{"id":179289,"date":"2024-05-02T11:01:25","date_gmt":"2024-05-02T10:01:25","guid":{"rendered":"https:\/\/datascientest.com\/en\/?p=179289"},"modified":"2024-02-18T12:44:07","modified_gmt":"2024-02-18T11:44:07","slug":"what-is-the-grad-cam-method","status":"publish","type":"post","link":"https:\/\/datascientest.com\/en\/what-is-the-grad-cam-method","title":{"rendered":"What is the Grad CAM method?"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"179289\" class=\"elementor elementor-179289\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-ed83ded e-flex e-con-boxed e-con\" data-id=\"ed83ded\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-51800f4 e-con-full e-flex e-con\" data-id=\"51800f4\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-d5cee03 elementor-widget elementor-widget-heading\" data-id=\"d5cee03\" 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\">In recent years, explainability has been a recurring but still niche issue in machine learning. The Grad-CAM method is a solution to this problem.<\/h2>\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\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-e1319f0 e-flex e-con-boxed e-con\" data-id=\"e1319f0\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-a5a09da e-con-full e-flex e-con\" data-id=\"a5a09da\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-c33b0cc elementor-widget elementor-widget-text-editor\" data-id=\"c33b0cc\" 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>More recently, interest in this subject has begun to accelerate. One of the reasons for this is the growing number of machine learning models in production.<\/p><p>On the one hand, this translates into a growing number of end users who need to understand how the models make their decisions. On the other hand, a <strong>growing number of Machine Learning developers<\/strong> need to understand why (or why not) a model works in a particular way.<\/p><p><a href=\"https:\/\/datascientest.com\/en\/convolutional-neural-network-everything-you-need-to-know\">Convolutional neural networks<\/a> have proved very effective in tasks such as image classification, face recognition and document analysis. But as efficiency and complexity have increased, the interpretability of these algorithms has gradually decreased. A solution to problems such as face recognition involves hundreds of layers and thousands of parameters to train, making it difficult to read, debug and trust the model.<strong> CNNs<\/strong> appear as black boxes that take inputs and give outputs with great precision without giving any intuition as to how they work.<\/p><p><a href=\"https:\/\/datascientest.com\/en\/roles-of-the-data-scientist-in-a-data-governance-strategy-data\">As data scientists<\/a>, it is our responsibility to ensure that the model works correctly. Suppose we are given the task of classifying different birds. The dataset contains images of different birds and plants\/trees in the background. If the network is looking at the plants and trees instead of the bird, there&#8217;s a good chance that it will misclassify the image and miss all the features of the bird. How can we tell if our model is looking at the right thing? In this article, we will look at one approach to identifying whether the<strong> CNN is working correctly with features that are important for <a href=\"https:\/\/datascientest.com\/en\/classification-algorithms-definition-and-main-models\">classification or recognition.<\/a><\/strong><\/p><p>\u00a0<\/p><p>\ud83d\udca1Related articles:<\/p><table dir=\"ltr\" border=\"1\" cellspacing=\"0\" cellpadding=\"0\" data-sheets-root=\"1\"><colgroup><col width=\"746\" \/><\/colgroup><tbody><tr><td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Unraveling Machine Learning vs Deep Learning: Key Differences Explained&quot;}\" data-sheets-hyperlink=\"https:\/\/datascientest.com\/en\/unraveling-machine-learning-vs-deep-learning-key-differences-explained\"><a class=\"in-cell-link\" href=\"https:\/\/datascientest.com\/en\/unraveling-machine-learning-vs-deep-learning-key-differences-explained\" target=\"_blank\" rel=\"noopener\">Unraveling Machine Learning vs Deep Learning: Key Differences 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href=\"https:\/\/datascientest.com\/en\/mlops-devops-applied-to-machine-learning-projects\" target=\"_blank\" rel=\"noopener\">MLOps: DevOps applied to Machine Learning projects <\/a><\/td><\/tr><tr><td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Feature Engineering: Importance for Machine Learning&quot;}\" data-sheets-hyperlink=\"https:\/\/datascientest.com\/en\/feature-engineering-importance-for-machine-learning\"><a class=\"in-cell-link\" href=\"https:\/\/datascientest.com\/en\/feature-engineering-importance-for-machine-learning\" target=\"_blank\" rel=\"noopener\">Feature Engineering: Importance for Machine Learning<\/a><\/td><\/tr><tr><td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Scikit Learn: Discover the Python library dedicated to Machine Learning&quot;}\" data-sheets-hyperlink=\"https:\/\/datascientest.com\/en\/scikit-learn-discover-the-python-library-dedicated-to-machine-learning\"><a class=\"in-cell-link\" href=\"https:\/\/datascientest.com\/en\/scikit-learn-discover-the-python-library-dedicated-to-machine-learning\" target=\"_blank\" rel=\"noopener\">Scikit Learn: Discover the Python library dedicated to Machine Learning<\/a><\/td><\/tr><\/tbody><\/table>\t\t\t\t\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\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-96b535d e-flex e-con-boxed e-con\" data-id=\"96b535d\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-ed56886 e-con-full e-flex e-con\" data-id=\"ed56886\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-90dd9fc elementor-widget elementor-widget-heading\" data-id=\"90dd9fc\" 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\">The Grad CAM method\n<\/h3>\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\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-d5cd764 e-flex e-con-boxed e-con\" data-id=\"d5cd764\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-18f5d91 e-con-full e-flex e-con\" data-id=\"18f5d91\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-3aff7c1 elementor-widget elementor-widget-text-editor\" data-id=\"3aff7c1\" 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>One way to ensure this is to <strong>visualise what CNNs are actually looking at,<\/strong> using the Grad-CAM method.<\/p><p><strong>The gradient-weighted class activation map (Grad CAM<\/strong>) produces a heat map that highlights important regions of an image using the target gradients (dog, cat) of the final convolutional layer.<\/p><p>The <strong>Grad CAM<\/strong> method is a popular visualisation technique that is useful for understanding how a convolutional neural network has been driven to make a classification decision. It is class-specific, meaning that it can produce a separate visualisation for each class present in the image.<\/p><p>In the event of a <a href=\"https:\/\/datascientest.com\/en\/management-of-unbalanced-classification-problems-ii\">classification error,<\/a> this method can be very useful for understanding where the problem lies in the convolutional network. It also makes the algorithm more transparent.<\/p>\t\t\t\t\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\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-0b61e22 e-flex e-con-boxed e-con\" data-id=\"0b61e22\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-0335fdb e-con-full e-flex e-con\" data-id=\"0335fdb\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-1c1794f elementor-widget elementor-widget-heading\" data-id=\"1c1794f\" 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\">How does it work?<\/h3>\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\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-21c4cfb e-flex e-con-boxed e-con\" data-id=\"21c4cfb\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-3daf45e e-con-full e-flex e-con\" data-id=\"3daf45e\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-7399267 elementor-widget elementor-widget-text-editor\" data-id=\"7399267\" 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><strong>Grad CAM<\/strong> consists in finding out which parts of the image have led a <strong>convolutional neural network to its final decision<\/strong>. This method consists of producing heat maps representing the activation classes on the images received as input. Each activation class is associated with a specific output class.<\/p><p>These classes are used to indicate the importance of each pixel in relation to the class in question by increasing or decreasing the intensity of the pixel.<\/p><p>For example, if an image is used in a convolutional network of dogs and cats, the <strong>Grad-CAM visualisation<\/strong> can generate a heatmap for the &#8216;cat&#8217; class, indicating the extent to which the different parts of the image correspond to a cat, and also a heatmap for the &#8216;dog&#8217; class, indicating the extent to which the parts of the image correspond to a dog.<\/p><p>For example, let&#8217;s consider a CNN of dogs and cats. The Grad-CAM method will generate a heatmap for the cat object class to indicate the extent to which each part of an image corresponds to a cat, and also a heatmap for the dog object class in the same way.<\/p><p>The class activation map assigns importance to each position (x, y) in the last convolutional layer by calculating the linear combination of activations, weighted by the corresponding output weights for the observed class (Australian terrier in the example below). The resulting class activation map is then resampled to the size of the input image. This is illustrated by the heatmap below.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3e227ee elementor-widget elementor-widget-image\" data-id=\"3e227ee\" 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<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"800\" height=\"359\" src=\"https:\/\/datascientest.com\/en\/files\/2021\/07\/Class-Activation-Mapping-1024x460.jpg\" class=\"attachment-large size-large wp-image-82237\" alt=\"Carte Thermique\" srcset=\"https:\/\/datascientest.com\/en\/files\/2021\/07\/Class-Activation-Mapping-1024x460.jpg 1024w, https:\/\/datascientest.com\/en\/files\/2021\/07\/Class-Activation-Mapping-300x135.jpg 300w, https:\/\/datascientest.com\/en\/files\/2021\/07\/Class-Activation-Mapping-768x345.jpg 768w, https:\/\/datascientest.com\/en\/files\/2021\/07\/Class-Activation-Mapping.jpg 1200w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Source : https:\/\/arxiv.org\/abs\/1512.04150<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b0eacbf elementor-widget elementor-widget-text-editor\" data-id=\"b0eacbf\" 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>Let&#8217;s take the example of the classification of dogs and cats again. The Grad-CAM method will be used to render the thermal zones used to classify each of the objects in our image, producing the following result:<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a0bbf05 elementor-widget elementor-widget-image\" data-id=\"a0bbf05\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"469\" height=\"181\" src=\"https:\/\/datascientest.com\/en\/files\/2021\/07\/Original-Image.jpg\" class=\"attachment-large size-large wp-image-82254\" alt=\"R\u00e9sultat de la m\u00e9thode Grad-CAM\" srcset=\"https:\/\/datascientest.com\/en\/files\/2021\/07\/Original-Image.jpg 469w, https:\/\/datascientest.com\/en\/files\/2021\/07\/Original-Image-300x116.jpg 300w\" sizes=\"(max-width: 469px) 100vw, 469px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Source : https:\/\/glassboxmedicine.com\/2020\/05\/29\/grad-cam-visual-explanations-from-deep-networks\/<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c78a13a elementor-widget elementor-widget-text-editor\" data-id=\"c78a13a\" 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>In this article, we have seen a new technique for interpreting convolutional neural networks, which are a state-of-the-art architecture, particularly for image-related tasks. Research in the field of interpretable machine learning is progressing at a rapid pace and is proving to be very important in gaining the confidence of users and contributing to the improvement of models.<\/p><p>Did you enjoy this article? If you are convinced by the importance of Machine Learning today, and by the effectiveness of our Bootcamp training course<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-380f26b elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"380f26b\" 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\/en\/data-scientist-course\">\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\">Find out more about our data scientist training<\/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<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>In recent years, explainability has been a recurring but still niche issue in machine learning. The Grad-CAM method is a solution to this problem. More recently, interest in this subject has begun to accelerate. One of the reasons for this is the growing number of machine learning models in production. On the one hand, this [&hellip;]<\/p>\n","protected":false},"author":67,"featured_media":179291,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[2425],"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>What is the Grad CAM method?<\/title>\n<meta name=\"description\" content=\"Grad CAM is widely used for interpretability and visualization in deep learning models, allowing researchers and practitioners to gain insights into the inner workings of CNNs and understand the factors influencing their predictions.\" \/>\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\/en\/what-is-the-grad-cam-method\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"What is the Grad CAM method?\" \/>\n<meta property=\"og:description\" content=\"In recent years, explainability has been a recurring but still niche issue in machine learning. The Grad-CAM method is a solution to this problem. 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