane Fellbaum, a researcher influential in the continued work on WordNet, during a 2006 visit to Princeton.
  13. ↑ Deng, Jia; Dong, Wei; Socher, Richard; Li, Li-Jia; Li, Kai; Fei-Fei, Li (2009), "ImageNet: A Large-Scale Hierarchical Image Database" (PDF), 2009 conference on Computer Vision and Pattern Recognition, archived from the original (PDF) on 15 January 2021, retrieved 26 July 2017
  14. ↑ Li, Fei-Fei (23 March 2015), How we're teaching computers to understand pictures , retrieved 16 December 2018
  15. ↑ "ImageNet". www.image-net.org. Retrieved 19 October 2022.
  16. ↑ Ridnik, Tal; Ben-Baruch, Emanuel; Noy, Asaf; Zelnik-Manor, Lihi (5 August 2021). "ImageNet-21K Pretraining for the Masses". arXiv: 2104.10972 [cs.CV].
  17. ↑ Robbins, Martin (6 May 2016). "Does an AI need to make love to Rembrandt's girlfriend to make art?". The Guardian. Retrieved 22 June 2016.
  18. ↑ Markoff, John (10 December 2015). "A Learning Advance in Artificial Intelligence Rivals Human Abilities". The New York Times. Retrieved 22 June 2016.
  19. ↑ Aron, Jacob (21 September 2015). "Forget the Turing test – there are better ways of judging AI". New Scientist. Retrieved 22 June 2016.
  20. ↑ Gershgorn, Dave (10 September 2017). "The Quartz guide to artificial intelligence: What is it, why is it important, and should we be afraid?". Quartz. Retrieved 3 February 2018.
  21. ↑ "The Viral App That Labels You Isn't Quite What You Think". Wired. ISSN   1059-1028 . Retrieved 22 September 2019.
  22. ↑ Wong, Julia Carrie (18 September 2019). "The viral selfie app ImageNet Roulette seemed fun – until it called me a racist slur". The Guardian. ISSN   0261-3077 . Retrieved 22 September 2019.
  23. ↑ Crawford, Kate; Paglen, Trevor (19 September 2019). "Excavating AI: The Politics of Training Sets for Machine Learning". -. Retrieved 22 September 2019.
  24. ↑ Lyons, Michael (24 December 2020). "Excavating "Excavating AI": The Elephant in the Gallery". arXiv: 2009.01215 .
  25. ↑ "Towards Fairer Datasets: Filtering and Balancing the Distribution of the People Subtree in the ImageNet Hierarchy". image-net.org. 17 September 2019. Retrieved 22 September 2019.
This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.