Wide ResNet | Papers With Code

<!doctype html> <html lang="en"> <head> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"> <script> const GTAG_ENABLED = true ; const GTAG_TRACKING_ID = "UA-121182717-1"; const SENTRY_DSN_FRONTEND = "".trim(); const GLOBAL_CSRF_TOKEN = 's6pyLuqAKJT5QmDu5l00lKXHej4W80T0T7y2kx0pbStAbaRwGVUNEvQJGRuLk60u'; const MEDIA_URL = "https://production-media.paperswithcode.com/"; const ASSETS_URL = "https://production-assets.paperswithcode.com"; run_after_frontend_loaded = window.run_after_frontend_loaded || []; </script> <link rel="preconnect" href="https://production-assets.paperswithcode.com"><link rel="dns-prefetch" href="https://production-assets.paperswithcode.com"><link rel="preload" as="font" type="font/woff2" href="https://production-assets.paperswithcode.com/perf/fonts/65e877e527022735c1a1.woff2" crossorigin><link rel="preload" as="font" type="font/woff2" href="https://production-assets.paperswithcode.com/perf/fonts/917632e36982ca7933c8.woff2" crossorigin><link rel="preload" as="font" type="font/woff2" href="https://production-assets.paperswithcode.com/perf/fonts/f1405bd8a987c2ea8a67.woff2" crossorigin><script>(()=>{if(GTAG_ENABLED){const t=document.createElement("script");function n(){window.dataLayer.push(arguments)}t.src=`https://www.googletagmanager.com/gtag/js?id=${GTAG_TRACKING_ID}`,document.head.appendChild(t),window.dataLayer=window.dataLayer||[],window.gtag=n,n("js",new Date),n("config",GTAG_TRACKING_ID),window.captureOutboundLink=function(t){n("event","click",{event_category:"outbound",event_label:t})}}else window.captureOutboundLink=function(n){document.location=n}})();</script><style>:root{--bs-blue: #0d6efd;--bs-indigo: #6610f2;--bs-purple: #6f42c1;--bs-pink: #d63384;--bs-red: #dc3545;--bs-orange: #fd7e14;--bs-yellow: #ffc107;--bs-green: #198754;--bs-teal: #20c997;--bs-cyan: #21cbce;--bs-white: #fff;--bs-gray: #6c757d;--bs-gray-dark: #343a40;--bs-primary: #0d6efd;--bs-secondary: #6c757d;--bs-success: #198754;--bs-info: #21cbce;--bs-warning: #ffc107;--bs-danger: #dc3545;--bs-light: #f8f9fa;--bs-dark: #212529;--bs-font-sans-serif: system-ui, -apple-system, "Segoe UI", Roboto, "Helvetica Neue", Arial, "Noto Sans", "Liberation Sans", sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol", "Noto Color Emoji";--bs-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace;--bs-gradient: linear-gradient(180deg, rgba(255, 255, 255, 0.15), rgba(255, 255, 255, 0))}@font-face{font-family:"Lato";font-style:normal;font-weight:300;font-display:swap;src:local(""),url(https://production-assets.paperswithcode.com/perf/fonts/917632e36982ca7933c8.woff2) format("woff2")}@font-face{font-family:"Lato";font-style:normal;font-weight:400;font-display:swap;src:local(""),url(https://production-assets.paperswithcode.com/perf/fonts/65e877e527022735c1a1.woff2) format("woff2")}@font-face{font-family:"Lato";font-style:normal;font-weight:700;font-display:swap;src:local(""),url(https://production-assets.paperswithcode.com/perf/fonts/f1405bd8a987c2ea8a67.woff2) format("woff2")}@font-face{font-family:"Computer Modern Serif";src:local(""),url(https://production-assets.paperswithcode.com/perf/fonts/b63de31899ec71cfb870.woff) format("woff");font-display:swap;font-weight:normal;font-style:normal}@font-face{font-family:"Computer Modern Serif";src:local(""),url(https://production-assets.paperswithcode.com/perf/fonts/5d5c7512cb539fb279b2.woff) format("woff");font-display:swap;font-weight:bold;font-style:normal}@font-face{font-family:"Computer Modern Serif";src:local(""),url(https://production-assets.paperswithcode.com/perf/fonts/357ce3503c6299bc1b58.woff) format("woff");font-display:swap;font-weight:normal;font-style:italic}@font-face{font-family:"Computer Modern Serif";src:local(""),url(https://production-assets.paperswithcode.com/perf/fonts/c83e6f15d4c7568ee872.woff) format("woff");font-display:swap;font-weight:bold;font-style:italic}@font-face{font-family:"Exo";font-style:normal;font-weight:100;src:local(""),url(https://production-assets.paperswithcode.com/perf/fonts/729c812ee9989426abb1.woff2) format("woff2");font-display:swap}@font-face{font-family:"Nunito";font-style:normal;font-weight:400;src:local(""),url(https://production-assets.paperswithcode.com/perf/fonts/39a18f443d434999b89b.woff2) format("woff2");font-display:swap}@font-face{font-family:"Nunito";font-style:normal;font-weight:700;src:local(""),url(https://production-assets.paperswithcode.com/perf/fonts/4ad349571e28bb59c5a5.woff2) format("woff2");font-display:swap}*,*::before,*::after{box-sizing:border-box}@media(prefers-reduced-motion: no-preference){:root{scroll-behavior:smooth}}body{margin:0;font-family:system-ui,-apple-system,"Segoe UI",Roboto,"Helvetica Neue",Arial,"Noto Sans","Liberation Sans",sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI Symbol","Noto Color Emoji";font-size:1rem;font-weight:400;line-height:1.5;color:#212529;background-color:#fff;-webkit-text-size-adjust:100%;-webkit-tap-highlight-color:rgba(0,0,0,0)}[tabindex="-1"]:focus:not(:focus-visible){outline:0 !important}hr{margin:1rem 0;color:#000;background-color:currentColor;border:0;opacity:.1}hr:not([size]){height:1px}h6,h5,h4,h3,h2,h1{margin-top:0;margin-bottom:.5rem;font-weight:500;line-height:1.2}h1{font-size:calc(1.375rem + 1.5vw)}@media(min-width: 1200px){h1{font-size:2.5rem}}h2{font-size:calc(1.325rem + 0.9vw)}@media(min-width: 1200px){h2{font-size:2rem}}h3{font-size:calc(1.3rem + 0.6vw)}@media(min-width: 1200px){h3{font-size:1.75rem}}h4{font-size:calc(1.275rem + 0.3vw)}@media(min-width: 1200px){h4{font-size:1.5rem}}h5{font-size:1.25rem}h6{font-size:1rem}p{margin-top:0;margin-bottom:1rem}ol,ul{padding-left:2rem}ol,ul{margin-top:0;margin-bottom:1rem}ul ul{margin-bottom:0}b,strong{font-weight:bolder}small{font-size:0.875em}a{color:#0d6efd;text-decoration:none}a:hover{color:#0a58ca;text-decoration:none}a:not([href]):not([class]),a:not([href]):not([class]):hover{color:inherit;text-decoration:none}pre,code{font-family:var(--bs-font-monospace);font-size:1em;direction:ltr /* rtl:ignore */;unicode-bidi:bidi-override}pre{display:block;margin-top:0;margin-bottom:1rem;overflow:auto;font-size:0.875em}code{font-size:0.875em;color:#d63384;word-wrap:break-word}a>code{color:inherit}figure{margin:0 0 1rem}img,svg{vertical-align:middle}table{caption-side:bottom;border-collapse:collapse}th{text-align:inherit;text-align:-webkit-match-parent}thead,tbody,tfoot,tr,td,th{border-color:inherit;border-style:solid;border-width:0}label{display:inline-block}button{border-radius:0}button:focus:not(:focus-visible){outline:0}input,button,select,optgroup,textarea{margin:0;font-family:inherit;font-size:inherit;line-height:inherit}button,select{text-transform:none}[role=button]{cursor:pointer}select{word-wrap:normal}[list]::-webkit-calendar-picker-indicator{display:none}button,[type=button],[type=reset],[type=submit]{-webkit-appearance:button}button:not(:disabled),[type=button]:not(:disabled),[type=reset]:not(:disabled),[type=submit]:not(:disabled){cursor:pointer}::-moz-focus-inner{padding:0;border-style:none}textarea{resize:vertical}::-webkit-datetime-edit-fields-wrapper,::-webkit-datetime-edit-text,::-webkit-datetime-edit-minute,::-webkit-datetime-edit-hour-field,::-webkit-datetime-edit-day-field,::-webkit-datetime-edit-month-field,::-webkit-datetime-edit-year-field{padding:0}::-webkit-inner-spin-button{height:auto}[type=search]{outline-offset:-2px;-webkit-appearance:textfield}::-webkit-search-decoration{-webkit-appearance:none}::-webkit-color-swatch-wrapper{padding:0}::file-selector-button{font:inherit}::-webkit-file-upload-button{font:inherit;-webkit-appearance:button}iframe{border:0}[hidden]{display:none !important}.list-unstyled{padding-left:0;list-style:none}small,.small{font-size:0.875em;font-weight:400}.footer{display:block;margin-top:30px;padding:15px;border-top:1px solid #e0e0e0;font-size:13px;color:#aaa;text-align:center}.footer a{color:#999}.footer-contact{margin-bottom:5px}.footer-contact-item{display:inline-block}.footer-links>*:not(:last-child){margin-right:1rem}.icon-wrapper{display:inline-block;width:1em;height:1em;contain:strict;fill:currentcolor;box-sizing:content-box !important}.icon-wrapper.icon-fa{position:relative;top:2px}.icon-wrapper svg{display:block;height:100%;width:100%}.icon-wrapper[data-name=slack] svg{width:200%;height:200%;transform:translate(-25%, -25%)}.icon-wrapper:not(.icon-color)>svg>*{stroke:currentColor}.navbar-brand .icon-wrapper{color:#21cbce;width:30px;height:30px;vertical-align:middle}.navbar-mobile-twitter{margin-right:18px !important;padding-top:1px}.navbar-mobile-twitter a{color:#1d9bf0}.navbar-mobile-twitter .icon-wrapper{width:23px;height:23px}.header-search{margin-bottom:26px}.header-search form{position:relative}.header-search .icon{color:gray;position:absolute !important;top:50% !important;left:initial !important;padding-right:0 !important;transform:translateY(-50%);right:22px;padding:0;height:20px;width:20px}.header-search .icon .icon-wrapper{width:100%;height:100%;top:0}.nav-link-social-icon{color:#1d9bf0;width:25px;height:25px}.nav-link-social-icon-slack{vertical-align:middle}@media(min-width: 992px){.header-search{margin:0}.header-search .icon{right:10px}.nav-link-social-icon{width:20px;height:20px}.nav-link-social-icon-slack{width:22px;height:22px}} </style><link href="https://production-assets.paperswithcode.com/static/css/13.a0e289cc.chunk.css" rel="stylesheet"><link href="https://production-assets.paperswithcode.com/static/css/main.cd7ec85b.chunk.css" rel="stylesheet">  <title>Wide ResNet | Papers With Code</title> <meta name="description" content="Summary Wide Residual Networks are a variant on ResNets where we decrease depth and increase the width of residual networks. This is achieved through the use of wide residual blocks. How do I load this model? To load a pretrained model: python import torchvision.models as models wide_resnet50_2 = models.wide_resnet50_2(pretrained=True) Replace the model name with the variant you want to use, e.g. wide_resnet50_2. You can find the IDs in the model summaries at the top of this page. To evaluate the model, use the image classification recipes from the library. bash python train.py --test-only --model='&lt;model_name&gt;' How do I train this model? You can follow the torchvision recipe on GitHub for training a new model afresh. Citation BibTeX @article{DBLP:journals/corr/ZagoruykoK16, author = {Sergey Zagoruyko and Nikos Komodakis}, title = {Wide Residual Networks}, journal = {CoRR}, volume = {abs/1605.07146}, year = {2016}, url = {http://arxiv.org/abs/1605.07146}, archivePrefix = {arXiv}, eprint = {1605.07146}, timestamp = {Mon, 13 Aug 2018 16:46:42 +0200}, biburl = {https://dblp.org/rec/journals/corr/ZagoruykoK16.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} }" />  <meta property="og:title" content="Papers with Code - Wide ResNet"> <meta property="og:description" content="Summary Wide Residual Networks are a variant on ResNets where we decrease depth and increase the width of residual networks. This is achieved through the use of wide residual blocks. How do I load this model? To load a pretrained model: python import torchvision.models as models wide_resnet50_2 = models.wide_resnet50_2(pretrained=True) Replace the model name with the variant you want to use, e.g. wide_resnet50_2. You can find the IDs in the model summaries at the top of this page. To evaluate the model, use the image classification recipes from the library. bash python train.py --test-only --model='&lt;model_name&gt;' How do I train this model? You can follow the torchvision recipe on GitHub for training a new model afresh. Citation BibTeX @article{DBLP:journals/corr/ZagoruykoK16, author = {Sergey Zagoruyko and Nikos Komodakis}, title = {Wide Residual Networks}, journal = {CoRR}, volume = {abs/1605.07146}, year = {2016}, url = {http://arxiv.org/abs/1605.07146}, archivePrefix = {arXiv}, eprint = {1605.07146}, timestamp = {Mon, 13 Aug 2018 16:46:42 +0200}, biburl = {https://dblp.org/rec/journals/corr/ZagoruykoK16.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} }"> <meta property="og:image" content="https://production-media.paperswithcode.com/models/wide_resnet50_2_aKAjVo7.png"> <meta property="og:url" content="https://paperswithcode.com/lib/torchvision/wide-resnet">  <meta name="twitter:card" content="summary_large_image"> <meta name="twitter:site" content="@paperswithcode"> <meta name="twitter:title" content="Papers with Code - Wide ResNet"> <meta name="twitter:description" content="Summary Wide Residual Networks are a variant on ResNets where we decrease depth and increase the width of residual networks. This is achieved through the use of wide residual blocks. How do I load this model? To load a pretrained model: python import torchvision.models as models wide_resnet50_2 = models.wide_resnet50_2(pretrained=True) Replace the model name with the variant you want to use, e.g. wide_resnet50_2. You can find the IDs in the model summaries at the top of this page. To evaluate the model, use the image classification recipes from the library. bash python train.py --test-only --model='&lt;model_name&gt;' How do I train this model? You can follow the torchvision recipe on GitHub for training a new model afresh. Citation BibTeX @article{DBLP:journals/corr/ZagoruykoK16, author = {Sergey Zagoruyko and Nikos Komodakis}, title = {Wide Residual Networks}, journal = {CoRR}, volume = {abs/1605.07146}, year = {2016}, url = {http://arxiv.org/abs/1605.07146}, archivePrefix = {arXiv}, eprint = {1605.07146}, timestamp = {Mon, 13 Aug 2018 16:46:42 +0200}, biburl = {https://dblp.org/rec/journals/corr/ZagoruykoK16.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} }"> <meta name="twitter:creator" content="@paperswithcode"> <meta name="twitter:url" content="https://paperswithcode.com/lib/torchvision/wide-resnet"> <meta name="twitter:domain" content="paperswithcode.com">  <script type="application/ld+json">{ "@context": "http://schema.org", "@graph": { "@type": "CreativeWork", "@id": "wide-resnet", "name": "Wide ResNet", "description": "Summary\nWide Residual Networks are a variant on ResNets where we decrease depth and increase the width of residual networks. This is achieved through the use of wide residual blocks.\n\nHow do I load this model?\nTo load a pretrained model:\n\npython\nimport torchvision.models as models\nwide_resnet50_2 = models.wide_resnet50_2(pretrained=True)\n\nReplace the model name with the variant you want to use, e.g. wide_resnet50_2. You can find \nthe IDs in the model summaries at the top of this page.\n\nTo evaluate the model, use the image classification recipes from the library.\n\nbash\npython train.py --test-only --model='\u0026lt;model_name\u0026gt;'\n\nHow do I train this model?\nYou can follow the torchvision recipe on GitHub for training a new model afresh.\n\nCitation\nBibTeX\n@article{DBLP:journals/corr/ZagoruykoK16,\n author = {Sergey Zagoruyko and\n Nikos Komodakis},\n title = {Wide Residual Networks},\n journal = {CoRR},\n volume = {abs/1605.07146},\n year = {2016},\n url = {http://arxiv.org/abs/1605.07146},\n archivePrefix = {arXiv},\n eprint = {1605.07146},\n timestamp = {Mon, 13 Aug 2018 16:46:42 +0200},\n biburl = {https://dblp.org/rec/journals/corr/ZagoruykoK16.bib},\n bibsource = {dblp computer science bibliography, https://dblp.org}\n}", "url": "https://paperswithcode.com/lib/torchvision/wide-resnet", "image": "https://production-media.paperswithcode.com/models/wide_resnet50_2_aKAjVo7.png", "headline": "Wide ResNet" } }</script> <meta name="theme-color" content="#fff"/> <link rel="manifest" href="https://production-assets.paperswithcode.com/static/manifest.web.json"> </head> <body> <nav class="navbar navbar-expand-lg navbar-light header"> <a class="navbar-brand" href="/"> <span class=" icon-wrapper" data-name="pwc"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512"><path d="M88 128h48v256H88zm144 0h48v256h-48zm-72 16h48v224h-48zm144 0h48v224h-48zm72-16h48v256h-48z"/><path d="M104 104V56H16v400h88v-48H64V104zm304-48v48h40v304h-40v48h88V56z"/></svg></span> </a> <div class="navbar-mobile-twitter d-lg-none"> <a rel="noreferrer" href="https://twitter.com/paperswithcode"> <span class=" icon-wrapper icon-fa icon-fa-brands" data-name="twitter"><svg viewBox="0 0 512.001 515.25" xmlns="http://www.w3.org/2000/svg"><path d="M459.37 152.016c.326 4.548.326 9.097.326 13.645 0 138.72-105.583 298.558-298.559 298.558C101.685 464.22 46.457 447 0 417.114c8.447.973 16.568 1.298 25.34 1.298 49.054 0 94.213-16.568 130.274-44.832-46.132-.975-84.792-31.188-98.113-72.772 6.499.975 12.996 1.624 19.819 1.624 9.42 0 18.843-1.3 27.613-3.573-48.08-9.747-84.142-51.98-84.142-102.984v-1.3c13.968 7.798 30.213 12.67 47.43 13.32-28.263-18.843-46.78-51.006-46.78-87.391 0-19.492 5.196-37.36 14.294-52.954 51.654 63.674 129.3 105.258 216.364 109.807-1.624-7.797-2.599-15.918-2.599-24.04 0-57.827 46.782-104.934 104.934-104.934 30.214 0 57.502 12.67 76.671 33.136 23.715-4.548 46.455-13.319 66.599-25.34-7.798 24.367-24.366 44.834-46.132 57.828 21.117-2.274 41.584-8.122 60.426-16.244-14.292 20.791-32.161 39.309-52.628 54.253z"/></svg></span> </a> </div> <button class="navbar-toggler" type="button" data-toggle="collapse" data-bs-toggle="collapse" data-target="#top-menu" data-bs-target="#top-menu" aria-controls="top-menu" aria-expanded="false" aria-label="Toggle navigation" > <span class="navbar-toggler-icon"></span> </button> <div class="collapse navbar-collapse" id="top-menu"> <ul class="navbar-nav mr-auto navbar-nav__left light-header"> <li class="nav-item header-search"> <form action="/search" method="get" id="id_global_search_form" autocomplete="off"> <input type="text" name="q_meta" style="display:none" id="q_meta" /> <input type="hidden" name="q_type" id="q_type" /> <input id="id_global_search_input" autocomplete="off" value="" name='q' class="global-search" type="search" placeholder='Search'/> <button type="submit" class="icon"><span class=" icon-wrapper icon-fa icon-fa-light" data-name="search"><svg viewBox="0 0 512.025 520.146" xmlns="http://www.w3.org/2000/svg"><path d="M508.5 482.6c4.7 4.7 4.7 12.3 0 17l-9.9 9.9c-4.7 4.7-12.3 4.7-17 0l-129-129c-2.2-2.3-3.5-5.3-3.5-8.5v-10.2C312 396 262.5 417 208 417 93.1 417 0 323.9 0 209S93.1 1 208 1s208 93.1 208 208c0 54.5-21 104-55.3 141.1H371c3.2 0 6.2 1.2 8.5 3.5zM208 385c97.3 0 176-78.7 176-176S305.3 33 208 33 32 111.7 32 209s78.7 176 176 176z"/></svg></span></button> </form> </li> <li class="nav-item"> <a class="nav-link" href="/sota"> Browse State-of-the-Art </a> </li> <li class="nav-item"> <a class="nav-link" href="/datasets"> Datasets </a> </li> <li class="nav-item"> <a class="nav-link" href="/methods">Methods</a> </li> <li class="nav-item dropdown"> <a class="nav-link dropdown-toggle" role="button" id="navbarDropdownRepro" data-toggle="dropdown" data-bs-toggle="dropdown" aria-haspopup="true" aria-expanded="false" > More </a> <div class="dropdown-menu" aria-labelledby="navbarDropdownRepro"> <a class="dropdown-item" href="/newsletter">Newsletter</a> <a class="dropdown-item" href="/rc2022">RC2022</a> <div class="dropdown-divider"></div> <a class="dropdown-item" href="/about">About</a> <a class="dropdown-item" href="/trends">Trends</a> <a class="dropdown-item" href="https://portal.paperswithcode.com/"> Portals </a> <a class="dropdown-item" href="/libraries"> Libraries </a> </div> </li> </ul> <ul class="navbar-nav ml-auto navbar-nav__right navbar-subscribe justify-content-center align-items-center"> <li class="nav-item"> <a class="nav-link" rel="noreferrer" href="https://twitter.com/paperswithcode"> <span class="nav-link-social-icon icon-wrapper icon-fa icon-fa-brands" data-name="twitter"><svg viewBox="0 0 512.001 515.25" xmlns="http://www.w3.org/2000/svg"><path d="M459.37 152.016c.326 4.548.326 9.097.326 13.645 0 138.72-105.583 298.558-298.559 298.558C101.685 464.22 46.457 447 0 417.114c8.447.973 16.568 1.298 25.34 1.298 49.054 0 94.213-16.568 130.274-44.832-46.132-.975-84.792-31.188-98.113-72.772 6.499.975 12.996 1.624 19.819 1.624 9.42 0 18.843-1.3 27.613-3.573-48.08-9.747-84.142-51.98-84.142-102.984v-1.3c13.968 7.798 30.213 12.67 47.43 13.32-28.263-18.843-46.78-51.006-46.78-87.391 0-19.492 5.196-37.36 14.294-52.954 51.654 63.674 129.3 105.258 216.364 109.807-1.624-7.797-2.599-15.918-2.599-24.04 0-57.827 46.782-104.934 104.934-104.934 30.214 0 57.502 12.67 76.671 33.136 23.715-4.548 46.455-13.319 66.599-25.34-7.798 24.367-24.366 44.834-46.132 57.828 21.117-2.274 41.584-8.122 60.426-16.244-14.292 20.791-32.161 39.309-52.628 54.253z"/></svg></span> </a> </li> <li class="nav-item"> <a id="signin-link" class="nav-link" href="/accounts/login?next=/lib/torchvision/wide-resnet">Sign In</a> </li> </ul> </div> </nav>  <div class="modal fade" id="emailModal" tabindex="-1" role="dialog" aria-labelledby="emailModalLabel" aria-hidden="true"> <div class="modal-dialog" role="document"> <div class="modal-content"> <div class="modal-header"> <h3 class="modal-title" id="emailModalLabel">Subscribe to the PwC Newsletter</h3> <button type="button" class="close" data-dismiss="modal" data-bs-dismiss="modal" aria-label="Close"> <span aria-hidden="true">×</span> </button> </div> <form action="" method="post"> <div class="modal-body"> <div class="modal-body-info-text"> Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets.<br/><br/> <a href="/newsletter">Read previous issues</a> </div> <input type="hidden" name="csrfmiddlewaretoken" value="s6pyLuqAKJT5QmDu5l00lKXHej4W80T0T7y2kx0pbStAbaRwGVUNEvQJGRuLk60u"> <input placeholder="Enter your email" type="email" class="form-control pwc-email" name="address" id="id_address" max_length="100" required> </div> <div class="modal-footer"> <button type="submit" class="btn btn-primary">Subscribe</button> </div> </form> </div> </div> </div>  <div class="modal fade" id="loginModal" tabindex="-1" role="dialog" aria-labelledby="loginModalLabel" aria-hidden="true"> <div class="modal-dialog" role="document"> <div class="modal-content"> <div class="modal-header"> <h5 class="modal-title" id="loginModalLabel">Join the community</h5> <button type="button" class="close btn-close" data-dismiss="modal" data-bs-dismiss="modal" aria-label="Close"> <span aria-hidden="true">×</span> </button> </div> <div class="login-modal-message"> You need to <a href="/accounts/login?next=/lib/torchvision/wide-resnet">log in</a> to edit.<br/> You can <a href="/accounts/register?next=/lib/torchvision/wide-resnet">create a new account</a> if you don't have one.<br/><br/> </div> </div> </div> </div>   <div class="modal fade" id="editModel" role="dialog" aria-labelledby="editModelLabel" aria-hidden="true"> <div class="modal-dialog modal-lg" role="document"> <div class="modal-content"> <div class="modal-header"> <h5 class="modal-title model-modal-title" id="editModelLabel">Edit Wide ResNet - RJT1990</h5> <button type="button" class="close" data-dismiss="modal" aria-label="Close"> <span aria-hidden="true">×</span> </button> </div> <div class="modal-body"> <form action="" method="post" enctype="multipart/form-data"> <input type="hidden" name="csrfmiddlewaretoken" value="s6pyLuqAKJT5QmDu5l00lKXHej4W80T0T7y2kx0pbStAbaRwGVUNEvQJGRuLk60u"> <div id="div_id_name" class="form-group"> <label for="id_name" class=" requiredField"> Model Name:<span class="asteriskField">*</span> </label> <div class=""> <input type="text" name="name" value="Wide ResNet" maxlength="200" class="textinput textInput form-control" required id="id_name"> </div> </div> <div id="div_id_description" class="form-group"> <label for="id_description" class=""> Description with Markdown (optional): </label> <div class=""> <textarea name="description" cols="40" rows="12" class="textarea form-control" id="id_description"> # Summary **Wide Residual Networks** are a variant on [ResNets](https://paperswithcode.com/method/resnet) where we decrease depth and increase the width of residual networks. This is achieved through the use of [wide residual blocks](https://paperswithcode.com/method/wide-residual-block). ## How do I load this model? To load a pretrained model: ```python import torchvision.models as models wide_resnet50_2 = models.wide_resnet50_2(pretrained=True) ``` Replace the model name with the variant you want to use, e.g. `wide_resnet50_2`. You can find the IDs in the model summaries at the top of this page. To evaluate the model, use the [image classification recipes](https://github.com/pytorch/vision/tree/master/references/classification) from the library. ```bash python train.py --test-only --model='<model_name>' ``` ## How do I train this model? You can follow the [torchvision recipe](https://github.com/pytorch/vision/tree/master/references/classification) on GitHub for training a new model afresh. ## Citation ```BibTeX @article{DBLP:journals/corr/ZagoruykoK16, author = {Sergey Zagoruyko and Nikos Komodakis}, title = {Wide Residual Networks}, journal = {CoRR}, volume = {abs/1605.07146}, year = {2016}, url = {http://arxiv.org/abs/1605.07146}, archivePrefix = {arXiv}, eprint = {1605.07146}, timestamp = {Mon, 13 Aug 2018 16:46:42 +0200}, biburl = {https://dblp.org/rec/journals/corr/ZagoruykoK16.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```</textarea> </div> </div> <div id="div_id_paper" class="form-group"> <label for="id_paper" class=" requiredField"> Paper:<span class="asteriskField">*</span> </label> <div class=""> <select name="paper" class="modelselect2 form-control" required id="id_paper" data-autocomplete-light-language="en" data-autocomplete-light-url="/paper-autocomplete/" data-autocomplete-light-function="select2"> <option value="">---------</option> <option value="21610" selected>Wide Residual Networks</option> </select> </div> </div> <div id="div_id_code_url" class="form-group"> <label for="id_code_url" class=""> Code URL (optional): </label> <div class=""> <input type="text" name="code_url" value="https://github.com/pytorch/vision" maxlength="200" class="textinput textInput form-control" id="id_code_url"> </div> </div> <div class="modal-footer"> <button type="submit" class="btn btn-primary"> Submit </button> </div> </form> </div> </div> </div> </div>  <div class="modal fade" id="addMetadata" role="dialog" aria-labelledby="addMetadata" aria-hidden="true"> <div class="modal-dialog" role="document"> <div class="modal-content"> <div class="modal-header"> <h5 class="modal-title model-modal-title" id="addMetadataLabel">Add Metadata</h5> <button type="button" class="close" data-dismiss="modal" aria-label="Close"> <span aria-hidden="true">×</span> </button> </div> <div class="modal-body"> <p>Your model lacks metadata. Adding metadata gives context on how your model was trained.</p> <p> Take the following JSON template, fill it in with your model's correct values:</p> <div class="codehilite"><pre><span></span>{ "Parameters": 62000000 "FLOPs": 524000000 "Training Time": "24 hours", "Training Resources": "8 NVIDIA V100 GPUs", "Training Data": ["ImageNet, Instagram"], "Training Techniques": ["AdamW, CutMix"] }</pre> </div> <p>[INSERT ADVICE HERE]</p> </div> </div> </div> </div>  <div class="modal fade" id="imagemodalwide-resnet-101-2" tabindex="-1" role="dialog" aria-labelledby="imagePreview" aria-hidden="true"> <div class="modal-dialog modal-lg"> <div class="modal-content"> <div class="modal-body text-center library-model-image-pop"> <img src="" id="imagepreviewwide-resnet-101-2"> </div> </div> </div> </div> <div class="modal fade" id="mobileimagemodalwide-resnet-101-2" tabindex="-1" role="dialog" aria-labelledby="mobileimagePreview" aria-hidden="true"> <div class="modal-dialog modal-lg"> <div class="modal-content"> <div class="modal-body text-center library-model-image-pop"> <img src="" id="mobileimagepreviewwide-resnet-101-2"> </div> </div> </div> </div> </div>  <div class="modal fade" id="addDatasetwide-resnet-101-2" role="dialog" aria-labelledby="addDatasetLabel" aria-hidden="true"> <div class="modal-dialog" role="document"> <div class="modal-content"> <div class="modal-header"> <h5 class="modal-title" id="addDatasetLabel">Add Training Data for Wide ResNet-101-2</h5> <button type="button" class="close" data-dismiss="modal" aria-label="Close"> <span aria-hidden="true">×</span> </button> </div> <div class="modal-body"> <div class="current-tasks-title">Attached datasets:</div> <ul class="list-unstyled"> <li> <span class="badge badge-primary">IMAGENET</span> </li> </ul> <form action="" method="post"> <div class="modal-body"> <input type="hidden" name="csrfmiddlewaretoken" value="s6pyLuqAKJT5QmDu5l00lKXHej4W80T0T7y2kx0pbStAbaRwGVUNEvQJGRuLk60u"> <input type="hidden" name="form-0-dataset_mlmodel_pk" value="173" id="id_form-0-dataset_mlmodel_pk"> <div id="div_id_form-0-dataset" class="form-group"> <label for="id_form-0-dataset" class=" requiredField"> Dataset:<span class="asteriskField">*</span> </label> <div class=""> <select name="form-0-dataset" class="modelselect2 form-control" id="id_form-0-dataset" data-autocomplete-light-language="en" data-autocomplete-light-url="/dataset-autocomplete/" data-autocomplete-light-function="select2"> <option value="" selected>---------</option> </select> </div> </div> </div> <div class="modal-footer"> <button type="submit" class="btn btn-primary"> Submit </button> </div> </form> </div> </div> </div> </div>  <div class="modal fade" id="removeDatasetwide-resnet-101-2" tabindex="-1" role="dialog" aria-labelledby="removeDatasetLabel" aria-hidden="true"> <div class="modal-dialog" role="document"> <div class="modal-content"> <div class="modal-header"> <h5 class="modal-title" id="removeDatasetLabel">Remove Dataset from Wide ResNet-101-2</h5> <button type="button" class="close" data-dismiss="modal" aria-label="Close"> <span aria-hidden="true">×</span> </button> </div> <form action="" method="post"> <div class="modal-body"> <ul class="list-unstyled"> <form action="" method="post"> <li> <span class="badge badge-primary">IMAGENET</span> <input type="hidden" name="csrfmiddlewaretoken" value="s6pyLuqAKJT5QmDu5l00lKXHej4W80T0T7y2kx0pbStAbaRwGVUNEvQJGRuLk60u"> <input type="hidden" name="dataset_mlmodel_pk" value="173"> <input type="hidden" name="remove_dataset_pk" value="8"> <button type="submit" class="btn btn-danger" style="width:2.5em">- </button> </li> </form> </ul> </div> </form> </div> </div> </div>  <div class="modal fade" id="addAttributewide-resnet-101-2" role="dialog" aria-labelledby="addAttributeLabel" aria-hidden="true"> <div class="modal-dialog" role="document"> <div class="modal-content add-attribute-content"> <div class="modal-header"> <h5 class="modal-title model-modal-title" id="addAttributeLabel">Add Attribute for Wide ResNet-101-2</h5> <button type="button" class="close" data-dismiss="modal" aria-label="Close"> <span aria-hidden="true">×</span> </button> </div> <div class="modal-body"> <form action="" method="post"> <div class="modal-body"> <input type="hidden" name="csrfmiddlewaretoken" value="s6pyLuqAKJT5QmDu5l00lKXHej4W80T0T7y2kx0pbStAbaRwGVUNEvQJGRuLk60u"> <input type="hidden" name="form-0-attribute_mlmodel_pk" value="173" id="id_form-0-attribute_mlmodel_pk"> <div id="div_id_form-0-name" class="form-group"> <label for="id_form-0-name" class=" requiredField"> Name: <i>(e.g. Layers)</i>:<span class="asteriskField">*</span> </label> <div class=""> <input type="text" name="form-0-name" maxlength="100" class="textinput textInput form-control" id="id_form-0-name"> </div> </div> <div id="div_id_form-0-value" class="form-group"> <label for="id_form-0-value" class=" requiredField"> Value <i>(e.g. 101)</i>:<span class="asteriskField">*</span> </label> <div class=""> <input type="text" name="form-0-value" maxlength="500" class="textinput textInput form-control" id="id_form-0-value"> </div> </div> </div> <div class="modal-footer"> <div style="width: 60%; font-size: 14px; float: left;"> <a href="#removeAttributewide-resnet-101-2" data-toggle="modal"> Remove Existing Attributes </a> </div> <button type="submit" class="btn btn-primary"> Submit </button> </div> </form> </div> </div> </div> </div>  <div class="modal fade" id="removeAttributewide-resnet-101-2" tabindex="-1" role="dialog" aria-labelledby="removeAttributeLabel" aria-hidden="true"> <div class="modal-dialog" role="document"> <div class="modal-content"> <div class="modal-header"> <h5 class="modal-title model-modal-title" id="removeAttributeLabel">Remove Attribute from Wide ResNet-101-2</h5> <button type="button" class="close" data-dismiss="modal" aria-label="Close"> <span aria-hidden="true">×</span> </button> </div> <form action="" method="post"> <div class="modal-body"> <table style="width: 100%;"> <tr> <form action="" method="post"> <th> ID </th> <td> wide_resnet101_2 </td> <td> <input type="hidden" name="csrfmiddlewaretoken" value="s6pyLuqAKJT5QmDu5l00lKXHej4W80T0T7y2kx0pbStAbaRwGVUNEvQJGRuLk60u"> <input type="hidden" name="attribute_mlmodel_pk" value="173"> <input type="hidden" name="remove_attribute_name" value="ID"> <button type="submit" class="btn btn-danger" style="width:2.5em">- </button> </td> </form> </tr> <tr> <form action="" method="post"> <th> LR </th> <td> 0.1 </td> <td> <input type="hidden" name="csrfmiddlewaretoken" value="s6pyLuqAKJT5QmDu5l00lKXHej4W80T0T7y2kx0pbStAbaRwGVUNEvQJGRuLk60u"> <input type="hidden" name="attribute_mlmodel_pk" value="173"> <input type="hidden" name="remove_attribute_name" value="LR"> <button type="submit" class="btn btn-danger" style="width:2.5em">- </button> </td> </form> </tr> <tr> <form action="" method="post"> <th> Epochs </th> <td> 90 </td> <td> <input type="hidden" name="csrfmiddlewaretoken" value="s6pyLuqAKJT5QmDu5l00lKXHej4W80T0T7y2kx0pbStAbaRwGVUNEvQJGRuLk60u"> <input type="hidden" name="attribute_mlmodel_pk" value="173"> <input type="hidden" name="remove_attribute_name" value="Epochs"> <button type="submit" class="btn btn-danger" style="width:2.5em">- </button> </td> </form> </tr> <tr> <form action="" method="post"> <th> LR Gamma </th> <td> 0.1 </td> <td> <input type="hidden" name="csrfmiddlewaretoken" value="s6pyLuqAKJT5QmDu5l00lKXHej4W80T0T7y2kx0pbStAbaRwGVUNEvQJGRuLk60u"> <input type="hidden" name="attribute_mlmodel_pk" value="173"> <input type="hidden" name="remove_attribute_name" value="LR Gamma"> <button type="submit" class="btn btn-danger" style="width:2.5em">- </button> </td> </form> </tr> <tr> <form action="" method="post"> <th> Momentum </th> <td> 0.9 </td> <td> <input type="hidden" name="csrfmiddlewaretoken" value="s6pyLuqAKJT5QmDu5l00lKXHej4W80T0T7y2kx0pbStAbaRwGVUNEvQJGRuLk60u"> <input type="hidden" name="attribute_mlmodel_pk" value="173"> <input type="hidden" name="remove_attribute_name" value="Momentum"> <button type="submit" class="btn btn-danger" style="width:2.5em">- </button> </td> </form> </tr> <tr> <form action="" method="post"> <th> Batch Size </th> <td> 32 </td> <td> <input type="hidden" name="csrfmiddlewaretoken" value="s6pyLuqAKJT5QmDu5l00lKXHej4W80T0T7y2kx0pbStAbaRwGVUNEvQJGRuLk60u"> <input type="hidden" name="attribute_mlmodel_pk" value="173"> <input type="hidden" name="remove_attribute_name" value="Batch Size"> <button type="submit" class="btn btn-danger" style="width:2.5em">- </button> </td> </form> </tr> <tr> <form action="" method="post"> <th> LR Step Size </th> <td> 30 </td> <td> <input type="hidden" name="csrfmiddlewaretoken" value="s6pyLuqAKJT5QmDu5l00lKXHej4W80T0T7y2kx0pbStAbaRwGVUNEvQJGRuLk60u"> <input type="hidden" name="attribute_mlmodel_pk" value="173"> <input type="hidden" name="remove_attribute_name" value="LR Step Size"> <button type="submit" class="btn btn-danger" style="width:2.5em">- </button> </td> </form> </tr> <tr> <form action="" method="post"> <th> Weight Decay </th> <td> 0.0001 </td> <td> <input type="hidden" name="csrfmiddlewaretoken" value="s6pyLuqAKJT5QmDu5l00lKXHej4W80T0T7y2kx0pbStAbaRwGVUNEvQJGRuLk60u"> <input type="hidden" name="attribute_mlmodel_pk" value="173"> <input type="hidden" name="remove_attribute_name" value="Weight Decay"> <button type="submit" class="btn btn-danger" style="width:2.5em">- </button> </td> </form> </tr> </table> </div> </form> </div> </div> </div>  <div class="modal fade" id="addTechniquewide-resnet-101-2" role="dialog" aria-labelledby="addTechniqueLabel" aria-hidden="true"> <div class="modal-dialog" role="document"> <div class="modal-content"> <div class="modal-header"> <h5 class="modal-title model-modal-title" id="addTechniqueLabel">Add Technique for Wide ResNet-101-2</h5> <button type="button" class="close" data-dismiss="modal" aria-label="Close"> <span aria-hidden="true">×</span> </button> </div> <div class="modal-body"> <div class="current-tasks-title">Attached techniques:</div> <ul class="list-unstyled"> <li> <span class="badge badge-primary">WEIGHT DECAY</span> </li> </ul> <ul class="list-unstyled"> <li> <span class="badge badge-primary">SGD WITH MOMENTUM</span> </li> </ul> <form action="" method="post"> <div class="modal-body"> <input type="hidden" name="csrfmiddlewaretoken" value="s6pyLuqAKJT5QmDu5l00lKXHej4W80T0T7y2kx0pbStAbaRwGVUNEvQJGRuLk60u"> <input type="hidden" name="form-0-technique_mlmodel_pk" value="173" id="id_form-0-technique_mlmodel_pk"> <div id="div_id_form-0-technique" class="form-group"> <label for="id_form-0-technique" class=" requiredField"> Technique:<span class="asteriskField">*</span> </label> <div class=""> <select name="form-0-technique" class="modelselect2 form-control" id="id_form-0-technique" data-autocomplete-light-language="en" data-autocomplete-light-url="/method-autocomplete/" data-autocomplete-light-function="select2"> <option value="" selected>---------</option> </select> </div> </div> </div> <div class="modal-footer"> <button type="submit" class="btn btn-primary"> Submit </button> </div> </form> </div> </div> </div> </div>  <div class="modal fade" id="removeTechniquewide-resnet-101-2" tabindex="-1" role="dialog" aria-labelledby="removeTechniqueLabel" aria-hidden="true"> <div class="modal-dialog" role="document"> <div class="modal-content"> <div class="modal-header"> <h5 class="modal-title model-modal-title" id="removeTechniqueLabel">Remove Technique from Wide ResNet-101-2</h5> <button type="button" class="close" data-dismiss="modal" aria-label="Close"> <span aria-hidden="true">×</span> </button> </div> <form action="" method="post"> <div class="modal-body"> <ul class="list-unstyled"> <form action="" method="post"> <li> <span class="badge badge-primary">WEIGHT DECAY</span> <input type="hidden" name="csrfmiddlewaretoken" value="s6pyLuqAKJT5QmDu5l00lKXHej4W80T0T7y2kx0pbStAbaRwGVUNEvQJGRuLk60u"> <input type="hidden" name="remove_technique_pk" value="564"> <input type="hidden" name="technique_mlmodel_pk" value="173"> <button type="submit" class="btn btn-danger" style="width:2.5em">- </button> </li> </form> </ul> <ul class="list-unstyled"> <form action="" method="post"> <li> <span class="badge badge-primary">SGD WITH MOMENTUM</span> <input type="hidden" name="csrfmiddlewaretoken" value="s6pyLuqAKJT5QmDu5l00lKXHej4W80T0T7y2kx0pbStAbaRwGVUNEvQJGRuLk60u"> <input type="hidden" name="remove_technique_pk" value="568"> <input type="hidden" name="technique_mlmodel_pk" value="173"> <button type="submit" class="btn btn-danger" style="width:2.5em">- </button> </li> </form> </ul> </div> </form> </div> </div> </div>  <div class="modal fade" id="addMotifwide-resnet-101-2" role="dialog" aria-labelledby="addMotifLabel" aria-hidden="true"> <div class="modal-dialog" role="document"> <div class="modal-content"> <div class="modal-header"> <h5 class="modal-title model-modal-title" id="addMotifLabel">Add Motif for Wide ResNet-101-2</h5> <button type="button" class="close" data-dismiss="modal" aria-label="Close"> <span aria-hidden="true">×</span> </button> </div> <div class="modal-body"> <div class="current-tasks-title">Attached motifs:</div> <ul class="list-unstyled"> <li> <span class="badge badge-primary">BATCH NORMALIZATION</span> </li> </ul> <ul class="list-unstyled"> <li> <span class="badge badge-primary">RESIDUAL CONNECTION</span> </li> </ul> <ul class="list-unstyled"> <li> <span class="badge badge-primary">WIDE RESIDUAL BLOCK</span> </li> </ul> <ul class="list-unstyled"> <li> <span class="badge badge-primary">GLOBAL AVERAGE POOLING</span> </li> </ul> <ul class="list-unstyled"> <li> <span class="badge badge-primary">SOFTMAX</span> </li> </ul> <ul class="list-unstyled"> <li> <span class="badge badge-primary">CONVOLUTION</span> </li> </ul> <ul class="list-unstyled"> <li> <span class="badge badge-primary">MAX POOLING</span> </li> </ul> <ul class="list-unstyled"> <li> <span class="badge badge-primary">1X1 CONVOLUTION</span> </li> </ul> <ul class="list-unstyled"> <li> <span class="badge badge-primary">RELU</span> </li> </ul> <form action="" method="post"> <div class="modal-body"> <input type="hidden" name="csrfmiddlewaretoken" value="s6pyLuqAKJT5QmDu5l00lKXHej4W80T0T7y2kx0pbStAbaRwGVUNEvQJGRuLk60u"> <input type="hidden" name="form-0-motif_mlmodel_pk" value="173" id="id_form-0-motif_mlmodel_pk"> <div id="div_id_form-0-motif" class="form-group"> <label for="id_form-0-motif" class=" requiredField"> Motif:<span class="asteriskField">*</span> </label> <div class=""> <select name="form-0-motif" class="modelselect2 form-control" id="id_form-0-motif" data-autocomplete-light-language="en" data-autocomplete-light-url="/method-autocomplete/" data-autocomplete-light-function="select2"> <option value="" selected>---------</option> </select> </div> </div> </div> <div class="modal-footer"> <button type="submit" class="btn btn-primary"> Submit </button> </div> </form> </div> </div> </div> </div>  <div class="modal fade" id="removeMotifwide-resnet-101-2" tabindex="-1" role="dialog" aria-labelledby="removeTechniqueLabel" aria-hidden="true"> <div class="modal-dialog" role="document"> <div class="modal-content"> <div class="modal-header"> <h5 class="modal-title model-modal-title" id="removeMotifLabel">Remove Motif from Wide ResNet-101-2</h5> <button type="button" class="close" data-dismiss="modal" aria-label="Close"> <span aria-hidden="true">×</span> </button> </div> <form action="" method="post"> <div class="modal-body"> <ul class="list-unstyled"> <form action="" method="post"> <li> <span class="badge badge-primary">BATCH NORMALIZATION</span> <input type="hidden" name="csrfmiddlewaretoken" value="s6pyLuqAKJT5QmDu5l00lKXHej4W80T0T7y2kx0pbStAbaRwGVUNEvQJGRuLk60u"> <input type="hidden" name="remove_motif_pk" value="427"> <input type="hidden" name="motif_mlmodel_pk" value="173"> <button type="submit" class="btn btn-danger" style="width:2.5em">- </button> </li> </form> </ul> <ul class="list-unstyled"> <form action="" method="post"> <li> <span class="badge badge-primary">RESIDUAL CONNECTION</span> <input type="hidden" name="csrfmiddlewaretoken" value="s6pyLuqAKJT5QmDu5l00lKXHej4W80T0T7y2kx0pbStAbaRwGVUNEvQJGRuLk60u"> <input type="hidden" name="remove_motif_pk" value="9"> <input type="hidden" name="motif_mlmodel_pk" value="173"> <button type="submit" class="btn btn-danger" style="width:2.5em">- </button> </li> </form> </ul> <ul class="list-unstyled"> <form action="" method="post"> <li> <span class="badge badge-primary">WIDE RESIDUAL BLOCK</span> <input type="hidden" name="csrfmiddlewaretoken" value="s6pyLuqAKJT5QmDu5l00lKXHej4W80T0T7y2kx0pbStAbaRwGVUNEvQJGRuLk60u"> <input type="hidden" name="remove_motif_pk" value="235"> <input type="hidden" name="motif_mlmodel_pk" value="173"> <button type="submit" class="btn btn-danger" style="width:2.5em">- </button> </li> </form> </ul> <ul class="list-unstyled"> <form action="" method="post"> <li> <span class="badge badge-primary">GLOBAL AVERAGE POOLING</span> <input type="hidden" name="csrfmiddlewaretoken" value="s6pyLuqAKJT5QmDu5l00lKXHej4W80T0T7y2kx0pbStAbaRwGVUNEvQJGRuLk60u"> <input type="hidden" name="remove_motif_pk" value="34"> <input type="hidden" name="motif_mlmodel_pk" value="173"> <button type="submit" class="btn btn-danger" style="width:2.5em">- </button> </li> </form> </ul> <ul class="list-unstyled"> <form action="" method="post"> <li> <span class="badge badge-primary">SOFTMAX</span> <input type="hidden" name="csrfmiddlewaretoken" value="s6pyLuqAKJT5QmDu5l00lKXHej4W80T0T7y2kx0pbStAbaRwGVUNEvQJGRuLk60u"> <input type="hidden" name="remove_motif_pk" value="537"> <input type="hidden" name="motif_mlmodel_pk" value="173"> <button type="submit" class="btn btn-danger" style="width:2.5em">- </button> </li> </form> </ul> <ul class="list-unstyled"> <form action="" method="post"> <li> <span class="badge badge-primary">CONVOLUTION</span> <input type="hidden" name="csrfmiddlewaretoken" value="s6pyLuqAKJT5QmDu5l00lKXHej4W80T0T7y2kx0pbStAbaRwGVUNEvQJGRuLk60u"> <input type="hidden" name="remove_motif_pk" value="315"> <input type="hidden" name="motif_mlmodel_pk" value="173"> <button type="submit" class="btn btn-danger" style="width:2.5em">- </button> </li> </form> </ul> <ul class="list-unstyled"> <form action="" method="post"> <li> <span class="badge badge-primary">MAX POOLING</span> <input type="hidden" name="csrfmiddlewaretoken" value="s6pyLuqAKJT5QmDu5l00lKXHej4W80T0T7y2kx0pbStAbaRwGVUNEvQJGRuLk60u"> <input type="hidden" name="remove_motif_pk" value="489"> <input type="hidden" name="motif_mlmodel_pk" value="173"> <button type="submit" class="btn btn-danger" style="width:2.5em">- </button> </li> </form> </ul> <ul class="list-unstyled"> <form action="" method="post"> <li> <span class="badge badge-primary">1X1 CONVOLUTION</span> <input type="hidden" name="csrfmiddlewaretoken" value="s6pyLuqAKJT5QmDu5l00lKXHej4W80T0T7y2kx0pbStAbaRwGVUNEvQJGRuLk60u"> <input type="hidden" name="remove_motif_pk" value="137"> <input type="hidden" name="motif_mlmodel_pk" value="173"> <button type="submit" class="btn btn-danger" style="width:2.5em">- </button> </li> </form> </ul> <ul class="list-unstyled"> <form action="" method="post"> <li> <span class="badge badge-primary">RELU</span> <input type="hidden" name="csrfmiddlewaretoken" value="s6pyLuqAKJT5QmDu5l00lKXHej4W80T0T7y2kx0pbStAbaRwGVUNEvQJGRuLk60u"> <input type="hidden" name="remove_motif_pk" value="515"> <input type="hidden" name="motif_mlmodel_pk" value="173"> <button type="submit" class="btn btn-danger" style="width:2.5em">- </button> </li> </form> </ul> </div> </form> </div> </div> </div>  <div class="modal fade" id="imagemodalwide-resnet-50-2" tabindex="-1" role="dialog" aria-labelledby="imagePreview" aria-hidden="true"> <div class="modal-dialog modal-lg"> <div class="modal-content"> <div class="modal-body text-center library-model-image-pop"> <img src="" id="imagepreviewwide-resnet-50-2"> </div> </div> </div> </div> <div class="modal fade" id="mobileimagemodalwide-resnet-50-2" tabindex="-1" role="dialog" aria-labelledby="mobileimagePreview" aria-hidden="true"> <div class="modal-dialog modal-lg"> <div class="modal-content"> <div class="modal-body text-center library-model-image-pop"> <img src="" id="mobileimagepreviewwide-resnet-50-2"> </div> </div> </div> </div> </div>  <div class="modal fade" id="addDatasetwide-resnet-50-2" role="dialog" aria-labelledby="addDatasetLabel" aria-hidden="true"> <div class="modal-dialog" role="document"> <div class="modal-content"> <div class="modal-header"> <h5 class="modal-title" id="addDatasetLabel">Add Training Data for Wide ResNet-50-2</h5> <button type="button" class="close" data-dismiss="modal" aria-label="Close"> <span aria-hidden="true">×</span> </button> </div> <div class="modal-body"> <div class="current-tasks-title">Attached datasets:</div> <ul class="list-unstyled"> <li> <span class="badge badge-primary">IMAGENET</span> </li> </ul> <form action="" method="post"> <div class="modal-body"> <input type="hidden" name="csrfmiddlewaretoken" value="s6pyLuqAKJT5QmDu5l00lKXHej4W80T0T7y2kx0pbStAbaRwGVUNEvQJGRuLk60u"> <input type="hidden" name="form-1-dataset_mlmodel_pk" value="162" id="id_form-1-dataset_mlmodel_pk"> <div id="div_id_form-1-dataset" class="form-group"> <label for="id_form-1-dataset" class=" requiredField"> Dataset:<span class="asteriskField">*</span> </label> <div class=""> <select name="form-1-dataset" class="modelselect2 form-control" id="id_form-1-dataset" data-autocomplete-light-language="en" data-autocomplete-light-url="/dataset-autocomplete/" data-autocomplete-light-function="select2"> <option value="" selected>---------</option> </select> </div> </div> </div> <div class="modal-footer"> <button type="submit" class="btn btn-primary"> Submit </button> </div> </form> </div> </div> </div> </div>  <div class="modal fade" id="removeDatasetwide-resnet-50-2" tabindex="-1" role="dialog" aria-labelledby="removeDatasetLabel" aria-hidden="true"> <div class="modal-dialog" role="document"> <div class="modal-content"> <div class="modal-header"> <h5 class="modal-title" id="removeDatasetLabel">Remove Dataset from Wide ResNet-50-2</h5> <button type="button" class="close" data-dismiss="modal" aria-label="Close"> <span aria-hidden="true">×</span> </button> </div> <form action="" method="post"> <div class="modal-body"> <ul class="list-unstyled"> <form action="" method="post"> <li> <span class="badge badge-primary">IMAGENET</span> <input type="hidden" name="csrfmiddlewaretoken" value="s6pyLuqAKJT5QmDu5l00lKXHej4W80T0T7y2kx0pbStAbaRwGVUNEvQJGRuLk60u"> <input type="hidden" name="dataset_mlmodel_pk" value="162"> <input type="hidden" name="remove_dataset_pk" value="8"> <button type="submit" class="btn btn-danger" style="width:2.5em">- </button> </li> </form> </ul> </div> </form> </div> </div> </div>  <div class="modal fade" id="addAttributewide-resnet-50-2" role="dialog" aria-labelledby="addAttributeLabel" aria-hidden="true"> <div class="modal-dialog" role="document"> <div class="modal-content add-attribute-content"> <div class="modal-header"> <h5 class="modal-title model-modal-title" id="addAttributeLabel">Add Attribute for Wide ResNet-50-2</h5> <button type="button" class="close" data-dismiss="modal" aria-label="Close"> <span aria-hidden="true">×</span> </button> </div> <div class="modal-body"> <form action="" method="post"> <div class="modal-body"> <input type="hidden" name="csrfmiddlewaretoken" value="s6pyLuqAKJT5QmDu5l00lKXHej4W80T0T7y2kx0pbStAbaRwGVUNEvQJGRuLk60u"> <input type="hidden" name="form-1-attribute_mlmodel_pk" value="162" id="id_form-1-attribute_mlmodel_pk"> <div id="div_id_form-1-name" class="form-group"> <label for="id_form-1-name" class=" requiredField"> Name: <i>(e.g. Layers)</i>:<span class="asteriskField">*</span> </label> <div class=""> <input type="text" name="form-1-name" maxlength="100" class="textinput textInput form-control" id="id_form-1-name"> </div> </div> <div id="div_id_form-1-value" class="form-group"> <label for="id_form-1-value" class=" requiredField"> Value <i>(e.g. 101)</i>:<span class="asteriskField">*</span> </label> <div class=""> <input type="text" name="form-1-value" maxlength="500" class="textinput textInput form-control" id="id_form-1-value"> </div> </div> </div> <div class="modal-footer"> <div style="width: 60%; font-size: 14px; float: left;"> <a href="#removeAttributewide-resnet-50-2" data-toggle="modal"> Remove Existing Attributes </a> </div> <button type="submit" class="btn btn-primary"> Submit </button> </div> </form> </div> </div> </div> </div>  <div class="modal fade" id="removeAttributewide-resnet-50-2" tabindex="-1" role="dialog" aria-labelledby="removeAttributeLabel" aria-hidden="true"> <div class="modal-dialog" role="document"> <div class="modal-content"> <div class="modal-header"> <h5 class="modal-title model-modal-title" id="removeAttributeLabel">Remove Attribute from Wide ResNet-50-2</h5> <button type="button" class="close" data-dismiss="modal" aria-label="Close"> <span aria-hidden="true">×</span> </button> </div> <form action="" method="post"> <div class="modal-body"> <table style="width: 100%;"> <tr> <form action="" method="post"> <th> ID </th> <td> wide_resnet50_2 </td> <td> <input type="hidden" name="csrfmiddlewaretoken" value="s6pyLuqAKJT5QmDu5l00lKXHej4W80T0T7y2kx0pbStAbaRwGVUNEvQJGRuLk60u"> <input type="hidden" name="attribute_mlmodel_pk" value="162"> <input type="hidden" name="remove_attribute_name" value="ID"> <button type="submit" class="btn btn-danger" style="width:2.5em">- </button> </td> </form> </tr> <tr> <form action="" method="post"> <th> LR </th> <td> 0.1 </td> <td> <input type="hidden" name="csrfmiddlewaretoken" value="s6pyLuqAKJT5QmDu5l00lKXHej4W80T0T7y2kx0pbStAbaRwGVUNEvQJGRuLk60u"> <input type="hidden" name="attribute_mlmodel_pk" value="162"> <input type="hidden" name="remove_attribute_name" value="LR"> <button type="submit" class="btn btn-danger" style="width:2.5em">- </button> </td> </form> </tr> <tr> <form action="" method="post"> <th> Epochs </th> <td> 90 </td> <td> <input type="hidden" name="csrfmiddlewaretoken" value="s6pyLuqAKJT5QmDu5l00lKXHej4W80T0T7y2kx0pbStAbaRwGVUNEvQJGRuLk60u"> <input type="hidden" name="attribute_mlmodel_pk" value="162"> <input type="hidden" name="remove_attribute_name" value="Epochs"> <button type="submit" class="btn btn-danger" style="width:2.5em">- </button> </td> </form> </tr> <tr> <form action="" method="post"> <th> LR Gamma </th> <td> 0.1 </td> <td> <input type="hidden" name="csrfmiddlewaretoken" value="s6pyLuqAKJT5QmDu5l00lKXHej4W80T0T7y2kx0pbStAbaRwGVUNEvQJGRuLk60u"> <input type="hidden" name="attribute_mlmodel_pk" value="162"> <input type="hidden" name="remove_attribute_name" value="LR Gamma"> <button type="submit" class="btn btn-danger" style="width:2.5em">- </button> </td> </form> </tr> <tr> <form action="" method="post"> <th> Momentum </th> <td> 0.9 </td> <td> <input type="hidden" name="csrfmiddlewaretoken" value="s6pyLuqAKJT5QmDu5l00lKXHej4W80T0T7y2kx0pbStAbaRwGVUNEvQJGRuLk60u"> <input type="hidden" name="attribute_mlmodel_pk" value="162"> <input type="hidden" name="remove_attribute_name" value="Momentum"> <button type="submit" class="btn btn-danger" style="width:2.5em">- </button> </td> </form> </tr> <tr> <form action="" method="post"> <th> Batch Size </th> <td> 32 </td> <td> <input type="hidden" name="csrfmiddlewaretoken" value="s6pyLuqAKJT5QmDu5l00lKXHej4W80T0T7y2kx0pbStAbaRwGVUNEvQJGRuLk60u"> <input type="hidden" name="attribute_mlmodel_pk" value="162"> <input type="hidden" name="remove_attribute_name" value="Batch Size"> <button type="submit" class="btn btn-danger" style="width:2.5em">- </button> </td> </form> </tr> <tr> <form action="" method="post"> <th> LR Step Size </th> <td> 30 </td> <td> <input type="hidden" name="csrfmiddlewaretoken" value="s6pyLuqAKJT5QmDu5l00lKXHej4W80T0T7y2kx0pbStAbaRwGVUNEvQJGRuLk60u"> <input type="hidden" name="attribute_mlmodel_pk" value="162"> <input type="hidden" name="remove_attribute_name" value="LR Step Size"> <button type="submit" class="btn btn-danger" style="width:2.5em">- </button> </td> </form> </tr> <tr> <form action="" method="post"> <th> Weight Decay </th> <td> 0.0001 </td> <td> <input type="hidden" name="csrfmiddlewaretoken" value="s6pyLuqAKJT5QmDu5l00lKXHej4W80T0T7y2kx0pbStAbaRwGVUNEvQJGRuLk60u"> <input type="hidden" name="attribute_mlmodel_pk" value="162"> <input type="hidden" name="remove_attribute_name" value="Weight Decay"> <button type="submit" class="btn btn-danger" style="width:2.5em">- </button> </td> </form> </tr> </table> </div> </form> </div> </div> </div>  <div class="modal fade" id="addTechniquewide-resnet-50-2" role="dialog" aria-labelledby="addTechniqueLabel" aria-hidden="true"> <div class="modal-dialog" role="document"> <div class="modal-content"> <div class="modal-header"> <h5 class="modal-title model-modal-title" id="addTechniqueLabel">Add Technique for Wide ResNet-50-2</h5> <button type="button" class="close" data-dismiss="modal" aria-label="Close"> <span aria-hidden="true">×</span> </button> </div> <div class="modal-body"> <div class="current-tasks-title">Attached techniques:</div> <ul class="list-unstyled"> <li> <span class="badge badge-primary">WEIGHT DECAY</span> </li> </ul> <ul class="list-unstyled"> <li> <span class="badge badge-primary">SGD WITH MOMENTUM</span> </li> </ul> <form action="" method="post"> <div class="modal-body"> <input type="hidden" name="csrfmiddlewaretoken" value="s6pyLuqAKJT5QmDu5l00lKXHej4W80T0T7y2kx0pbStAbaRwGVUNEvQJGRuLk60u"> <input type="hidden" name="form-1-technique_mlmodel_pk" value="162" id="id_form-1-technique_mlmodel_pk"> <div id="div_id_form-1-technique" class="form-group"> <label for="id_form-1-technique" class=" requiredField"> Technique:<span class="asteriskField">*</span> </label> <div class=""> <select name="form-1-technique" class="modelselect2 form-control" id="id_form-1-technique" data-autocomplete-light-language="en" data-autocomplete-light-url="/method-autocomplete/" data-autocomplete-light-function="select2"> <option value="" selected>---------</option> </select> </div> </div> </div> <div class="modal-footer"> <button type="submit" class="btn btn-primary"> Submit </button> </div> </form> </div> </div> </div> </div>  <div class="modal fade" id="removeTechniquewide-resnet-50-2" tabindex="-1" role="dialog" aria-labelledby="removeTechniqueLabel" aria-hidden="true"> <div class="modal-dialog" role="document"> <div class="modal-content"> <div class="modal-header"> <h5 class="modal-title model-modal-title" id="removeTechniqueLabel">Remove Technique from Wide ResNet-50-2</h5> <button type="button" class="close" data-dismiss="modal" aria-label="Close"> <span aria-hidden="true">×</span> </button> </div> <form action="" method="post"> <div class="modal-body"> <ul class="list-unstyled"> <form action="" method="post"> <li> <span class="badge badge-primary">WEIGHT DECAY</span> <input type="hidden" name="csrfmiddlewaretoken" value="s6pyLuqAKJT5QmDu5l00lKXHej4W80T0T7y2kx0pbStAbaRwGVUNEvQJGRuLk60u"> <input type="hidden" name="remove_technique_pk" value="564"> <input type="hidden" name="technique_mlmodel_pk" value="162"> <button type="submit" class="btn btn-danger" style="width:2.5em">- </button> </li> </form> </ul> <ul class="list-unstyled"> <form action="" method="post"> <li> <span class="badge badge-primary">SGD WITH MOMENTUM</span> <input type="hidden" name="csrfmiddlewaretoken" value="s6pyLuqAKJT5QmDu5l00lKXHej4W80T0T7y2kx0pbStAbaRwGVUNEvQJGRuLk60u"> <input type="hidden" name="remove_technique_pk" value="568"> <input type="hidden" name="technique_mlmodel_pk" value="162"> <button type="submit" class="btn btn-danger" style="width:2.5em">- </button> </li> </form> </ul> </div> </form> </div> </div> </div>  <div class="modal fade" id="addMotifwide-resnet-50-2" role="dialog" aria-labelledby="addMotifLabel" aria-hidden="true"> <div class="modal-dialog" role="document"> <div class="modal-content"> <div class="modal-header"> <h5 class="modal-title model-modal-title" id="addMotifLabel">Add Motif for Wide ResNet-50-2</h5> <button type="button" class="close" data-dismiss="modal" aria-label="Close"> <span aria-hidden="true">×</span> </button> </div> <div class="modal-body"> <div class="current-tasks-title">Attached motifs:</div> <ul class="list-unstyled"> <li> <span class="badge badge-primary">BATCH NORMALIZATION</span> </li> </ul> <ul class="list-unstyled"> <li> <span class="badge badge-primary">RESIDUAL CONNECTION</span> </li> </ul> <ul class="list-unstyled"> <li> <span class="badge badge-primary">WIDE RESIDUAL BLOCK</span> </li> </ul> <ul class="list-unstyled"> <li> <span class="badge badge-primary">GLOBAL AVERAGE POOLING</span> </li> </ul> <ul class="list-unstyled"> <li> <span class="badge badge-primary">SOFTMAX</span> </li> </ul> <ul class="list-unstyled"> <li> <span class="badge badge-primary">CONVOLUTION</span> </li> </ul> <ul class="list-unstyled"> <li> <span class="badge badge-primary">MAX POOLING</span> </li> </ul> <ul class="list-unstyled"> <li> <span class="badge badge-primary">1X1 CONVOLUTION</span> </li> </ul> <ul class="list-unstyled"> <li> <span class="badge badge-primary">RELU</span> </li> </ul> <form action="" method="post"> <div class="modal-body"> <input type="hidden" name="csrfmiddlewaretoken" value="s6pyLuqAKJT5QmDu5l00lKXHej4W80T0T7y2kx0pbStAbaRwGVUNEvQJGRuLk60u"> <input type="hidden" name="form-1-motif_mlmodel_pk" value="162" id="id_form-1-motif_mlmodel_pk"> <div id="div_id_form-1-motif" class="form-group"> <label for="id_form-1-motif" class=" requiredField"> Motif:<span class="asteriskField">*</span> </label> <div class=""> <select name="form-1-motif" class="modelselect2 form-control" id="id_form-1-motif" data-autocomplete-light-language="en" data-autocomplete-light-url="/method-autocomplete/" data-autocomplete-light-function="select2"> <option value="" selected>---------</option> </select> </div> </div> </div> <div class="modal-footer"> <button type="submit" class="btn btn-primary"> Submit </button> </div> </form> </div> </div> </div> </div>  <div class="modal fade" id="removeMotifwide-resnet-50-2" tabindex="-1" role="dialog" aria-labelledby="removeTechniqueLabel" aria-hidden="true"> <div class="modal-dialog" role="document"> <div class="modal-content"> <div class="modal-header"> <h5 class="modal-title model-modal-title" id="removeMotifLabel">Remove Motif from Wide ResNet-50-2</h5> <button type="button" class="close" data-dismiss="modal" aria-label="Close"> <span aria-hidden="true">×</span> </button> </div> <form action="" method="post"> <div class="modal-body"> <ul class="list-unstyled"> <form action="" method="post"> <li> <span class="badge badge-primary">BATCH NORMALIZATION</span> <input type="hidden" name="csrfmiddlewaretoken" value="s6pyLuqAKJT5QmDu5l00lKXHej4W80T0T7y2kx0pbStAbaRwGVUNEvQJGRuLk60u"> <input type="hidden" name="remove_motif_pk" value="427"> <input type="hidden" name="motif_mlmodel_pk" value="162"> <button type="submit" class="btn btn-danger" style="width:2.5em">- </button> </li> </form> </ul> <ul class="list-unstyled"> <form action="" method="post"> <li> <span class="badge badge-primary">RESIDUAL CONNECTION</span> <input type="hidden" name="csrfmiddlewaretoken" value="s6pyLuqAKJT5QmDu5l00lKXHej4W80T0T7y2kx0pbStAbaRwGVUNEvQJGRuLk60u"> <input type="hidden" name="remove_motif_pk" value="9"> <input type="hidden" name="motif_mlmodel_pk" value="162"> <button type="submit" class="btn btn-danger" style="width:2.5em">- </button> </li> </form> </ul> <ul class="list-unstyled"> <form action="" method="post"> <li> <span class="badge badge-primary">WIDE RESIDUAL BLOCK</span> <input type="hidden" name="csrfmiddlewaretoken" value="s6pyLuqAKJT5QmDu5l00lKXHej4W80T0T7y2kx0pbStAbaRwGVUNEvQJGRuLk60u"> <input type="hidden" name="remove_motif_pk" value="235"> <input type="hidden" name="motif_mlmodel_pk" value="162"> <button type="submit" class="btn btn-danger" style="width:2.5em">- </button> </li> </form> </ul> <ul class="list-unstyled"> <form action="" method="post"> <li> <span class="badge badge-primary">GLOBAL AVERAGE POOLING</span> <input type="hidden" name="csrfmiddlewaretoken" value="s6pyLuqAKJT5QmDu5l00lKXHej4W80T0T7y2kx0pbStAbaRwGVUNEvQJGRuLk60u"> <input type="hidden" name="remove_motif_pk" value="34"> <input type="hidden" name="motif_mlmodel_pk" value="162"> <button type="submit" class="btn btn-danger" style="width:2.5em">- </button> </li> </form> </ul> <ul class="list-unstyled"> <form action="" method="post"> <li> <span class="badge badge-primary">SOFTMAX</span> <input type="hidden" name="csrfmiddlewaretoken" value="s6pyLuqAKJT5QmDu5l00lKXHej4W80T0T7y2kx0pbStAbaRwGVUNEvQJGRuLk60u"> <input type="hidden" name="remove_motif_pk" value="537"> <input type="hidden" name="motif_mlmodel_pk" value="162"> <button type="submit" class="btn btn-danger" style="width:2.5em">- </button> </li> </form> </ul> <ul class="list-unstyled"> <form action="" method="post"> <li> <span class="badge badge-primary">CONVOLUTION</span> <input type="hidden" name="csrfmiddlewaretoken" value="s6pyLuqAKJT5QmDu5l00lKXHej4W80T0T7y2kx0pbStAbaRwGVUNEvQJGRuLk60u"> <input type="hidden" name="remove_motif_pk" value="315"> <input type="hidden" name="motif_mlmodel_pk" value="162"> <button type="submit" class="btn btn-danger" style="width:2.5em">- </button> </li> </form> </ul> <ul class="list-unstyled"> <form action="" method="post"> <li> <span class="badge badge-primary">MAX POOLING</span> <input type="hidden" name="csrfmiddlewaretoken" value="s6pyLuqAKJT5QmDu5l00lKXHej4W80T0T7y2kx0pbStAbaRwGVUNEvQJGRuLk60u"> <input type="hidden" name="remove_motif_pk" value="489"> <input type="hidden" name="motif_mlmodel_pk" value="162"> <button type="submit" class="btn btn-danger" style="width:2.5em">- </button> </li> </form> </ul> <ul class="list-unstyled"> <form action="" method="post"> <li> <span class="badge badge-primary">1X1 CONVOLUTION</span> <input type="hidden" name="csrfmiddlewaretoken" value="s6pyLuqAKJT5QmDu5l00lKXHej4W80T0T7y2kx0pbStAbaRwGVUNEvQJGRuLk60u"> <input type="hidden" name="remove_motif_pk" value="137"> <input type="hidden" name="motif_mlmodel_pk" value="162"> <button type="submit" class="btn btn-danger" style="width:2.5em">- </button> </li> </form> </ul> <ul class="list-unstyled"> <form action="" method="post"> <li> <span class="badge badge-primary">RELU</span> <input type="hidden" name="csrfmiddlewaretoken" value="s6pyLuqAKJT5QmDu5l00lKXHej4W80T0T7y2kx0pbStAbaRwGVUNEvQJGRuLk60u"> <input type="hidden" name="remove_motif_pk" value="515"> <input type="hidden" name="motif_mlmodel_pk" value="162"> <button type="submit" class="btn btn-danger" style="width:2.5em">- </button> </li> </form> </ul> </div> </form> </div> </div> </div> <div class="container content content-buffer "> <div class="container model-page"> <h1>Wide ResNet</h1> <div class="row subheader"> <div class="col-lg-10"> <h4> <img src="https://production-media.paperswithcode.com/libraries/pytorch_v82PHL5.png"> <a href="/owner/pytorch">pytorch</a> / <a href="/lib/torchvision"><b> vision</b></a> </h4> </div> <div class="col-lg-2"> <span class="text-muted">Last updated on Feb 12, 2021</span> </div> </div> <hr> <div class="variant-select"> <span class=" icon-wrapper icon-ion" data-name="git-network-outline"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><circle cx="128" cy="96" r="48" fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32"/><circle cx="256" cy="416" r="48" fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32"/><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 256v112"/><circle cx="384" cy="96" r="48" fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32"/><path d="M128 144c0 74.67 68.92 112 128 112m128-112c0 74.67-68.92 112-128 112" fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32"/></svg></span> <select id='modelSelect' name="variant" class="form-control model-select" style="height: initial; margin-bottom: 10px;"> <option value="wide-resnet-101-2" selected >Wide ResNet-101-2</option> <option value="wide-resnet-50-2" >Wide ResNet-50-2</option> </select> </div> <div class="tab-content model-border"> <div class="tab-pane active in " id="wide-resnet-101-2"> <div class="model-menu"> <div class="row model-choose"> <div class="col-lg-8"> <div class="variant-header"> Wide ResNet-101-2 </div> </div> <div class="col-lg-4 model-align"> </div> </div> </div> <div class="row variant-card"> <div class="col-lg-8 variant-details"> <div id="modelImageDivmobile" style="height: 250px !important;" class="mobile-wrapper img-wrapper d-block d-sm-none"> <a href="#" id="mobilepopwide-resnet-101-2"> <img id="mobileimageresourcewide-resnet-101-2" src="https://production-media.paperswithcode.com/models/wide_resnet101_2_4NMITvS.png"> </a> </div> <hr> <div class="container mobile-parameters"> <div class="row"> <div class="col"> <span class="feature-title">Parameters</span> 127 Million </div> <div class="col"><span class="feature-title">FLOPs</span> 23 Billion </div> <div class="col"> <span class="feature-title">File Size</span> 242.90 MB </div> <div class="w-100"></div> <div class="col"><span class="feature-title">Training Data</span> <a href="/dataset/imagenet">ImageNet</a> </div> <div class="col"><span class="feature-title">Training Resources</span> 8x NVIDIA V100 GPUs </div> <div class="col"> <span class="feature-title">Training Time</span> </div> </div> </div> <hr> <table> <tbody> <tr> <th>Training Techniques</th> <td> <a href="/method/weight-decay">Weight Decay</a>, <a href="/method/sgd-with-momentum">SGD with Momentum</a> </td> </tr> <tr> <th>Architecture</th> <td> <a href="/method/1x1-convolution">1x1 Convolution</a>, <a href="/method/wide-residual-block">Wide Residual Block</a>, <a href="/method/batch-normalization">Batch Normalization</a>, <a href="/method/convolution">Convolution</a>, <a href="/method/global-average-pooling">Global Average Pooling</a>, <a href="/method/residual-connection">Residual Connection</a>, <a href="/method/relu">ReLU</a>, <a href="/method/max-pooling">Max Pooling</a>, <a href="/method/softmax">Softmax</a> </td> </tr> <tr> <th>ID</th> <td>wide_resnet101_2</td> </tr> <tr id='hiddenRow2' class="hidden-row hide-row"> <th>LR</th> <td>0.1</td> </tr> <tr id='hiddenRow3' class="hidden-row hide-row"> <th>Epochs</th> <td>90</td> </tr> <tr id='hiddenRow4' class="hidden-row hide-row"> <th>LR Gamma</th> <td>0.1</td> </tr> <tr id='hiddenRow5' class="hidden-row hide-row"> <th>Momentum</th> <td>0.9</td> </tr> <tr id='hiddenRow6' class="hidden-row hide-row"> <th>Batch Size</th> <td>32</td> </tr> <tr id='hiddenRow7' class="hidden-row hide-row"> <th>LR Step Size</th> <td>30</td> </tr> <tr id='hiddenRow8' class="hidden-row hide-row"> <th>Weight Decay</th> <td>0.0001</td> </tr> <tr class="hidden-row"> <td style="padding-top: 15px; padding-left: 0px;"> <a class="contribution toggle"> SHOW MORE <span class="hidden-element icon-wrapper icon-ion" data-name="caret-forward-outline"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M190.06 414l163.12-139.78a24 24 0 0 0 0-36.44L190.06 98c-15.57-13.34-39.62-2.28-39.62 18.22v279.6c0 20.5 24.05 31.56 39.62 18.18z"/></svg></span> </a> </td> <td style="padding-top: 15px; padding-left: 0px;"> </td> </tr> <tr class="hidden-row hide-row"> <td style="padding-top: 15px; padding-left: 0px;"> <a class="contribution toggle"> SHOW LESS <span class="hidden-element icon-wrapper icon-ion" data-name="caret-back-outline"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M321.94 98L158.82 237.78a24 24 0 0 0 0 36.44L321.94 414c15.57 13.34 39.62 2.28 39.62-18.22v-279.6c0-20.5-24.05-31.56-39.62-18.18z"/></svg></span> </a> </td> <td style="padding-top: 15px; padding-left: 0px;"> </td> </tr> </tbody> </table> <div class="align-mobile"> <a href="https://arxiv.org/abs/1605.07146v4"> <button class="badge badge-light"> <span class=" icon-wrapper icon-ion" data-name="document-outline"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M416 221.25V416a48 48 0 0 1-48 48H144a48 48 0 0 1-48-48V96a48 48 0 0 1 48-48h98.75a32 32 0 0 1 22.62 9.37l141.26 141.26a32 32 0 0 1 9.37 22.62z" fill="none" stroke="#000" stroke-linejoin="round" stroke-width="32"/><path d="M256 56v120a32 32 0 0 0 32 32h120" fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32"/></svg></span> Paper </button> </a> <a target="_blank" href="https://github.com/pytorch/vision/blob/5a315453da5089d66de94604ea49334a66552524/torchvision/models/resnet.py#L374"> <button class="badge badge-light"> <span class=" icon-wrapper icon-ion" data-name="code-outline"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M160 368L32 256l128-112m192 224l128-112-128-112"/></svg></span> Code </button> </a> <a target="_blank" href="https://github.com/pytorch/vision/tree/master/references/classification"> <button class="badge badge-light"> <span class=" icon-wrapper icon-ion" data-name="hammer-outline"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M277.42 247a24.68 24.68 0 0 0-4.08-5.47L255 223.44a21.63 21.63 0 0 0-6.56-4.57 20.93 20.93 0 0 0-23.28 4.27c-6.36 6.26-18 17.68-39 38.43C146 301.3 71.43 367.89 37.71 396.29a16 16 0 0 0-1.09 23.54l39 39.43a16.13 16.13 0 0 0 23.67-.89c29.24-34.37 96.3-109 136-148.23 20.39-20.06 31.82-31.58 38.29-37.94a21.76 21.76 0 0 0 3.84-25.2zm201.01-46l-34.31-34a5.44 5.44 0 0 0-4-1.59 5.59 5.59 0 0 0-4 1.59h0a11.41 11.41 0 0 1-9.55 3.27c-4.48-.49-9.25-1.88-12.33-4.86-7-6.86 1.09-20.36-5.07-29a242.88 242.88 0 0 0-23.08-26.72c-7.06-7-34.81-33.47-81.55-52.53a123.79 123.79 0 0 0-47-9.24c-26.35 0-46.61 11.76-54 18.51-5.88 5.32-12 13.77-12 13.77a91.29 91.29 0 0 1 10.81-3.2 79.53 79.53 0 0 1 23.28-1.49C241.19 76.8 259.94 84.1 270 92c16.21 13 23.18 30.39 24.27 52.83.8 16.69-15.23 37.76-30.44 54.94a7.85 7.85 0 0 0 .4 10.83l21.24 21.23a8 8 0 0 0 11.14.1c13.93-13.51 31.09-28.47 40.82-34.46s17.58-7.68 21.35-8.09a35.71 35.71 0 0 1 21.3 4.62 13.65 13.65 0 0 1 3.08 2.38c6.46 6.56 6.07 17.28-.5 23.74l-2 1.89a5.5 5.5 0 0 0 0 7.84l34.31 34a5.5 5.5 0 0 0 4 1.58 5.65 5.65 0 0 0 4-1.58L478.43 209a5.82 5.82 0 0 0 0-8z" fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32"/></svg></span> Config </button> </a> <a target="_blank" href="https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth"> <button class="badge badge-light"> <span class=" icon-wrapper icon-ion" data-name="barbell-outline"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M48 256h416"/><rect x="384" y="128" width="32" height="256" rx="16" ry="16" fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32"/><rect x="96" y="128" width="32" height="256" rx="16" ry="16" fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32"/><rect x="32" y="192" width="16" height="128" rx="8" ry="8" fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32"/><rect x="464" y="192" width="16" height="128" rx="8" ry="8" fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32"/></svg></span> Weights </button> </a> </div> </div> <div class="col-lg-4"> <div id="modelImageDiv" class="img-wrapper hidden-element"> <a href="#" id="popwide-resnet-101-2"> <img id="imageresourcewide-resnet-101-2" src="https://production-media.paperswithcode.com/models/wide_resnet101_2_4NMITvS.png"> </a> </div> </div> </div> </div> <div class="tab-pane" id="wide-resnet-50-2"> <div class="model-menu"> <div class="row model-choose"> <div class="col-lg-8"> <div class="variant-header"> Wide ResNet-50-2 </div> </div> <div class="col-lg-4 model-align"> </div> </div> </div> <div class="row variant-card"> <div class="col-lg-8 variant-details"> <div id="modelImageDivmobile" style="height: 250px !important;" class="mobile-wrapper img-wrapper d-block d-sm-none"> <a href="#" id="mobilepopwide-resnet-50-2"> <img id="mobileimageresourcewide-resnet-50-2" src="https://production-media.paperswithcode.com/models/wide_resnet50_2_aKAjVo7.png"> </a> </div> <hr> <div class="container mobile-parameters"> <div class="row"> <div class="col"> <span class="feature-title">Parameters</span> 69 Million </div> <div class="col"><span class="feature-title">FLOPs</span> 11 Billion </div> <div class="col"> <span class="feature-title">File Size</span> 131.82 MB </div> <div class="w-100"></div> <div class="col"><span class="feature-title">Training Data</span> <a href="/dataset/imagenet">ImageNet</a> </div> <div class="col"><span class="feature-title">Training Resources</span> 8x NVIDIA V100 GPUs </div> <div class="col"> <span class="feature-title">Training Time</span> </div> </div> </div> <hr> <table> <tbody> <tr> <th>Training Techniques</th> <td> <a href="/method/weight-decay">Weight Decay</a>, <a href="/method/sgd-with-momentum">SGD with Momentum</a> </td> </tr> <tr> <th>Architecture</th> <td> <a href="/method/1x1-convolution">1x1 Convolution</a>, <a href="/method/wide-residual-block">Wide Residual Block</a>, <a href="/method/batch-normalization">Batch Normalization</a>, <a href="/method/convolution">Convolution</a>, <a href="/method/global-average-pooling">Global Average Pooling</a>, <a href="/method/residual-connection">Residual Connection</a>, <a href="/method/relu">ReLU</a>, <a href="/method/max-pooling">Max Pooling</a>, <a href="/method/softmax">Softmax</a> </td> </tr> <tr> <th>ID</th> <td>wide_resnet50_2</td> </tr> <tr id='hiddenRow2' class="hidden-row hide-row"> <th>LR</th> <td>0.1</td> </tr> <tr id='hiddenRow3' class="hidden-row hide-row"> <th>Epochs</th> <td>90</td> </tr> <tr id='hiddenRow4' class="hidden-row hide-row"> <th>LR Gamma</th> <td>0.1</td> </tr> <tr id='hiddenRow5' class="hidden-row hide-row"> <th>Momentum</th> <td>0.9</td> </tr> <tr id='hiddenRow6' class="hidden-row hide-row"> <th>Batch Size</th> <td>32</td> </tr> <tr id='hiddenRow7' class="hidden-row hide-row"> <th>LR Step Size</th> <td>30</td> </tr> <tr id='hiddenRow8' class="hidden-row hide-row"> <th>Weight Decay</th> <td>0.0001</td> </tr> <tr class="hidden-row"> <td style="padding-top: 15px; padding-left: 0px;"> <a class="contribution toggle"> SHOW MORE <span class="hidden-element icon-wrapper icon-ion" data-name="caret-forward-outline"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M190.06 414l163.12-139.78a24 24 0 0 0 0-36.44L190.06 98c-15.57-13.34-39.62-2.28-39.62 18.22v279.6c0 20.5 24.05 31.56 39.62 18.18z"/></svg></span> </a> </td> <td style="padding-top: 15px; padding-left: 0px;"> </td> </tr> <tr class="hidden-row hide-row"> <td style="padding-top: 15px; padding-left: 0px;"> <a class="contribution toggle"> SHOW LESS <span class="hidden-element icon-wrapper icon-ion" data-name="caret-back-outline"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M321.94 98L158.82 237.78a24 24 0 0 0 0 36.44L321.94 414c15.57 13.34 39.62 2.28 39.62-18.22v-279.6c0-20.5-24.05-31.56-39.62-18.18z"/></svg></span> </a> </td> <td style="padding-top: 15px; padding-left: 0px;"> </td> </tr> </tbody> </table> <div class="align-mobile"> <a href="https://arxiv.org/abs/1605.07146v4"> <button class="badge badge-light"> <span class=" icon-wrapper icon-ion" data-name="document-outline"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M416 221.25V416a48 48 0 0 1-48 48H144a48 48 0 0 1-48-48V96a48 48 0 0 1 48-48h98.75a32 32 0 0 1 22.62 9.37l141.26 141.26a32 32 0 0 1 9.37 22.62z" fill="none" stroke="#000" stroke-linejoin="round" stroke-width="32"/><path d="M256 56v120a32 32 0 0 0 32 32h120" fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32"/></svg></span> Paper </button> </a> <a target="_blank" href="https://github.com/pytorch/vision/blob/5a315453da5089d66de94604ea49334a66552524/torchvision/models/resnet.py#L356"> <button class="badge badge-light"> <span class=" icon-wrapper icon-ion" data-name="code-outline"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M160 368L32 256l128-112m192 224l128-112-128-112"/></svg></span> Code </button> </a> <a target="_blank" href="https://github.com/pytorch/vision/tree/master/references/classification"> <button class="badge badge-light"> <span class=" icon-wrapper icon-ion" data-name="hammer-outline"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M277.42 247a24.68 24.68 0 0 0-4.08-5.47L255 223.44a21.63 21.63 0 0 0-6.56-4.57 20.93 20.93 0 0 0-23.28 4.27c-6.36 6.26-18 17.68-39 38.43C146 301.3 71.43 367.89 37.71 396.29a16 16 0 0 0-1.09 23.54l39 39.43a16.13 16.13 0 0 0 23.67-.89c29.24-34.37 96.3-109 136-148.23 20.39-20.06 31.82-31.58 38.29-37.94a21.76 21.76 0 0 0 3.84-25.2zm201.01-46l-34.31-34a5.44 5.44 0 0 0-4-1.59 5.59 5.59 0 0 0-4 1.59h0a11.41 11.41 0 0 1-9.55 3.27c-4.48-.49-9.25-1.88-12.33-4.86-7-6.86 1.09-20.36-5.07-29a242.88 242.88 0 0 0-23.08-26.72c-7.06-7-34.81-33.47-81.55-52.53a123.79 123.79 0 0 0-47-9.24c-26.35 0-46.61 11.76-54 18.51-5.88 5.32-12 13.77-12 13.77a91.29 91.29 0 0 1 10.81-3.2 79.53 79.53 0 0 1 23.28-1.49C241.19 76.8 259.94 84.1 270 92c16.21 13 23.18 30.39 24.27 52.83.8 16.69-15.23 37.76-30.44 54.94a7.85 7.85 0 0 0 .4 10.83l21.24 21.23a8 8 0 0 0 11.14.1c13.93-13.51 31.09-28.47 40.82-34.46s17.58-7.68 21.35-8.09a35.71 35.71 0 0 1 21.3 4.62 13.65 13.65 0 0 1 3.08 2.38c6.46 6.56 6.07 17.28-.5 23.74l-2 1.89a5.5 5.5 0 0 0 0 7.84l34.31 34a5.5 5.5 0 0 0 4 1.58 5.65 5.65 0 0 0 4-1.58L478.43 209a5.82 5.82 0 0 0 0-8z" fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32"/></svg></span> Config </button> </a> <a target="_blank" href="https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth"> <button class="badge badge-light"> <span class=" icon-wrapper icon-ion" data-name="barbell-outline"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M48 256h416"/><rect x="384" y="128" width="32" height="256" rx="16" ry="16" fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32"/><rect x="96" y="128" width="32" height="256" rx="16" ry="16" fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32"/><rect x="32" y="192" width="16" height="128" rx="8" ry="8" fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32"/><rect x="464" y="192" width="16" height="128" rx="8" ry="8" fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32"/></svg></span> Weights </button> </a> </div> </div> <div class="col-lg-4"> <div id="modelImageDiv" class="img-wrapper hidden-element"> <a href="#" id="popwide-resnet-50-2"> <img id="imageresourcewide-resnet-50-2" src="https://production-media.paperswithcode.com/models/wide_resnet50_2_aKAjVo7.png"> </a> </div> </div> </div> </div> </div> <div class="model-card model-card-border"> <div class="row"> <div class="col-lg-10"> <div class="model-card-header">README.md</div> </div> <div class="col-lg-2"> </div> </div> <h1>Summary</h1> <p><strong>Wide Residual Networks</strong> are a variant on <a href="https://paperswithcode.com/method/resnet">ResNets</a> where we decrease depth and increase the width of residual networks. This is achieved through the use of <a href="https://paperswithcode.com/method/wide-residual-block">wide residual blocks</a>.</p> <h2>How do I load this model?</h2> <p>To load a pretrained model:</p> <div class="codehilite"><pre><span></span><code><span class="kn">import</span> <span class="nn">torchvision.models</span> <span class="k">as</span> <span class="nn">models</span> <span class="n">wide_resnet50_2</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">wide_resnet50_2</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> </code></pre></div> <p>Replace the model name with the variant you want to use, e.g. <code>wide_resnet50_2</code>. You can find the IDs in the model summaries at the top of this page.</p> <p>To evaluate the model, use the <a href="https://github.com/pytorch/vision/tree/master/references/classification">image classification recipes</a> from the library.</p> <div class="codehilite"><pre><span></span><code>python train.py --test-only --model<span class="o">=</span><span class="s1">'<model_name>'</span> </code></pre></div> <h2>How do I train this model?</h2> <p>You can follow the <a href="https://github.com/pytorch/vision/tree/master/references/classification">torchvision recipe</a> on GitHub for training a new model afresh.</p> <h2>Citation</h2> <div class="codehilite"><pre><span></span><code><span class="nc">@article</span><span class="p">{</span><span class="nl">DBLP:journals/corr/ZagoruykoK16</span><span class="p">,</span> <span class="na">author</span> <span class="p">=</span> <span class="s">{Sergey Zagoruyko and</span> <span class="s"> Nikos Komodakis}</span><span class="p">,</span> <span class="na">title</span> <span class="p">=</span> <span class="s">{Wide Residual Networks}</span><span class="p">,</span> <span class="na">journal</span> <span class="p">=</span> <span class="s">{CoRR}</span><span class="p">,</span> <span class="na">volume</span> <span class="p">=</span> <span class="s">{abs/1605.07146}</span><span class="p">,</span> <span class="na">year</span> <span class="p">=</span> <span class="s">{2016}</span><span class="p">,</span> <span class="na">url</span> <span class="p">=</span> <span class="s">{http://arxiv.org/abs/1605.07146}</span><span class="p">,</span> <span class="na">archivePrefix</span> <span class="p">=</span> <span class="s">{arXiv}</span><span class="p">,</span> <span class="na">eprint</span> <span class="p">=</span> <span class="s">{1605.07146}</span><span class="p">,</span> <span class="na">timestamp</span> <span class="p">=</span> <span class="s">{Mon, 13 Aug 2018 16:46:42 +0200}</span><span class="p">,</span> <span class="na">biburl</span> <span class="p">=</span> <span class="s">{https://dblp.org/rec/journals/corr/ZagoruykoK16.bib}</span><span class="p">,</span> <span class="na">bibsource</span> <span class="p">=</span> <span class="s">{dblp computer science bibliography, https://dblp.org}</span> <span class="p">}</span> </code></pre></div> </div> <div class="results model-card-border"> <h1>Results</h1> <h4 class="d-block d-sm-none"> Image Classification on ImageNet </h4> <p class="options hidden-element"> <select id="benchmark_slug" name="benchmark_slug" class="form-control"> <option id="image-classification-on-imagenet" value="image-classification-on-imagenet"> Image Classification on ImageNet </option> </select> <select id="y_axis" name="y_axis" class="form-control hidden-element"> <option id="Top 1 Accuracy" value="Top 1 Accuracy">Top 1 Accuracy</option> <option id="Top 5 Accuracy" value="Top 5 Accuracy">Top 5 Accuracy</option> <option id="Momentum" value="Momentum">Momentum</option> <option id="Weight Decay" value="Weight Decay">Weight Decay</option> <option id="Batch Size" value="Batch Size">Batch Size</option> <option id="LR" value="LR">LR</option> <option id="LR Step Size" value="LR Step Size">LR Step Size</option> <option id="LR Gamma" value="LR Gamma">LR Gamma</option> <option id="Parameters" value="Parameters">Parameters</option> <option id="Epochs" value="Epochs">Epochs</option> <option id="FLOPs" value="FLOPs">FLOPs</option> </select> <select id="x_axis" name="x_axis" class="form-control hidden-element"> <option id="Parameters" value="Parameters">Parameters</option> <option id="FLOPs" value="FLOPs">FLOPs</option> <option id="Top 1 Accuracy" value="Top 1 Accuracy">Top 1 Accuracy</option> <option id="Top 5 Accuracy" value="Top 5 Accuracy">Top 5 Accuracy</option> <option id="Momentum" value="Momentum">Momentum</option> <option id="Weight Decay" value="Weight Decay">Weight Decay</option> <option id="Batch Size" value="Batch Size">Batch Size</option> <option id="LR" value="LR">LR</option> <option id="LR Step Size" value="LR Step Size">LR Step Size</option> <option id="LR Gamma" value="LR Gamma">LR Gamma</option> <option id="Epochs" value="Epochs">Epochs</option> </select> <select id="model_option" name="model_option" class="form-control hidden-element"> <option id="all-library" value="all-library">torchvision</option> <option id="all-models" value="all-models">All Models</option> <option id="model-only" value="model-only">Wide ResNet</option> </select> </p> <p> <figure class="highcharts-figure"> <div class="highcharts-container" id="perform-vs-complexity-container"></div> </figure> </p> <a href="/task/image-classification"> <h5><img src=" https://production-media.paperswithcode.com/thumbnails/task/task-0000000951-52325f45_O0tAMly.jpg"> Image Classification </h5> </a> <table> <tr> <th>BENCHMARK</th> <th>MODEL</th> <th>METRIC NAME</th> <th>METRIC VALUE</th> <th>GLOBAL RANK</th> </tr> <tr> <td> ImageNet </td> <td> Wide ResNet-101-2 </td> <td>Top 1 Accuracy</td> <td class="text-center">78.84%</td> <td class="text-center"># 141</td> </tr> <tr> <td> </td> <td> </td> <td>Top 5 Accuracy</td> <td class="text-center">94.28%</td> <td class="text-center"># 141</td> </tr> <tr> <td> ImageNet </td> <td> Wide ResNet-50-2 </td> <td>Top 1 Accuracy</td> <td class="text-center">78.51%</td> <td class="text-center"># 151</td> </tr> <tr> <td> </td> <td> </td> <td>Top 5 Accuracy</td> <td class="text-center">94.09%</td> <td class="text-center"># 151</td> </tr> </table> </div> </div> </div> <div class="footer"> <div class="footer-contact"> <span class="footer-contact-item">Contact us on:</span> <a class="footer-contact-item" href="mailto:hello@paperswithcode.com"> <span class=" icon-wrapper icon-ion" data-name="mail"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M424 80H88a56.06 56.06 0 0 0-56 56v240a56.06 56.06 0 0 0 56 56h336a56.06 56.06 0 0 0 56-56V136a56.06 56.06 0 0 0-56-56zm-14.18 92.63l-144 112a16 16 0 0 1-19.64 0l-144-112a16 16 0 1 1 19.64-25.26L256 251.73l134.18-104.36a16 16 0 0 1 19.64 25.26z"/></svg></span> hello@paperswithcode.com </a>. <span class="footer-contact-item"> Papers With Code is a free resource with all data licensed under <a rel="noreferrer" href="https://creativecommons.org/licenses/by-sa/4.0/">CC-BY-SA</a>. </span> </div> <div class="footer-links"> <a href="/site/terms">Terms</a> <a href="/site/data-policy">Data policy</a> <a href="/site/cookies-policy">Cookies policy</a> <a href="/about#team" class="fair-logo"> from <img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAANAAAAAgCAMAAABU6AZfAAAABGdBTUEAALGPC/xhBQAAAAFzUkdCAK7OHOkAAAAJcEhZcwAAFiUAABYlAUlSJPAAAABFUExURUdwTBwqMhwqMxsqMhkqMxsqMhwqMgCA+hwrMxJIgBsrMxsqMgJ28AF58wF38BsqMwB58hsqMwF17wF07hwrMwRm4QJz7Wj6SIIAAAAUdFJOUwDP87wcPIT+4A1tVti1Ta0smZVzG3JP8wAABR9JREFUWMO1memWpCoMgF0QxX1//0e9kCAkAadq5tzKjzndQmM+szNFEWQ9puu6xn02BXm4j23bTsdapKJAMguFgRVT/Ejyx4uH5hgvL1PUfm69jEd6bN05GTJvXF5X/hfRcPyWe2kTLDFdRA4ENVMbZZJGMt3ppEttNMDC2X/Qa7MK1OrveZoKz2/445I+U4znuvaExxKZLFCqtym/A6rzn+OjbHj8ubwDmfESslvtgWea13WeckQPUKJTf/4USHkDnVXzCrT74DnmeX+8rjgcxA4QBmPpyAKdOm+5XwFpgHH/bG9AMzLMqM9DxxCQaM0qLr7U4xE/AgIDVRBHlcoDeYd7lFee6GZOBvaaskD8S6nut0Dg0ItZEt+IQAfjseIzRDvS/WCxWQJ17phqEGqepQBS/VaXZa0H/4XUYMVt6nr309DEjYvduPT2gWELQTr0iQbC1+SADOg/kjVvspGqX6zSRAgEKbqOf6zgd82AVB+8s0YNm5NL6Y8MGzttwKt0krP9+9A/+hzQTALoUX5MnxW7iCIEUmD7IVZb8G0G1HRE9UqbWKkEUFPSR0MWqH5eB65XmgzQdN3WGjxReROxPD2LROeBIEiD7UGLraBAjMcS9W9AquTPckBgoMqEWG1SIGN57otn5KO9Y30N4rq6MQFC5TX1cEWBfJLY+mbQ5ZMUm8UK7F1A9GNc90T3enkpCZhCdUzfdQq0Wp774gnZao55YU3SgkmAVBez1eDfR4BABd/XqY36ichyaLUnyJZ8jatimUBjqQTouK2M3OGs4miiiduN5bkHCL15C9Zw7heBRMHYSMRxIGyYFsPqpwTqactT8w0P0OSA9iRY9jQvrDyIAhCoAjrrR90I1PNCpcivHEh+cATUmS5xoCaNB3ggMzqgRO/RYPIb1WviDkB4sv22kB8ghQcgUIFWzyUmaQ6kpf5DCoTFh5fwQQCt493e9ypD5Xjq7S5cMQeEubpBf2oKCoSMohPzduBAi2yimhRIc3NvrOd+gCxPexvhcGPM3SRoJpbmIhAGSudTNgNCR+qIRL05UCebsxTIiAYOX6sEkONphRkw9A9ZjADIZIDg857we5MBSiQHVMlWJgXyeTBIyVpGD4RttHC4yVtENHn7K5ASdeM3QGX2sKcKBCBmITYmrGii9TOQT7JYwxOgrhbyby4XJrvs54kuR8vlCg4XEgEOEs8Q8R5DYZboCwEESpTmi/Hhc1Lo8zxPlghZjpbLqWVGUGxSes1y4W2lkkC+Wf0C6GPaxtZo0VQW4nOhsJLqAg01HXqgGN0+083MegKoYLdisbDqzHVG1iZJYe0EUDoB+dj149gDRCCgt2lZ1zA5nhvCyEwvrc/b3N/HiZlMgINmZaR/aX3MJluf7Kepo8+F5tRfUh1wR0odzg8Srnm9w7L5SyB/p6H9Ptt0Vj310ngAlDHbnLo3mGc00sJiQ+4KEM+I8xC7fWv5VGcz3Y0C2ZCa70sgf0tXbnbY1jXpln3W6jYXDG4jNthdrfVWn8n4gAVAZe+0GgaEaeGFx4XRQyTM9yWQnNuIAy5/HPAWPuDJ8Yc66sYvSeY/8dhlYqH0kuQzkFQ03nnHCyI/gtc0GfM7BVPmL5J0yHPkXm6d3u6v/TLw3GL5ayDr6WW47awHYmS1VC+XJOVQcCCZBPk13SCvgmcb8uI/UqjqdvlOlk3j5OU20C0putdO1ZWNo0a8oumXslx0vMYaNrfPURt2hnp5G2rhtsEP5j/3Wqt0fQd1YgAAAABJRU5ErkJggg=="> </a> </div> </div> <script> run_after_frontend_loaded.push(() => { $("#popwide-resnet-101-2").on("click", function () { $('#imagepreviewwide-resnet-101-2').attr('src', $('#imageresourcewide-resnet-101-2').attr('src')); $('#imagemodalwide-resnet-101-2').modal('show'); }); $("#mobilepopwide-resnet-101-2").on("click", function () { $('#mobileimagepreviewwide-resnet-101-2').attr('src', $('#mobileimageresourcewide-resnet-101-2').attr('src')); $('#mobileimagemodalwide-resnet-101-2').modal('show'); }); $("#popwide-resnet-50-2").on("click", function () { $('#imagepreviewwide-resnet-50-2').attr('src', $('#imageresourcewide-resnet-50-2').attr('src')); $('#imagemodalwide-resnet-50-2').modal('show'); }); $("#mobilepopwide-resnet-50-2").on("click", function () { $('#mobileimagepreviewwide-resnet-50-2').attr('src', $('#mobileimageresourcewide-resnet-50-2').attr('src')); $('#mobileimagemodalwide-resnet-50-2').modal('show'); }); }); </script> <script id="benchmark_series" type="application/json">[{"name": "MNASNet 1.0", "color": "rgba(205, 205, 205, 1)", "data": [{"name": "MNASNet 1.0", "image": "https://production-media.paperswithcode.com/thumbnails/libraries/library-0000000001-4518157b_e8yTyIr.jpg", "x": 4.38331, "y": 73.51, "url": "/model/mnasnet-1-0"}]}, {"name": "MobileNet V2", "color": "rgba(205, 205, 205, 1)", "data": [{"name": "MobileNet V2", "image": "https://production-media.paperswithcode.com/thumbnails/libraries/library-0000000001-4518157b_e8yTyIr.jpg", "x": 3.50487, "y": 71.88, "url": "/model/mobilenet-v2"}]}, {"name": "AlexNet", "color": "rgba(205, 205, 205, 1)", "data": [{"name": "AlexNet", "image": "https://production-media.paperswithcode.com/thumbnails/libraries/library-0000000001-4518157b_e8yTyIr.jpg", "x": 61.10084, "y": 56.55, "url": "/model/alexnet"}]}, {"name": "ShuffleNet V2", "color": "rgba(205, 205, 205, 1)", "data": [{"name": "ShuffleNet V2", "image": "https://production-media.paperswithcode.com/thumbnails/libraries/library-0000000001-4518157b_e8yTyIr.jpg", "x": 2.2786, "y": 69.36, "url": "/model/shufflenet-v2"}]}, {"name": "GoogleNet", "color": "rgba(205, 205, 205, 1)", "data": [{"name": "GoogleNet", "image": "https://production-media.paperswithcode.com/thumbnails/libraries/library-0000000001-4518157b_e8yTyIr.jpg", "x": 6.6249, "y": 69.78, "url": "/model/googlenet"}]}, {"name": "Inception v3", "color": "rgba(205, 205, 205, 1)", "data": [{"name": "Inception v3", "image": "https://production-media.paperswithcode.com/thumbnails/libraries/library-0000000001-4518157b_e8yTyIr.jpg", "x": 23.83457, "y": 77.45, "url": "/model/inception-v3"}]}, {"name": "SqueezeNet 1.1", "color": "rgba(205, 205, 205, 1)", "data": [{"name": "SqueezeNet 1.1", "image": "https://production-media.paperswithcode.com/thumbnails/libraries/library-0000000001-4518157b_e8yTyIr.jpg", "x": 1.2355, "y": 58.19, "url": "/model/squeezenet?variant=squeezenet-1-1"}]}, {"name": "ResNet-101", "color": "rgba(205, 205, 205, 1)", "data": [{"name": "ResNet-101", "image": "https://production-media.paperswithcode.com/thumbnails/libraries/library-0000000001-4518157b_e8yTyIr.jpg", "x": 44.54916, "y": 77.37, "url": "/model/resnet?variant=resnet-101"}]}, {"name": "ResNet-152", "color": "rgba(205, 205, 205, 1)", "data": [{"name": "ResNet-152", "image": "https://production-media.paperswithcode.com/thumbnails/libraries/library-0000000001-4518157b_e8yTyIr.jpg", "x": 60.19281, "y": 78.31, "url": "/model/resnet?variant=resnet-152"}]}, {"name": "ResNeXt-50-32x4d", "color": "rgba(205, 205, 205, 1)", "data": [{"name": "ResNeXt-50-32x4d", "image": "https://production-media.paperswithcode.com/thumbnails/libraries/library-0000000001-4518157b_e8yTyIr.jpg", "x": 25.0289, "y": 77.62, "url": "/model/resnext?variant=resnext-50-32x4d"}]}, {"name": "VGG-11", "color": "rgba(205, 205, 205, 1)", "data": [{"name": "VGG-11", "image": "https://production-media.paperswithcode.com/thumbnails/libraries/library-0000000001-4518157b_e8yTyIr.jpg", "x": 132.86334, "y": 69.02, "url": "/model/vgg?variant=vgg-11"}]}, {"name": "ResNet-18", "color": "rgba(205, 205, 205, 1)", "data": [{"name": "ResNet-18", "image": "https://production-media.paperswithcode.com/thumbnails/libraries/library-0000000001-4518157b_e8yTyIr.jpg", "x": 11.68951, "y": 69.76, "url": "/model/resnet?variant=resnet-18"}]}, {"name": "SqueezeNet 1.0", "color": "rgba(205, 205, 205, 1)", "data": [{"name": "SqueezeNet 1.0", "image": "https://production-media.paperswithcode.com/thumbnails/libraries/library-0000000001-4518157b_e8yTyIr.jpg", "x": 1.24842, "y": 58.1, "url": "/model/squeezenet?variant=squeezenet-1-0"}]}, {"name": "Densenet-201", "color": "rgba(205, 205, 205, 1)", "data": [{"name": "Densenet-201", "image": "https://production-media.paperswithcode.com/thumbnails/libraries/library-0000000001-4518157b_e8yTyIr.jpg", "x": 20.01393, "y": 77.2, "url": "/model/densenet?variant=densenet-201"}]}, {"name": "ResNeXt-101-32x8d", "color": "rgba(205, 205, 205, 1)", "data": [{"name": "ResNeXt-101-32x8d", "image": "https://production-media.paperswithcode.com/thumbnails/libraries/library-0000000001-4518157b_e8yTyIr.jpg", "x": 88.79134, "y": 79.31, "url": "/model/resnext?variant=resnext-101-32x8d"}]}, {"name": "Densenet-121", "color": "rgba(205, 205, 205, 1)", "data": [{"name": "Densenet-121", "image": "https://production-media.paperswithcode.com/thumbnails/libraries/library-0000000001-4518157b_e8yTyIr.jpg", "x": 7.97886, "y": 74.65, "url": "/model/densenet?variant=densenet-121"}]}, {"name": "Densenet-169", "color": "rgba(205, 205, 205, 1)", "data": [{"name": "Densenet-169", "image": "https://production-media.paperswithcode.com/thumbnails/libraries/library-0000000001-4518157b_e8yTyIr.jpg", "x": 14.14948, "y": 76.0, "url": "/model/densenet?variant=densenet-169"}]}, {"name": "Densenet-161", "color": "rgba(205, 205, 205, 1)", "data": [{"name": "Densenet-161", "image": "https://production-media.paperswithcode.com/thumbnails/libraries/library-0000000001-4518157b_e8yTyIr.jpg", "x": 28.681, "y": 77.65, "url": "/model/densenet?variant=densenet-161"}]}, {"name": "ResNet-34", "color": "rgba(205, 205, 205, 1)", "data": [{"name": "ResNet-34", "image": "https://production-media.paperswithcode.com/thumbnails/libraries/library-0000000001-4518157b_e8yTyIr.jpg", "x": 21.79767, "y": 73.3, "url": "/model/resnet?variant=resnet-34"}]}, {"name": "MobileNet V3 Large", "color": "rgba(205, 205, 205, 1)", "data": [{"name": "MobileNet V3 Large", "image": "https://production-media.paperswithcode.com/thumbnails/libraries/library-0000000001-4518157b_e8yTyIr.jpg", "x": 5.48303, "y": 74.042, "url": "/model/mobilenet-v3?variant=mobilenet-v3-large"}]}, {"name": "MobileNet V3 Small", "color": "rgba(205, 205, 205, 1)", "data": [{"name": "MobileNet V3 Small", "image": "https://production-media.paperswithcode.com/thumbnails/libraries/library-0000000001-4518157b_e8yTyIr.jpg", "x": 2.54286, "y": 67.668, "url": "/model/mobilenet-v3?variant=mobilenet-v3-small"}]}, {"name": "ResNet-50", "color": "rgba(205, 205, 205, 1)", "data": [{"name": "ResNet-50", "image": "https://production-media.paperswithcode.com/thumbnails/libraries/library-0000000001-4518157b_e8yTyIr.jpg", "x": 25.55703, "y": 76.15, "url": "/model/resnet?variant=resnet-50"}]}, {"name": "VGG-11 with batch normalization", "color": "rgba(205, 205, 205, 1)", "data": [{"name": "VGG-11 with batch normalization", "image": "https://production-media.paperswithcode.com/thumbnails/libraries/library-0000000001-4518157b_e8yTyIr.jpg", "x": 132.86884, "y": 70.38, "url": "/model/vgg?variant=vgg-11-with-batch-normalization"}]}, {"name": "VGG-19", "color": "rgba(205, 205, 205, 1)", "data": [{"name": "VGG-19", "image": "https://production-media.paperswithcode.com/thumbnails/libraries/library-0000000001-4518157b_e8yTyIr.jpg", "x": 143.66724, "y": 72.38, "url": "/model/vgg?variant=vgg-19"}]}, {"name": "VGG-13", "color": "rgba(205, 205, 205, 1)", "data": [{"name": "VGG-13", "image": "https://production-media.paperswithcode.com/thumbnails/libraries/library-0000000001-4518157b_e8yTyIr.jpg", "x": 133.04785, "y": 69.93, "url": "/model/vgg?variant=vgg-13"}]}, {"name": "VGG-13 with batch normalization", "color": "rgba(205, 205, 205, 1)", "data": [{"name": "VGG-13 with batch normalization", "image": "https://production-media.paperswithcode.com/thumbnails/libraries/library-0000000001-4518157b_e8yTyIr.jpg", "x": 133.05374, "y": 71.55, "url": "/model/vgg?variant=vgg-13-with-batch-normalization"}]}, {"name": "VGG-16", "color": "rgba(205, 205, 205, 1)", "data": [{"name": "VGG-16", "image": "https://production-media.paperswithcode.com/thumbnails/libraries/library-0000000001-4518157b_e8yTyIr.jpg", "x": 138.35754, "y": 71.59, "url": "/model/vgg?variant=vgg-16"}]}, {"name": "VGG-16 with batch normalization", "color": "rgba(205, 205, 205, 1)", "data": [{"name": "VGG-16 with batch normalization", "image": "https://production-media.paperswithcode.com/thumbnails/libraries/library-0000000001-4518157b_e8yTyIr.jpg", "x": 138.36599, "y": 73.37, "url": "/model/vgg?variant=vgg-16-with-batch-normalization"}]}, {"name": "VGG-19 with batch normalization", "color": "rgba(205, 205, 205, 1)", "data": [{"name": "VGG-19 with batch normalization", "image": "https://production-media.paperswithcode.com/thumbnails/libraries/library-0000000001-4518157b_e8yTyIr.jpg", "x": 143.67825, "y": 74.24, "url": "/model/vgg?variant=vgg-19-with-batch-normalization"}]}, {"name": "Wide ResNet-101-2", "color": "#ee9617", "data": [{"name": "Wide ResNet-50-2", "dataLabels": {"enabled": "true", "format": "Wide ResNet-50-2"}, "image": "https://production-media.paperswithcode.com/thumbnails/libraries/library-0000000001-4518157b_e8yTyIr.jpg", "url": "/model/wide-resnet?variant=wide-resnet-50-2", "x": 68.88324, "y": 78.51}, {"name": "Wide ResNet-101-2", "dataLabels": {"enabled": "true", "format": "Wide ResNet-101-2"}, "image": "https://production-media.paperswithcode.com/thumbnails/libraries/library-0000000001-4518157b_e8yTyIr.jpg", "url": "/model/wide-resnet?variant=wide-resnet-101-2", "x": 126.8867, "y": 78.84}]}]</script> <script> run_after_frontend_loaded.push(() => { var chart = Highcharts.chart('perform-vs-complexity-container', { chart: { type: 'scatter', zoomType: 'xy' }, legend: { enabled: false }, title: { text: '' }, credits: { enabled: false }, xAxis: { min: 0, title: { margin: 15, enabled: true, text: 'Parameters (M)', style: { fontSize: '15px', } }, startOnTick: true, endOnTick: true, showLastLabel: true }, yAxis: { gridLineColor: '#f2f2f2', minorGridLineWidth: 0, title: { margin: 20, text: 'Top 1 Accuracy (%)', style: { fontSize: '15px', } }, }, plotOptions: { series: { stickyTracking: false }, scatter: { point: { events: { click: function () { if (this.url != '/model/') { window.location.href = this.url; } }, } }, lineWidth:2, dashStyle: 'dot', marker: { symbol: 'circle', radius: 5, states: { hover: { enabled: true, lineColor: 'rgb(100,100,100)' } } }, states: { hover: { marker: { enabled: false } } }, } }, series: JSON.parse(document.getElementById('benchmark_series').textContent), tooltip: { useHTML: true, formatter: function () { var s = '<b>' + this.point.name + '</b>'; s += '<br/>' + 'Top 1 Accuracy' + ': ' + this.point.y; if ('%') { s += '%'; } s += '<br/>' + 'Parameters' + ': ' + this.point.x; if ('M') { s += 'M'; } if (this.point.image) { s += '<br/><img style="margin-top: 5px" height=20 src=' + this.point.image + '>'; } return s; } }, }); let benchmark = {}; const $xAxisNameInput = $("#x_axis"); const $yAxisNameInput = $("#y_axis"); const $benchmarkSlugInput = $("#benchmark_slug"); const $modelOptionInput = $("#model_option"); $('select[name=x_axis]').on("change", () => { $.ajax({ url: "/api/v0/benchmark_items/", data: { model_slug: "wide-resnet", x_axis_name: $xAxisNameInput.val(), y_axis_name: $yAxisNameInput.val(), benchmark_slug: $benchmarkSlugInput.val(), library_slug: "torchvision", model_option: $modelOptionInput.val(), redraw_benchmark: false }, success: function (data) { benchmark = data.data["0"]; chart.destroy(); chart = Highcharts.chart('perform-vs-complexity-container', { tooltip: { useHTML: true, formatter: function () { var s = '<b>' + this.point.name + '</b>'; s += '<br/>' + benchmark.y_axis_name + ': ' + this.point.y; if (benchmark.last_y_char) { s += benchmark.last_y_char; } s += '<br/>' + benchmark.x_axis_name + ': ' + this.point.x; if (benchmark.last_x_char) { s += benchmark.last_x_char; } if (this.point.image) { s += '<br/><img style="margin-top: 5px" height=20 src=' + this.point.image + '>'; } return s; } }, chart: { type: 'scatter', zoomType: 'xy' }, legend: { enabled: false }, title: { text: '' }, credits: { enabled: false }, series: benchmark["series"], plotOptions: { lineWidth: function () { if(benchmark.dotted_line) { return 2; } else { return 'null'; } }(), dashStyle: function () { if(benchmark.dotted_line) { return 'dot'; } else { return 'null'; } }(), series: { stickyTracking: false }, scatter: { point: { events: { click: function () { if (this.url != '/model/') { window.location.href = this.url; } }, } }, marker: { symbol: 'circle', radius: 5, states: { hover: { enabled: true, lineColor: 'rgb(100,100,100)' } } }, states: { hover: { marker: { enabled: false } } }, } }, xAxis: { min: 0, title: { margin: 15, enabled: true, text: benchmark["long_x_axis_name"], style: { fontSize: '15px', } }, startOnTick: true, endOnTick: true, showLastLabel: true }, yAxis: { gridLineColor: '#f2f2f2', minorGridLineWidth: 0, title: { margin: 20, text: benchmark["long_y_axis_name"], style: { fontSize: '15px', } }, }, }); } }); }); $('select[name=y_axis]').on("change", () => { $.ajax({ url: "/api/v0/benchmark_items/", data: { model_slug: "wide-resnet", x_axis_name: $xAxisNameInput.val(), y_axis_name: $yAxisNameInput.val(), benchmark_slug: $benchmarkSlugInput.val(), library_slug: "torchvision", model_option: $modelOptionInput.val(), redraw_benchmark: false }, success: function (data) { benchmark = data.data["0"]; chart.destroy(); chart = Highcharts.chart('perform-vs-complexity-container', { tooltip: { useHTML: true, formatter: function () { var s = '<b>' + this.point.name + '</b>'; s += '<br/>' + benchmark.y_axis_name + ': ' + this.point.y; if (benchmark.last_y_char) { s += benchmark.last_y_char; } s += '<br/>' + benchmark.x_axis_name + ': ' + this.point.x; if (benchmark.last_x_char) { s += benchmark.last_x_char; } if (this.point.image) { s += '<br/><img style="margin-top: 5px" height=20 src=' + this.point.image + '>'; } return s; } }, chart: { type: 'scatter', zoomType: 'xy' }, legend: { enabled: false }, title: { text: '' }, credits: { enabled: false }, series: benchmark["series"], plotOptions: { lineWidth: function () { if(benchmark.dotted_line) { return 2; } else { return 'null'; } }(), dashStyle: function () { if(benchmark.dotted_line) { return 'dot'; } else { return 'null'; } }(), series: { stickyTracking: false }, scatter: { point: { events: { click: function () { if (this.url != '/model/') { window.location.href = this.url; } }, } }, marker: { symbol: 'circle', radius: 5, states: { hover: { enabled: true, lineColor: 'rgb(100,100,100)' } } }, states: { hover: { marker: { enabled: false } } }, } }, xAxis: { min: 0, title: { margin: 15, enabled: true, text: benchmark["long_x_axis_name"], style: { fontSize: '15px', } }, startOnTick: true, endOnTick: true, showLastLabel: true }, yAxis: { gridLineColor: '#f2f2f2', minorGridLineWidth: 0, title: { margin: 20, text: benchmark["long_y_axis_name"], style: { fontSize: '15px', } }, }, }); } }); }); $('select[name=benchmark_slug]').on("change", () => { $.ajax({ url: "/api/v0/benchmark_items/", data: { model_slug: "wide-resnet", x_axis_name: $xAxisNameInput.val(), y_axis_name: $yAxisNameInput.val(), benchmark_slug: $benchmarkSlugInput.val(), library_slug: "torchvision", model_option: $modelOptionInput.val(), redraw_benchmark: true }, success: function (data) { $("select[name=x_axis] option").remove(); $("select[name=y_axis] option").remove(); benchmark = data.data["0"]; $.each(benchmark["y_axis_metrics"], function (key, value) { $('select[name=y_axis]') .append($("<option></option>") .attr("value", value) .text(value)); }); $.each(benchmark["x_axis_metrics"], function (key, value) { $('select[name=x_axis]') .append($("<option></option>") .attr("value", value) .text(value)); }); chart.destroy(); chart = Highcharts.chart('perform-vs-complexity-container', { tooltip: { useHTML: true, formatter: function () { var s = '<b>' + this.point.name + '</b>'; s += '<br/>' + benchmark.y_axis_name + ': ' + this.point.y; if (benchmark.last_y_char) { s += benchmark.last_y_char; } s += '<br/>' + benchmark.x_axis_name + ': ' + this.point.x; if (benchmark.last_x_char) { s += benchmark.last_x_char; } if (this.point.image) { s += '<br/><img style="margin-top: 5px" height=20 src=' + this.point.image + '>'; } return s; } }, chart: { type: 'scatter', zoomType: 'xy' }, legend: { enabled: false }, title: { text: '' }, credits: { enabled: false }, series: benchmark["series"], plotOptions: { lineWidth: function () { if(benchmark.dotted_line) { return 2; } else { return 'null'; } }(), dashStyle: function () { if(benchmark.dotted_line) { return 'dot'; } else { return 'null'; } }(), series: { stickyTracking: false }, scatter: { point: { events: { click: function () { if (this.url != '/model/') { window.location.href = this.url; } }, } }, marker: { symbol: 'circle', radius: 5, states: { hover: { enabled: true, lineColor: 'rgb(100,100,100)' } } }, states: { hover: { marker: { enabled: false } } }, } }, xAxis: { min: 0, title: { margin: 15, enabled: true, text: benchmark["long_x_axis_name"], style: { fontSize: '15px', } }, startOnTick: true, endOnTick: true, showLastLabel: true }, yAxis: { gridLineColor: '#f2f2f2', minorGridLineWidth: 0, title: { margin: 20, text: benchmark["long_y_axis_name"], style: { fontSize: '15px', } }, }, }); } }); }); $('select[name=model_option]').on("change", () => { $.ajax({ url: "/api/v0/benchmark_items/", data: { model_slug: "wide-resnet", x_axis_name: $xAxisNameInput.val(), y_axis_name: $yAxisNameInput.val(), benchmark_slug: $benchmarkSlugInput.val(), library_slug: "torchvision", model_option: $modelOptionInput.val(), redraw_benchmark: false }, success: function (data) { benchmark = data.data["0"]; chart.destroy(); chart = Highcharts.chart('perform-vs-complexity-container', { tooltip: { useHTML: true, formatter: function () { var s = '<b>' + this.point.name + '</b>'; s += '<br/>' + benchmark.y_axis_name + ': ' + this.point.y; if (benchmark.last_y_char) { s += benchmark.last_y_char; } s += '<br/>' + benchmark.x_axis_name + ': ' + this.point.x; if (benchmark.last_x_char) { s += benchmark.last_x_char; } if (this.point.image) { s += '<br/><img style="margin-top: 5px" height=20 src=' + this.point.image + '>'; } return s; } }, chart: { type: 'scatter', zoomType: 'xy' }, legend: { enabled: false }, title: { text: '' }, credits: { enabled: false }, series: benchmark["series"], plotOptions: { lineWidth: function () { if(benchmark.dotted_line) { return 2; } else { return 'null'; } }(), dashStyle: function () { if(benchmark.dotted_line) { return 'dot'; } else { return 'null'; } }(), series: { stickyTracking: false }, scatter: { point: { events: { click: function () { if (this.url != '/model/') { window.location.href = this.url; } }, } }, marker: { symbol: 'circle', radius: 5, states: { hover: { enabled: true, lineColor: 'rgb(100,100,100)' } } }, states: { hover: { marker: { enabled: false } } }, } }, xAxis: { min: 0, title: { margin: 15, enabled: true, text: benchmark["long_x_axis_name"], style: { fontSize: '15px', } }, startOnTick: true, endOnTick: true, showLastLabel: true }, yAxis: { gridLineColor: '#f2f2f2', minorGridLineWidth: 0, title: { margin: 20, text: benchmark["long_y_axis_name"], style: { fontSize: '15px', } }, }, }); chart.redraw(); } }); }); $('.model-select').on('change', function (e) { $('.tab-pane').removeClass('active in') $('#' + $(e.currentTarget).val()).addClass("active in"); $('#menu-' + $(e.currentTarget).val()).addClass("active in"); }) $(".toggle").on("click", function () { $(".hidden-row").toggleClass("hide-row"); }); $(document).ready(function() { $('[data-toggle="tooltip"]').tooltip({html: true}); }); }); </script> <script> run_after_frontend_loaded.push(() => { $(function() { $.fn.modal.Constructor.prototype._enforceFocus = function() {}; $.widget( "custom.catcomplete", $.ui.autocomplete, { _create: function() { this._super(); this.widget().menu( "option", "items", "> :not(.ui-autocomplete-category)" ); }, /** Overrides the _renderItem method in jquery to allow for search result images and icons **/ _renderItem: function( ul, item ) { /** If we have an image in the seearch item then render it; if no task image available, use default **/ if ( "image" in item ) { if ( item.image ) { var image_url = item.image; } else { var image_url = "https://production-media.paperswithcode.com/" + "tasks/default.gif"; } return $( "<li>" ) .append( $( "<div>" ).text( item.label ).prepend( $( "<img src=" + image_url + ">") ) ) .appendTo( ul ); } else { return $( "<li>" ) .append($("<div>").text(item.label)) .appendTo( ul ); } }, _renderMenu: function( ul, items ) { var that = this, currentCategory = ""; $.each( items, function( index, item ) { var li; if ( item.category != currentCategory ) { ul.append( "<li class='ui-autocomplete-category'>" + item.category + "</li>" ); currentCategory = item.category; } li = that._renderItemData( ul, item ); if (item.meta !== null) { li.attr('data-qmeta', item.meta); } if ( item.category ) { li.attr( "aria-label", item.category + " : " + item.label ); } }); } }); $( "#id_global_search_input" ).catcomplete({ minLength: 2, source: function( request, response ) { var term = request.term; $.get( "/api/search-autocomplete/", {"q": term}, function(data){ let t = data.tasks, lb = data.leaderboards, p = data.papers, d = data.datasets, m = data.methods; let ts = [], lbs = [], ps = [], ds = [], ms = []; let total = 0; let maxItems = 12; for (let i=0; i<5 && total < maxItems; i++) { if (t.length && total < maxItems) { ts.push({ label: t[0].name, image: t[0].image, category: "Tasks", meta: null, }); t.shift(); total ++; } if (lb.length && total < maxItems) { lbs.push({ label: lb[0].name, image: lb[0].image, category: "Leaderboards", meta: lb[0].slug }); lb.shift(); total ++; } if (p.length && total < maxItems) { ps.push({label: p[0].title, category: "Papers", meta: null}); p.shift(); total ++; } if (d.length && total < maxItems) { ds.push({ label: d[0].name, image: d[0].image, category: "Datasets", meta: d[0].slug, }); d.shift(); total ++; } if (m.length && total < maxItems) { ms.push({ label: m[0].name, image: m[0].image, category: "Methods", meta: null }); m.shift(); total ++; } } let searchData = ts.concat(lbs, ps, ds, ms); response(searchData); }); }, select: function(event, ui) { $("#id_global_search_input").val(ui.item.label); if (typeof gtag !== 'undefined') { gtag('event', 'SiteActions', { 'event_category': 'Search', 'event_label': ui.item.category, }); } if (ui.item.meta === null) { $('#q_meta').val(''); $('#q_meta').removeAttr('name'); } else { if(!$('#q_meta').attr("name")) { $('#q_meta').attr('name', 'q_meta'); } $('#q_meta').val(ui.item.meta); } $('#q_type').val(ui.item.category.toLowerCase()); $("#id_global_search_form").submit(); } }); if ($(window).width() < 1200 && $(window).width() > 992 ) { $("#id_global_search_input").attr("placeholder", "Search"); } // Setup csrf token for ajax requests let getCookie = (name) => { var cookieValue = null; if (document.cookie && document.cookie !== '') { var cookies = document.cookie.split(';'); for (var i = 0; i < cookies.length; i++) { var cookie = jQuery.trim(cookies[i]); // Does this cookie string begin with the name we want? if (cookie.substring(0, name.length + 1) === (name + '=')) { cookieValue = decodeURIComponent(cookie.substring(name.length + 1)); break; } } } return cookieValue; }; let csrftoken = getCookie('csrftoken'); // Make sure we use the most up-to-date CSRF token $("input[name='csrfmiddlewaretoken']").val(csrftoken); function csrfSafeMethod(method) { // these HTTP methods do not require CSRF protection return (/^(GET|HEAD|OPTIONS|TRACE)$/.test(method)); } $.ajaxSetup({ beforeSend: function(xhr, settings) { if (!csrfSafeMethod(settings.type) && !this.crossDomain) { xhr.setRequestHeader("X-CSRFToken", csrftoken); } } }); }); }); </script> <script>!function(e){function t(t){for(var n,a,s=t[0],u=t[1],f=t[2],i=0,d=[];i<s.length;i++)a=s[i],Object.prototype.hasOwnProperty.call(o,a)&&o[a]&&d.push(o[a][0]),o[a]=0;for(n in u)Object.prototype.hasOwnProperty.call(u,n)&&(e[n]=u[n]);for(l&&l(t);d.length;)d.shift()();return c.push.apply(c,f||[]),r()}function r(){for(var e,t=0;t<c.length;t++){for(var r=c[t],n=!0,a=1;a<r.length;a++){var u=r[a];0!==o[u]&&(n=!1)}n&&(c.splice(t--,1),e=s(s.s=r[0]))}return e}var n={},a={11:0},o={11:0},c=[];function s(t){if(n[t])return n[t].exports;var r=n[t]={i:t,l:!1,exports:{}};return e[t].call(r.exports,r,r.exports,s),r.l=!0,r.exports}s.e=function(e){var t=[];a[e]?t.push(a[e]):0!==a[e]&&{2:1,3:1,5:1,6:1,8:1,9:1,10:1}[e]&&t.push(a[e]=new Promise((function(t,r){for(var n="static/css/"+({4:"chart",5:"conference-page",6:"example-page",8:"newsletters-create-page",9:"newsletters-edit-page",10:"newsletters-list-page",12:"table"}[e]||e)+"."+{0:"31d6cfe0",1:"31d6cfe0",2:"5745a9fd",3:"05600cd7",4:"31d6cfe0",5:"67565070",6:"8444f163",8:"f8a273b3",9:"f8a273b3",10:"db3e0a85",12:"31d6cfe0",14:"31d6cfe0",15:"31d6cfe0"}[e]+".chunk.css",o=s.p+n,c=document.getElementsByTagName("link"),u=0;u<c.length;u++){var f=(l=c[u]).getAttribute("data-href")||l.getAttribute("href");if("stylesheet"===l.rel&&(f===n||f===o))return t()}var i=document.getElementsByTagName("style");for(u=0;u<i.length;u++){var l;if((f=(l=i[u]).getAttribute("data-href"))===n||f===o)return t()}var d=document.createElement("link");d.rel="stylesheet",d.type="text/css",d.onload=t,d.onerror=function(t){var n=t&&t.target&&t.target.src||o,c=new Error("Loading CSS chunk "+e+" failed.\n("+n+")");c.code="CSS_CHUNK_LOAD_FAILED",c.request=n,delete a[e],d.parentNode.removeChild(d),r(c)},d.href=o,document.getElementsByTagName("head")[0].appendChild(d)})).then((function(){a[e]=0})));var r=o[e];if(0!==r)if(r)t.push(r[2]);else{var n=new Promise((function(t,n){r=o[e]=[t,n]}));t.push(r[2]=n);var c,u=document.createElement("script");u.charset="utf-8",u.timeout=120,s.nc&&u.setAttribute("nonce",s.nc),u.src=function(e){return s.p+"static/js/"+({4:"chart",5:"conference-page",6:"example-page",8:"newsletters-create-page",9:"newsletters-edit-page",10:"newsletters-list-page",12:"table"}[e]||e)+"."+{0:"041a0327",1:"eb8f85bf",2:"57df0e43",3:"dd682e9c",4:"934a42ca",5:"ddc33be8",6:"f5234ef0",8:"c76f72bd",9:"aa24afbf",10:"a749f71a",12:"c5756280",14:"be7b1031",15:"b8393014"}[e]+".chunk.js"}(e);var f=new Error;c=function(t){u.onerror=u.onload=null,clearTimeout(i);var r=o[e];if(0!==r){if(r){var n=t&&("load"===t.type?"missing":t.type),a=t&&t.target&&t.target.src;f.message="Loading chunk "+e+" failed.\n("+n+": "+a+")",f.name="ChunkLoadError",f.type=n,f.request=a,r[1](f)}o[e]=void 0}};var i=setTimeout((function(){c({type:"timeout",target:u})}),12e4);u.onerror=u.onload=c,document.head.appendChild(u)}return Promise.all(t)},s.m=e,s.c=n,s.d=function(e,t,r){s.o(e,t)||Object.defineProperty(e,t,{enumerable:!0,get:r})},s.r=function(e){"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(e,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(e,"__esModule",{value:!0})},s.t=function(e,t){if(1&t&&(e=s(e)),8&t)return e;if(4&t&&"object"==typeof e&&e&&e.__esModule)return e;var r=Object.create(null);if(s.r(r),Object.defineProperty(r,"default",{enumerable:!0,value:e}),2&t&&"string"!=typeof e)for(var n in e)s.d(r,n,function(t){return e[t]}.bind(null,n));return r},s.n=function(e){var t=e&&e.__esModule?function(){return e.default}:function(){return e};return s.d(t,"a",t),t},s.o=function(e,t){return Object.prototype.hasOwnProperty.call(e,t)},s.p="https://production-assets.paperswithcode.com/",s.oe=function(e){throw console.error(e),e};var u=this.webpackJsonpfrontend=this.webpackJsonpfrontend||[],f=u.push.bind(u);u.push=t,u=u.slice();for(var i=0;i<u.length;i++)t(u[i]);var l=f;r()}([])</script><script src="https://production-assets.paperswithcode.com/static/js/13.aa3fa037.chunk.js"></script><script src="https://production-assets.paperswithcode.com/static/js/main.99ee382b.chunk.js"></script> </body> </html>

CINXE.COM

Wide ResNet | Papers With Code