{"id":68297,"date":"2021-06-09T11:10:00","date_gmt":"2021-06-09T10:10:00","guid":{"rendered":"https:\/\/datascientest.com\/?p=68297"},"modified":"2023-11-09T14:07:09","modified_gmt":"2023-11-09T14:07:09","slug":"u-net","status":"publish","type":"post","link":"https:\/\/datascientest.com\/u-net","title":{"rendered":"U-NET : le r\u00e9seau de neurones de Computer Vision"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"68297\" class=\"elementor elementor-68297\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-1b1324a elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"1b1324a\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-2992877\" data-id=\"2992877\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-286aae5 elementor-widget elementor-widget-heading\" data-id=\"286aae5\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h2 class=\"elementor-heading-title elementor-size-default\">U-NET est un mod\u00e8le de r\u00e9seau de neurones d\u00e9di\u00e9 aux taches de Vision par Ordinateur (Computer Vision) et plus particuli\u00e8rement aux probl\u00e8mes de Segmentation S\u00e9mantique. D\u00e9couvrez tout ce que vous devez savoir : pr\u00e9sentation, fonctionnement, architecture, avantages, formations...<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-17b18f6 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"17b18f6\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-ef310ef\" data-id=\"ef310ef\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-848ca89 elementor-widget elementor-widget-text-editor\" data-id=\"848ca89\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<p>L&rsquo;intelligence artificielle est une vaste technologie aux multiples branches. La <a href=\"https:\/\/datascientest.com\/computer-vision\">\u00a0\u00bb Computer Vision \u00a0\u00bb ou vision par ordinateur<\/a> est l&rsquo;une de ces sous-cat\u00e9gories.<\/p><p>Il s&rsquo;agit d&rsquo;un champ scientifique interdisciplinaire visant \u00e0 permettre aux ordinateurs de<strong> \u00a0\u00bb comprendre \u00a0\u00bb les images et les vid\u00e9os<\/strong>. L&rsquo;objectif est d&rsquo;automatiser les t\u00e2ches effectu\u00e9es par le syst\u00e8me visuel humain.<\/p><p>Gr\u00e2ce <a href=\"https:\/\/datascientest.com\/deep-learning\">au Deep Learning<\/a>, de formidables progr\u00e8s ont pu \u00eatre r\u00e9alis\u00e9s dans le domaine de la Computer Vision au fil des derni\u00e8res ann\u00e9es. D\u00e9sormais, l<strong>es machines sont capables de rivaliser<\/strong> avec la vision humaine dans certaines situations.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-1362e19 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"1362e19\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-f94c03f\" data-id=\"f94c03f\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-09592b8 elementor-widget elementor-widget-video\" data-id=\"09592b8\" data-element_type=\"widget\" data-settings=\"{&quot;youtube_url&quot;:&quot;https:\\\/\\\/www.youtube.com\\\/watch?v=G7WxP0dcPC8&amp;t=20s&quot;,&quot;video_type&quot;:&quot;youtube&quot;,&quot;controls&quot;:&quot;yes&quot;}\" data-widget_type=\"video.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-video .elementor-widget-container{overflow:hidden;transform:translateZ(0)}.elementor-widget-video .elementor-wrapper{aspect-ratio:var(--video-aspect-ratio)}.elementor-widget-video .elementor-wrapper iframe,.elementor-widget-video .elementor-wrapper video{height:100%;width:100%;display:flex;border:none;background-color:#000}@supports not (aspect-ratio:1\/1){.elementor-widget-video .elementor-wrapper{position:relative;overflow:hidden;height:0;padding-bottom:calc(100% \/ var(--video-aspect-ratio))}.elementor-widget-video .elementor-wrapper iframe,.elementor-widget-video .elementor-wrapper video{position:absolute;top:0;right:0;bottom:0;left:0}}.elementor-widget-video .elementor-open-inline .elementor-custom-embed-image-overlay{position:absolute;top:0;right:0;bottom:0;left:0;background-size:cover;background-position:50%}.elementor-widget-video .elementor-custom-embed-image-overlay{cursor:pointer;text-align:center}.elementor-widget-video .elementor-custom-embed-image-overlay:hover .elementor-custom-embed-play i{opacity:1}.elementor-widget-video .elementor-custom-embed-image-overlay img{display:block;width:100%;aspect-ratio:var(--video-aspect-ratio);-o-object-fit:cover;object-fit:cover;-o-object-position:center center;object-position:center center}@supports not (aspect-ratio:1\/1){.elementor-widget-video .elementor-custom-embed-image-overlay{position:relative;overflow:hidden;height:0;padding-bottom:calc(100% \/ var(--video-aspect-ratio))}.elementor-widget-video .elementor-custom-embed-image-overlay img{position:absolute;top:0;right:0;bottom:0;left:0}}.elementor-widget-video .e-hosted-video .elementor-video{-o-object-fit:cover;object-fit:cover}.e-con-inner>.elementor-widget-video,.e-con>.elementor-widget-video{width:var(--container-widget-width);--flex-grow:var(--container-widget-flex-grow)}<\/style>\t\t<div class=\"elementor-wrapper elementor-open-inline\">\n\t\t\t<div class=\"elementor-video\"><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-035e0ba elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"035e0ba\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-18fe33d\" data-id=\"18fe33d\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-068900b elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"068900b\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/formation-data-scientist\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Suivre une formation Deep Learning<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-ae5e201 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"ae5e201\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-cb45ccc\" data-id=\"cb45ccc\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-d65d41f elementor-widget elementor-widget-heading\" data-id=\"d65d41f\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Les diff\u00e9rentes t\u00e2ches de Vision par Ordinateur<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-b1593d6 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"b1593d6\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-2cd77de\" data-id=\"2cd77de\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-214ad4c elementor-widget elementor-widget-text-editor\" data-id=\"214ad4c\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Il existe diff\u00e9rentes t\u00e2ches de vision par ordinateur. L&rsquo;une des applications les plus courantes est <strong>la classification d&rsquo;image<\/strong>. Elle consiste \u00e0 laisser l&rsquo;ordinateur identifier l&rsquo;objet principal d&rsquo;une image et \u00e0 lui attribuer une \u00e9tiquette pour classifier cette image.<\/p><p>Il est aussi possible de <strong>laisser l&rsquo;ordinateur localiser l&#8217;emplacement de l&rsquo;objet sur l&rsquo;image<\/strong>. Pour ce faire, il encadre l&rsquo;objet d&rsquo;une \u00a0\u00bb Bounding Box \u00a0\u00bb (bo\u00eete englobante) pouvant \u00eatre identifi\u00e9e par des param\u00e8tres num\u00e9riques relatifs aux bordures de l&rsquo;image.<\/p><p>La classification d&rsquo;objet se limite \u00e0 un seul objet par image. Plus complexe, <strong>la d\u00e9tection d&rsquo;objet<\/strong> va plus loin et exige de l&rsquo;ordinateur de d\u00e9tecter et de localiser tous les diff\u00e9rents objets au sein d&rsquo;une m\u00eame image.<\/p><p>La <strong>segmentation s\u00e9mantique<\/strong> consiste \u00e0 \u00e9tiqueter chaque pixel d&rsquo;une image avec une classe correspondante \u00e0 ce qui est repr\u00e9sent\u00e9. On parle aussi de \u00a0\u00bb pr\u00e9diction dense \u00ab\u00a0, car chaque pixel doit \u00eatre pr\u00e9dit.<\/p><p>Contrairement aux autres t\u00e2ches de vision par ordinateur, la segmentation s\u00e9mantique ne produit pas seulement des \u00e9tiquettes et des Bounding Boxes. Elle g\u00e9n\u00e8re <strong>une image en haute d\u00e9finition<\/strong>, sur laquelle chaque pixel est classifi\u00e9.<\/p><p>La <strong>segmentation d&rsquo;instance<\/strong> va encore plus loin, en classifiant chaque instance d&rsquo;une m\u00eame classe s\u00e9par\u00e9ment. Par exemple, si une image pr\u00e9sente trois chiens, chaque chien est une instance de la classe \u00a0\u00bb Chien \u00ab\u00a0. Chacun sera classifi\u00e9 s\u00e9par\u00e9ment, par exemple en utilisant diff\u00e9rentes couleurs.<\/p><p>\u00c0 travers ces diff\u00e9rentes t\u00e2ches, <strong>l&rsquo;ordinateur \u00a0\u00bb comprend \u00a0\u00bb le contenu des images<\/strong> avec un niveau de granularit\u00e9 de plus en plus pr\u00e9cis. Dans ce dossier, nous allons nous int\u00e9resser sp\u00e9cifiquement \u00e0 la t\u00e2che de segmentation s\u00e9mantique.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-25124e2 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"25124e2\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-fd15f2b\" data-id=\"fd15f2b\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-bd2a848 elementor-widget elementor-widget-video\" data-id=\"bd2a848\" data-element_type=\"widget\" data-settings=\"{&quot;youtube_url&quot;:&quot;https:\\\/\\\/youtu.be\\\/P27XN7-N_Bk&quot;,&quot;video_type&quot;:&quot;youtube&quot;,&quot;controls&quot;:&quot;yes&quot;}\" data-widget_type=\"video.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-wrapper elementor-open-inline\">\n\t\t\t<div class=\"elementor-video\"><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-486ea28 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"486ea28\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-4057564\" data-id=\"4057564\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-3776e6a elementor-widget elementor-widget-heading\" data-id=\"3776e6a\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Les applications et cas d'usage de Segmentation S\u00e9mantique<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-335cfde elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"335cfde\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-f390d69\" data-id=\"f390d69\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-b2b9671 elementor-widget elementor-widget-text-editor\" data-id=\"b2b9671\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>La segmentation s\u00e9mantique est utilis\u00e9e pour une large vari\u00e9t\u00e9 d&rsquo;applications. Les <strong>v\u00e9hicules autonomes<\/strong>, par exemple, requi\u00e8rent une perception, une planification et une ex\u00e9cution au sein d&rsquo;environnements en constante \u00e9volution.<\/p><p>Ils n\u00e9cessitent aussi une haute pr\u00e9cision, puisque <strong>la s\u00e9curit\u00e9 doit \u00eatre infaillible<\/strong> sur la route. Gr\u00e2ce \u00e0 la segmentation s\u00e9mantique, il est possible pour les voitures sans pilote de d\u00e9tecter les espaces libres sur les voies, les marquages au sol et les panneaux de signalisation.<\/p><p>On utilise aussi cette technique d&rsquo;IA pour <strong>les diagnostics m\u00e9dicaux<\/strong>. Les machines peuvent \u00e9pauler les analyses effectu\u00e9es par les radiologues, afin de r\u00e9duire le temps n\u00e9cessaire pour \u00e9tablir des diagnostics.<\/p><p>Un autre cas d&rsquo;usage est <strong>la cartographie par satellite<\/strong>, tr\u00e8s importante pour la surveillance des zones de d\u00e9forestation ou pour l&rsquo;urbanisation. La segmentation s\u00e9mantique permet de distinguer les diff\u00e9rents types de terrains de mani\u00e8re automatis\u00e9e. La d\u00e9tection de b\u00e2timents et de routes est aussi tr\u00e8s utile pour la gestion du trafic ou la planification urbaine.<\/p><p>Enfin, <strong>l<\/strong><strong>es robots d&rsquo;agriculture de pr\u00e9cision<\/strong> peuvent utiliser la segmentation s\u00e9mantique pour distinguer les plantations des mauvaises herbes. Ceci leur permet d&rsquo;automatiser le d\u00e9sherbage en utilisant moins d&rsquo;herbicide.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-bb157cd elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"bb157cd\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-65e08af\" data-id=\"65e08af\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-30eb891 elementor-widget elementor-widget-video\" data-id=\"30eb891\" data-element_type=\"widget\" data-settings=\"{&quot;youtube_url&quot;:&quot;https:\\\/\\\/youtu.be\\\/uiE56h5LyXc&quot;,&quot;video_type&quot;:&quot;youtube&quot;,&quot;controls&quot;:&quot;yes&quot;}\" data-widget_type=\"video.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-wrapper elementor-open-inline\">\n\t\t\t<div class=\"elementor-video\"><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-59fa527 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"59fa527\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-916b47a\" data-id=\"916b47a\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-804c050 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"804c050\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/formation-data-scientist\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Apprendre \u00e0 utiliser U-Net<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-ecbb693 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"ecbb693\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-cff3122\" data-id=\"cff3122\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-842e72a elementor-widget elementor-widget-heading\" data-id=\"842e72a\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Qu'est-ce que U-NET ?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-7178143 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"7178143\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-baf7023\" data-id=\"baf7023\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-a30b5f3 elementor-widget elementor-widget-text-editor\" data-id=\"a30b5f3\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Il existe <strong>diff\u00e9rentes m\u00e9thodes pour r\u00e9soudre les probl\u00e8mes<\/strong> de segmentation s\u00e9mantique. Les approches traditionnelles consistent \u00e0 d\u00e9tecter les points, les lignes ou les bordures. Il est aussi possible de se baser sur la morphologie, ou d&rsquo;assembler des grappes de pixels.<\/p><p>D\u00e9sormais, <a href=\"https:\/\/datascientest.com\/deep-neural-network\" target=\"_blank\" rel=\"noopener\">les r\u00e9seaux de neurones<\/a> convolutifs de Deep Learning sont tr\u00e8s utilis\u00e9s. Ils permettent de <strong>s&rsquo;attaquer \u00e0 des probl\u00e8mes plus complexes<\/strong> gr\u00e2ce \u00e0 la segmentation d&rsquo;image.<\/p><p>L&rsquo;un des r\u00e9seaux de neurones les plus utilis\u00e9s <strong>pour la segmentation d&rsquo;image est U-NET<\/strong>. Il s&rsquo;agit d&rsquo;un Mod\u00e8le de R\u00e9seau de Neurones Enti\u00e8rement Convolutif. Ce mod\u00e8le fut initialement d\u00e9velopp\u00e9 par Olaf Ronneberger, Phillip Fischer, et Thomas Brox en 2015 pour la segmentation d&rsquo;images m\u00e9dicales.<\/p><p>L&rsquo;<strong>architecture de U-NET<\/strong> est compos\u00e9e de deux \u00a0\u00bb chemins \u00ab\u00a0. Le premier est le chemin de contraction, aussi appel\u00e9 encodeur. Il est utilis\u00e9 pour capturer le contexte d&rsquo;une image.<\/p><p>Il s&rsquo;agit en fait d&rsquo;un <strong>assemblage de couches de convolution<\/strong> et de couches de \u00a0\u00bb max pooling \u00a0\u00bb permettant de cr\u00e9er une carte de caract\u00e9ristiques d&rsquo;une image et de r\u00e9duire sa taille pour diminuer le nombre de param\u00e8tres du r\u00e9seau.<\/p><p>Le second chemin est celui de l&rsquo;expansion sym\u00e9trique, <strong>aussi appel\u00e9 d\u00e9codeur<\/strong>. Il permet aussi une localisation pr\u00e9cise gr\u00e2ce \u00e0 la convolution transpos\u00e9e.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-862b388 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"862b388\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-fb8bda1\" data-id=\"fb8bda1\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-a7af1d6 elementor-widget elementor-widget-image\" data-id=\"a7af1d6\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-image{text-align:center}.elementor-widget-image a{display:inline-block}.elementor-widget-image a img[src$=\".svg\"]{width:48px}.elementor-widget-image img{vertical-align:middle;display:inline-block}<\/style>\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"800\" height=\"533\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2021\/05\/u-net-architecture-1024x682.png\" class=\"attachment-large size-large wp-image-68301\" alt=\"u-net architecture\" srcset=\"https:\/\/datascientest.com\/wp-content\/uploads\/2021\/05\/u-net-architecture-1024x682.png 1024w, https:\/\/datascientest.com\/wp-content\/uploads\/2021\/05\/u-net-architecture-300x200.png 300w, https:\/\/datascientest.com\/wp-content\/uploads\/2021\/05\/u-net-architecture-768x512.png 768w, https:\/\/datascientest.com\/wp-content\/uploads\/2021\/05\/u-net-architecture-1536x1023.png 1536w, https:\/\/datascientest.com\/wp-content\/uploads\/2021\/05\/u-net-architecture.png 1555w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-b5bfc91 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"b5bfc91\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-625469d\" data-id=\"625469d\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-361da18 elementor-widget elementor-widget-heading\" data-id=\"361da18\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Les avantages de U-NET<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-0990249 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"0990249\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-38c7bac\" data-id=\"38c7bac\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-813c915 elementor-widget elementor-widget-text-editor\" data-id=\"813c915\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Dans le domaine du Deep Learning, il est n\u00e9cessaire d&rsquo;utiliser <strong>de larges ensembles de donn\u00e9es pour entra\u00eener<\/strong> les mod\u00e8les. Il peut \u00eatre difficile d&rsquo;assembler de tels volumes de donn\u00e9es pour r\u00e9soudre un probl\u00e8me de classification d&rsquo;images, en termes de temps, de budget et de ressources hardware.<\/p><p>L&rsquo;<strong>\u00e9tiquetage des donn\u00e9es<\/strong> requiert aussi l&rsquo;expertise de plusieurs d\u00e9veloppeurs et ing\u00e9nieurs. C&rsquo;est particuli\u00e8rement le cas pour des domaines hautement sp\u00e9cialis\u00e9s comme les diagnostics m\u00e9dicaux.<\/p><p>U-NET permet de rem\u00e9dier \u00e0 ces probl\u00e8mes, puisqu&rsquo;il s&rsquo;av\u00e8re <strong>efficace m\u00eame avec un ensemble de donn\u00e9es limit\u00e9<\/strong>. Il offre aussi une pr\u00e9cision sup\u00e9rieure aux mod\u00e8les conventionnels.<\/p><p>Une <strong>architecture autoencoder classique<\/strong> r\u00e9duit la taille des informations entr\u00e9es, puis les couches suivantes. Le d\u00e9codage commence ensuite, la repr\u00e9sentation de caract\u00e9ristiques lin\u00e9aire est apprise et la traille augmente progressivement. \u00c0 la fin de cette architecture, la taille de sortie est \u00e9gale \u00e0 la taille d&rsquo;entr\u00e9e.<\/p><p>Une telle architecture est id\u00e9ale pour pr\u00e9server la taille initiale. Le <strong>probl\u00e8me est qu&rsquo;elle compresse l&rsquo;input<\/strong> de fa\u00e7on linaire, ce qui emp\u00eache la transmission de la totalit\u00e9 des caract\u00e9ristiques.<\/p><p>C&rsquo;est <strong>l\u00e0 que U-NET tire son \u00e9pingle du jeu<\/strong> gr\u00e2ce \u00e0 son architecture en U. La d\u00e9convolution est effectu\u00e9e du c\u00f4t\u00e9 du d\u00e9codeur, ce qui permet d&rsquo;\u00e9viter le probl\u00e8me de goulot rencontr\u00e9 avec une architecture auto-encodeur et donc d&rsquo;\u00e9viter la perte de caract\u00e9ristiques.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-c79a4a7 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"c79a4a7\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-391d28e\" data-id=\"391d28e\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-be7d4b2 elementor-widget elementor-widget-heading\" data-id=\"be7d4b2\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Comment apprendre \u00e0 utiliser U-NET ?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-5657867 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"5657867\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-7f516be\" data-id=\"7f516be\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-e8be1fe elementor-widget elementor-widget-video\" data-id=\"e8be1fe\" data-element_type=\"widget\" data-settings=\"{&quot;youtube_url&quot;:&quot;https:\\\/\\\/youtu.be\\\/PZlNVCqsHyc&quot;,&quot;video_type&quot;:&quot;youtube&quot;,&quot;controls&quot;:&quot;yes&quot;}\" data-widget_type=\"video.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-wrapper elementor-open-inline\">\n\t\t\t<div class=\"elementor-video\"><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-84acf40 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"84acf40\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-7861b7b\" data-id=\"7861b7b\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-f65da42 elementor-widget elementor-widget-text-editor\" data-id=\"f65da42\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Alors que l&rsquo;IA et la Vision par Ordinateur sont de plus en plus exploit\u00e9s dans tous les secteurs, la ma\u00eetrise du Deep Learning et des diff\u00e9rents mod\u00e8les comme U-NET est <strong>une comp\u00e9tence pr\u00e9cieuse et convoit\u00e9e<\/strong>.<\/p><p>Pour l&rsquo;acqu\u00e9rir, vous pouvez vous tourner vers <a href=\"https:\/\/datascientest.com\/formations-data\" target=\"_blank\" rel=\"noopener\">les formations DataScientest<\/a>. Le Machine Learning et le Deep Learning sont au coeur de <strong>notre parcours Data Scientist<\/strong>.<\/p><p>\u00c0 travers cette formation, vous apprendrez aussi la programmation en Python, la DataViz, et l&rsquo;utilisation des bases de donn\u00e9es et outils Big Data. \u00c0 l&rsquo;issue du parcours, vous aurez <strong>toutes les comp\u00e9tences n\u00e9cessaires<\/strong> pour exercer le m\u00e9tier en plein essor de scientifique des donn\u00e9es.<\/p><p>Nos formations professionnalisantes sont con\u00e7ues pour r\u00e9pondre aux besoins r\u00e9els des entreprises et des organisations. Elles peuvent \u00eatre effectu\u00e9es <strong>en BootCamp, ou en Formation Continue<\/strong>.<\/p><p>Nous vous proposons <strong>une approche innovante de Blended Learning<\/strong>, alliant apprentissage en pr\u00e9sentiel et \u00e0 distance. Au terme du programme, vous recevrez un dipl\u00f4me certifi\u00e9 par l&rsquo;Universit\u00e9 de la Sorbonne.<\/p><p>Nos formations peuvent \u00eatre financ\u00e9es \u00e0 l&rsquo;aide du Compte Personnel de Formation, ou par P\u00f4le Emploi via l&rsquo;AIF. Parmi nos alumnis, 93% ont trouv\u00e9 un emploi imm\u00e9diatement. N&rsquo;attendez plus et d\u00e9couvrez d\u00e8s maintenant <a href=\"https:\/\/datascientest.com\/formation-data-scientist\" target=\"_blank\" rel=\"noopener\">la formation Data Scientist<\/a> !<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-0113a0b elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"0113a0b\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-6b833f6\" data-id=\"6b833f6\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-6f60f61 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"6f60f61\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/formation-data-scientist\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Commencer une formation Data Scientist<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-e245d68 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"e245d68\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-05df5c9\" data-id=\"05df5c9\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-e103eaa elementor-widget elementor-widget-text-editor\" data-id=\"e103eaa\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Vous savez tout sur U-NET. D\u00e9couvrez notre dossier complet <a href=\"https:\/\/datascientest.com\/computer-vision\" target=\"_blank\" rel=\"noopener\">sur la Computer Vision<\/a>, et notre dossier plus g\u00e9n\u00e9ral <a href=\"https:\/\/datascientest.com\/deep-learning\" target=\"_blank\" rel=\"noopener\">sur le Deep Learning<\/a>.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-f67db3b elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"f67db3b\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-eedacdd\" data-id=\"eedacdd\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-59b2211 elementor-widget elementor-widget-html\" data-id=\"59b2211\" data-element_type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<script type=\"application\/ld+json\">\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"FAQPage\",\n  \"mainEntity\": [{\n    \"@type\": \"Question\",\n    \"name\": \"Les diff\u00e9rentes t\u00e2ches de Vision par Ordinateur\",\n    \"acceptedAnswer\": {\n      \"@type\": \"Answer\",\n      \"text\": \"Il existe diff\u00e9rentes t\u00e2ches de vision par ordinateur. 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Les approches traditionnelles consistent \u00e0 d\u00e9tecter les points, les lignes ou les bordures. Il est aussi possible de se baser sur la morphologie, ou d'assembler des grappes de pixels.\"\n    }\n  },{\n    \"@type\": \"Question\",\n    \"name\": \"Les avantages de U-NET\u200b\",\n    \"acceptedAnswer\": {\n      \"@type\": \"Answer\",\n      \"text\": \"Dans le domaine du Deep Learning, il est n\u00e9cessaire d'utiliser de larges ensembles de donn\u00e9es pour entra\u00eener les mod\u00e8les. Il peut \u00eatre difficile d'assembler de tels volumes de donn\u00e9es pour r\u00e9soudre un probl\u00e8me de classification d'images, en termes de temps, de budget et de ressources hardware.\"\n    }\n  }]\n}\n<\/script>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>U-NET est un mod\u00e8le de r\u00e9seau de neurones d\u00e9di\u00e9 aux taches de Vision par Ordinateur (Computer Vision) et plus particuli\u00e8rement aux probl\u00e8mes de Segmentation S\u00e9mantique. D\u00e9couvrez tout ce que vous devez savoir : pr\u00e9sentation, fonctionnement, architecture, avantages, formations&#8230; L&rsquo;intelligence artificielle est une vaste technologie aux multiples branches. La \u00a0\u00bb Computer Vision \u00a0\u00bb ou vision par [&hellip;]<\/p>\n","protected":false},"author":77,"featured_media":75712,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[2497],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.7 (Yoast SEO v21.7) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>U-NET : le r\u00e9seau de neurones de Computer Vision<\/title>\n<meta name=\"description\" content=\"U-NET est un mod\u00e8le de r\u00e9seau de neurones con\u00e7u pour la Computer Vision ou vision par ordinateur en intelligence artificielle. Lisez cet article pour tout comprendre sur le mod\u00e8le U-NET\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/datascientest.com\/u-net\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"U-NET : le r\u00e9seau de neurones de Computer Vision\" \/>\n<meta property=\"og:url\" content=\"https:\/\/datascientest.com\/u-net\" \/>\n<meta property=\"og:site_name\" content=\"Formation Data Science | DataScientest.com\" \/>\n<meta property=\"article:published_time\" content=\"2021-06-09T10:10:00+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2023-11-09T14:07:09+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/datascientest.com\/wp-content\/uploads\/2021\/06\/illu_UNET-08.png\" \/>\n\t<meta property=\"og:image:width\" content=\"2560\" \/>\n\t<meta property=\"og:image:height\" content=\"1405\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Raphael Kassel\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"\u00c9crit par\" \/>\n\t<meta name=\"twitter:data1\" content=\"Raphael Kassel\" \/>\n\t<meta name=\"twitter:label2\" content=\"Dur\u00e9e de lecture estim\u00e9e\" \/>\n\t<meta name=\"twitter:data2\" content=\"7 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/datascientest.com\/u-net\",\"url\":\"https:\/\/datascientest.com\/u-net\",\"name\":\"U-NET : le r\u00e9seau de neurones de Computer Vision\",\"isPartOf\":{\"@id\":\"https:\/\/datascientest.com\/#website\"},\"datePublished\":\"2021-06-09T10:10:00+00:00\",\"dateModified\":\"2023-11-09T14:07:09+00:00\",\"author\":{\"@id\":\"https:\/\/datascientest.com\/#\/schema\/person\/6eca16f63daaafcbc96e15549b9d4ee1\"},\"description\":\"U-NET est un mod\u00e8le de r\u00e9seau de neurones con\u00e7u pour la Computer Vision ou vision par ordinateur en intelligence artificielle. 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