{"id":16219,"date":"2022-05-31T05:46:11","date_gmt":"2022-05-31T05:46:11","guid":{"rendered":"https:\/\/blog.datumo.com\/en\/?p=16219"},"modified":"2024-10-22T08:28:03","modified_gmt":"2024-10-22T08:28:03","slug":"role-of-image-labeling-in-your-deep-machine-learning-model","status":"publish","type":"post","link":"https:\/\/blog.datumo.com\/en\/tech\/16219","title":{"rendered":"Role of Image Labeling in your Deep\/Machine Learning Model"},"content":{"rendered":"[vc_row pix_particles_check=&#8221;&#8221;][vc_column][vc_raw_html]JTNDbWV0YSUyMGh0dHAtZXF1aXYlM0QlMjJyZWZyZXNoJTIyJTIwY29udGVudCUzRCUyMjAlM0IlMjB1cmwlM0RodHRwcyUzQSUyRiUyRmRhdHVtby5jb20lMkZlbiUyRnJvbGUtb2YtaW1hZ2UtbGFiZWxpbmctaW4teW91ci1kZWVwLW1hY2hpbmUtbGVhcm5pbmctbW9kZWwlMkYlMjIlM0U=[\/vc_raw_html]<div id=\"el1646799961152-e3ee06c0-4e82\" class=\"w-100 d-block \"><\/div><div class=\"pix-content-box card      vc_custom_1654577545529 custom-responsive-198423716   rounded-lg bg- w-100  \"   ><div class=\"\" style=\"z-index:30;position:relative;\">[vc_column_text]\r\n<p style=\"text-align: left;\"><span style=\"font-size: 14pt;\"><strong>\ud83d\udd11<\/strong> <strong>In 9 minutes you will learn:<\/strong><\/span><\/p>\r\n&nbsp;\r\n<ul>\r\n \t<li>The lifecycle of a data science project<\/li>\r\n \t<li>The applications of image labeling<\/li>\r\n \t<li>The importance of data labeling<\/li>\r\n<\/ul>\r\n[\/vc_column_text]<\/div><\/div>[\/vc_column][\/vc_row][vc_row pix_particles_check=&#8221;&#8221;][vc_column]<div id=\"el1650294698986-a1b962b5-ef42\" class=\"w-100 d-block \"><\/div>[vc_column_text css=&#8221;.vc_custom_1653976813621{padding-top: 40px !important;padding-right: 20px !important;padding-bottom: 40px !important;padding-left: 20px !important;}&#8221;]\r\n<p id=\"c862\" class=\"pw-post-body-paragraph zs zt yn bn b zu zv hk zw zx zy ho zz aba abb abc abd abe abf abg abh abi abj abk abl abm jn iz\" data-selectable-paragraph=\"\">Furthermore, since we are talking about <strong class=\"bn ml\"><em class=\"pn\">image<\/em>\u00a0<em class=\"pn\">labeling<\/em><\/strong>, it is implicit that we are going to talk strictly about the Computer Vision domain of Machine Learning and ignore textual data completely. Now, before we can get on to that, it is important to know what a Data Science Project\u2019s lifecycle is. So let\u2019s start with that!<\/p>\r\n\r\n<figure class=\"abo abp abq abr xj abs jc jd paragraph-image\">\r\n<div class=\"abt abu dq abv cf abw\" tabindex=\"0\" role=\"button\"><img fetchpriority=\"high\" decoding=\"async\" class=\"aligncenter size-full wp-image-16221\" src=\"https:\/\/blog.datumo.com\/en\/wp-content\/uploads\/2022\/05\/Frame-2728.png\" alt=\"\" width=\"1330\" height=\"776\" srcset=\"https:\/\/blog.datumo.com\/en\/wp-content\/uploads\/2022\/05\/Frame-2728.png 1330w, https:\/\/blog.datumo.com\/en\/wp-content\/uploads\/2022\/05\/Frame-2728-300x175.png 300w, https:\/\/blog.datumo.com\/en\/wp-content\/uploads\/2022\/05\/Frame-2728-1024x597.png 1024w, https:\/\/blog.datumo.com\/en\/wp-content\/uploads\/2022\/05\/Frame-2728-768x448.png 768w\" sizes=\"(max-width: 1330px) 100vw, 1330px\" \/><\/div><\/figure>\r\n<div tabindex=\"0\" role=\"button\"><\/div>\r\n<div tabindex=\"0\" role=\"button\">\r\n<p id=\"11d9\" class=\"pw-post-body-paragraph zs zt yn bn b zu acc hk zw zx acd ho zz aba ace abc abd abe acf abg abh abi acg abk abl abm jn iz\" data-selectable-paragraph=\"\">As you can see above, after understanding and defining your problem statement, the first three steps to initiate your project revolve around \u2018Data\u2019. In the first phase, i.e.\u00a0<strong class=\"bn ml\"><em class=\"pn\">Data Collection<\/em><\/strong>, you either scrape images from the Internet or collect them yourself through a camera. Image labeling falls in the second phase i.e.\u00a0<strong class=\"bn ml\"><em class=\"pn\">Data Understanding<\/em><\/strong>\u00a0because this is where you are providing information \/ description to your model so that it can understand what the data represents. The third phase i.e.\u00a0<strong class=\"bn ml\"><em class=\"pn\">Data Preparation<\/em><\/strong>\u00a0is where you process your images to convert them to a format in which they can be easily fed to your model, and also ensure consistency in the dataset \u2014 for instance, all images should have the same dimensions (64 x 64). This is all you need to know about the lifecycle, understanding the other phases is not relevant for this tutorial.<\/p>\r\n\r\n<\/div>\r\n[\/vc_column_text][\/vc_column][\/vc_row][vc_section full_width=&#8221;stretch_row&#8221; pix_over_visibility=&#8221;&#8221; css=&#8221;.vc_custom_1650444445523{padding-top: 80px !important;padding-bottom: 80px !important;background-color: #f8f9fa !important;}&#8221; el_id=&#8221;pix_section_program&#8221;][vc_row full_width=&#8221;stretch_row&#8221; pix_particles_check=&#8221;&#8221;][vc_column content_align=&#8221;text-center&#8221; offset=&#8221;vc_col-lg-offset-0 vc_col-lg-12 vc_col-md-offset-1 vc_col-md-10&#8243;]<div id=\"el1650442503491-f5da6b2f-fa35\" class=\"mb-3 text-left \"><h2 class=\"mb-32 pix-sliding-headline font-weight-bold secondary-font\" data-class=\"secondary-font text-heading-default\" data-style=\"\">Application<\/h2><\/div>[vc_column_text css=&#8221;.vc_custom_1653976239106{padding-top: 40px !important;padding-bottom: 40px !important;}&#8221;]\r\n<p style=\"text-align: left;\">To discuss the applications of Image labeling, we need to know the types of learning in Machine Learning and in which types exactly is image labeling important. For that, see the image below:<\/p>\r\n<p style=\"text-align: left;\"><img decoding=\"async\" class=\"aligncenter size-full wp-image-16223\" src=\"https:\/\/blog.datumo.com\/en\/wp-content\/uploads\/2022\/05\/Frame-2729-1.png\" alt=\"\" width=\"1330\" height=\"776\" srcset=\"https:\/\/blog.datumo.com\/en\/wp-content\/uploads\/2022\/05\/Frame-2729-1.png 1330w, https:\/\/blog.datumo.com\/en\/wp-content\/uploads\/2022\/05\/Frame-2729-1-300x175.png 300w, https:\/\/blog.datumo.com\/en\/wp-content\/uploads\/2022\/05\/Frame-2729-1-1024x597.png 1024w, https:\/\/blog.datumo.com\/en\/wp-content\/uploads\/2022\/05\/Frame-2729-1-768x448.png 768w\" sizes=\"(max-width: 1330px) 100vw, 1330px\" \/><\/p>\r\n<p id=\"6707\" class=\"pw-post-body-paragraph zs zt yn bn b zu acc hk zw zx acd ho zz aba ace abc abd abe acf abg abh abi acg abk abl abm jn iz\" style=\"text-align: left;\" data-selectable-paragraph=\"\">Supervised learning is when you first train your model on a set of labeled data i.e. let it practice on some data for which you already know the answer. Think of it as doing practice questions before an actual exam (you have the solutions for the practice questions). Semi-supervised learning is similar to that as well, except that we introduce some unlabeled data as well and let the model train on a mixture of both. Unsupervised learning is where you let the model find meaningful patterns in your unlabeled dataset. As you probably would have guessed by now, image labeling is only relevant\/ important when you are either performing supervised learning or semi-supervised learning. It plays no role whatsoever if you are going for unsupervised learning.<\/p>\r\n<p id=\"60f2\" class=\"pw-post-body-paragraph zs zt yn bn b zu acc hk zw zx acd ho zz aba ace abc abd abe acf abg abh abi acg abk abl abm jn iz\" style=\"text-align: left;\" data-selectable-paragraph=\"\">Image labeling happens to be one of the starting processes in a computer vision application. It is divided into two main categories:<\/p>\r\n[\/vc_column_text][\/vc_column][\/vc_row][\/vc_section][vc_row pix_particles_check=&#8221;&#8221;][vc_column]<div id=\"el1650442607008-a85a832d-43f0\" class=\"w-100 d-block \"><\/div><div  class=\"pix-heading-el text-left \"><div><div class=\"slide-in-container\"><h2 class=\"text-heading-default font-weight-bold heading-text el-title_custom_color mb-12\" style=\"\" data-anim-type=\"\" data-anim-delay=\"0\">Labeling for Image Classification<\/h2><\/div><\/div><\/div>[vc_column_text css=&#8221;.vc_custom_1653976262387{padding-top: 40px !important;}&#8221;]\r\n<p id=\"3f10\" class=\"pw-post-body-paragraph zs zt yn bn b zu acc hk zw zx acd ho zz aba ace abc abd abe acf abg abh abi acg abk abl abm jn iz\" data-selectable-paragraph=\"\">Image classification is a basic application of computer vision where we classify images into different categories. For instance, let us consider a cat vs dog classifier \u2014 what it would do is if we give an image as input, it would tell us whether the given image is a cat image or a dog image. To build this classifier, the first thing we would need is a labeled dataset (that\u2019s the only part we are going to talk about, let us ignore the machine learning\/algorithm part for now). To construct this dataset, we would need to collect thousands of cat &amp; dog images and then rename those files with either\u00a0<strong class=\"bn ml\"><em class=\"pn\">cat<\/em><\/strong>\u00a0label or\u00a0<strong class=\"bn ml\"><em class=\"pn\">dog<\/em><\/strong>\u00a0label. Once we have done that, we would feed it (i.e. the dataset) into the model for training. This is all you need to know as far as image labeling is concerned.<\/p>\r\n[\/vc_column_text]<div id=\"el1650442651668-7359ff25-270a\" class=\"w-100 d-block \"><\/div><div id=\"el1650294913061-211813f5-5f2d\" class=\"w-100 d-block \"><\/div>[\/vc_column][\/vc_row][vc_section full_width=&#8221;stretch_row&#8221; pix_over_visibility=&#8221;&#8221; css=&#8221;.vc_custom_1650444445523{padding-top: 80px !important;padding-bottom: 80px !important;background-color: #f8f9fa !important;}&#8221;][vc_row full_width=&#8221;stretch_row&#8221; pix_particles_check=&#8221;&#8221;][vc_column content_align=&#8221;text-center&#8221; offset=&#8221;vc_col-lg-offset-0 vc_col-lg-12 vc_col-md-offset-1 vc_col-md-10&#8243;]<div  class=\"pix-heading-el text-left \"><div><div class=\"slide-in-container\"><h2 class=\"text-heading-default font-weight-bold heading-text el-title_custom_color mb-12\" style=\"\" data-anim-type=\"\" data-anim-delay=\"0\">Labeling for Object Detection<\/h2><\/div><\/div><\/div>[vc_column_text css=&#8221;.vc_custom_1653976604653{padding-top: 40px !important;padding-right: 20px !important;padding-bottom: px !important;padding-left: 20px !important;}&#8221;]\r\n<p style=\"text-align: left;\">Object Detection is a comparatively complex application of computer vision that requires a more sophisticated kind of image labeling. For instance, let\u2019s consider an application that detects cats and dogs in an image and draws a\u00a0<em class=\"pn\">bounding box\u00a0<\/em>to identify wherein the image is\/are the cat(s) or dog(s). To differentiate between the above application from this one, think of it like this: In classification, we assign a single label to the\u00a0<strong class=\"bn ml\"><em class=\"pn\">whole\u00a0<\/em><\/strong>image; whereas, in object detection, we label parts of an image with different labels. To elaborate further, in object detection, a single image could contain one or more cats AND dogs; hence, a single image would have multiple labels.<\/p>\r\n\r\n\r\n<figure id=\"attachment_16224\" aria-describedby=\"caption-attachment-16224\" style=\"width: 608px\" class=\"wp-caption aligncenter\"><img decoding=\"async\" class=\"wp-image-16224 size-full\" src=\"https:\/\/blog.datumo.com\/en\/wp-content\/uploads\/2022\/05\/1_8Kucv3QDMCZJdnrkQCOvrA.jpg\" alt=\"\" width=\"608\" height=\"330\" srcset=\"https:\/\/blog.datumo.com\/en\/wp-content\/uploads\/2022\/05\/1_8Kucv3QDMCZJdnrkQCOvrA.jpg 608w, https:\/\/blog.datumo.com\/en\/wp-content\/uploads\/2022\/05\/1_8Kucv3QDMCZJdnrkQCOvrA-300x163.jpg 300w\" sizes=\"(max-width: 608px) 100vw, 608px\" \/><figcaption id=\"caption-attachment-16224\" class=\"wp-caption-text\">Bounding box captures the position of an object in an image by drawing a box around that object and noting down the spatial coordinates of the edges of that box. This is an image that shows how bounding boxes are drawn on an image to label motor vehicles.<\/figcaption><\/figure>\r\n\r\n&nbsp;\r\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"xml\">\/\/squares\r\n\r\n\"squares\": [{\r\n\"coordinates\": {\r\n\"xmin\":0.30,\r\n\"ymin\":0.04,\r\n\"xmax\":0.71,\r\n\"ymax\":0.59,\r\n},\r\n\"label\":\"car\"\r\n}]<\/pre>\r\nThis is a sample XML file showing how the information of a bounding box is stored.[\/vc_column_text][\/vc_column][\/vc_row][\/vc_section][vc_row pix_particles_check=&#8221;&#8221;][vc_column]<div id=\"el1650362147064-486b7dc2-a9b3\" class=\"w-100 d-block \"><\/div><div id=\"el1650450433074-0be5e40e-928e\" class=\"w-100 d-block \"><\/div><div  class=\"pix-heading-el text-left \"><div><div class=\"slide-in-container\"><h2 class=\"text-heading-default font-weight-bold heading-text el-title_custom_color mb-12\" style=\"\" data-anim-type=\"\" data-anim-delay=\"0\">Importance of Labeling<\/h2><\/div><\/div><\/div>[vc_column_text css=&#8221;.vc_custom_1653976639518{padding-top: 40px !important;padding-bottom: 0px !important;}&#8221;]The fact that a labeled dataset is the input to your machine learning model is in itself a strong indicator of the importance labeling holds for your deep\/machine learning model. If the dataset is not accurately labeled, the accuracy of your model will suffer, no matter how state-of-the-art your model\u2019s architecture is. I have seen it a few times in my own professional experience as well that engineers have had to go back to the data labeling phase after getting poor accuracy from the model(s) they use; hence, wasting a lot of project resources in terms of time as well as costs. Therefore, it is important to give this phase due time and diligence to ensure the success of your project.[\/vc_column_text]<div id=\"el1650362652282-42ee7789-aa09\" class=\"w-100 d-block \"><\/div>[\/vc_column][\/vc_row][vc_section full_width=&#8221;stretch_row&#8221; pix_over_visibility=&#8221;&#8221; css=&#8221;.vc_custom_1650444445523{padding-top: 80px !important;padding-bottom: 80px !important;background-color: #f8f9fa !important;}&#8221;][vc_row full_width=&#8221;stretch_row&#8221; pix_particles_check=&#8221;&#8221;][vc_column content_align=&#8221;text-center&#8221; offset=&#8221;vc_col-lg-offset-0 vc_col-lg-12 vc_col-md-offset-1 vc_col-md-10&#8243;]<div  class=\"pix-heading-el text-left \"><div><div class=\"slide-in-container\"><h2 class=\"text-heading-default font-weight-bold heading-text el-title_custom_color mb-12\" style=\"\" data-anim-type=\"\" data-anim-delay=\"0\">Tools<\/h2><\/div><\/div><\/div>[vc_column_text css=&#8221;.vc_custom_1653976708968{padding-top: 40px !important;}&#8221;]\r\n<p id=\"e090\" class=\"pw-post-body-paragraph zs zt yn bn b zu zv hk zw zx zy ho zz aba abb abc abd abe abf abg abh abi abj abk abl abm jn iz\" style=\"text-align: left;\" data-selectable-paragraph=\"\">By now, we have learned pretty much all the background knowledge we need to have about image labeling, so lets now move to the last step i.e. learning how to gather a labeled dataset. The most convenient way is to find a public labeled dataset online which is fit for your application; however, more times than not, such a dataset doesn\u2019t exist. The second approach is to make use of crowdsourcing platforms and incentivize its users to collect and label the dataset for you. For instance, say you want a labeled dataset of pens, laptops and mobile phones \u2014 you can ask people to take pictures of any of these things and submit them in a Cloud Storage folder to get some rewards. However, this approach would only work if you\u2019re labeling images for classification; you cannot expect or even trust people to draw accurate bounding boxes on these images. For those who cannot afford these options and are an individual who is building an application for research purposes or for your own interest, worry not! There are many tools available for labeling images yourself such as\u00a0<em class=\"pn\">Colabeler\u00a0<\/em>and\u00a0<em class=\"pn\">LabelImg<\/em>;<em class=\"pn\">\u00a0<\/em>they are the two most popular ones that I, too, have used and found to be quite user-friendly.<\/p>\r\n<p id=\"19e3\" class=\"pw-post-body-paragraph zs zt yn bn b zu acc hk zw zx acd ho zz aba ace abc abd abe acf abg abh abi acg abk abl abm jn iz\" style=\"text-align: left;\" data-selectable-paragraph=\"\">Moreover, one of the best ways to handle all these burdens is to hire a professional team to do the task for you;\u00a0<a class=\"au mn\" href=\"https:\/\/www.datumo.com\" target=\"_blank\" rel=\"noopener ugc nofollow\"><strong class=\"bn ml\"><em class=\"pn\">DATUMO <\/em><\/strong><\/a>is your best solution! Here at\u00a0<a class=\"au mn\" href=\"https:\/\/www.datumo.com\" target=\"_blank\" rel=\"noopener ugc nofollow\"><strong class=\"bn ml\"><em class=\"pn\">DATUMO<\/em><\/strong><\/a>, we crowdsource our tasks to diverse users located globally to ensure the quality and quantity on time. Moreover, our in-house managers double-check the quality of the collected or processed data.<\/p>\r\n[\/vc_column_text][\/vc_column][\/vc_row][\/vc_section][vc_row pix_particles_check=&#8221;&#8221;][vc_column]<div id=\"el1653971463480-ce74a014-4ae9\" class=\"w-100 d-block \"><\/div>[vc_column_text css=&#8221;.vc_custom_1653976721829{padding-top: 40px !important;padding-bottom: 0px !important;}&#8221;]To sum it all up, we discussed the \u2018<strong class=\"bn ml\"><em class=\"pn\">Data<\/em><\/strong>\u2019 related steps in a Data Science Project\u2019s lifecycle, uncovered the importance of image labeling in computer vision domain of Machine Learning, learned about the types of image labeling and when each is used, and lastly, we talked about some popular tools that are most frequently used for doing this task. After following this tutorial, you should be able to annotate your image datasets with good accuracy, keeping in mind the role it would play in increasing or decreasing your deep learning model\u2019s accuracy.[\/vc_column_text]<div id=\"el1653971463481-f4f34d7c-39ce\" class=\"w-100 d-block \"><\/div><div  class=\"pix-heading-el text-left  vc_custom_1653976763369\"><div><div class=\"slide-in-container\"><h3 class=\"text-heading-default font-weight-bold heading-text el-title_custom_color mb-12\" style=\"\" data-anim-type=\"\" data-anim-delay=\"0\">Contents References<\/h3><\/div><\/div><\/div>[vc_column_text css=&#8221;.vc_custom_1653976752455{padding-top: 40px !important;padding-bottom: 0px !important;}&#8221;]<a class=\"au mn\" href=\"https:\/\/towardsdatascience.com\/life-cycle-of-a-data-science-project-3962b9670e5b\" target=\"_blank\" rel=\"noopener\">Life Cycle of a Data Science Project<\/a>\r\n<a class=\"au mn\" href=\"https:\/\/www.analyticsvidhya.com\/blog\/2017\/09\/pseudo-labelling-semi-supervised-learning-technique\" target=\"_blank\" rel=\"noopener ugc nofollow\">Introduction to Pseudo-Labelling : A Semi-Supervised learning technique<\/a>\r\n<a class=\"au mn\" href=\"https:\/\/www.cloudfactory.com\/data-labeling-guide\" target=\"_blank\" rel=\"noopener ugc nofollow\">The Ultimate Guide to Data Labeling for Machine Learning<\/a>[\/vc_column_text]<div id=\"el1653971463480-31c1935f-3278\" class=\"w-100 d-block \"><\/div>[\/vc_column][\/vc_row][vc_row pix_particles_check=&#8221;&#8221;][vc_column width=&#8221;1\/2&#8243;]<div id=\"el1646794934167-c0c94dd3-ea74\" class=\"w-100 d-block \"><\/div><div class=\" mb-3 mb-md-0 \"  ><div class=\"card w-100 h-100 bg-white  vc_custom_1652982865548  pix-hover-item rounded-10 position-relative overflow-hidden2 text-white tilt fancy_card\" ><div class=\"card-img-overlay overflow-visible d-inline-block w-100 pix-img-overlay pix-p-30 d-flex align-items-end text-left\"><div class=\"w-100 \"><h3 class=\"card-title  text-black font-weight-bold mb-0 animate-in\" style=\"\">See what we can do for you.<\/h3><p class=\"card-text pix-pt-10 text-black \" style=\"\">Build smarter AI with us.<\/p><div class=\"card-btn-div mt-4 d-inline-block w-100\"><a  href=\"https:\/\/datumo.com\" class=\"btn mb-2     text-white btn-black d-inline-block      btn-md\" target=\"_blank\" rel=\"noopener\"    ><span class=\"font-weight-bold \" >Learn More<\/span><\/a><\/div><\/div><\/div><\/div><\/div>[\/vc_column][vc_column width=&#8221;1\/2&#8243;]<div id=\"el1646794982519-9a19190b-7fde\" class=\"w-100 d-block \"><\/div><div class=\" mb-3 mb-md-0 \"  ><div class=\"card w-100 h-100 bg-black  vc_custom_1653971438710  pix-hover-item rounded-10 position-relative overflow-hidden2 text-white tilt fancy_card\" ><div class=\"card-img-overlay overflow-visible d-inline-block w-100 pix-img-overlay pix-p-30 d-flex align-items-end text-left\"><div class=\"w-100 \"><h3 class=\"card-title  text-white font-weight-bold mb-0 animate-in\" style=\"\">We would like to support the AI industry by sharing.<\/h3><p class=\"card-text pix-pt-10 text-white \" style=\"\"><\/p><div class=\"card-btn-div mt-4 d-inline-block w-100\"><a  href=\"https:\/\/open.datumo.com\/en\" class=\"btn mb-2    vc_custom_1653971438714  btn-primary d-inline-block      btn-md\" target=\"_blank\" rel=\"noopener\"    ><span class=\"font-weight-bold \" >Download Open Datasets<\/span><\/a><\/div><\/div><\/div><\/div><\/div>[\/vc_column][\/vc_row][vc_row pix_particles_check=&#8221;&#8221;][vc_column]<div id=\"el1646799961152-e3ee06c0-4e82\" class=\"w-100 d-block \"><\/div>[\/vc_column][\/vc_row]","protected":false},"excerpt":{"rendered":"[vc_row pix_particles_check=&#8221;&#8221;][vc_column][vc_raw_html]JTNDbWV0YSUyMGh0dHAtZXF1aXYlM0QlMjJyZWZyZXNoJTIyJTIwY29udGVudCUzRCUyMjAlM0IlMjB1cmwlM0RodHRwcyUzQSUyRiUyRmRhdHVtby5jb20lMkZlbiUyRnJvbGUtb2YtaW1hZ2UtbGFiZWxpbmctaW4teW91ci1kZWVwLW1hY2hpbmUtbGVhcm5pbmctbW9kZWwlMkYlMjIlM0U=[\/vc_raw_html][\/vc_column][\/vc_row][vc_row pix_particles_check=&#8221;&#8221;][vc_column][vc_column_text css=&#8221;.vc_custom_1653976813621{padding-top: 40px !important;padding-right: 20px !important;padding-bottom: 40px !important;padding-left: 20px !important;}&#8221;] Furthermore, since we are talking about image\u00a0labeling, it is implicit that we are going to talk strictly about the Computer Vision domain of Machine Learning and ignore textual&#8230;","protected":false},"author":1,"featured_media":16227,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[131],"tags":[26,174,127,173,175],"class_list":["post-16219","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-tech","tag-ai","tag-computer-visionmodel","tag-datumo","tag-deep-machine-learning","tag-image-labeling"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v23.3 - 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