YOLOv1 Explained | Papers With Code

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Object detection is framed as a regression problem to spatially separated bounding boxes and associated class probabilities. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation. Since the whole detection pipeline is a single network, it can be optimized end-to-end directly on detection performance. The network uses features from the entire image to predict each bounding box. It also predicts all bounding boxes across all classes for an image simultaneously. This means the network reasons globally about the full image and all the objects in the image." />  <meta property="og:title" content="Papers with Code - YOLOv1 Explained"> <meta property="og:description" content="YOLOv1 is a single-stage object detection model. Object detection is framed as a regression problem to spatially separated bounding boxes and associated class probabilities. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation. Since the whole detection pipeline is a single network, it can be optimized end-to-end directly on detection performance. The network uses features from the entire image to predict each bounding box. It also predicts all bounding boxes across all classes for an image simultaneously. This means the network reasons globally about the full image and all the objects in the image."> <meta property="og:image" content="https://production-media.paperswithcode.com/methods/Screen_Shot_2020-06-24_at_12.22.30_PM.png"> <meta property="og:url" content="https://paperswithcode.com/method/yolov1">  <meta name="twitter:card" content="summary_large_image"> <meta name="twitter:site" content="@paperswithcode"> <meta name="twitter:title" content="Papers with Code - YOLOv1 Explained"> <meta name="twitter:description" content="YOLOv1 is a single-stage object detection model. Object detection is framed as a regression problem to spatially separated bounding boxes and associated class probabilities. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation. Since the whole detection pipeline is a single network, it can be optimized end-to-end directly on detection performance. The network uses features from the entire image to predict each bounding box. It also predicts all bounding boxes across all classes for an image simultaneously. 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It also predicts all bounding boxes across all classes for an image simultaneously. This means the network reasons globally about the full image and all the objects in the image.", "url": "https://paperswithcode.com/method/yolov1", "image": "https://production-media.paperswithcode.com/methods/Screen_Shot_2020-06-24_at_12.22.30_PM.png", "headline": "YOLOv1 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" href="https://twitter.com/paperswithcode"> <span class=" icon-wrapper icon-fa 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Object detection is framed as a regression problem to spatially separated bounding boxes and associated class probabilities. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation. Since the whole detection pipeline is a single network, it can be optimized end-to-end directly on detection performance. The network uses features from the entire image to predict each bounding box. It also predicts all bounding boxes across all classes for an image simultaneously. This means the network reasons globally about the full image and all the objects in the image.</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 href="https://production-media.paperswithcode.com/methods/Screen_Shot_2020-06-24_at_12.22.30_PM.png">methods/Screen_Shot_2020-06-24_at_12.22.30_PM.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/*" class="clearablefileinput form-control-file" id="id_image"> </div> </div> <div class="modal-footer"> 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role="dialog" aria-labelledby="removeComponentLabel" aria-hidden="true"> <div class="modal-dialog" role="document"> <div class="modal-content"> <div class="modal-header"> <h5 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/1x1-convolution"> <span class="badge badge-primary">1X1 CONVOLUTION</span> </a> <input type="hidden" name="csrfmiddlewaretoken" value="EIz0giSHtHHt2zAfo1Fm6xnotg3U0UW5Wd8qaMWsRXhSMMP6QbC3fQs5DhTzYibW"> <input type="hidden" name="remove_component_pk" value="137"> <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/convolution"> <span 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<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/non-maximum-suppression"> <span class="badge badge-primary">NON MAXIMUM SUPPRESSION</span> </a> <input type="hidden" name="csrfmiddlewaretoken" value="EIz0giSHtHHt2zAfo1Fm6xnotg3U0UW5Wd8qaMWsRXhSMMP6QbC3fQs5DhTzYibW"> <input type="hidden" name="remove_component_pk" value="380"> <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>YOLOv1</h1> <span class="method-subtitle">Introduced by Redmon et al. in <a href="/paper/you-only-look-once-unified-real-time-object">You Only Look Once: Unified, Real-Time 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>YOLOv1</strong> is a single-stage object detection model. Object detection is framed as a regression problem to spatially separated bounding boxes and associated class probabilities. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation. Since the whole detection pipeline is a single network, it can be optimized end-to-end directly on detection performance. </p> <p>The network uses features from the entire image to predict each bounding box. It also predicts all bounding boxes across all classes for an image simultaneously. This means the network reasons globally about the full image and all the objects in the image.</p> <span class="description-source"> Source: <a href="http://arxiv.org/abs/1506.02640v5"><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> You Only Look Once: Unified, Real-Time Object Detection</a> </span> <div class="context"> <div class="row"> <div class="col-md-12"> <a href="https://arxiv.org/abs/1506.02640v5" onclick="captureOutboundLink('https://arxiv.org/abs/1506.02640v5'); 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_12.22.30_PM.png" data-lightbox="imageresource"> <img id="imageresource" width=100% src="https://production-media.paperswithcode.com/methods/Screen_Shot_2020-06-24_at_12.22.30_PM.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 = '218'; </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">5</td> <td class="text-right">29.41%</td> </tr> <tr> <td><span class="dot" style="background-color: #0d233a"></span> <a href="/task/real-time-object-detection">Real-Time Object Detection</a> </td> <td class="text-right">2</td> <td class="text-right">11.76%</td> </tr> <tr> <td><span class="dot" style="background-color: #8bbc21"></span> <a href="/task/object">Object</a> </td> <td class="text-right">2</td> <td class="text-right">11.76%</td> </tr> <tr> <td><span class="dot" style="background-color: #f28f43"></span> <a href="/task/survey">Survey</a> </td> <td class="text-right">1</td> <td class="text-right">5.88%</td> </tr> <tr> <td><span class="dot" style="background-color: rgb(128, 133, 233)"></span> <a href="/task/computational-efficiency">Computational Efficiency</a> </td> <td class="text-right">1</td> <td class="text-right">5.88%</td> </tr> <tr> <td><span class="dot" style="background-color: rgb(241, 92, 128)"></span> <a href="/task/machine-learning">BIG-bench Machine Learning</a> </td> <td class="text-right">1</td> <td class="text-right">5.88%</td> </tr> <tr> <td><span class="dot" style="background-color: rgb(228, 211, 84)"></span> <a href="/task/blood-cell-count">Blood Cell Count</a> </td> <td class="text-right">1</td> <td class="text-right">5.88%</td> </tr> <tr> <td><span class="dot" style="background-color: rgb(43, 144, 143)"></span> <a href="/task/blood-cell-detection">Blood Cell Detection</a> </td> <td class="text-right">1</td> <td class="text-right">5.88%</td> </tr> <tr> <td><span class="dot" style="background-color: rgb(255, 116, 116)"></span> <a href="/task/cbc-test">CBC TEST</a> </td> <td class="text-right">1</td> <td class="text-right">5.88%</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/1x1-convolution"> <div class="method-image"> <img src="https://production-media.paperswithcode.com/thumbnails/method/8af57ab4-844d-4f77-93db-f0ad3a582b9c.jpg"> 1x1 Convolution </div> </a> </td> <td> <a href="/methods/category/convolutions"> Convolutions </a> </td> <td class="hidden-element"> </td> </tr> <tr> <td><a href="/method/convolution"> <div class="method-image"> <img src="https://production-media.paperswithcode.com/thumbnails/method/7aae5df0-922a-4806-beaf-8e49ea229a17.jpg"> Convolution </div> </a> </td> <td> <a href="/methods/category/convolutions"> Convolutions </a> </td> <td class="hidden-element"> </td> </tr> <tr> <td><a href="/method/dense-connections"> <div class="method-image"> <img src="https://production-media.paperswithcode.com/thumbnails/method/method-0000000009-bc4d4a07_xuCMY0c.jpg"> Dense Connections </div> </a> </td> <td> <a href="/methods/category/feedforward-networks"> Feedforward Networks </a> </td> <td class="hidden-element"> </td> </tr> <tr> <td><a href="/method/dropout"> <div class="method-image"> <img src="https://production-media.paperswithcode.com/thumbnails/method/method-0000000169-91318315_CHtBAl1.jpg"> Dropout </div> </a> </td> <td> <a href="/methods/category/regularization"> Regularization </a> </td> <td class="hidden-element"> </td> </tr> <tr> <td><a href="/method/leaky-relu"> <div class="method-image"> <img src="https://production-media.paperswithcode.com/thumbnails/method/1ad67ddd-dc17-4786-8dbb-fa9f383688cb.jpg"> Leaky ReLU </div> </a> </td> <td> <a href="/methods/category/activation-functions"> Activation Functions </a> </td> <td class="hidden-element"> </td> </tr> <tr> <td><a href="/method/max-pooling"> <div class="method-image"> <img src="https://production-media.paperswithcode.com/thumbnails/method/method-0000036659-3913323a_WdD9TuY.jpg"> Max Pooling </div> </a> </td> <td> <a href="/methods/category/pooling-operation"> Pooling Operations </a> </td> <td class="hidden-element"> </td> </tr> <tr> <td><a href="/method/non-maximum-suppression"> <div class="method-image"> <img src="https://production-media.paperswithcode.com/thumbnails/method/method-0000054844-107a911f.jpg"> Non Maximum Suppression </div> </a> </td> <td> <a href="/methods/category/proposal-filtering"> Proposal Filtering </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" 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