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<a class="vector-toc-link" href="#Categories"> <div class="vector-toc-text"> <span class="vector-toc-numb">2.1</span> <span>Categories</span> </div> </a> <ul id="toc-Categories-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-Image_format" class="vector-toc-list-item vector-toc-level-2"> <a class="vector-toc-link" href="#Image_format"> <div class="vector-toc-text"> <span class="vector-toc-numb">2.2</span> <span>Image format</span> </div> </a> <ul id="toc-Image_format-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-Labels_and_annotations" class="vector-toc-list-item vector-toc-level-2"> <a class="vector-toc-link" href="#Labels_and_annotations"> <div class="vector-toc-text"> <span class="vector-toc-numb">2.3</span> <span>Labels and annotations</span> </div> </a> <ul id="toc-Labels_and_annotations-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-ImageNet-21K" class="vector-toc-list-item vector-toc-level-2"> <a class="vector-toc-link" href="#ImageNet-21K"> <div class="vector-toc-text"> <span class="vector-toc-numb">2.4</span> <span>ImageNet-21K</span> </div> </a> <ul id="toc-ImageNet-21K-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-ImageNet-1K" class="vector-toc-list-item vector-toc-level-2"> <a class="vector-toc-link" href="#ImageNet-1K"> <div class="vector-toc-text"> <span class="vector-toc-numb">2.5</span> <span>ImageNet-1K</span> </div> </a> <ul id="toc-ImageNet-1K-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-Later_developments" class="vector-toc-list-item vector-toc-level-2"> <a class="vector-toc-link" href="#Later_developments"> <div class="vector-toc-text"> <span class="vector-toc-numb">2.6</span> <span>Later developments</span> </div> </a> <ul id="toc-Later_developments-sublist" class="vector-toc-list"> </ul> </li> </ul> </li> <li id="toc-History_of_the_ImageNet_challenge" class="vector-toc-list-item vector-toc-level-1 vector-toc-list-item-expanded"> <a class="vector-toc-link" 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dir="ltr"><div class="shortdescription nomobile noexcerpt noprint searchaux" style="display:none">Image dataset</div><p>The <b>ImageNet</b> project is a large visual <a href="/wiki/Database" title="Database">database</a> designed for use in <a href="/wiki/Outline_of_object_recognition" title="Outline of object recognition">visual object recognition software</a> research. More than 14 million<sup id="cite_ref-New_Scientist_1-0" class="reference"><a href="#cite_note-New_Scientist-1"><span class="cite-bracket">[</span>1<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-nytimes_2012_2-0" class="reference"><a href="#cite_note-nytimes_2012-2"><span class="cite-bracket">[</span>2<span class="cite-bracket">]</span></a></sup> 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.<sup id="cite_ref-3" class="reference"><a href="#cite_note-3"><span class="cite-bracket">[</span>3<span class="cite-bracket">]</span></a></sup> ImageNet contains more than 20,000 categories,<sup id="cite_ref-nytimes_2012_2-1" class="reference"><a href="#cite_note-nytimes_2012-2"><span class="cite-bracket">[</span>2<span class="cite-bracket">]</span></a></sup> with a typical category, such as "balloon" or "strawberry", consisting of several hundred images.<sup id="cite_ref-economist_4-0" class="reference"><a href="#cite_note-economist-4"><span class="cite-bracket">[</span>4<span class="cite-bracket">]</span></a></sup> The database of annotations of third-party image <a href="/wiki/URL" title="URL">URLs</a> is freely available directly from ImageNet, though the actual images are not owned by ImageNet.<sup id="cite_ref-5" class="reference"><a href="#cite_note-5"><span class="cite-bracket">[</span>5<span class="cite-bracket">]</span></a></sup> Since 2010, the ImageNet project runs an annual software contest, the ImageNet Large Scale Visual Recognition Challenge (<a href="#History_of_the_ImageNet_challenge">ILSVRC</a>), where software programs compete to correctly classify and detect objects and scenes. The challenge uses a "trimmed" list of one thousand non-overlapping classes.<sup id="cite_ref-ILJVRC-2015_6-0" class="reference"><a href="#cite_note-ILJVRC-2015-6"><span class="cite-bracket">[</span>6<span class="cite-bracket">]</span></a></sup> </p><meta property="mw:PageProp/toc" /> <div class="mw-heading mw-heading2"><h2 id="History">History</h2><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=ImageNet&action=edit&section=1" title="Edit section: History"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p>AI researcher <a href="/wiki/Fei-Fei_Li" title="Fei-Fei Li">Fei-Fei Li</a> 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.<sup id="cite_ref-WiredQuest_7-0" class="reference"><a href="#cite_note-WiredQuest-7"><span class="cite-bracket">[</span>7<span class="cite-bracket">]</span></a></sup> In 2007, Li met with Princeton professor <a href="/wiki/Christiane_Fellbaum" title="Christiane Fellbaum">Christiane Fellbaum</a>, one of the creators of <a href="/wiki/WordNet" title="WordNet">WordNet</a> , to discuss the project. As a result of this meeting, Li went on to build ImageNet starting from the roughly 22,000 nouns of WordNet and using many of its features.<sup id="cite_ref-Gershgorn_8-0" class="reference"><a href="#cite_note-Gershgorn-8"><span class="cite-bracket">[</span>8<span class="cite-bracket">]</span></a></sup> She was also inspired by a 1987 estimate<sup id="cite_ref-9" class="reference"><a href="#cite_note-9"><span class="cite-bracket">[</span>9<span class="cite-bracket">]</span></a></sup> that the average person recognizes roughly 30,000 different kinds of objects.<sup id="cite_ref-:1_10-0" class="reference"><a href="#cite_note-:1-10"><span class="cite-bracket">[</span>10<span class="cite-bracket">]</span></a></sup> </p><p>As an assistant professor at <a href="/wiki/Princeton_University" title="Princeton University">Princeton</a>, Li assembled a team of researchers to work on the ImageNet project. They used <a href="/wiki/Amazon_Mechanical_Turk" title="Amazon Mechanical Turk">Amazon Mechanical Turk</a> to help with the classification of images. Labeling started in July 2008 and ended in April 2010. It took 2.5 years to complete the labeling.<sup id="cite_ref-Gershgorn_8-1" class="reference"><a href="#cite_note-Gershgorn-8"><span class="cite-bracket">[</span>8<span class="cite-bracket">]</span></a></sup> They had enough budget to have each of the 14 million images labelled three times.<sup id="cite_ref-:1_10-1" class="reference"><a href="#cite_note-:1-10"><span class="cite-bracket">[</span>10<span class="cite-bracket">]</span></a></sup> </p><p>The original plan called for 10,000 images per category, for 40,000 categories at 400 million images, each verified 3 times. They found that humans can classify at most 2 images/sec. At this rate, it was estimated to take 19 human-years of labor (without rest).<sup id="cite_ref-:5_11-0" class="reference"><a href="#cite_note-:5-11"><span class="cite-bracket">[</span>11<span class="cite-bracket">]</span></a></sup> </p><p>They presented their database for the first time as a poster at the 2009 <a href="/wiki/Conference_on_Computer_Vision_and_Pattern_Recognition" title="Conference on Computer Vision and Pattern Recognition">Conference on Computer Vision and Pattern Recognition</a> (CVPR) in Florida, titled "ImageNet: A Preview of a Large-scale Hierarchical Dataset".<sup id="cite_ref-12" class="reference"><a href="#cite_note-12"><span class="cite-bracket">[</span>12<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-Gershgorn_8-2" class="reference"><a href="#cite_note-Gershgorn-8"><span class="cite-bracket">[</span>8<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-:2_13-0" class="reference"><a href="#cite_note-:2-13"><span class="cite-bracket">[</span>13<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-14" class="reference"><a href="#cite_note-14"><span class="cite-bracket">[</span>14<span class="cite-bracket">]</span></a></sup> The poster was reused at Vision Sciences Society 2009.<sup id="cite_ref-15" class="reference"><a href="#cite_note-15"><span class="cite-bracket">[</span>15<span class="cite-bracket">]</span></a></sup> </p><p>In 2009, Alex Berg suggested adding object localization as a task. Li approached <a rel="nofollow" class="external text" href="http://host.robots.ox.ac.uk/pascal/VOC/">PASCAL Visual Object Classes</a> contest in 2009 for a collaboration. It resulted in the subsequent <a href="/wiki/ImageNet_Large_Scale_Visual_Recognition_Challenge" class="mw-redirect" title="ImageNet Large Scale Visual Recognition Challenge">ImageNet Large Scale Visual Recognition Challenge</a> starting in 2010, which has 1000 classes and object localization, as compared to <a rel="nofollow" class="external text" href="http://host.robots.ox.ac.uk/pascal/VOC/">PASCAL VOC</a> which had just 20 classes and 19,737 images (in 2010).<sup id="cite_ref-ILJVRC-2015_6-1" class="reference"><a href="#cite_note-ILJVRC-2015-6"><span class="cite-bracket">[</span>6<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-Gershgorn_8-3" class="reference"><a href="#cite_note-Gershgorn-8"><span class="cite-bracket">[</span>8<span class="cite-bracket">]</span></a></sup> </p> <div class="mw-heading mw-heading3"><h3 id="Significance_for_deep_learning">Significance for deep learning</h3><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=ImageNet&action=edit&section=2" title="Edit section: Significance for deep learning"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p>On 30 September 2012, a <a href="/wiki/Convolutional_neural_network" title="Convolutional neural network">convolutional neural network</a> (CNN) called <a href="/wiki/AlexNet" title="AlexNet">AlexNet</a><sup id="cite_ref-:0_16-0" class="reference"><a href="#cite_note-:0-16"><span class="cite-bracket">[</span>16<span class="cite-bracket">]</span></a></sup> 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 <a href="/wiki/Graphics_processing_unit" title="Graphics processing unit">graphics processing units</a> (GPUs) during training,<sup id="cite_ref-:0_16-1" class="reference"><a href="#cite_note-:0-16"><span class="cite-bracket">[</span>16<span class="cite-bracket">]</span></a></sup> an essential ingredient of the <a href="/wiki/Deep_learning" title="Deep learning">deep learning</a> revolution. According to <i><a href="/wiki/The_Economist" title="The Economist">The Economist</a></i>, "Suddenly people started to pay attention, not just within the AI community but across the technology industry as a whole."<sup id="cite_ref-economist_4-1" class="reference"><a href="#cite_note-economist-4"><span class="cite-bracket">[</span>4<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-17" class="reference"><a href="#cite_note-17"><span class="cite-bracket">[</span>17<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-18" class="reference"><a href="#cite_note-18"><span class="cite-bracket">[</span>18<span class="cite-bracket">]</span></a></sup> </p><p>In 2015, AlexNet was outperformed by <a href="/wiki/Microsoft" title="Microsoft">Microsoft</a>'s <a href="/wiki/ResNets" class="mw-redirect" title="ResNets">very deep CNN</a> with over 100 layers, which won the ImageNet 2015 contest.<sup id="cite_ref-microsoft2015_19-0" class="reference"><a href="#cite_note-microsoft2015-19"><span class="cite-bracket">[</span>19<span class="cite-bracket">]</span></a></sup> </p> <div class="mw-heading mw-heading2"><h2 id="Dataset">Dataset</h2><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=ImageNet&action=edit&section=3" title="Edit section: Dataset"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p>ImageNet <a href="/wiki/Crowdsources" class="mw-redirect" title="Crowdsources">crowdsources</a> 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 <a href="/wiki/WordNet" title="WordNet">WordNet</a> schema to categorize objects, augmented with 120 categories of <a href="/wiki/Dog_breeds" class="mw-redirect" title="Dog breeds">dog breeds</a> to showcase fine-grained classification.<sup id="cite_ref-ILJVRC-2015_6-2" class="reference"><a href="#cite_note-ILJVRC-2015-6"><span class="cite-bracket">[</span>6<span class="cite-bracket">]</span></a></sup> </p><p>In 2012, ImageNet was the world's largest academic user of <a href="/wiki/Amazon_Mechanical_Turk" title="Amazon Mechanical Turk">Mechanical Turk</a>. The average worker identified 50 images per minute.<sup id="cite_ref-nytimes_2012_2-2" class="reference"><a href="#cite_note-nytimes_2012-2"><span class="cite-bracket">[</span>2<span class="cite-bracket">]</span></a></sup> </p><p>The original plan of the full ImageNet would have roughly 50M clean, diverse and full resolution images spread over approximately 50K synsets.<sup id="cite_ref-:2_13-1" class="reference"><a href="#cite_note-:2-13"><span class="cite-bracket">[</span>13<span class="cite-bracket">]</span></a></sup> This was not achieved. </p><p>The summary statistics given on April 30, 2010:<sup id="cite_ref-20" class="reference"><a href="#cite_note-20"><span class="cite-bracket">[</span>20<span class="cite-bracket">]</span></a></sup> </p> <ul><li>Total number of non-empty synsets: 21841</li> <li>Total number of images: 14,197,122</li> <li>Number of images with bounding box annotations: 1,034,908</li> <li>Number of synsets with SIFT features: 1000</li> <li>Number of images with SIFT features: 1.2 million</li></ul> <div class="mw-heading mw-heading3"><h3 id="Categories">Categories</h3><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=ImageNet&action=edit&section=4" title="Edit section: Categories"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p>The categories of ImageNet were filtered from the WordNet concepts. Each concept, since it can contain multiple synonyms (for example, "kitty" and "young cat"), so each concept is called a "synonym set" or "<a href="/wiki/Synset" title="Synset">synset</a>". There were more than 100,000 synsets in WordNet 3.0, majority of them are nouns (80,000+). The ImageNet dataset filtered these to 21,841 synsets that are <a href="/wiki/Count_noun" title="Count noun">countable nouns</a> that can be visually illustrated. </p><p>Each synset in WordNet 3.0 has a "WordNet ID" (wnid), which is a concatenation of <a href="/wiki/Part_of_speech" title="Part of speech">part of speech</a> and an "offset" (a <a href="/wiki/Unique_key" title="Unique key">unique identifying number</a>). Every wnid starts with "n" because ImageNet only includes <a href="/wiki/Noun" title="Noun">nouns</a>. For example, the wnid of synset "<a href="/wiki/Dog" title="Dog">dog, domestic dog, Canis familiaris</a>" is "n02084071".<sup id="cite_ref-21" class="reference"><a href="#cite_note-21"><span class="cite-bracket">[</span>21<span class="cite-bracket">]</span></a></sup> </p><p>The categories in ImageNet fall into 9 levels, from level 1 (such as "mammal") to level 9 (such as "German shepherd").<sup id="cite_ref-:5_11-1" class="reference"><a href="#cite_note-:5-11"><span class="cite-bracket">[</span>11<span class="cite-bracket">]</span></a></sup> </p> <div class="mw-heading mw-heading3"><h3 id="Image_format">Image format</h3><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=ImageNet&action=edit&section=5" title="Edit section: Image format"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p>The images were scraped from online image search (<a href="/wiki/Google_Search" title="Google Search">Google</a>, <a href="/wiki/Picsearch" title="Picsearch">Picsearch</a>, <a href="/wiki/MSN" title="MSN">MSN</a>, <a href="/wiki/Yahoo" title="Yahoo">Yahoo</a>, <a href="/wiki/Flickr" title="Flickr">Flickr</a>, etc) using synonyms in multiple languages. For example: <i>German shepherd, German police dog, German shepherd dog, Alsatian, ovejero alemán, pastore tedesco, 德国牧羊犬</i>.<sup id="cite_ref-:4_22-0" class="reference"><a href="#cite_note-:4-22"><span class="cite-bracket">[</span>22<span class="cite-bracket">]</span></a></sup> </p><p>ImageNet consists of images in <a href="/wiki/RGB_color_model" title="RGB color model">RGB</a> format with varying resolutions. For example, in ImageNet 2012, "fish" category, the 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. </p><p>For example, in PyTorch, ImageNet images are by default normalized by dividing the pixel values so that they fall between 0 and 1, then subtracting by [0.485, 0.456, 0.406], then dividing by [0.229, 0.224, 0.225]. These are the mean and standard deviations, for ImageNet, so these <a href="/wiki/Whitening_transformation" title="Whitening transformation">whitens</a> the input data.<sup id="cite_ref-23" class="reference"><a href="#cite_note-23"><span class="cite-bracket">[</span>23<span class="cite-bracket">]</span></a></sup> </p> <div class="mw-heading mw-heading3"><h3 id="Labels_and_annotations">Labels and annotations</h3><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=ImageNet&action=edit&section=6" title="Edit section: Labels and annotations"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p>Each image is labelled with exactly one wnid. </p><p>Dense <a href="/wiki/Scale-invariant_feature_transform" title="Scale-invariant feature transform">SIFT features</a> (raw SIFT descriptors, quantized codewords, and coordinates of each descriptor/codeword) for ImageNet-1K were available for download, designed for <a href="/wiki/Bag_of_visual_words" class="mw-redirect" title="Bag of visual words">bag of visual words</a>.<sup id="cite_ref-24" class="reference"><a href="#cite_note-24"><span class="cite-bracket">[</span>24<span class="cite-bracket">]</span></a></sup> </p><p>The bounding boxes of objects were available for about 3000 popular synsets<sup id="cite_ref-25" class="reference"><a href="#cite_note-25"><span class="cite-bracket">[</span>25<span class="cite-bracket">]</span></a></sup> with on average 150 images in each synset.<sup id="cite_ref-26" class="reference"><a href="#cite_note-26"><span class="cite-bracket">[</span>26<span class="cite-bracket">]</span></a></sup> </p><p>Furthermore, some images have attributes. They released 25 attributes for ~400 popular synsets:<sup id="cite_ref-27" class="reference"><a href="#cite_note-27"><span class="cite-bracket">[</span>27<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-28" class="reference"><a href="#cite_note-28"><span class="cite-bracket">[</span>28<span class="cite-bracket">]</span></a></sup> </p> <ul><li><b>Color</b>: black, blue, brown, gray, green, orange, pink, red, violet, white, yellow</li> <li><b>Pattern</b>: spotted, striped</li> <li><b>Shape</b>: long, round, rectangular, square</li> <li><b>Texture</b>: furry, smooth, rough, shiny, metallic, vegetation, wooden, wet</li></ul> <div class="mw-heading mw-heading3"><h3 id="ImageNet-21K">ImageNet-21K</h3><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=ImageNet&action=edit&section=7" title="Edit section: ImageNet-21K"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p>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.<sup id="cite_ref-:3_29-0" class="reference"><a href="#cite_note-:3-29"><span class="cite-bracket">[</span>29<span class="cite-bracket">]</span></a></sup> </p><p>The full ImageNet-21k was released in Fall of 2011, as <code>fall11_whole.tar</code>. There is no official train-validation-test split for ImageNet-21k. Some classes contain only 1-10 samples, while others contain thousands.<sup id="cite_ref-:3_29-1" class="reference"><a href="#cite_note-:3-29"><span class="cite-bracket">[</span>29<span class="cite-bracket">]</span></a></sup> </p> <div class="mw-heading mw-heading3"><h3 id="ImageNet-1K">ImageNet-1K</h3><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=ImageNet&action=edit&section=8" title="Edit section: ImageNet-1K"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p>There are various subsets of the ImageNet dataset used in various context, sometimes referred to as "versions".<sup id="cite_ref-:0_16-2" class="reference"><a href="#cite_note-:0-16"><span class="cite-bracket">[</span>16<span class="cite-bracket">]</span></a></sup> </p><p>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.<sup id="cite_ref-30" class="reference"><a href="#cite_note-30"><span class="cite-bracket">[</span>30<span class="cite-bracket">]</span></a></sup> </p><p>Each category in ImageNet-1K is a leaf category, meaning that there are no child nodes below it, unlike ImageNet-21K. For example, in ImageNet-21K, there are some images categorized as simply "mammal", whereas in ImageNet-1K, there are only images categorized as things like "German shepherd", since there are no child-words below "German shepherd".<sup id="cite_ref-:4_22-1" class="reference"><a href="#cite_note-:4-22"><span class="cite-bracket">[</span>22<span class="cite-bracket">]</span></a></sup> </p> <div class="mw-heading mw-heading3"><h3 id="Later_developments">Later developments</h3><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=ImageNet&action=edit&section=9" title="Edit section: Later developments"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p>In 2021 winter, ImageNet-21k was updated. 2,702 categories in the "person" subtree were filtered to prevent "problematic behaviors" in a trained model. In 2021, ImageNet-1k was updated by annotating faces appearing in the 997 non-person categories. They found training models on the dataset with these faces blurred caused minimal loss in performance.<sup id="cite_ref-31" class="reference"><a href="#cite_note-31"><span class="cite-bracket">[</span>31<span class="cite-bracket">]</span></a></sup> </p><p>ImageNetV2 was a new dataset containing three test sets with 10,000 each, constructed by the same methodology as the original ImageNet.<sup id="cite_ref-32" class="reference"><a href="#cite_note-32"><span class="cite-bracket">[</span>32<span class="cite-bracket">]</span></a></sup> </p><p>ImageNet-21K-P was a filtered and cleaned subset of ImageNet-21K, with 12,358,688 images from 11,221 categories.<sup id="cite_ref-:3_29-2" class="reference"><a href="#cite_note-:3-29"><span class="cite-bracket">[</span>29<span class="cite-bracket">]</span></a></sup> </p> <table class="wikitable"> <caption>Table of datasets </caption> <tbody><tr> <th>Name </th> <th>Published </th> <th>Classes </th> <th>Training </th> <th>Validation </th> <th>Test </th> <th>Size </th></tr> <tr> <td>PASCAL VOC </td> <td>2005 </td> <td>20 </td> <td> </td> <td> </td> <td> </td> <td> </td></tr> <tr> <td>ImageNet-1K </td> <td>2009 </td> <td>1,000 </td> <td>1,281,167 </td> <td>50,000 </td> <td>100,000 </td> <td>130 GB </td></tr> <tr> <td>ImageNet-21K </td> <td>2011 </td> <td>21,841 </td> <td>14,197,122 </td> <td> </td> <td> </td> <td>1.31 TB </td></tr> <tr> <td>ImageNetV2 </td> <td>2019 </td> <td> </td> <td> </td> <td> </td> <td>30,000 </td> <td> </td></tr> <tr> <td>ImageNet-21K-P </td> <td>2021 </td> <td>11,221 </td> <td>11,797,632 </td> <td> </td> <td>561,052 </td> <td> </td></tr></tbody></table> <div class="mw-heading mw-heading2"><h2 id="History_of_the_ImageNet_challenge">History of the ImageNet challenge</h2><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=ImageNet&action=edit&section=10" title="Edit section: History of the ImageNet challenge"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <figure class="mw-default-size" typeof="mw:File/Thumb"><a href="/wiki/File:ImageNet_error_rate_history_(just_systems).svg" class="mw-file-description"><img src="//upload.wikimedia.org/wikipedia/commons/thumb/4/4f/ImageNet_error_rate_history_%28just_systems%29.svg/220px-ImageNet_error_rate_history_%28just_systems%29.svg.png" decoding="async" width="220" height="269" class="mw-file-element" srcset="//upload.wikimedia.org/wikipedia/commons/thumb/4/4f/ImageNet_error_rate_history_%28just_systems%29.svg/330px-ImageNet_error_rate_history_%28just_systems%29.svg.png 1.5x, //upload.wikimedia.org/wikipedia/commons/thumb/4/4f/ImageNet_error_rate_history_%28just_systems%29.svg/440px-ImageNet_error_rate_history_%28just_systems%29.svg.png 2x" data-file-width="810" data-file-height="990" /></a><figcaption>Error rate history on ImageNet (showing best result per team and up to 10 entries per year)</figcaption></figure> <p>The ILSVRC aims to "follow in the footsteps" of the smaller-scale <a rel="nofollow" class="external text" href="http://host.robots.ox.ac.uk/pascal/VOC/">PASCAL VOC</a> challenge, established in 2005, which contained only about 20,000 images and twenty object classes.<sup id="cite_ref-ILJVRC-2015_6-3" class="reference"><a href="#cite_note-ILJVRC-2015-6"><span class="cite-bracket">[</span>6<span class="cite-bracket">]</span></a></sup> To "democratize" ImageNet, Fei-Fei Li proposed to the <a rel="nofollow" class="external text" href="http://host.robots.ox.ac.uk/pascal/VOC/">PASCAL VOC</a> 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.<sup id="cite_ref-Gershgorn_8-4" class="reference"><a href="#cite_note-Gershgorn-8"><span class="cite-bracket">[</span>8<span class="cite-bracket">]</span></a></sup> </p><p>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.<sup id="cite_ref-ILJVRC-2015_6-4" class="reference"><a href="#cite_note-ILJVRC-2015-6"><span class="cite-bracket">[</span>6<span class="cite-bracket">]</span></a></sup> </p><p>The 2010s saw dramatic progress in image processing. </p><p>The first competition in 2010 had 11 participating teams. The winning team was a linear <a href="/wiki/Support_vector_machine" title="Support vector machine">support vector machine</a> (SVM). The features are a dense grid of <a href="/wiki/Histogram_of_oriented_gradients" title="Histogram of oriented gradients">HoG</a> and <a href="/wiki/Local_binary_patterns" title="Local binary patterns">LBP</a>, sparsified by local coordinate coding and pooling.<sup id="cite_ref-33" class="reference"><a href="#cite_note-33"><span class="cite-bracket">[</span>33<span class="cite-bracket">]</span></a></sup> It achieved 52.9% in classification accuracy and 71.8% in top-5 accuracy. It was trained for 4 days on three 8-core machines (dual quad-core 2GHz <a href="/wiki/Xeon" title="Xeon">Intel Xeon</a> CPU).<sup id="cite_ref-34" class="reference"><a href="#cite_note-34"><span class="cite-bracket">[</span>34<span class="cite-bracket">]</span></a></sup> </p><p>The second competition in 2011 had fewer teams, with another SVM winning at top-5 error rate 25%.<sup id="cite_ref-:1_10-2" class="reference"><a href="#cite_note-:1-10"><span class="cite-bracket">[</span>10<span class="cite-bracket">]</span></a></sup> The winning team was XRCE by Florent Perronnin, Jorge Sanchez. The system was another linear SVM, running on quantized<sup id="cite_ref-35" class="reference"><a href="#cite_note-35"><span class="cite-bracket">[</span>35<span class="cite-bracket">]</span></a></sup> <a href="/wiki/Fisher_kernel" title="Fisher kernel">Fisher vectors</a>.<sup id="cite_ref-36" class="reference"><a href="#cite_note-36"><span class="cite-bracket">[</span>36<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-37" class="reference"><a href="#cite_note-37"><span class="cite-bracket">[</span>37<span class="cite-bracket">]</span></a></sup> It achieved 74.2% in top-5 accuracy. </p><p>In 2012, a deep <a href="/wiki/Convolutional_neural_network" title="Convolutional neural network">convolutional neural net</a> called <a href="/wiki/AlexNet" title="AlexNet">AlexNet</a> achieved 84.7% in top-5 accuracy, a great leap forward.<sup id="cite_ref-38" class="reference"><a href="#cite_note-38"><span class="cite-bracket">[</span>38<span class="cite-bracket">]</span></a></sup> In the next couple of years, top-5 accuracy grew to above 90%. 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.<sup id="cite_ref-economist_4-2" class="reference"><a href="#cite_note-economist-4"><span class="cite-bracket">[</span>4<span class="cite-bracket">]</span></a></sup> </p><p>By 2014, more than fifty institutions participated in the ILSVRC.<sup id="cite_ref-ILJVRC-2015_6-5" class="reference"><a href="#cite_note-ILJVRC-2015-6"><span class="cite-bracket">[</span>6<span class="cite-bracket">]</span></a></sup> In 2017, 29 of 38 competing teams had greater than 95% accuracy.<sup id="cite_ref-39" class="reference"><a href="#cite_note-39"><span class="cite-bracket">[</span>39<span class="cite-bracket">]</span></a></sup> 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 <a href="/wiki/Augmented_reality" title="Augmented reality">augmented reality</a>.<sup id="cite_ref-New_Scientist_1-1" class="reference"><a href="#cite_note-New_Scientist-1"><span class="cite-bracket">[</span>1<span class="cite-bracket">]</span></a></sup> </p><p>By 2015, researchers at Microsoft reported that their CNNs exceeded human ability at the narrow ILSVRC tasks.<sup id="cite_ref-microsoft2015_19-1" class="reference"><a href="#cite_note-microsoft2015-19"><span class="cite-bracket">[</span>19<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-40" class="reference"><a href="#cite_note-40"><span class="cite-bracket">[</span>40<span class="cite-bracket">]</span></a></sup> However, as one of the challenge's organizers, <a href="/wiki/Olga_Russakovsky" title="Olga Russakovsky">Olga Russakovsky</a>, pointed out in 2015, the contest is over only 1000 categories; humans can recognize a larger number of categories, and also (unlike the programs) can judge the context of an image.<sup id="cite_ref-41" class="reference"><a href="#cite_note-41"><span class="cite-bracket">[</span>41<span class="cite-bracket">]</span></a></sup> </p> <div class="mw-heading mw-heading2"><h2 id="Bias_in_ImageNet">Bias in ImageNet</h2><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=ImageNet&action=edit&section=11" title="Edit section: Bias in ImageNet"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p>It is estimated that over 6% of labels in the ImageNet-1k validation set are wrong.<sup id="cite_ref-42" class="reference"><a href="#cite_note-42"><span class="cite-bracket">[</span>42<span class="cite-bracket">]</span></a></sup> It is also found that around 10% of ImageNet-1k contains ambiguous or erroneous labels, and that, when presented with a model's prediction and the original ImageNet label, human annotators prefer the prediction of a state of the art model in 2020 trained on the original ImageNet, suggesting that ImageNet-1k has been saturated.<sup id="cite_ref-43" class="reference"><a href="#cite_note-43"><span class="cite-bracket">[</span>43<span class="cite-bracket">]</span></a></sup> </p><p>A study of the history of the multiple layers (<a href="/wiki/Taxonomy_(general)" class="mw-redirect" title="Taxonomy (general)">taxonomy</a>, object classes and labeling) of ImageNet and WordNet in 2019 described how <a href="/wiki/Algorithmic_bias" title="Algorithmic bias">bias</a><sup class="noprint Inline-Template" style="margin-left:0.1em; white-space:nowrap;">[<i><a href="/wiki/Wikipedia:Please_clarify" title="Wikipedia:Please clarify"><span title="The text near this tag may need clarification or removal of jargon. (December 2023)">clarification needed</span></a></i>]</sup> is deeply embedded in most classification approaches for all sorts of images.<sup id="cite_ref-44" class="reference"><a href="#cite_note-44"><span class="cite-bracket">[</span>44<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-45" class="reference"><a href="#cite_note-45"><span class="cite-bracket">[</span>45<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-46" class="reference"><a href="#cite_note-46"><span class="cite-bracket">[</span>46<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-47" class="reference"><a href="#cite_note-47"><span class="cite-bracket">[</span>47<span class="cite-bracket">]</span></a></sup> ImageNet is working to address various sources of bias.<sup id="cite_ref-48" class="reference"><a href="#cite_note-48"><span class="cite-bracket">[</span>48<span class="cite-bracket">]</span></a></sup> </p><p>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 <a href="/wiki/Diplodocus" title="Diplodocus">diplodocus</a>."<sup class="noprint Inline-Template" style="margin-left:0.1em; white-space:nowrap;">[<i><a href="/wiki/Wikipedia:Please_clarify" title="Wikipedia:Please clarify"><span title="The text near this tag may need clarification or removal of jargon. (August 2019)">clarification needed</span></a></i>]</sup> </p> <div class="mw-heading mw-heading2"><h2 id="See_also">See also</h2><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=ImageNet&action=edit&section=12" title="Edit section: See also"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <ul><li><a href="/wiki/Computer_vision" title="Computer vision">Computer vision</a></li> <li><a href="/wiki/List_of_datasets_for_machine_learning_research" class="mw-redirect" title="List of datasets for machine learning research">List of datasets for machine learning research</a></li> <li><a href="/wiki/WordNet" title="WordNet">WordNet</a></li></ul> <div class="mw-heading mw-heading2"><h2 id="References">References</h2><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=ImageNet&action=edit&section=13" title="Edit section: References"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <style data-mw-deduplicate="TemplateStyles:r1239543626">.mw-parser-output .reflist{margin-bottom:0.5em;list-style-type:decimal}@media screen{.mw-parser-output .reflist{font-size:90%}}.mw-parser-output .reflist .references{font-size:100%;margin-bottom:0;list-style-type:inherit}.mw-parser-output .reflist-columns-2{column-width:30em}.mw-parser-output .reflist-columns-3{column-width:25em}.mw-parser-output .reflist-columns{margin-top:0.3em}.mw-parser-output .reflist-columns ol{margin-top:0}.mw-parser-output .reflist-columns li{page-break-inside:avoid;break-inside:avoid-column}.mw-parser-output .reflist-upper-alpha{list-style-type:upper-alpha}.mw-parser-output .reflist-upper-roman{list-style-type:upper-roman}.mw-parser-output .reflist-lower-alpha{list-style-type:lower-alpha}.mw-parser-output .reflist-lower-greek{list-style-type:lower-greek}.mw-parser-output .reflist-lower-roman{list-style-type:lower-roman}</style><div class="reflist reflist-columns references-column-width" style="column-width: 30em;"> <ol class="references"> <li id="cite_note-New_Scientist-1"><span class="mw-cite-backlink">^ <a href="#cite_ref-New_Scientist_1-0"><sup><i><b>a</b></i></sup></a> <a href="#cite_ref-New_Scientist_1-1"><sup><i><b>b</b></i></sup></a></span> <span class="reference-text"><style data-mw-deduplicate="TemplateStyles:r1238218222">.mw-parser-output cite.citation{font-style:inherit;word-wrap:break-word}.mw-parser-output .citation q{quotes:"\"""\"""'""'"}.mw-parser-output .citation:target{background-color:rgba(0,127,255,0.133)}.mw-parser-output .id-lock-free.id-lock-free a{background:url("//upload.wikimedia.org/wikipedia/commons/6/65/Lock-green.svg")right 0.1em center/9px no-repeat}.mw-parser-output .id-lock-limited.id-lock-limited a,.mw-parser-output .id-lock-registration.id-lock-registration a{background:url("//upload.wikimedia.org/wikipedia/commons/d/d6/Lock-gray-alt-2.svg")right 0.1em center/9px no-repeat}.mw-parser-output .id-lock-subscription.id-lock-subscription a{background:url("//upload.wikimedia.org/wikipedia/commons/a/aa/Lock-red-alt-2.svg")right 0.1em center/9px no-repeat}.mw-parser-output .cs1-ws-icon a{background:url("//upload.wikimedia.org/wikipedia/commons/4/4c/Wikisource-logo.svg")right 0.1em center/12px no-repeat}body:not(.skin-timeless):not(.skin-minerva) .mw-parser-output .id-lock-free a,body:not(.skin-timeless):not(.skin-minerva) .mw-parser-output .id-lock-limited a,body:not(.skin-timeless):not(.skin-minerva) .mw-parser-output .id-lock-registration a,body:not(.skin-timeless):not(.skin-minerva) .mw-parser-output .id-lock-subscription a,body:not(.skin-timeless):not(.skin-minerva) .mw-parser-output .cs1-ws-icon a{background-size:contain;padding:0 1em 0 0}.mw-parser-output .cs1-code{color:inherit;background:inherit;border:none;padding:inherit}.mw-parser-output .cs1-hidden-error{display:none;color:var(--color-error,#d33)}.mw-parser-output .cs1-visible-error{color:var(--color-error,#d33)}.mw-parser-output .cs1-maint{display:none;color:#085;margin-left:0.3em}.mw-parser-output .cs1-kern-left{padding-left:0.2em}.mw-parser-output .cs1-kern-right{padding-right:0.2em}.mw-parser-output .citation .mw-selflink{font-weight:inherit}@media screen{.mw-parser-output .cs1-format{font-size:95%}html.skin-theme-clientpref-night .mw-parser-output .cs1-maint{color:#18911f}}@media screen and (prefers-color-scheme:dark){html.skin-theme-clientpref-os .mw-parser-output .cs1-maint{color:#18911f}}</style><cite class="citation news cs1"><a rel="nofollow" class="external text" href="https://www.newscientist.com/article/2127131-new-computer-vision-challenge-wants-to-teach-robots-to-see-in-3d/">"New computer vision challenge wants to teach robots to see in 3D"</a>. <i>New Scientist</i>. 7 April 2017<span class="reference-accessdate">. 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style="width:1%"><a href="/wiki/Differentiable_function" title="Differentiable function">General</a></th><td class="navbox-list-with-group navbox-list navbox-odd" style="width:100%;padding:0"><div style="padding:0 0.25em"> <ul><li><b><a href="/wiki/Differentiable_programming" title="Differentiable programming">Differentiable programming</a></b></li> <li><a href="/wiki/Information_geometry" title="Information geometry">Information geometry</a></li> <li><a href="/wiki/Statistical_manifold" title="Statistical manifold">Statistical manifold</a></li> <li><a href="/wiki/Automatic_differentiation" title="Automatic differentiation">Automatic differentiation</a></li> <li><a href="/wiki/Neuromorphic_computing" title="Neuromorphic computing">Neuromorphic computing</a></li> <li><a href="/wiki/Pattern_recognition" title="Pattern recognition">Pattern recognition</a></li> <li><a href="/wiki/Ricci_calculus" title="Ricci calculus">Ricci calculus</a></li> <li><a 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srcset="//upload.wikimedia.org/wikipedia/en/thumb/e/e2/Symbol_portal_class.svg/23px-Symbol_portal_class.svg.png 1.5x, //upload.wikimedia.org/wikipedia/en/thumb/e/e2/Symbol_portal_class.svg/31px-Symbol_portal_class.svg.png 2x" data-file-width="180" data-file-height="185" /></a></span> Portals <ul><li><a href="/wiki/Portal:Computer_programming" title="Portal:Computer programming">Computer programming</a></li> <li><a href="/wiki/Portal:Technology" title="Portal:Technology">Technology</a></li></ul></li></ul> </div></td></tr></tbody></table></div> <div class="navbox-styles"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1129693374"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1236075235"></div><div role="navigation" class="navbox" aria-labelledby="Standard_test_items" style="padding:3px"><table class="nowraplinks mw-collapsible autocollapse navbox-inner" style="border-spacing:0;background:transparent;color:inherit"><tbody><tr><th scope="col" class="navbox-title" colspan="2"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1129693374"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1239400231"><div class="navbar plainlinks hlist navbar-mini"><ul><li class="nv-view"><a href="/wiki/Template:Standard_test_item" title="Template:Standard test item"><abbr title="View this template">v</abbr></a></li><li class="nv-talk"><a href="/wiki/Template_talk:Standard_test_item" title="Template talk:Standard test item"><abbr title="Discuss this template">t</abbr></a></li><li class="nv-edit"><a href="/wiki/Special:EditPage/Template:Standard_test_item" title="Special:EditPage/Template:Standard test item"><abbr title="Edit this template">e</abbr></a></li></ul></div><div id="Standard_test_items" style="font-size:114%;margin:0 4em">Standard test items</div></th></tr><tr><td class="navbox-abovebelow hlist" colspan="2"><div> <ul><li><a href="/wiki/Pangram" title="Pangram">Pangram</a></li> <li><a href="/wiki/Reference_implementation" title="Reference implementation">Reference implementation</a></li> <li><a href="/wiki/Sanity_check" title="Sanity check">Sanity check</a></li> <li><a href="/wiki/Standard_test_image" title="Standard test image">Standard test image</a></li></ul> </div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%"><a href="/wiki/Artificial_intelligence" title="Artificial intelligence">Artificial intelligence</a></th><td class="navbox-list-with-group navbox-list navbox-odd hlist" style="width:100%;padding:0"><div style="padding:0 0.25em"> <ul><li><a href="/wiki/Chinese_room" title="Chinese room">Chinese room</a></li> <li><a href="/wiki/Turing_test" title="Turing test">Turing test</a></li></ul> </div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%">Television (<a href="/wiki/Test_card" title="Test card">test card</a>)</th><td class="navbox-list-with-group navbox-list navbox-even hlist" style="width:100%;padding:0"><div style="padding:0 0.25em"> <ul><li><a href="/wiki/SMPTE_color_bars" title="SMPTE color bars">SMPTE color bars</a></li> <li><a href="/wiki/EBU_colour_bars" title="EBU colour bars">EBU colour bars</a></li> <li><a href="/wiki/Indian-head_test_pattern" title="Indian-head test pattern">Indian-head test pattern</a></li> <li><a href="/wiki/EIA_1956_resolution_chart" title="EIA 1956 resolution chart">EIA 1956 resolution chart</a></li> <li><a href="/wiki/List_of_BBC_test_cards" title="List of BBC test cards">BBC Test Card</a> <a href="/wiki/List_of_BBC_test_cards#Test_Card_A" title="List of BBC test cards">A</a>, <a href="/wiki/List_of_BBC_test_cards#Test_Card_B" title="List of BBC test cards">B</a>, <a href="/wiki/List_of_BBC_test_cards#Test_Card_C" title="List of BBC test cards">C</a>, <a href="/wiki/List_of_BBC_test_cards#Test_Card_D" title="List of BBC test cards">D</a>, <a href="/wiki/List_of_BBC_test_cards#Test_Card_E_(later_Test_Card_C)" title="List of BBC test cards">E</a>, <a href="/wiki/Test_Card_F" title="Test Card F">F</a>, <a href="/wiki/List_of_BBC_test_cards#Test_Card_G" title="List of BBC test cards">G</a>, <a href="/wiki/List_of_BBC_test_cards#Test_Card_H" title="List of BBC test cards">H</a>, <a href="/wiki/Test_Card_J" class="mw-redirect" title="Test Card J">J</a>, <a href="/wiki/Test_Card_W" class="mw-redirect" title="Test Card W">W</a>, <a href="/wiki/Test_Card_X" class="mw-redirect" title="Test Card X">X</a></li> <li><a href="/wiki/ETP-1" title="ETP-1">ETP-1</a></li> <li><a href="/wiki/Philips_circle_pattern" title="Philips circle pattern">Philips circle pattern</a> (<a href="/wiki/Philips_circle_pattern#PM5534" title="Philips circle pattern">PM 5538</a>, <a href="/wiki/Philips_PM5540" title="Philips PM5540">PM 5540</a>, <a href="/wiki/Philips_circle_pattern#PM5544" title="Philips circle pattern">PM 5544</a>, <a href="/wiki/Philips_circle_pattern#PM5644" title="Philips circle pattern">PM 5644</a>)</li> <li><a href="/wiki/Snell_%26_Wilcox_Zone_Plate" title="Snell & Wilcox Zone Plate">Snell & Wilcox SW2/SW4</a></li> <li><a href="/wiki/Telefunken_FuBK" title="Telefunken FuBK">Telefunken FuBK</a></li> <li><a href="/wiki/TVE_test_card" title="TVE test card">TVE test card</a></li> <li><a href="/wiki/Universal_Electronic_Test_Chart" title="Universal Electronic Test Chart">UEIT</a></li></ul> </div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%"><a href="/wiki/Computer_language" title="Computer language">Computer languages</a></th><td class="navbox-list-with-group navbox-list navbox-odd hlist" style="width:100%;padding:0"><div style="padding:0 0.25em"> <ul><li><a href="/wiki/%22Hello,_World!%22_program" title=""Hello, World!" program">"Hello, World!" program</a></li> <li><a href="/wiki/Quine_(computing)" title="Quine (computing)">Quine</a></li> <li><a href="/wiki/TPK_algorithm" title="TPK algorithm">Trabb Pardo–Knuth algorithm</a></li> <li><a href="/wiki/Man_or_boy_test" title="Man or boy test">Man or boy test</a></li> <li><a href="/wiki/Just_another_Perl_hacker" class="mw-redirect" title="Just another Perl hacker">Just another Perl hacker</a></li></ul> </div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%"><a href="/wiki/Data_compression" title="Data compression">Data compression</a></th><td class="navbox-list-with-group navbox-list navbox-even hlist" style="width:100%;padding:0"><div style="padding:0 0.25em"> <ul><li><a href="/wiki/Calgary_corpus" title="Calgary corpus">Calgary corpus</a></li> <li><a href="/wiki/Canterbury_corpus" title="Canterbury corpus">Canterbury corpus</a></li> <li><a href="/w/index.php?title=Silesia_corpus&action=edit&redlink=1" class="new" title="Silesia corpus (page does not exist)">Silesia corpus</a></li> <li><a href="/wiki/Hutter_Prize" title="Hutter Prize">enwik8, enwik9</a></li></ul> </div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%"><a href="/wiki/3D_computer_graphics" title="3D computer graphics">3D computer graphics</a></th><td class="navbox-list-with-group navbox-list navbox-odd hlist" style="width:100%;padding:0"><div style="padding:0 0.25em"> <ul><li><a href="/wiki/Cornell_box" title="Cornell box">Cornell box</a></li> <li><a href="/wiki/Stanford_bunny" title="Stanford bunny">Stanford bunny</a></li> <li><a href="/wiki/Stanford_dragon" title="Stanford dragon">Stanford dragon</a></li> <li><a href="/wiki/Utah_teapot" title="Utah teapot">Utah teapot</a></li> <li><a href="/wiki/List_of_common_3D_test_models" title="List of common 3D test models">List</a></li></ul> </div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%"><a href="/wiki/Machine_learning" title="Machine learning">Machine learning</a></th><td class="navbox-list-with-group navbox-list navbox-even hlist" style="width:100%;padding:0"><div style="padding:0 0.25em"> <ul><li><a class="mw-selflink selflink">ImageNet</a></li> <li><a href="/wiki/MNIST_database" title="MNIST database">MNIST database</a></li> <li><a href="/wiki/List_of_datasets_for_machine-learning_research" title="List of datasets for machine-learning research">List</a></li></ul> </div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%"><a href="/wiki/Typography" title="Typography">Typography</a> (<a href="/wiki/Filler_text" title="Filler text">filler text</a>)</th><td class="navbox-list-with-group navbox-list navbox-odd hlist" style="width:100%;padding:0"><div style="padding:0 0.25em"> <ul><li><a href="/wiki/Etaoin_shrdlu" title="Etaoin shrdlu">Etaoin shrdlu</a></li> <li><a href="/wiki/Hamburgevons" title="Hamburgevons">Hamburgevons</a></li> <li><a href="/wiki/Lorem_ipsum" title="Lorem ipsum">Lorem ipsum</a></li> <li><a href="/wiki/The_quick_brown_fox_jumps_over_the_lazy_dog" title="The quick brown fox jumps over the lazy dog">The quick brown fox jumps over the lazy dog</a></li></ul> </div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%">Other</th><td class="navbox-list-with-group navbox-list navbox-even hlist" style="width:100%;padding:0"><div style="padding:0 0.25em"> <ul><li><a href="/wiki/3DBenchy" title="3DBenchy">3DBenchy</a></li> <li>Acid <ul><li><a href="/wiki/Acid1" title="Acid1">1</a></li> <li><a href="/wiki/Acid2" title="Acid2">2</a></li> <li><a href="/wiki/Acid3" title="Acid3">3</a></li></ul></li> <li><a href="/wiki/Bad_Apple!!#Use_as_a_graphical_and_audio_test" title="Bad Apple!!">"Bad Apple!!"</a></li> <li><a href="/wiki/EICAR_test_file" title="EICAR test file">EICAR test file</a></li> <li><a href="/wiki/Test_functions_for_optimization" title="Test functions for optimization">functions for optimization</a></li> <li><a href="/wiki/GTUBE" title="GTUBE">GTUBE</a></li> <li><a href="/wiki/Harvard_sentences" title="Harvard sentences">Harvard sentences</a></li> <li><a href="/wiki/Lenna" title="Lenna">Lenna</a></li> <li><a href="/wiki/The_North_Wind_and_the_Sun#Use_in_phonetic_demonstrations" title="The North Wind and the Sun">"The North Wind and the Sun"</a></li> <li><a href="/wiki/Tom%27s_Diner#The_"Mother_of_the_MP3"" title="Tom's Diner">"Tom's Diner"</a></li> <li><a href="/wiki/Film_leader" title="Film leader">SMPTE universal leader</a></li> <li><a href="/wiki/EURion_constellation" title="EURion constellation">EURion constellation</a></li> <li><a href="/wiki/Shakedown_(testing)" title="Shakedown (testing)">Shakedown</a></li> <li><a href="/wiki/Webdriver_Torso" title="Webdriver Torso">Webdriver Torso</a></li> <li><a href="/wiki/1951_USAF_resolution_test_chart" title="1951 USAF resolution test chart">1951 USAF resolution test chart</a></li></ul> </div></td></tr></tbody></table></div>    </div><noscript><img src="https://login.wikimedia.org/wiki/Special:CentralAutoLogin/start?type=1x1" alt="" width="1" height="1" style="border: none; position: absolute;"></noscript> <div class="printfooter" data-nosnippet="">Retrieved from "<a dir="ltr" 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