YOLOv3 Explained | Papers With Code

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Improvements include the use of a new backbone network, Darknet-53 that utilises residual connections, or in the words of the author, "those newfangled residual network stuff", as well as some improvements to the bounding box prediction step, and use of three different scales from which to extract features (similar to an FPN)." />  <meta property="og:title" content="Papers with Code - YOLOv3 Explained"> <meta property="og:description" content="YOLOv3 is a real-time, single-stage object detection model that builds on YOLOv2 with several improvements. Improvements include the use of a new backbone network, Darknet-53 that utilises residual connections, or in the words of the author, "those newfangled residual network stuff", as well as some improvements to the bounding box prediction step, and use of three different scales from which to extract features (similar to an FPN)."> <meta property="og:image" content="https://production-media.paperswithcode.com/methods/Screen_Shot_2020-06-24_at_12.52.19_PM_awcwYBa.png"> <meta property="og:url" content="https://paperswithcode.com/method/yolov3">  <meta name="twitter:card" content="summary_large_image"> <meta name="twitter:site" content="@paperswithcode"> <meta name="twitter:title" content="Papers with Code - YOLOv3 Explained"> <meta name="twitter:description" content="YOLOv3 is a real-time, single-stage object detection model that builds on YOLOv2 with several improvements. Improvements include the use of a new backbone network, Darknet-53 that utilises residual connections, or in the words of the author, "those newfangled residual network stuff", as well as some improvements to the bounding box prediction step, and use of three different scales from which to extract features (similar to an FPN)."> <meta name="twitter:creator" content="@paperswithcode"> <meta name="twitter:url" content="https://paperswithcode.com/method/yolov3"> <meta name="twitter:domain" content="paperswithcode.com">  <script type="application/ld+json">{ "@context": "http://schema.org", "@graph": { "@type": "CreativeWork", "@id": "yolov3", "name": "YOLOv3 Explained", "description": "YOLOv3 is a real-time, single-stage object detection model that builds on YOLOv2 with several improvements. Improvements include the use of a new backbone network, Darknet-53 that utilises residual connections, or in the words of the author, \"those newfangled residual network stuff\", as well as some improvements to the bounding box prediction step, and use of three different scales from which to extract features (similar to an FPN).", "url": "https://paperswithcode.com/method/yolov3", "image": "https://production-media.paperswithcode.com/methods/Screen_Shot_2020-06-24_at_12.52.19_PM_awcwYBa.png", "headline": "YOLOv3 Explained" } }</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 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Description with Markdown (optional): </label> <div class=""> <textarea name="description" cols="40" rows="12" class="textarea form-control" id="id_description"> **YOLOv3** is a real-time, single-stage object detection model that builds on [YOLOv2](https://paperswithcode.com/method/yolov2) with several improvements. Improvements include the use of a new backbone network, [Darknet-53](https://paperswithcode.com/method/darknet-53) that utilises residual connections, or in the words of the author, "those newfangled residual network stuff", as well as some improvements to the bounding box prediction step, and use of three different scales from which to extract features (similar to an [FPN](https://paperswithcode.com/method/fpn)).</textarea> </div> </div> <div id="div_id_code_snippet_url" class="form-group"> <label for="id_code_snippet_url" class=""> Code Snippet URL (optional): </label> <div class=""> <input type="text" name="code_snippet_url" value="https://github.com/pjreddie/darknet" maxlength="200" class="textinput textInput form-control" id="id_code_snippet_url"> </div> </div> <div id="div_id_image" class="form-group"> <label for="id_image" class=""> Image </label> <div class=""> Currently: <a 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class="modal-title" id="removeCompoenntnLabel">Remove a method component</h5> <button type="button" class="close btn-close" data-bs-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> <a href="/method/darknet-53"> <span class="badge badge-primary">DARKNET-53</span> </a> <input type="hidden" name="csrfmiddlewaretoken" value="vGEobrmeEPdBuCcgVlXJwokN1cOxF11g1uh0lzzalb7SuSWmoaEFOUvDiR6oJREn"> <input type="hidden" name="remove_component_pk" value="238"> <button type="submit" class="btn btn-danger" style="width:2.5em">- </button> </li> </form> </ul> <ul class="list-unstyled"> <form action="" method="post"> <li> <a href="/method/k-means-clustering"> <span class="badge badge-primary">K-MEANS CLUSTERING</span> </a> <input type="hidden" name="csrfmiddlewaretoken" value="vGEobrmeEPdBuCcgVlXJwokN1cOxF11g1uh0lzzalb7SuSWmoaEFOUvDiR6oJREn"> <input type="hidden" name="remove_component_pk" value="578"> <button type="submit" class="btn btn-danger" style="width:2.5em">- </button> </li> </form> </ul> <ul class="list-unstyled"> <form action="" method="post"> <li> <a href="/method/logistic-regression"> <span class="badge badge-primary">LOGISTIC REGRESSION</span> </a> <input type="hidden" name="csrfmiddlewaretoken" value="vGEobrmeEPdBuCcgVlXJwokN1cOxF11g1uh0lzzalb7SuSWmoaEFOUvDiR6oJREn"> <input type="hidden" name="remove_component_pk" value="222"> <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="mobile-width"> <div class="method-header"> <a href="/methods/category/object-detection-models"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/method/c9f8b125-84f6-4d45-b4a2-84eb1b59f311.jpg"> <span>Object Detection Models</span> </span> </a> <div class="method-title"> <div class="row"> <div class="col-md-11"> <h1>YOLOv3</h1> <span class="method-subtitle">Introduced by Redmon et al. in <a href="/paper/yolov3-an-incremental-improvement">YOLOv3: An Incremental Improvement</a></span> </div> <div class="col-md-1"> <div class="float-right">聽 <div class="dropdown edit-button"> <a data-bs-toggle="modal" data-bs-target="#loginModal"> <span class="badge badge-method-edit" style="padding-top:10px;"><span class=" icon-wrapper icon-fa icon-fa-solid" data-name="edit"><svg viewBox="0 0 576 514.999" xmlns="http://www.w3.org/2000/svg"><path d="M402.6 85.198l90.2 90.2c3.8 3.8 3.8 10 0 13.8l-218.399 218.4-92.8 10.3c-12.4 1.4-22.9-9.1-21.5-21.5l10.3-92.8 218.4-218.4c3.799-3.8 10-3.8 13.799 0zm162-22.9c15.2 15.2 15.2 39.9 0 55.2l-35.4 35.4c-3.8 3.8-10 3.8-13.8 0l-90.2-90.2c-3.8-3.8-3.8-10 0-13.8l35.4-35.4c15.3-15.2 40-15.2 55.2 0zM384 348.198c0-3.2 1.3-6.2 3.5-8.5l40-40c7.6-7.5 20.5-2.2 20.5 8.5v157.8c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48v-352c0-26.5 21.5-48 48-48h285.8c10.7 0 16.1 12.9 8.5 20.5l-40 40c-2.3 2.2-5.3 3.5-8.5 3.5H64v320h320v-101.8z"/></svg></span> Edit</span> </a> </div> </div> </div> </div> </div> <div class="method-content" style="margin-top: 2rem;"> <div class="row"> <div class="col-md-8 description"> <p><strong>YOLOv3</strong> is a real-time, single-stage object detection model that builds on <a href="https://paperswithcode.com/method/yolov2">YOLOv2</a> with several improvements. Improvements include the use of a new backbone network, <a href="https://paperswithcode.com/method/darknet-53">Darknet-53</a> that utilises residual connections, or in the words of the author, "those newfangled residual network stuff", as well as some improvements to the bounding box prediction step, and use of three different scales from which to extract features (similar to an <a href="https://paperswithcode.com/method/fpn">FPN</a>).</p> <span class="description-source"> Source: <a href="http://arxiv.org/abs/1804.02767v1"><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> YOLOv3: An Incremental Improvement</a> </span> <div class="context"> <div class="row"> <div class="col-md-12"> <a href="https://arxiv.org/abs/1804.02767v1" onclick="captureOutboundLink('https://arxiv.org/abs/1804.02767v1'); return true;" class="badge badge-light"> Read Paper </a> <a href="https://github.com/pjreddie/darknet" onclick="captureOutboundLink('https://github.com/pjreddie/darknet'); return true;" class="badge badge-light"> See Code </a> </div> </div> </div> </div> <div class="col-md-4"> <a href="#" id="pop"> <a href="https://production-media.paperswithcode.com/methods/Screen_Shot_2020-06-24_at_12.52.19_PM_awcwYBa.png" data-lightbox="imageresource"> <img id="imageresource" width=100% src="https://production-media.paperswithcode.com/methods/Screen_Shot_2020-06-24_at_12.52.19_PM_awcwYBa.png"> </a> </a> <div class="modal fade" id="imagemodal" 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"> <img class="method-image" src="" id="imagepreview" style="max-width: 750px;"> </div> </div> </div> </div> </div> </div> <div id="papers"> <h4 style="margin-bottom: 0.5rem">Papers</h4> <hr> <div class="sota-table-preview papers-datatable-component"> <table style="width: 100% !important;" id="datatable-papers" class="table-striped table-responsive"> <thead style="width: 100% !important;"> <tr> <th style="text-left"><span>Paper</span></th> <th class="text-center"><span>Code</span></th> <th class="text-center"><span>Results</span></th> <th class="text-right"><span>Date</span></th> <th class="text-center"><span>Stars</span></th> </tr> </thead> </table> </div> <script> const DATATABLE_PAPERS_FILTER_NAME = 'papermethod__method_id'; const DATATABLE_PAPERS_FILTER_VALUE = '269'; </script> </div> <div id="tasks"> <h4 style="margin-bottom: 0.5rem">Tasks</h4> <hr> <div class="row"> <div class="col-lg-6"> <figure class="highcharts-figure"> <div id="usage-container"></div> </figure> </div> <div class="col-lg-6 task-methods"> <table> <tr> <th>Task</th> <th class="text-right">Papers</th> <th class="text-right">Share</th> </tr> <tr> <td><span class="dot" style="background-color: #2f7ed8"></span> <a href="/task/object-detection">Object Detection</a> </td> <td class="text-right">143</td> <td class="text-right">27.93%</td> </tr> <tr> <td><span class="dot" style="background-color: #0d233a"></span> <a href="/task/object">Object</a> </td> <td class="text-right">75</td> <td class="text-right">14.65%</td> </tr> <tr> <td><span class="dot" style="background-color: #8bbc21"></span> <a href="/task/autonomous-driving">Autonomous Driving</a> </td> <td class="text-right">19</td> <td class="text-right">3.71%</td> </tr> <tr> <td><span class="dot" style="background-color: #f28f43"></span> <a href="/task/real-time-object-detection">Real-Time Object Detection</a> </td> <td class="text-right">16</td> <td class="text-right">3.13%</td> </tr> <tr> <td><span class="dot" style="background-color: rgb(128, 133, 233)"></span> <a href="/task/deep-learning">Deep Learning</a> </td> <td class="text-right">14</td> <td class="text-right">2.73%</td> </tr> <tr> <td><span class="dot" style="background-color: rgb(241, 92, 128)"></span> <a href="/task/classification-1">Classification</a> </td> <td class="text-right">9</td> <td class="text-right">1.76%</td> </tr> <tr> <td><span class="dot" style="background-color: rgb(228, 211, 84)"></span> <a href="/task/pedestrian-detection">Pedestrian Detection</a> </td> <td class="text-right">8</td> <td class="text-right">1.56%</td> </tr> <tr> <td><span class="dot" style="background-color: rgb(43, 144, 143)"></span> <a href="/task/instance-segmentation">Instance Segmentation</a> </td> <td class="text-right">7</td> <td class="text-right">1.37%</td> </tr> <tr> <td><span class="dot" style="background-color: rgb(255, 116, 116)"></span> <a href="/task/small-object-detection">Small Object Detection</a> </td> <td class="text-right">6</td> <td class="text-right">1.17%</td> </tr> </table> </div> </div> </div> <div id="trends"> <h4 style="margin-bottom: 0.5rem">Usage Over Time</h4> <hr> <figure style="margin-top: 2.5rem" class="highcharts-figure"> <div id="container"></div> </figure> <span class="experimental-note hidden-element"><span class=" icon-wrapper icon-ion" data-name="flask-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-miterlimit="10" stroke-width="32" d="M176 48h160M118 304h276M208 48v93.48a64.09 64.09 0 0 1-9.88 34.18L73.21 373.49C48.4 412.78 76.63 464 123.08 464h265.84c46.45 0 74.68-51.22 49.87-90.51L313.87 175.66a64.09 64.09 0 0 1-9.87-34.18V48"/></svg></span> This feature is experimental; we are continuously improving our matching algorithm.</span> </div> <div id="components"> <h4 style="margin-bottom: 0.5rem">Components</h4> <hr> <table> <tr> <th>Component</th> <th>Type</th> <th class="hidden-element"> <div class="float-right"> <div class="dropdown edit-button"> <button class="dropdown-toggle badge badge-edit" type="button" id="compEditMenu" data-bs-toggle="dropdown" aria-haspopup="true" aria-expanded="false"> <span class=" icon-wrapper icon-fa icon-fa-solid" data-name="edit"><svg viewBox="0 0 576 514.999" xmlns="http://www.w3.org/2000/svg"><path d="M402.6 85.198l90.2 90.2c3.8 3.8 3.8 10 0 13.8l-218.399 218.4-92.8 10.3c-12.4 1.4-22.9-9.1-21.5-21.5l10.3-92.8 218.4-218.4c3.799-3.8 10-3.8 13.799 0zm162-22.9c15.2 15.2 15.2 39.9 0 55.2l-35.4 35.4c-3.8 3.8-10 3.8-13.8 0l-90.2-90.2c-3.8-3.8-3.8-10 0-13.8l35.4-35.4c15.3-15.2 40-15.2 55.2 0zM384 348.198c0-3.2 1.3-6.2 3.5-8.5l40-40c7.6-7.5 20.5-2.2 20.5 8.5v157.8c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48v-352c0-26.5 21.5-48 48-48h285.8c10.7 0 16.1 12.9 8.5 20.5l-40 40c-2.3 2.2-5.3 3.5-8.5 3.5H64v320h320v-101.8z"/></svg></span> Edit </button> <div class="dropdown-menu dropdown-menu-end" aria-labelledby="compEditMenu" x-placement="bottom-end" style="position: absolute; transform: translate3d(55px, 35px, 0px); top: 0px; left: 0px; will-change: transform;"> <a class="dropdown-item" href="#loginModal" data-bs-toggle="modal"> <span class=" icon-wrapper icon-ion" data-name="add"><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="M256 112v288m144-144H112"/></svg></span> Add</a> <a class="dropdown-item" href="#loginModal" data-bs-toggle="modal"> <span class=" icon-wrapper icon-ion" data-name="remove"><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="M400 256H112"/></svg></span> Remove</a> </div> </div> </div> </th> </tr> <tr> <td><a href="/method/darknet-53"> <div class="method-image"> <img src="https://production-media.paperswithcode.com/thumbnails/method/method-0000000238-e5282d2f_6URXJsm.jpg"> Darknet-53 </div> </a> </td> <td> <a href="/methods/category/convolutional-neural-networks"> Convolutional Neural Networks </a> </td> <td class="hidden-element"> </td> </tr> <tr> <td><a href="/method/k-means-clustering"> <div class="method-image"> <img src="https://production-media.paperswithcode.com/thumbnails/method/22679b69-c7fc-40e5-9ea8-3dc006b48c59.jpg"> k-Means Clustering </div> </a> </td> <td> <a href="/methods/category/clustering"> Clustering </a> </td> <td class="hidden-element"> </td> </tr> <tr> <td><a href="/method/logistic-regression"> <div class="method-image"> <img src="https://production-media.paperswithcode.com/thumbnails/method/method-0000020529-5e65bc59.jpg"> Logistic Regression </div> </a> </td> <td> <a href="/methods/category/generalized-linear-models"> Generalized Linear Models </a> </td> <td class="hidden-element"> </td> </tr> </table> </div> <div class="collections"> <h4> Categories <div class="float-right"> <div class="dropdown edit-button"> <button class="dropdown-toggle badge badge-edit" type="button" id="evalEditMenu" data-bs-toggle="dropdown" aria-haspopup="true" aria-expanded="false"> <span class=" icon-wrapper icon-fa icon-fa-solid" data-name="edit"><svg viewBox="0 0 576 514.999" xmlns="http://www.w3.org/2000/svg"><path d="M402.6 85.198l90.2 90.2c3.8 3.8 3.8 10 0 13.8l-218.399 218.4-92.8 10.3c-12.4 1.4-22.9-9.1-21.5-21.5l10.3-92.8 218.4-218.4c3.799-3.8 10-3.8 13.799 0zm162-22.9c15.2 15.2 15.2 39.9 0 55.2l-35.4 35.4c-3.8 3.8-10 3.8-13.8 0l-90.2-90.2c-3.8-3.8-3.8-10 0-13.8l35.4-35.4c15.3-15.2 40-15.2 55.2 0zM384 348.198c0-3.2 1.3-6.2 3.5-8.5l40-40c7.6-7.5 20.5-2.2 20.5 8.5v157.8c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48v-352c0-26.5 21.5-48 48-48h285.8c10.7 0 16.1 12.9 8.5 20.5l-40 40c-2.3 2.2-5.3 3.5-8.5 3.5H64v320h320v-101.8z"/></svg></span> Edit </button> <div class="dropdown-menu dropdown-menu-end" aria-labelledby="evalEditMenu" x-placement="bottom-end" style="position: absolute; transform: translate3d(55px, 35px, 0px); top: 0px; left: 0px; will-change: transform;"> <a class="dropdown-item" href="#loginModal" data-bs-toggle="modal"> <span class=" icon-wrapper icon-ion" data-name="add"><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="M256 112v288m144-144H112"/></svg></span> Add</a> <a class="dropdown-item" href="#loginModal" data-bs-toggle="modal"> <span class=" icon-wrapper icon-ion" data-name="remove"><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="M400 256H112"/></svg></span> Remove</a> </div> </div> </div><hr> </h4> </div> <div class="row"> <div class="col-md-12"> <ul class="list-unstyled"> <li> <a href="/methods/category/object-detection-models"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/method/c9f8b125-84f6-4d45-b4a2-84eb1b59f311.jpg"> <span>Object Detection Models</span> </span> </a> </li> </ul> <ul class="list-unstyled"> <li> <a href="/methods/category/one-stage-object-detection-models"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>One-Stage Object Detection Models</span> </span> </a> </li> </ul> </div> </div> </div> </div> <script id="task_data" type="application/json">[{"name": "Object Detection", "slug": "object-detection", "papers": 143, "y": 27.9296875, "dot_color": "#2f7ed8"}, {"name": "Object", "slug": "object", "papers": 75, "y": 14.6484375, "dot_color": "#0d233a"}, {"name": "Autonomous Driving", "slug": "autonomous-driving", "papers": 19, "y": 3.7109375, "dot_color": "#8bbc21"}, {"name": "Real-Time Object 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