# ImageNet Dataset Created

ImageNet is a large-scale visual database created by Fei-Fei Li and colleagues, containing over 14 million hand-annotated images across more than 20,000 categories, and it has been pivotal in advancing deep learning through its annual recognition challenge.

ImageNet is a large-scale visual database designed for use in visual object recognition software research. It contains more than 14 million hand-annotated images indicating what objects are pictured, with at least one million images also providing bounding boxes. The database covers more than 20,000 categories, each typically consisting of several hundred images. Since 2010, the ImageNet project has run 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.

The project was conceived by AI researcher Fei-Fei Li, who began working on the idea in 2006. At a time when most AI research focused on models and algorithms, Li aimed to expand and improve the data available for training AI algorithms. In 2007, she met with Princeton professor Christiane Fellbaum, one of the creators of WordNet, to discuss the project. This led to building ImageNet starting from the roughly 22,000 nouns of WordNet and using many of its features. Li was also inspired by a 1987 estimate that the average person recognizes roughly 30,000 different kinds of objects.

## History and Construction

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. Labeling started in July 2008 and ended in April 2010. It took 49,000 workers from 167 countries to filter and label over 160 million candidate images. The budget allowed for each of the 14 million images to be labelled three times.

The original plan called for 10,000 images per category, for 40,000 categories at 400 million images, each verified three times. However, humans can classify at most two images per second, and at this rate it was estimated to take 19 human-years of labor (without rest). The team presented the database for the first time as a poster at the 2009 Conference on Computer Vision and Pattern Recognition (CVPR) in Florida, titled "ImageNet: A Preview of a Large-scale Hierarchical Dataset." The poster was reused at Vision Sciences Society 2009.

In 2009, Alex Berg suggested adding object localization as a task. Li approached the PASCAL Visual Object Classes contest in 2009 for a collaboration, which resulted in the subsequent ImageNet Large Scale Visual Recognition Challenge starting in 2010. The challenge featured 1000 classes and object localization, compared to PASCAL VOC which had just 20 classes and 19,737 images in 2010.

## Significance for Deep Learning

On 30 September 2012, a convolutional neural network (CNN) called AlexNet 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 CNNs was feasible due to the use of graphics processing units (GPUs) during training, an essential ingredient of the [deep learning](https://www.wikiprompt.org/wiki/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."

In 2015, AlexNet was outperformed by Microsoft's very deep CNN with over 100 layers, which won the ImageNet 2015 contest with a 3.57% error on the test set. Andrej Karpathy estimated in 2014 that with concentrated effort, he could reach a 5.1% error rate, and about 10 people from his lab reached roughly 12-13% with less effort. It was estimated that with maximal effort, a human could reach 2.4% error.

## Dataset Structure

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.

In 2012, ImageNet was the world's largest academic user of Mechanical Turk, with the average worker identifying 50 images per minute. The original plan for the full ImageNet would have roughly 50 million clean, diverse, and full-resolution images spread over approximately 50,000 synsets, but this was not achieved.

As of April 30, 2010, the summary statistics were: total number of non-empty synsets: 21,841; total number of images: 14,197,122; number of images with bounding box annotations: 1,034,908; number of synsets with SIFT features: 1,000; number of images with SIFT features: 1.2 million.

### Categories

The categories of ImageNet were filtered from WordNet concepts. Each concept, since it can contain multiple synonyms (for example, "kitty" and "young cat"), is called a "synonym set" or "synset." There were more than 100,000 synsets in WordNet 3.0, the majority being nouns (80,000+). The ImageNet dataset filtered these to 21,841 synsets that are countable nouns that can be visually illustrated.

Each synset in WordNet 3.0 has a "WordNet ID" (wnid), which is a concatenation of part of speech and an "offset" (a unique identifying number). Every wnid starts with "n" because ImageNet only includes nouns. For example, the wnid of synset "dog, domestic dog, Canis familiaris" is "n02084071." The categories fall into 9 levels, from level 1 (such as "mammal") to level 9 (such as "German shepherd").

### Image Format

The images were scraped from online image search engines (Google, Picsearch, MSN, Yahoo, Flickr, etc.) using synonyms in multiple languages. For example, for German shepherd: German police dog, Alsatian, ovejero alemán, pastore tedesco, 德国牧羊犬. ImageNet consists of images in RGB format with varying resolutions. For instance, in the ImageNet 2012 "fish" category, resolution ranges from 4288 x 2848 to 75 x 56. In machine learning, these are typically preprocessed into a standard constant resolution and whitened before further processing by [neural networks](https://www.wikiprompt.org/wiki/neural-network).

For example, in PyTorch, ImageNet images are by default normalized by dividing pixel values to fall between 0 and 1, then subtracting [0.485, 0.456, 0.406] and dividing by [0.229, 0.224, 0.225]. These are the mean and standard deviations for ImageNet, so this whitens the input data.

### Labels and Annotations

Each image is labelled with exactly one wnid. Dense SIFT features (raw SIFT descriptors, quantized codewords, and coordinates of each descriptor/codeword) for ImageNet-1K were available for download, designed for bag of visual words. Bounding boxes of objects were available for about 3,000 popular synsets with an average of 150 images in each synset. Additionally, some images have attribute annotations, though details are limited.

## Legacy and Impact

ImageNet has become a benchmark for [machine learning](https://www.wikiprompt.org/wiki/machine-learning) and [computer vision](https://www.wikiprompt.org/wiki/computer-vision) research, driving progress in [artificial intelligence](https://www.wikiprompt.org/wiki/artificial-intelligence). Its annual challenge has spurred innovations in [convolutional neural networks](https://www.wikiprompt.org/wiki/convolutional-neural-network) and other architectures, influencing the broader field of [deep learning](https://www.wikiprompt.org/wiki/deep-learning). The dataset's scale and structure have also informed subsequent large-scale data projects and the development of [data augmentation](https://www.wikiprompt.org/wiki/data-augmentation) techniques.

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Source: https://www.wikiprompt.org/wiki/imagenet-2010
License: CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0/)
Last updated: 2026-09-13T04:00:28.322699+00:00