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data-mw-ve-target-container> <div class="vector-body-before-content"> <div class="mw-indicators"> </div> <div id="siteSub" class="noprint">From Wikipedia, the free encyclopedia</div> </div> <div id="contentSub"><div id="mw-content-subtitle"></div></div> <div id="mw-content-text" class="mw-body-content"><div class="mw-content-ltr mw-parser-output" lang="en" dir="ltr"><div class="shortdescription nomobile noexcerpt noprint searchaux" style="display:none">Family of computer vision models designed for efficient inference</div> <style data-mw-deduplicate="TemplateStyles:r1257001546">.mw-parser-output .infobox-subbox{padding:0;border:none;margin:-3px;width:auto;min-width:100%;font-size:100%;clear:none;float:none;background-color:transparent}.mw-parser-output .infobox-3cols-child{margin:auto}.mw-parser-output .infobox .navbar{font-size:100%}@media screen{html.skin-theme-clientpref-night .mw-parser-output .infobox-full-data:not(.notheme)>div:not(.notheme)[style]{background:#1f1f23!important;color:#f8f9fa}}@media screen and (prefers-color-scheme:dark){html.skin-theme-clientpref-os .mw-parser-output .infobox-full-data:not(.notheme) div:not(.notheme){background:#1f1f23!important;color:#f8f9fa}}@media(min-width:640px){body.skin--responsive .mw-parser-output .infobox-table{display:table!important}body.skin--responsive .mw-parser-output .infobox-table>caption{display:table-caption!important}body.skin--responsive .mw-parser-output .infobox-table>tbody{display:table-row-group}body.skin--responsive .mw-parser-output .infobox-table tr{display:table-row!important}body.skin--responsive .mw-parser-output .infobox-table th,body.skin--responsive .mw-parser-output .infobox-table td{padding-left:inherit;padding-right:inherit}}</style><table class="infobox vevent"><caption class="infobox-title summary">EfficientNet</caption><tbody><tr><th scope="row" class="infobox-label" style="white-space: nowrap;"><a href="/wiki/Programmer" title="Programmer">Developer(s)</a></th><td class="infobox-data"><a href="/wiki/Google_AI" title="Google AI">Google AI</a></td></tr><tr><th scope="row" class="infobox-label" style="white-space: nowrap;">Initial release</th><td class="infobox-data">May 2019</td></tr><tr><th scope="row" class="infobox-label" style="white-space: nowrap;"><a href="/wiki/Repository_(version_control)" title="Repository (version control)">Repository</a></th><td class="infobox-data"><span class="url"><a rel="nofollow" class="external text" href="https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet">github<wbr />.com<wbr />/tensorflow<wbr />/tpu<wbr />/tree<wbr />/master<wbr />/models<wbr />/official<wbr />/efficientnet</a></span></td></tr><tr><th scope="row" class="infobox-label" style="white-space: nowrap;">Written in</th><td class="infobox-data"><a href="/wiki/Python_(programming_language)" title="Python (programming language)">Python</a></td></tr><tr><th scope="row" class="infobox-label" style="white-space: nowrap;"><a href="/wiki/Software_license" title="Software license">License</a></th><td class="infobox-data"><a href="/wiki/Apache_License_2.0" class="mw-redirect" title="Apache License 2.0">Apache License 2.0</a></td></tr><tr><th scope="row" class="infobox-label" style="white-space: nowrap;">Website</th><td class="infobox-data"><a rel="nofollow" class="external text" href="https://research.google/blog/efficientnet-improving-accuracy-and-efficiency-through-automl-and-model-scaling/">Google AI Blog</a></td></tr></tbody></table> <p><b>EfficientNet</b> is a family of <a href="/wiki/Convolutional_neural_network" title="Convolutional neural network">convolutional neural networks</a> (CNNs) for <a href="/wiki/Computer_vision" title="Computer vision">computer vision</a> published by researchers at <a href="/wiki/Google_AI" title="Google AI">Google AI</a> in 2019.<sup id="cite_ref-:0_1-0" class="reference"><a href="#cite_note-:0-1"><span class="cite-bracket">[</span>1<span class="cite-bracket">]</span></a></sup> Its key innovation is <b>compound scaling</b>, which uniformly scales all dimensions of depth, width, and resolution using a single parameter. </p><p>EfficientNet models have been adopted in various computer vision tasks, including <a href="/wiki/Image_classification" class="mw-redirect" title="Image classification">image classification</a>, <a href="/wiki/Object_detection" title="Object detection">object detection</a>, and <a href="/wiki/Image_segmentation" title="Image segmentation">segmentation</a>. </p> <meta property="mw:PageProp/toc" /> <div class="mw-heading mw-heading2"><h2 id="Compound_scaling">Compound scaling</h2><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=EfficientNet&action=edit&section=1" title="Edit section: Compound scaling"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p>EfficientNet introduces <b>compound scaling</b>, which, instead of scaling one dimension of the network at a time, such as depth (number of layers), width (number of channels), or resolution (input image size), uses a <b>compound coefficient</b> <span class="mwe-math-element"><span class="mwe-math-mathml-inline mwe-math-mathml-a11y" style="display: none;"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle \phi }"> <semantics> <mrow class="MJX-TeXAtom-ORD"> <mstyle displaystyle="true" scriptlevel="0"> <mi>ϕ</mi> </mstyle> </mrow> <annotation encoding="application/x-tex">{\displaystyle \phi }</annotation> </semantics> </math></span><img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/72b1f30316670aee6270a28334bdf4f5072cdde4" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.671ex; width:1.385ex; height:2.509ex;" alt="{\displaystyle \phi }" /></span> to scale all three dimensions simultaneously. Specifically, given a baseline network, the depth, width, and resolution are scaled according to the following equations:<sup id="cite_ref-:0_1-1" class="reference"><a href="#cite_note-:0-1"><span class="cite-bracket">[</span>1<span class="cite-bracket">]</span></a></sup><span class="mwe-math-element"><span class="mwe-math-mathml-display mwe-math-mathml-a11y" style="display: none;"><math display="block" xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle {\begin{aligned}{\text{depth multiplier: }}d&=\alpha ^{\phi }\\{\text{width multiplier: }}w&=\beta ^{\phi }\\{\text{resolution multiplier: }}r&=\gamma ^{\phi }\end{aligned}}}"> <semantics> <mrow class="MJX-TeXAtom-ORD"> <mstyle displaystyle="true" scriptlevel="0"> <mrow class="MJX-TeXAtom-ORD"> <mtable columnalign="right left right left right left right left right left right left" rowspacing="3pt" columnspacing="0em 2em 0em 2em 0em 2em 0em 2em 0em 2em 0em" displaystyle="true"> <mtr> <mtd> <mrow class="MJX-TeXAtom-ORD"> <mtext>depth multiplier: </mtext> </mrow> <mi>d</mi> </mtd> <mtd> <mi></mi> <mo>=</mo> <msup> <mi>α</mi> <mrow class="MJX-TeXAtom-ORD"> <mi>ϕ</mi> </mrow> </msup> </mtd> </mtr> <mtr> <mtd> <mrow class="MJX-TeXAtom-ORD"> <mtext>width multiplier: </mtext> </mrow> <mi>w</mi> </mtd> <mtd> <mi></mi> <mo>=</mo> <msup> <mi>β</mi> <mrow class="MJX-TeXAtom-ORD"> <mi>ϕ</mi> </mrow> </msup> </mtd> </mtr> <mtr> <mtd> <mrow class="MJX-TeXAtom-ORD"> <mtext>resolution multiplier: </mtext> </mrow> <mi>r</mi> </mtd> <mtd> <mi></mi> <mo>=</mo> <msup> <mi>γ</mi> <mrow class="MJX-TeXAtom-ORD"> <mi>ϕ</mi> </mrow> </msup> </mtd> </mtr> </mtable> </mrow> </mstyle> </mrow> <annotation encoding="application/x-tex">{\displaystyle {\begin{aligned}{\text{depth multiplier: }}d&=\alpha ^{\phi }\\{\text{width multiplier: }}w&=\beta ^{\phi }\\{\text{resolution multiplier: }}r&=\gamma ^{\phi }\end{aligned}}}</annotation> </semantics> </math></span><img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/e50326e1a3e60f9f4584e66e232e14e59f1e5529" class="mwe-math-fallback-image-display mw-invert skin-invert" aria-hidden="true" style="vertical-align: -4.171ex; width:29.332ex; height:9.509ex;" alt="{\displaystyle {\begin{aligned}{\text{depth multiplier: }}d&=\alpha ^{\phi }\\{\text{width multiplier: }}w&=\beta ^{\phi }\\{\text{resolution multiplier: }}r&=\gamma ^{\phi }\end{aligned}}}" /></span>subject to <span class="mwe-math-element"><span class="mwe-math-mathml-inline mwe-math-mathml-a11y" style="display: none;"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle \alpha \cdot \beta ^{2}\cdot \gamma ^{2}\approx 2}"> <semantics> <mrow class="MJX-TeXAtom-ORD"> <mstyle displaystyle="true" scriptlevel="0"> <mi>α</mi> <mo>⋅</mo> <msup> <mi>β</mi> <mrow class="MJX-TeXAtom-ORD"> <mn>2</mn> </mrow> </msup> <mo>⋅</mo> <msup> <mi>γ</mi> <mrow class="MJX-TeXAtom-ORD"> <mn>2</mn> </mrow> </msup> <mo>≈</mo> <mn>2</mn> </mstyle> </mrow> <annotation encoding="application/x-tex">{\displaystyle \alpha \cdot \beta ^{2}\cdot \gamma ^{2}\approx 2}</annotation> </semantics> </math></span><img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/215f91a63ddc859c4acf01fa48a8db0e177224bb" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.838ex; width:13.832ex; height:3.176ex;" alt="{\displaystyle \alpha \cdot \beta ^{2}\cdot \gamma ^{2}\approx 2}" /></span> and <span class="mwe-math-element"><span class="mwe-math-mathml-inline mwe-math-mathml-a11y" style="display: none;"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle \alpha \geq 1,\beta \geq 1,\gamma \geq 1}"> <semantics> <mrow class="MJX-TeXAtom-ORD"> <mstyle displaystyle="true" scriptlevel="0"> <mi>α</mi> <mo>≥</mo> <mn>1</mn> <mo>,</mo> <mi>β</mi> <mo>≥</mo> <mn>1</mn> <mo>,</mo> <mi>γ</mi> <mo>≥</mo> <mn>1</mn> </mstyle> </mrow> <annotation encoding="application/x-tex">{\displaystyle \alpha \geq 1,\beta \geq 1,\gamma \geq 1}</annotation> </semantics> </math></span><img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/1c47ddb2121db8da4448cea25e2b8f7cafaed304" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.838ex; width:18.933ex; height:2.676ex;" alt="{\displaystyle \alpha \geq 1,\beta \geq 1,\gamma \geq 1}" /></span>. The <span class="mwe-math-element"><span class="mwe-math-mathml-inline mwe-math-mathml-a11y" style="display: none;"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle \alpha \cdot \beta ^{2}\cdot \gamma ^{2}\approx 2}"> <semantics> <mrow class="MJX-TeXAtom-ORD"> <mstyle displaystyle="true" scriptlevel="0"> <mi>α</mi> <mo>⋅</mo> <msup> <mi>β</mi> <mrow class="MJX-TeXAtom-ORD"> <mn>2</mn> </mrow> </msup> <mo>⋅</mo> <msup> <mi>γ</mi> <mrow class="MJX-TeXAtom-ORD"> <mn>2</mn> </mrow> </msup> <mo>≈</mo> <mn>2</mn> </mstyle> </mrow> <annotation encoding="application/x-tex">{\displaystyle \alpha \cdot \beta ^{2}\cdot \gamma ^{2}\approx 2}</annotation> </semantics> </math></span><img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/215f91a63ddc859c4acf01fa48a8db0e177224bb" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.838ex; width:13.832ex; height:3.176ex;" alt="{\displaystyle \alpha \cdot \beta ^{2}\cdot \gamma ^{2}\approx 2}" /></span> condition is such that increasing <span class="mwe-math-element"><span class="mwe-math-mathml-inline mwe-math-mathml-a11y" style="display: none;"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle \phi }"> <semantics> <mrow class="MJX-TeXAtom-ORD"> <mstyle displaystyle="true" scriptlevel="0"> <mi>ϕ</mi> </mstyle> </mrow> <annotation encoding="application/x-tex">{\displaystyle \phi }</annotation> </semantics> </math></span><img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/72b1f30316670aee6270a28334bdf4f5072cdde4" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.671ex; width:1.385ex; height:2.509ex;" alt="{\displaystyle \phi }" /></span> by a factor of <span class="mwe-math-element"><span class="mwe-math-mathml-inline mwe-math-mathml-a11y" style="display: none;"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle \phi _{0}}"> <semantics> <mrow class="MJX-TeXAtom-ORD"> <mstyle displaystyle="true" scriptlevel="0"> <msub> <mi>ϕ</mi> <mrow class="MJX-TeXAtom-ORD"> <mn>0</mn> </mrow> </msub> </mstyle> </mrow> <annotation encoding="application/x-tex">{\displaystyle \phi _{0}}</annotation> </semantics> </math></span><img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/085cd18f16ab03e701c109e6c68d16addab32c3a" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.671ex; width:2.44ex; height:2.509ex;" alt="{\displaystyle \phi _{0}}" /></span> would increase the total FLOPs of running the network on an image approximately <span class="mwe-math-element"><span class="mwe-math-mathml-inline mwe-math-mathml-a11y" style="display: none;"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle 2^{\phi _{0}}}"> <semantics> <mrow class="MJX-TeXAtom-ORD"> <mstyle displaystyle="true" scriptlevel="0"> <msup> <mn>2</mn> <mrow class="MJX-TeXAtom-ORD"> <msub> <mi>ϕ</mi> <mrow class="MJX-TeXAtom-ORD"> <mn>0</mn> </mrow> </msub> </mrow> </msup> </mstyle> </mrow> <annotation encoding="application/x-tex">{\displaystyle 2^{\phi _{0}}}</annotation> </semantics> </math></span><img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/6185eec524f73b573bfd1b020b6c6df1387bd14a" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.338ex; width:3.206ex; height:2.676ex;" alt="{\displaystyle 2^{\phi _{0}}}" /></span> times. The <a href="/wiki/Hyperparameter_(machine_learning)" title="Hyperparameter (machine learning)">hyperparameters</a> <span class="mwe-math-element"><span class="mwe-math-mathml-inline mwe-math-mathml-a11y" style="display: none;"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle \alpha }"> <semantics> <mrow class="MJX-TeXAtom-ORD"> <mstyle displaystyle="true" scriptlevel="0"> <mi>α</mi> </mstyle> </mrow> <annotation encoding="application/x-tex">{\displaystyle \alpha }</annotation> </semantics> </math></span><img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/b79333175c8b3f0840bfb4ec41b8072c83ea88d3" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.338ex; width:1.488ex; height:1.676ex;" alt="{\displaystyle \alpha }" /></span>, <span class="mwe-math-element"><span class="mwe-math-mathml-inline mwe-math-mathml-a11y" style="display: none;"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle \beta }"> <semantics> <mrow class="MJX-TeXAtom-ORD"> <mstyle displaystyle="true" scriptlevel="0"> <mi>β</mi> </mstyle> </mrow> <annotation encoding="application/x-tex">{\displaystyle \beta }</annotation> </semantics> </math></span><img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/7ed48a5e36207156fb792fa79d29925d2f7901e8" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.671ex; width:1.332ex; height:2.509ex;" alt="{\displaystyle \beta }" /></span>, and <span class="mwe-math-element"><span class="mwe-math-mathml-inline mwe-math-mathml-a11y" style="display: none;"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle \gamma }"> <semantics> <mrow class="MJX-TeXAtom-ORD"> <mstyle displaystyle="true" scriptlevel="0"> <mi>γ</mi> </mstyle> </mrow> <annotation encoding="application/x-tex">{\displaystyle \gamma }</annotation> </semantics> </math></span><img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/a223c880b0ce3da8f64ee33c4f0010beee400b1a" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.838ex; width:1.262ex; height:2.176ex;" alt="{\displaystyle \gamma }" /></span> are determined by a small <a href="/wiki/Grid_search" class="mw-redirect" title="Grid search">grid search</a>. The original paper suggested 1.2, 1.1, and 1.15, respectively. </p><p>Architecturally, they optimized the choice of modules by <a href="/wiki/Neural_architecture_search" title="Neural architecture search">neural architecture search</a> (NAS), and found that the inverted bottleneck convolution (which they called <b>MBConv</b>) used in <a href="/wiki/MobileNet" title="MobileNet">MobileNet</a> worked well. </p><p>The EfficientNet family is a stack of MBConv layers, with shapes determined by the compound scaling. The original publication consisted of 8 models, from EfficientNet-B0 to EfficientNet-B7, with increasing model size and accuracy. EfficientNet-B0 is the baseline network, and subsequent models are obtained by scaling the baseline network by increasing <span class="mwe-math-element"><span class="mwe-math-mathml-inline mwe-math-mathml-a11y" style="display: none;"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle \phi }"> <semantics> <mrow class="MJX-TeXAtom-ORD"> <mstyle displaystyle="true" scriptlevel="0"> <mi>ϕ</mi> </mstyle> </mrow> <annotation encoding="application/x-tex">{\displaystyle \phi }</annotation> </semantics> </math></span><img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/72b1f30316670aee6270a28334bdf4f5072cdde4" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.671ex; width:1.385ex; height:2.509ex;" alt="{\displaystyle \phi }" /></span>. </p> <div class="mw-heading mw-heading2"><h2 id="Variants">Variants</h2><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=EfficientNet&action=edit&section=2" title="Edit section: Variants"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p>EfficientNet has been adapted for fast inference on <a href="/wiki/Edge_computing" title="Edge computing">edge</a> <a href="/wiki/Tensor_Processing_Unit" title="Tensor Processing Unit">TPUs</a><sup id="cite_ref-2" class="reference"><a href="#cite_note-2"><span class="cite-bracket">[</span>2<span class="cite-bracket">]</span></a></sup> and centralized TPU or <a href="/wiki/Graphics_processing_unit" title="Graphics processing unit">GPU</a> <a href="/wiki/Computer_cluster" title="Computer cluster">clusters</a> by NAS.<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> </p><p>EfficientNet V2 was published in June 2021. The architecture was improved by further NAS search with more types of convolutional layers.<sup id="cite_ref-4" class="reference"><a href="#cite_note-4"><span class="cite-bracket">[</span>4<span class="cite-bracket">]</span></a></sup> It also introduced a training method, which progressively increases image size during training, and uses regularization techniques like <a href="/wiki/Dropout_training" class="mw-redirect" title="Dropout training">dropout</a>, <a href="/wiki/Data_augmentation" title="Data augmentation">RandAugment</a>,<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> and Mixup.<sup id="cite_ref-6" class="reference"><a href="#cite_note-6"><span class="cite-bracket">[</span>6<span class="cite-bracket">]</span></a></sup> The authors claim this approach mitigates accuracy drops often associated with progressive resizing. </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=EfficientNet&action=edit&section=3" title="Edit section: See also"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <ul><li><a href="/wiki/Convolutional_neural_network" title="Convolutional neural network">Convolutional neural network</a></li> <li><a href="/wiki/SqueezeNet" title="SqueezeNet">SqueezeNet</a></li> <li><a href="/wiki/MobileNet" title="MobileNet">MobileNet</a></li> <li><a href="/wiki/You_Only_Look_Once" title="You Only Look Once">You Only Look Once</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=EfficientNet&action=edit&section=4" title="Edit section: References"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <div class="mw-references-wrap"><ol class="references"> <li id="cite_note-:0-1"><span class="mw-cite-backlink">^ <a href="#cite_ref-:0_1-0"><sup><i><b>a</b></i></sup></a> <a href="#cite_ref-:0_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 id="CITEREFTanLe2020" class="citation cs2">Tan, Mingxing; Le, Quoc V. (2020-09-11), <i>EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks</i>, <a href="/wiki/ArXiv_(identifier)" class="mw-redirect" title="ArXiv (identifier)">arXiv</a>:<span class="id-lock-free" title="Freely accessible"><a rel="nofollow" class="external text" href="https://arxiv.org/abs/1905.11946">1905.11946</a></span></cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=book&rft.btitle=EfficientNet%3A+Rethinking+Model+Scaling+for+Convolutional+Neural+Networks&rft.date=2020-09-11&rft_id=info%3Aarxiv%2F1905.11946&rft.aulast=Tan&rft.aufirst=Mingxing&rft.au=Le%2C+Quoc+V.&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEfficientNet" class="Z3988"></span></span> </li> <li id="cite_note-2"><span class="mw-cite-backlink"><b><a href="#cite_ref-2">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222" /><cite class="citation web cs1"><a rel="nofollow" class="external text" href="https://research.google/blog/efficientnet-edgetpu-creating-accelerator-optimized-neural-networks-with-automl/">"EfficientNet-EdgeTPU: Creating Accelerator-Optimized Neural Networks with AutoML"</a>. <i>research.google</i>. August 6, 2019<span class="reference-accessdate">. Retrieved <span class="nowrap">2024-10-18</span></span>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=unknown&rft.jtitle=research.google&rft.atitle=EfficientNet-EdgeTPU%3A+Creating+Accelerator-Optimized+Neural+Networks+with+AutoML&rft.date=2019-08-06&rft_id=https%3A%2F%2Fresearch.google%2Fblog%2Fefficientnet-edgetpu-creating-accelerator-optimized-neural-networks-with-automl%2F&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEfficientNet" class="Z3988"></span></span> </li> <li id="cite_note-3"><span class="mw-cite-backlink"><b><a href="#cite_ref-3">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222" /><cite id="CITEREFLiTanPangLi2021" class="citation cs2">Li, Sheng; Tan, Mingxing; Pang, Ruoming; Li, Andrew; Cheng, Liqun; Le, Quoc; Jouppi, Norman P. (2021-02-10), <i>Searching for Fast Model Families on Datacenter Accelerators</i>, <a href="/wiki/ArXiv_(identifier)" class="mw-redirect" title="ArXiv (identifier)">arXiv</a>:<span class="id-lock-free" title="Freely accessible"><a rel="nofollow" class="external text" href="https://arxiv.org/abs/2102.05610">2102.05610</a></span></cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=book&rft.btitle=Searching+for+Fast+Model+Families+on+Datacenter+Accelerators&rft.date=2021-02-10&rft_id=info%3Aarxiv%2F2102.05610&rft.aulast=Li&rft.aufirst=Sheng&rft.au=Tan%2C+Mingxing&rft.au=Pang%2C+Ruoming&rft.au=Li%2C+Andrew&rft.au=Cheng%2C+Liqun&rft.au=Le%2C+Quoc&rft.au=Jouppi%2C+Norman+P.&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEfficientNet" class="Z3988"></span></span> </li> <li id="cite_note-4"><span class="mw-cite-backlink"><b><a href="#cite_ref-4">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222" /><cite id="CITEREFTanLe2021" class="citation cs2">Tan, Mingxing; Le, Quoc V. (2021-06-23), <i>EfficientNetV2: Smaller Models and Faster Training</i>, <a href="/wiki/ArXiv_(identifier)" class="mw-redirect" title="ArXiv (identifier)">arXiv</a>:<span class="id-lock-free" title="Freely accessible"><a rel="nofollow" class="external text" href="https://arxiv.org/abs/2104.00298">2104.00298</a></span></cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=book&rft.btitle=EfficientNetV2%3A+Smaller+Models+and+Faster+Training&rft.date=2021-06-23&rft_id=info%3Aarxiv%2F2104.00298&rft.aulast=Tan&rft.aufirst=Mingxing&rft.au=Le%2C+Quoc+V.&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEfficientNet" class="Z3988"></span></span> </li> <li id="cite_note-5"><span class="mw-cite-backlink"><b><a href="#cite_ref-5">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222" /><cite id="CITEREFCubukZophShlensLe2020" class="citation journal cs1">Cubuk, Ekin D.; Zoph, Barret; Shlens, Jonathon; Le, Quoc V. (2020). <a rel="nofollow" class="external text" href="https://openaccess.thecvf.com/content_CVPRW_2020/html/w40/Cubuk_Randaugment_Practical_Automated_Data_Augmentation_With_a_Reduced_Search_Space_CVPRW_2020_paper.html">"Randaugment: Practical Automated Data Augmentation With a Reduced Search Space"</a>: <span class="nowrap">702–</span>703. <a href="/wiki/ArXiv_(identifier)" class="mw-redirect" title="ArXiv (identifier)">arXiv</a>:<span class="id-lock-free" title="Freely accessible"><a rel="nofollow" class="external text" href="https://arxiv.org/abs/1909.13719">1909.13719</a></span>.</cite><span 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title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=book&rft.btitle=mixup%3A+Beyond+Empirical+Risk+Minimization&rft.date=2018-04-27&rft_id=info%3Aarxiv%2F1710.09412&rft.aulast=Zhang&rft.aufirst=Hongyi&rft.au=Cisse%2C+Moustapha&rft.au=Dauphin%2C+Yann+N.&rft.au=Lopez-Paz%2C+David&rfr_id=info%3Asid%2Fen.wikipedia.org%3AEfficientNet" class="Z3988"></span></span> </li> </ol></div> <div class="mw-heading mw-heading2"><h2 id="External_links">External links</h2><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=EfficientNet&action=edit&section=5" title="Edit section: External links"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <ul><li><a rel="nofollow" class="external text" href="https://ai.googleblog.com/2019/05/efficientnet-improving-accuracy-and.html">EfficientNet: Improving Accuracy and Efficiency through AutoML and Model Scaling (Google AI Blog)</a></li></ul> <div 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<li><a href="/wiki/Google_Brain" title="Google Brain">Google Brain</a></li> <li><a href="/wiki/Google_DeepMind" title="Google DeepMind">Google DeepMind</a></li></ul> </div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%">Computer programs</th><td class="navbox-list-with-group navbox-list navbox-odd" style="width:100%;padding:0"><div style="padding:0 0.25em"></div><table class="nowraplinks navbox-subgroup" style="border-spacing:0"><tbody><tr><th scope="row" class="navbox-group" style="width:1%">AlphaGo</th><td class="navbox-list-with-group navbox-list navbox-odd" style="width:100%;padding:0"><div style="padding:0 0.25em"></div><table class="nowraplinks navbox-subgroup" style="border-spacing:0"><tbody><tr><th scope="row" class="navbox-group" style="width:1%">Versions</th><td class="navbox-list-with-group navbox-list navbox-odd" style="width:100%;padding:0"><div style="padding:0 0.25em"> <ul><li><a href="/wiki/AlphaGo" title="AlphaGo">AlphaGo</a> (2015)</li> <li><a href="/wiki/Master_(software)" title="Master (software)">Master</a> (2016)</li> <li><a href="/wiki/AlphaGo_Zero" title="AlphaGo Zero">AlphaGo Zero</a> (2017)</li> <li><a href="/wiki/AlphaZero" title="AlphaZero">AlphaZero</a> (2017)</li> <li><a href="/wiki/MuZero" title="MuZero">MuZero</a> (2019)</li></ul> </div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%">Competitions</th><td class="navbox-list-with-group navbox-list navbox-even" style="width:100%;padding:0"><div style="padding:0 0.25em"> <ul><li><a href="/wiki/AlphaGo_versus_Fan_Hui" title="AlphaGo versus Fan Hui">Fan Hui</a> (2015)</li> <li><a href="/wiki/AlphaGo_versus_Lee_Sedol" title="AlphaGo versus Lee Sedol">Lee Sedol</a> (2016)</li> <li><a href="/wiki/AlphaGo_versus_Ke_Jie" title="AlphaGo versus Ke Jie">Ke Jie</a> (2017)</li></ul> </div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%">In popular culture</th><td class="navbox-list-with-group navbox-list navbox-odd" style="width:100%;padding:0"><div style="padding:0 0.25em"> <ul><li><i><a href="/wiki/AlphaGo_(film)" title="AlphaGo (film)">AlphaGo</a></i> (2017)</li> <li><i><a href="/wiki/The_MANIAC" title="The MANIAC">The MANIAC</a></i> (2023)</li></ul> </div></td></tr></tbody></table><div></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" style="width:100%;padding:0"><div style="padding:0 0.25em"> <ul><li><a href="/wiki/AlphaFold" title="AlphaFold">AlphaFold</a> (2018)</li> <li><a href="/wiki/AlphaStar_(software)" title="AlphaStar (software)">AlphaStar</a> (2019)</li> <li><a href="/wiki/AlphaDev" title="AlphaDev">AlphaDev</a> (2023)</li> <li><a href="/wiki/AlphaGeometry" title="AlphaGeometry">AlphaGeometry</a> (2024)</li></ul> </div></td></tr></tbody></table><div></div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%">Machine learning</th><td class="navbox-list-with-group navbox-list navbox-odd" style="width:100%;padding:0"><div style="padding:0 0.25em"></div><table class="nowraplinks navbox-subgroup" style="border-spacing:0"><tbody><tr><th scope="row" class="navbox-group" style="width:1%">Neural networks</th><td class="navbox-list-with-group navbox-list navbox-odd" style="width:100%;padding:0"><div style="padding:0 0.25em"> <ul><li><a href="/wiki/Inception_(deep_learning_architecture)" title="Inception (deep learning architecture)">Inception</a> (2014)</li> <li><a href="/wiki/WaveNet" title="WaveNet">WaveNet</a> (2016)</li> <li><a href="/wiki/MobileNet" title="MobileNet">MobileNet</a> (2017)</li> <li><a href="/wiki/Transformer_(deep_learning_architecture)" title="Transformer (deep learning architecture)">Transformer</a> (2017)</li> <li><a class="mw-selflink selflink">EfficientNet</a> (2019)</li> <li><a href="/wiki/Gato_(DeepMind)" title="Gato (DeepMind)">Gato</a> (2022)</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" style="width:100%;padding:0"><div style="padding:0 0.25em"> <ul><li><a href="/wiki/Quantum_Artificial_Intelligence_Lab" title="Quantum Artificial Intelligence Lab">Quantum Artificial Intelligence Lab</a></li> <li><a href="/wiki/TensorFlow" title="TensorFlow">TensorFlow</a></li> <li><a href="/wiki/Tensor_Processing_Unit" title="Tensor Processing Unit">Tensor Processing Unit</a></li></ul> </div></td></tr></tbody></table><div></div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%">Generative AI</th><td class="navbox-list-with-group navbox-list navbox-odd" style="width:100%;padding:0"><div style="padding:0 0.25em"></div><table class="nowraplinks navbox-subgroup" style="border-spacing:0"><tbody><tr><th scope="row" class="navbox-group" style="width:1%">Chatbots</th><td class="navbox-list-with-group navbox-list navbox-odd" style="width:100%;padding:0"><div style="padding:0 0.25em"> <ul><li><a href="/wiki/Google_Assistant" title="Google Assistant">Assistant</a> (2016)</li> <li><a href="/wiki/Sparrow_(chatbot)" title="Sparrow (chatbot)">Sparrow</a> (2022)</li> <li><a href="/wiki/Gemini_(chatbot)" title="Gemini (chatbot)">Gemini</a> (2023)</li></ul> </div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%">Language models</th><td class="navbox-list-with-group navbox-list navbox-even" style="width:100%;padding:0"><div style="padding:0 0.25em"> <ul><li><a href="/wiki/BERT_(language_model)" title="BERT (language model)">BERT</a> (2018)</li> <li><a href="/wiki/XLNet" title="XLNet">XLNet</a> (2019)</li> <li><a href="/wiki/T5_(language_model)" title="T5 (language model)">T5</a> (2019)</li> <li><a href="/wiki/LaMDA" title="LaMDA">LaMDA</a> (2021)</li> <li><a href="/wiki/Chinchilla_(language_model)" title="Chinchilla (language model)">Chinchilla</a> (2022)</li> <li><a href="/wiki/PaLM" title="PaLM">PaLM</a> (2022)</li> <li><a href="/wiki/Gemini_(language_model)" title="Gemini (language model)">Gemini</a> (2023)</li> <li><a href="/wiki/VideoPoet" title="VideoPoet">VideoPoet</a> (2024)</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-odd" style="width:100%;padding:0"><div style="padding:0 0.25em"> <ul><li><a href="/wiki/DreamBooth" title="DreamBooth">DreamBooth</a> (2022)</li> <li><a href="/wiki/NotebookLM" title="NotebookLM">NotebookLM</a> (2023)</li> <li><a href="/wiki/Google_Vids" title="Google Vids">Vids</a> (2024)</li></ul> </div></td></tr></tbody></table><div></div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%">See also</th><td class="navbox-list-with-group navbox-list navbox-even" style="width:100%;padding:0"><div style="padding:0 0.25em"> <ul><li>"<a href="/wiki/Attention_Is_All_You_Need" title="Attention Is All You Need">Attention Is All You Need</a>"</li> <li><a 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