CSPDarknet53 Explained | Papers With Code

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It employs a CSPNet strategy to partition the feature map of the base layer into two parts and then merges them through a cross-stage hierarchy. The use of a split and merge strategy allows for more gradient flow through the network. This CNN is used as the backbone for YOLOv4." />  <meta property="og:title" content="Papers with Code - CSPDarknet53 Explained"> <meta property="og:description" content="CSPDarknet53 is a convolutional neural network and backbone for object detection that uses DarkNet-53. It employs a CSPNet strategy to partition the feature map of the base layer into two parts and then merges them through a cross-stage hierarchy. The use of a split and merge strategy allows for more gradient flow through the network. This CNN is used as the backbone for YOLOv4."> <meta property="og:image" content="https://production-media.paperswithcode.com/methods/Screen_Shot_2020-06-25_at_3.55.20_PM_fTGbeXg.png"> <meta property="og:url" content="https://paperswithcode.com/method/cspdarknet53">  <meta name="twitter:card" content="summary_large_image"> <meta name="twitter:site" content="@paperswithcode"> <meta name="twitter:title" content="Papers with Code - CSPDarknet53 Explained"> <meta name="twitter:description" content="CSPDarknet53 is a convolutional neural network and backbone for object detection that uses DarkNet-53. It employs a CSPNet strategy to partition the feature map of the base layer into two parts and then merges them through a cross-stage hierarchy. The use of a split and merge strategy allows for more gradient flow through the network. 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The use of a split and merge strategy allows for more gradient flow through the network. \n\nThis CNN is used as the backbone for YOLOv4.", "url": "https://paperswithcode.com/method/cspdarknet53", "image": "https://production-media.paperswithcode.com/methods/Screen_Shot_2020-06-25_at_3.55.20_PM_fTGbeXg.png", "headline": "CSPDarknet53 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 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" 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**CSPDarknet53** is a convolutional neural network and backbone for object detection that uses [DarkNet-53](https://paperswithcode.com/method/darknet-53). It employs a CSPNet strategy to partition the feature map of the base layer into two parts and then merges them through a cross-stage hierarchy. The use of a split and merge strategy allows for more gradient flow through the network. This CNN is used as the backbone for [YOLOv4](https://paperswithcode.com/method/yolov4).</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/Tianxiaomo/pytorch-YOLOv4/blob/be3a20bb4a87988b30dddb018d74ee677d1434e8/tool/darknet2pytorch.py#L134" 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 href="https://production-media.paperswithcode.com/methods/Screen_Shot_2020-06-25_at_3.55.20_PM_fTGbeXg.png">methods/Screen_Shot_2020-06-25_at_3.55.20_PM_fTGbeXg.png</a> <input type="checkbox" name="image-clear" id="image-clear_id"> <label for="image-clear_id">Clear</label><br> Change: <input type="file" name="image" accept="image/*" 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method="post"> <li> <a href="/method/darknet-53"> <span class="badge badge-primary">DARKNET-53</span> </a> <input type="hidden" name="csrfmiddlewaretoken" value="nwgDByhxZapjaY8UgH0MCZAA5jNZVISveQSOf8UdcSxiOwNST7y5PwfDUXX0ucL0"> <input type="hidden" name="remove_component_pk" value="238"> <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/convolutional-neural-networks"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/method/89d208bf-ac5b-4330-9f3b-b87f820f3ac2.jpg"> <span>Convolutional Neural Networks</span> </span> </a> <div class="method-title"> <div class="row"> <div class="col-md-11"> <h1>CSPDarknet53</h1> <span class="method-subtitle">Introduced by Bochkovskiy et al. in <a href="/paper/yolov4-optimal-speed-and-accuracy-of-object">YOLOv4: Optimal Speed and Accuracy of Object Detection</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>CSPDarknet53</strong> is a convolutional neural network and backbone for object detection that uses <a href="https://paperswithcode.com/method/darknet-53">DarkNet-53</a>. It employs a CSPNet strategy to partition the feature map of the base layer into two parts and then merges them through a cross-stage hierarchy. The use of a split and merge strategy allows for more gradient flow through the network. </p> <p>This CNN is used as the backbone for <a href="https://paperswithcode.com/method/yolov4">YOLOv4</a>.</p> <span class="description-source"> Source: <a href="https://arxiv.org/abs/2004.10934v1"><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> YOLOv4: Optimal Speed and Accuracy of Object Detection</a> </span> <div class="context"> <div class="row"> <div class="col-md-12"> <a href="https://arxiv.org/abs/2004.10934v1" onclick="captureOutboundLink('https://arxiv.org/abs/2004.10934v1'); return true;" class="badge badge-light"> Read Paper </a> <a href="https://github.com/Tianxiaomo/pytorch-YOLOv4/blob/be3a20bb4a87988b30dddb018d74ee677d1434e8/tool/darknet2pytorch.py#L134" onclick="captureOutboundLink('https://github.com/Tianxiaomo/pytorch-YOLOv4/blob/be3a20bb4a87988b30dddb018d74ee677d1434e8/tool/darknet2pytorch.py#L134'); 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_2020-06-25_at_3.55.20_PM_fTGbeXg.png" data-lightbox="imageresource"> <img id="imageresource" width=100% src="https://production-media.paperswithcode.com/methods/Screen_Shot_2020-06-25_at_3.55.20_PM_fTGbeXg.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 = '30'; </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">73</td> <td class="text-right">25.35%</td> </tr> <tr> <td><span class="dot" style="background-color: #0d233a"></span> <a href="/task/object">Object</a> </td> <td class="text-right">42</td> <td class="text-right">14.58%</td> </tr> <tr> <td><span class="dot" style="background-color: #8bbc21"></span> <a href="/task/real-time-object-detection">Real-Time Object Detection</a> </td> <td class="text-right">15</td> <td class="text-right">5.21%</td> </tr> <tr> <td><span class="dot" style="background-color: #f28f43"></span> <a href="/task/deep-learning">Deep Learning</a> </td> <td class="text-right">9</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/autonomous-driving">Autonomous Driving</a> </td> <td class="text-right">8</td> <td class="text-right">2.78%</td> </tr> <tr> <td><span class="dot" style="background-color: rgb(241, 92, 128)"></span> <a href="/task/semantic-segmentation">Semantic Segmentation</a> </td> <td class="text-right">6</td> <td class="text-right">2.08%</td> </tr> <tr> <td><span class="dot" style="background-color: rgb(228, 211, 84)"></span> <a href="/task/object-tracking">Object Tracking</a> </td> <td class="text-right">6</td> <td class="text-right">2.08%</td> </tr> <tr> <td><span class="dot" style="background-color: rgb(43, 144, 143)"></span> <a href="/task/2d-object-detection">2D Object Detection</a> </td> <td class="text-right">6</td> <td class="text-right">2.08%</td> </tr> <tr> <td><span class="dot" style="background-color: rgb(255, 116, 116)"></span> <a href="/task/multi-object-tracking">Multi-Object Tracking</a> </td> <td class="text-right">5</td> <td class="text-right">1.74%</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> </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/convolutional-neural-networks"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/method/89d208bf-ac5b-4330-9f3b-b87f820f3ac2.jpg"> <span>Convolutional Neural Networks</span> </span> </a> </li> </ul> </div> </div> </div> </div> <script id="task_data" type="application/json">[{"name": "Object Detection", "slug": "object-detection", "papers": 73, "y": 25.34722222222222, "dot_color": "#2f7ed8"}, {"name": "Object", "slug": "object", "papers": 42, "y": 14.583333333333334, "dot_color": "#0d233a"}, {"name": "Real-Time Object Detection", "slug": "real-time-object-detection", "papers": 15, "y": 5.208333333333334, "dot_color": "#8bbc21"}, 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