{"id":19828,"date":"2021-08-19T07:34:17","date_gmt":"2021-08-19T06:34:17","guid":{"rendered":"https:\/\/conclusionintelligence.de\/?p=19828"},"modified":"2025-02-28T11:47:25","modified_gmt":"2025-02-28T10:47:25","slug":"computer-vision-training-your-model","status":"publish","type":"post","link":"https:\/\/conclusionintelligence.de\/de\/blog-de\/computer-vision-training-your-model","title":{"rendered":"Computer Vision: Training Your Model"},"content":{"rendered":"<p><div class=\"fusion-fullwidth fullwidth-box fusion-builder-row-1 nonhundred-percent-fullwidth non-hundred-percent-height-scrolling\"  style='background-color: rgba(255,255,255,0);background-position: center center;background-repeat: no-repeat;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px;'><div class=\"fusion-builder-row fusion-row \"><div  class=\"fusion-layout-column fusion_builder_column fusion_builder_column_1_1 fusion-builder-column-0 fusion-one-full fusion-column-first fusion-column-last 1_1\"  style='margin-top:15px;margin-bottom:15px;'><div class=\"fusion-column-wrapper\" style=\"padding: 0px 0px 0px 0px;background-position:left top;background-repeat:no-repeat;-webkit-background-size:cover;-moz-background-size:cover;-o-background-size:cover;background-size:cover;\"   data-bg-url=\"\"><style type=\"text\/css\"><\/style><div class=\"fusion-title title fusion-title-1 fusion-sep-none fusion-title-text fusion-title-size-one\" style=\"font-size:40px;margin-top:15px;margin-bottom:15px;\"><h1 class=\"title-heading-left\" style=\"font-family:&quot;Montserrat&quot;;font-weight:500;margin:0;font-size:1em;color:#000000;\">Computer Vision: Training Your Model<\/h1><\/div><div class=\"fusion-clearfix\"><\/div><\/div><\/div><div  class=\"fusion-layout-column fusion_builder_column fusion_builder_column_1_1 fusion-builder-column-1 fusion-one-full fusion-column-first fusion-column-last 1_1\"  style='margin-top:15px;margin-bottom:15px;'><div class=\"fusion-column-wrapper\" style=\"padding: 0px 0px 0px 0px;background-position:left top;background-repeat:no-repeat;-webkit-background-size:cover;-moz-background-size:cover;-o-background-size:cover;background-size:cover;\"   data-bg-url=\"\"><div class=\"fusion-text\"><p>Last time, we discussed <a href=\"https:\/\/conclusionintelligence.de\/blog\/computer-vision-labelling\" rel=\"nofollow\" target=\"_blank\">how computer vision can learn everything we see<\/a> and <a href=\"https:\/\/conclusionintelligence.de\/blog\/computer-vision-data-augmentation\" rel=\"nofollow\" target=\"_blank\">what to do if little data is available<\/a>. Now that we have enough data, the next step would be to determine what type of model we want to train. The questions are: &#8222;How long do we have to train the model?&#8220; and &#8222;How do we know whether we train it well enough?&#8220;. In this blog, we will show you how to check the performance of a model, along with how to quantify this performance to prove that a new model is trained better than its predecessor.<\/p>\n<\/div><div class=\"fusion-clearfix\"><\/div><\/div><\/div><\/div><\/div><style type=\"text\/css\">.fusion-fullwidth.fusion-builder-row-1 a:not(.fusion-button):not(.fusion-builder-module-control):not(.fusion-social-network-icon):not(.fb-icon-element):not(.fusion-countdown-link):not(.fusion-rollover-link):not(.fusion-rollover-gallery):not(.fusion-button-bar):not(.add_to_cart_button):not(.show_details_button):not(.product_type_external):not(.fusion-quick-view):not(.fusion-rollover-title-link):not(.fusion-breadcrumb-link) , .fusion-fullwidth.fusion-builder-row-1 a:not(.fusion-button):not(.fusion-builder-module-control):not(.fusion-social-network-icon):not(.fb-icon-element):not(.fusion-countdown-link):not(.fusion-rollover-link):not(.fusion-rollover-gallery):not(.fusion-button-bar):not(.add_to_cart_button):not(.show_details_button):not(.product_type_external):not(.fusion-quick-view):not(.fusion-rollover-title-link):not(.fusion-breadcrumb-link):before, .fusion-fullwidth.fusion-builder-row-1 a:not(.fusion-button):not(.fusion-builder-module-control):not(.fusion-social-network-icon):not(.fb-icon-element):not(.fusion-countdown-link):not(.fusion-rollover-link):not(.fusion-rollover-gallery):not(.fusion-button-bar):not(.add_to_cart_button):not(.show_details_button):not(.product_type_external):not(.fusion-quick-view):not(.fusion-rollover-title-link):not(.fusion-breadcrumb-link):after {color: #e63232;}.fusion-fullwidth.fusion-builder-row-1 a:not(.fusion-button):not(.fusion-builder-module-control):not(.fusion-social-network-icon):not(.fb-icon-element):not(.fusion-countdown-link):not(.fusion-rollover-link):not(.fusion-rollover-gallery):not(.fusion-button-bar):not(.add_to_cart_button):not(.show_details_button):not(.product_type_external):not(.fusion-quick-view):not(.fusion-rollover-title-link):not(.fusion-breadcrumb-link):hover, .fusion-fullwidth.fusion-builder-row-1 a:not(.fusion-button):not(.fusion-builder-module-control):not(.fusion-social-network-icon):not(.fb-icon-element):not(.fusion-countdown-link):not(.fusion-rollover-link):not(.fusion-rollover-gallery):not(.fusion-button-bar):not(.add_to_cart_button):not(.show_details_button):not(.product_type_external):not(.fusion-quick-view):not(.fusion-rollover-title-link):not(.fusion-breadcrumb-link):hover:before, .fusion-fullwidth.fusion-builder-row-1 a:not(.fusion-button):not(.fusion-builder-module-control):not(.fusion-social-network-icon):not(.fb-icon-element):not(.fusion-countdown-link):not(.fusion-rollover-link):not(.fusion-rollover-gallery):not(.fusion-button-bar):not(.add_to_cart_button):not(.show_details_button):not(.product_type_external):not(.fusion-quick-view):not(.fusion-rollover-title-link):not(.fusion-breadcrumb-link):hover:after {color: #e63232;}.fusion-fullwidth.fusion-builder-row-1 .pagination a.inactive:hover, .fusion-fullwidth.fusion-builder-row-1 .fusion-filters .fusion-filter.fusion-active a {border-color: #e63232;}.fusion-fullwidth.fusion-builder-row-1 .pagination .current {border-color: #e63232; background-color: #e63232;}.fusion-fullwidth.fusion-builder-row-1 .fusion-filters .fusion-filter.fusion-active a, .fusion-fullwidth.fusion-builder-row-1 .fusion-date-and-formats .fusion-format-box, .fusion-fullwidth.fusion-builder-row-1 .fusion-popover, .fusion-fullwidth.fusion-builder-row-1 .tooltip-shortcode {color: #e63232;}#main .fusion-fullwidth.fusion-builder-row-1 .post .blog-shortcode-post-title a:hover {color: #e63232;}<\/style><div class=\"fusion-fullwidth fullwidth-box fusion-builder-row-2 nonhundred-percent-fullwidth non-hundred-percent-height-scrolling\"  style='background-color: rgba(255,255,255,0);background-position: center center;background-repeat: no-repeat;padding-top:0px;padding-right:0px;padding-bottom:75px;padding-left:0px;'><div class=\"fusion-builder-row fusion-row \"><div  class=\"fusion-layout-column fusion_builder_column fusion_builder_column_1_1 fusion-builder-column-2 fusion-one-full fusion-column-first fusion-column-last 1_1\"  style='margin-top:15px;margin-bottom:0px;'><div class=\"fusion-column-wrapper\" style=\"padding: 0px 0px 0px 0px;background-position:left top;background-repeat:no-repeat;-webkit-background-size:cover;-moz-background-size:cover;-o-background-size:cover;background-size:cover;\"   data-bg-url=\"\"><style type=\"text\/css\"><\/style><div class=\"fusion-title title fusion-title-2 fusion-sep-none fusion-title-text fusion-title-size-two\" style=\"font-size:24px;margin-top:15px;margin-bottom:15px;\"><h2 class=\"title-heading-left\" style=\"margin:0;font-size:1em;color:#e63232;\">Performance<\/h2><\/div><div class=\"fusion-text\"><p>The model&#8217;s detections can be categorized as the following: true positive, true negative, false positive and false negative. The first two categories mean that the detection was done correctly. The last two categories are a bit trickier. False positive means that the model detects something where there is nothing, whereas false negative is when the model fails to detect the object(s) on the image.<\/p>\n<\/div><div class=\"fusion-clearfix\"><\/div><\/div><\/div><div  class=\"fusion-layout-column fusion_builder_column fusion_builder_column_1_2 fusion-builder-column-3 fusion-one-half fusion-column-first 1_2\"  style='margin-top:0px;margin-bottom:15px;width:50%;width:calc(50% - ( ( 4% ) * 0.5 ) );margin-right: 4%;'><div class=\"fusion-column-wrapper\" style=\"padding: 0px 0px 0px 0px;background-position:left top;background-repeat:no-repeat;-webkit-background-size:cover;-moz-background-size:cover;-o-background-size:cover;background-size:cover;\"   data-bg-url=\"\"><div class=\"fusion-text\"><p>On the image you can see a confusion matrix for a classification model. When looking at the results on the matrix, it is very important that they show many true positive and true negative predictions, as it means that<span class=\"NormalTextRun SCXW95018345 BCX0\">\u00a0the model<\/span><span class=\"NormalTextRun SCXW95018345 BCX0\">\u00a0<\/span><span class=\"NormalTextRun SCXW95018345 BCX0\">predicts the<\/span><span class=\"NormalTextRun SCXW95018345 BCX0\">\u00a0correct<\/span><span class=\"NormalTextRun SCXW95018345 BCX0\"> labels for the images. To make it easier to understand, let\u2019s say that we want to train the model to detect persons who wear protective helmets.\u00a0<\/span><\/p>\n<\/div><ul class=\"fusion-checklist fusion-checklist-1\" style=\"font-size:18px;line-height:30.6px;\"><li class=\"fusion-li-item\"><span style=\"height:30.6px;width:30.6px;margin-right:12.6px;\" class=\"icon-wrapper circle-no\"><i class=\"fusion-li-icon fa-check fas\" style=\"color:#e63232;\"><\/i><\/span><div class=\"fusion-li-item-content\" style=\"margin-left:43.2px;\">\n<p>True positive \u2013 the model correctly detects persons wearing helmets.<\/p>\n<\/div><\/li><li class=\"fusion-li-item\"><span style=\"height:30.6px;width:30.6px;margin-right:12.6px;\" class=\"icon-wrapper circle-no\"><i class=\"fusion-li-icon fa-check fas\" style=\"color:#e63232;\"><\/i><\/span><div class=\"fusion-li-item-content\" style=\"margin-left:43.2px;\">\n<p>True negative \u2013 the model correctly detects persons wearing no helmets.<\/p>\n<\/div><\/li><li class=\"fusion-li-item\"><span style=\"height:30.6px;width:30.6px;margin-right:12.6px;\" class=\"icon-wrapper circle-no\"><i class=\"fusion-li-icon fa-check fas\" style=\"color:#e63232;\"><\/i><\/span><div class=\"fusion-li-item-content\" style=\"margin-left:43.2px;\">\n<p>False positive \u2013 the model detects persons wearing helmets, but in fact they aren&#8217;t wearing helmets.<\/p>\n<\/div><\/li><li class=\"fusion-li-item\"><span style=\"height:30.6px;width:30.6px;margin-right:12.6px;\" class=\"icon-wrapper circle-no\"><i class=\"fusion-li-icon fa-check fas\" style=\"color:#e63232;\"><\/i><\/span><div class=\"fusion-li-item-content\" style=\"margin-left:43.2px;\">\n<p>False negative \u2013 the model detects persons wearing no helmets, but in fact they are wearing helmets.<\/p>\n<\/div><\/li><\/ul><div class=\"fusion-clearfix\"><\/div><\/div><\/div><div  class=\"fusion-layout-column fusion_builder_column fusion_builder_column_1_2 fusion-builder-column-4 fusion-one-half fusion-column-last 1_2\"  style='margin-top:15px;margin-bottom:15px;width:50%;width:calc(50% - ( ( 4% ) * 0.5 ) );'><div class=\"fusion-column-wrapper\" style=\"padding: 0px 0px 0px 0px;background-position:left top;background-repeat:no-repeat;-webkit-background-size:cover;-moz-background-size:cover;-o-background-size:cover;background-size:cover;\"   data-bg-url=\"\"><span style=\"width:100%;max-width:400px;\" class=\"fusion-imageframe imageframe-none imageframe-1 hover-type-none\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/conclusionintelligence.de\/wp-content\/uploads\/2021\/08\/Basic-confusion-matric-1.png\" data-orig-src=\"https:\/\/conclusionintelligence.de\/wp-content\/uploads\/2021\/08\/Basic-confusion-matric-1.png\" width=\"1200\" height=\"1200\" alt=\"\" title=\"Basic-confusion-matric\" class=\"lazyload img-responsive wp-image-36449\" srcset=\"data:image\/svg+xml,%3Csvg%20xmlns%3D%27http%3A%2F%2Fwww.w3.org%2F2000%2Fsvg%27%20width%3D%271200%27%20height%3D%271200%27%20viewBox%3D%270%200%201200%201200%27%3E%3Crect%20width%3D%271200%27%20height%3D%2731200%27%20fill-opacity%3D%220%22%2F%3E%3C%2Fsvg%3E\" data-srcset=\"https:\/\/conclusionintelligence.de\/wp-content\/uploads\/2021\/08\/Basic-confusion-matric-1-200x200.png 200w, https:\/\/conclusionintelligence.de\/wp-content\/uploads\/2021\/08\/Basic-confusion-matric-1-400x400.png 400w, https:\/\/conclusionintelligence.de\/wp-content\/uploads\/2021\/08\/Basic-confusion-matric-1-600x600.png 600w, https:\/\/conclusionintelligence.de\/wp-content\/uploads\/2021\/08\/Basic-confusion-matric-1-800x800.png 800w, https:\/\/conclusionintelligence.de\/wp-content\/uploads\/2021\/08\/Basic-confusion-matric-1.png 1200w\" data-sizes=\"auto\" data-orig-sizes=\"auto, (max-width: 800px) 100vw, 600px\" \/><\/span><div class=\"fusion-clearfix\"><\/div><\/div><\/div><div  class=\"fusion-layout-column fusion_builder_column fusion_builder_column_1_2 fusion-builder-column-5 fusion-one-half fusion-column-first 1_2\"  style='margin-top:15px;margin-bottom:15px;width:50%;width:calc(50% - ( ( 4% ) * 0.5 ) );margin-right: 4%;'><div class=\"fusion-column-wrapper\" style=\"padding: 0px 0px 0px 0px;background-position:left top;background-repeat:no-repeat;-webkit-background-size:cover;-moz-background-size:cover;-o-background-size:cover;background-size:cover;\"   data-bg-url=\"\"><div class=\"fusion-sep-clear\"><\/div><div class=\"fusion-separator fusion-full-width-sep sep-none\" style=\"margin-left: auto;margin-right: auto;margin-top:50px;\"><\/div><div class=\"fusion-text\"><p>The previous example was for binary classification, where we only have two classes (true vs. false), but a confusion matrix can also be used when there are more classes. The figure below shows a confusion matrix for 10 classes.<\/p>\n<\/div><span style=\"width:100%;max-width:500px;\" class=\"fusion-imageframe imageframe-none imageframe-2 hover-type-none\"><a class=\"fusion-no-lightbox\" href=\"https:\/\/www.scikit-yb.org\/en\/latest\/_images\/confusion_matrix-1.png\" target=\"_self\" aria-label=\"confusion matrix with different classes\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/conclusionintelligence.de\/wp-content\/uploads\/2021\/08\/confusion-matrix-with-different-classes.png\" data-orig-src=\"https:\/\/conclusionintelligence.de\/wp-content\/uploads\/2021\/08\/confusion-matrix-with-different-classes.png\" width=\"640\" height=\"440\" alt=\"\" class=\"lazyload img-responsive wp-image-19725\" srcset=\"data:image\/svg+xml,%3Csvg%20xmlns%3D%27http%3A%2F%2Fwww.w3.org%2F2000%2Fsvg%27%20width%3D%27640%27%20height%3D%27440%27%20viewBox%3D%270%200%20640%20440%27%3E%3Crect%20width%3D%27640%27%20height%3D%273440%27%20fill-opacity%3D%220%22%2F%3E%3C%2Fsvg%3E\" data-srcset=\"https:\/\/conclusionintelligence.de\/wp-content\/uploads\/2021\/08\/confusion-matrix-with-different-classes-200x138.png 200w, https:\/\/conclusionintelligence.de\/wp-content\/uploads\/2021\/08\/confusion-matrix-with-different-classes-400x275.png 400w, https:\/\/conclusionintelligence.de\/wp-content\/uploads\/2021\/08\/confusion-matrix-with-different-classes-600x413.png 600w, https:\/\/conclusionintelligence.de\/wp-content\/uploads\/2021\/08\/confusion-matrix-with-different-classes.png 640w\" data-sizes=\"auto\" data-orig-sizes=\"auto, (max-width: 800px) 100vw, 600px\" \/><\/a><\/span><div class=\"fusion-sep-clear\"><\/div><div class=\"fusion-separator fusion-full-width-sep sep-none\" style=\"margin-left: auto;margin-right: auto;margin-top:35px;\"><\/div><div class=\"fusion-clearfix\"><\/div><\/div><\/div><div  class=\"fusion-layout-column fusion_builder_column fusion_builder_column_1_2 fusion-builder-column-6 fusion-one-half fusion-column-last 1_2\"  style='margin-top:15px;margin-bottom:0px;width:50%;width:calc(50% - ( ( 4% ) * 0.5 ) );'><div class=\"fusion-column-wrapper\" style=\"padding: 0px 0px 0px 0px;background-position:left top;background-repeat:no-repeat;-webkit-background-size:cover;-moz-background-size:cover;-o-background-size:cover;background-size:cover;\"   data-bg-url=\"\"><div class=\"fusion-sep-clear\"><\/div><div class=\"fusion-separator fusion-full-width-sep sep-none\" style=\"margin-left: auto;margin-right: auto;margin-top:35px;\"><\/div><style type=\"text\/css\"><\/style><div class=\"fusion-title title fusion-title-3 fusion-sep-none fusion-title-text fusion-title-size-two\" style=\"font-size:24px;margin-top:15px;margin-bottom:15px;\"><h2 class=\"title-heading-left\" style=\"margin:0;font-size:1em;color:#e63232;\">When to stop training your model?<\/h2><\/div><div class=\"fusion-text\"><p>Now we know how we can check if a model performs well. It&#8217;s not time to celebrate yet! The next question would be, what data should we use to test the performance of the model? When we use data that has been previously used for training, the model would obviously show great performance. However, it does not guarantee that it will do well in real-life performance. This is what we called &#8222;overfitting&#8220;. It usually occurs when we have too little training data, or when we train the same data for too long. It means that the weights of the model are too optimized for the training data. So how do we fix this? By just stopping the training earlier, right? Yes, but when is &#8222;early enough&#8220;? Also, if we stop too early, we would get what we called &#8222;underfitting&#8220;, which means we have not trained the model long enough.<\/p>\n<\/div><div class=\"fusion-clearfix\"><\/div><\/div><\/div><div  class=\"fusion-layout-column fusion_builder_column fusion_builder_column_1_1 fusion-builder-column-7 fusion-one-full fusion-column-first fusion-column-last 1_1\"  style='margin-top:0px;margin-bottom:15px;'><div class=\"fusion-column-wrapper\" style=\"padding: 0px 0px 0px 0px;background-position:left top;background-repeat:no-repeat;-webkit-background-size:cover;-moz-background-size:cover;-o-background-size:cover;background-size:cover;\"   data-bg-url=\"\"><div class=\"fusion-text\"><p>A way to counter underfitting or overfitting is to take some data that we have annotated out of the training loop (many other techniques are being used to counter under-\/overfitting, but we&#8217;ll keep that for the next blog). These unseen images for the training are now the test set. We are going to use this data to check how well the model works by processing these images. The output of these images can be visualized in the confusion matrix, and now we can see how well the model is generalized (how well it works on unseen data). What if, after some time, we notice that the model does not work well? Do we have to restart the complete training? No! We can always retrain the model from the last checkpoint with some extra data.<\/p>\n<\/div><span class=\"fusion-imageframe imageframe-none imageframe-3 hover-type-none\"><a class=\"fusion-no-lightbox\" href=\"https:\/\/media.geeksforgeeks.org\/wp-content\/cdn-uploads\/20190523171258\/overfitting_2.png\" target=\"_self\" aria-label=\"overfitting_2\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/conclusionintelligence.de\/wp-content\/uploads\/2021\/08\/overfitting_2.png\" data-orig-src=\"https:\/\/conclusionintelligence.de\/wp-content\/uploads\/2021\/08\/overfitting_2.png\" width=\"1200\" height=\"492\" alt=\"underfitting, appropriate fitting, overfitting\" class=\"lazyload img-responsive wp-image-19738\" srcset=\"data:image\/svg+xml,%3Csvg%20xmlns%3D%27http%3A%2F%2Fwww.w3.org%2F2000%2Fsvg%27%20width%3D%271200%27%20height%3D%27492%27%20viewBox%3D%270%200%201200%20492%27%3E%3Crect%20width%3D%271200%27%20height%3D%273492%27%20fill-opacity%3D%220%22%2F%3E%3C%2Fsvg%3E\" data-srcset=\"https:\/\/conclusionintelligence.de\/wp-content\/uploads\/2021\/08\/overfitting_2-200x82.png 200w, https:\/\/conclusionintelligence.de\/wp-content\/uploads\/2021\/08\/overfitting_2-400x164.png 400w, https:\/\/conclusionintelligence.de\/wp-content\/uploads\/2021\/08\/overfitting_2-600x246.png 600w, https:\/\/conclusionintelligence.de\/wp-content\/uploads\/2021\/08\/overfitting_2-800x328.png 800w, https:\/\/conclusionintelligence.de\/wp-content\/uploads\/2021\/08\/overfitting_2.png 1200w\" data-sizes=\"auto\" data-orig-sizes=\"auto, (max-width: 800px) 100vw, 1200px\" \/><\/a><\/span><div class=\"fusion-clearfix\"><\/div><\/div><\/div><\/div><\/div><style type=\"text\/css\">.fusion-fullwidth.fusion-builder-row-2 a:not(.fusion-button):not(.fusion-builder-module-control):not(.fusion-social-network-icon):not(.fb-icon-element):not(.fusion-countdown-link):not(.fusion-rollover-link):not(.fusion-rollover-gallery):not(.fusion-button-bar):not(.add_to_cart_button):not(.show_details_button):not(.product_type_external):not(.fusion-quick-view):not(.fusion-rollover-title-link):not(.fusion-breadcrumb-link) , .fusion-fullwidth.fusion-builder-row-2 a:not(.fusion-button):not(.fusion-builder-module-control):not(.fusion-social-network-icon):not(.fb-icon-element):not(.fusion-countdown-link):not(.fusion-rollover-link):not(.fusion-rollover-gallery):not(.fusion-button-bar):not(.add_to_cart_button):not(.show_details_button):not(.product_type_external):not(.fusion-quick-view):not(.fusion-rollover-title-link):not(.fusion-breadcrumb-link):before, .fusion-fullwidth.fusion-builder-row-2 a:not(.fusion-button):not(.fusion-builder-module-control):not(.fusion-social-network-icon):not(.fb-icon-element):not(.fusion-countdown-link):not(.fusion-rollover-link):not(.fusion-rollover-gallery):not(.fusion-button-bar):not(.add_to_cart_button):not(.show_details_button):not(.product_type_external):not(.fusion-quick-view):not(.fusion-rollover-title-link):not(.fusion-breadcrumb-link):after {color: #000000;}.fusion-fullwidth.fusion-builder-row-2 a:not(.fusion-button):not(.fusion-builder-module-control):not(.fusion-social-network-icon):not(.fb-icon-element):not(.fusion-countdown-link):not(.fusion-rollover-link):not(.fusion-rollover-gallery):not(.fusion-button-bar):not(.add_to_cart_button):not(.show_details_button):not(.product_type_external):not(.fusion-quick-view):not(.fusion-rollover-title-link):not(.fusion-breadcrumb-link):hover, .fusion-fullwidth.fusion-builder-row-2 a:not(.fusion-button):not(.fusion-builder-module-control):not(.fusion-social-network-icon):not(.fb-icon-element):not(.fusion-countdown-link):not(.fusion-rollover-link):not(.fusion-rollover-gallery):not(.fusion-button-bar):not(.add_to_cart_button):not(.show_details_button):not(.product_type_external):not(.fusion-quick-view):not(.fusion-rollover-title-link):not(.fusion-breadcrumb-link):hover:before, .fusion-fullwidth.fusion-builder-row-2 a:not(.fusion-button):not(.fusion-builder-module-control):not(.fusion-social-network-icon):not(.fb-icon-element):not(.fusion-countdown-link):not(.fusion-rollover-link):not(.fusion-rollover-gallery):not(.fusion-button-bar):not(.add_to_cart_button):not(.show_details_button):not(.product_type_external):not(.fusion-quick-view):not(.fusion-rollover-title-link):not(.fusion-breadcrumb-link):hover:after {color: #000000;}.fusion-fullwidth.fusion-builder-row-2 .pagination a.inactive:hover, .fusion-fullwidth.fusion-builder-row-2 .fusion-filters .fusion-filter.fusion-active a {border-color: #000000;}.fusion-fullwidth.fusion-builder-row-2 .pagination .current {border-color: #000000; background-color: #000000;}.fusion-fullwidth.fusion-builder-row-2 .fusion-filters .fusion-filter.fusion-active a, .fusion-fullwidth.fusion-builder-row-2 .fusion-date-and-formats .fusion-format-box, .fusion-fullwidth.fusion-builder-row-2 .fusion-popover, .fusion-fullwidth.fusion-builder-row-2 .tooltip-shortcode {color: #000000;}#main .fusion-fullwidth.fusion-builder-row-2 .post .blog-shortcode-post-title a:hover {color: #000000;}<\/style><div class=\"fusion-fullwidth fullwidth-box fusion-builder-row-3 nonhundred-percent-fullwidth non-hundred-percent-height-scrolling\"  style='background-color: rgba(255,255,255,0);background-position: center center;background-repeat: no-repeat;padding-top:0px;padding-right:0px;padding-bottom:75px;padding-left:0px;'><div class=\"fusion-builder-row fusion-row \"><div  class=\"fusion-layout-column fusion_builder_column fusion_builder_column_1_1 fusion-builder-column-8 fusion-one-full fusion-column-first fusion-column-last 1_1\"  style='margin-top:15px;margin-bottom:15px;'><div class=\"fusion-column-wrapper\" style=\"padding: 0px 0px 0px 0px;background-position:left top;background-repeat:no-repeat;-webkit-background-size:cover;-moz-background-size:cover;-o-background-size:cover;background-size:cover;\"   data-bg-url=\"\"><style type=\"text\/css\"><\/style><div class=\"fusion-title title fusion-title-4 fusion-sep-none fusion-title-text fusion-title-size-two\" style=\"font-size:24px;margin-top:15px;margin-bottom:15px;\"><h2 class=\"title-heading-left\" style=\"margin:0;font-size:1em;color:#e63232;\">Conclusion<\/h2><\/div><div class=\"fusion-text\"><p>As you can see, training and evaluating the performance of a model is not as straightforward as it looks at first glance. And as always, there is no one-size-fits-all technique for every situation. At Conclusion Intelligence, we always try and experiment with different techniques to train the model as good as possible for your use case! Not sure how to start your <a href=\"https:\/\/conclusionintelligence.de\/computer-vision-for-quality-improvement\" rel=\"nofollow\" target=\"_blank\">computer vision<\/a> adventure? Our experts are always ready to help! Have a look at our related blogs to find out numerous opportunities that computer vision can offer to your company:<\/p>\n<\/div><ul class=\"fusion-checklist fusion-checklist-2\" style=\"font-size:18px;line-height:30.6px;\"><li class=\"fusion-li-item\"><span style=\"height:30.6px;width:30.6px;margin-right:12.6px;\" class=\"icon-wrapper circle-no\"><i class=\"fusion-li-icon fa fa-check\" style=\"color:#e63232;\"><\/i><\/span><div class=\"fusion-li-item-content\" style=\"margin-left:43.2px;\"><a href=\"https:\/\/conclusionintelligence.de\/blog\/four-steps-computer-vision\" rel=\"nofollow\" target=\"_blank\">4 Easy &amp; Simple Steps To Start Using Computer Vision<\/a><\/div><\/li><li class=\"fusion-li-item\"><span style=\"height:30.6px;width:30.6px;margin-right:12.6px;\" class=\"icon-wrapper circle-no\"><i class=\"fusion-li-icon fa fa-check\" style=\"color:#e63232;\"><\/i><\/span><div class=\"fusion-li-item-content\" style=\"margin-left:43.2px;\"><a href=\"https:\/\/conclusionintelligence.de\/blog\/computer-vision-labelling\" rel=\"nofollow\" target=\"_blank\">How Computer Vision Can Learn Everything That We See<\/a><\/div><\/li><li class=\"fusion-li-item\"><span style=\"height:30.6px;width:30.6px;margin-right:12.6px;\" class=\"icon-wrapper circle-no\"><i class=\"fusion-li-icon fa fa-check\" style=\"color:#e63232;\"><\/i><\/span><div class=\"fusion-li-item-content\" style=\"margin-left:43.2px;\"><a href=\"https:\/\/conclusionintelligence.de\/blog\/computer-vision-data-augmentation\" rel=\"nofollow\" target=\"_blank\">Computer Vision: What To Do If There Is Little Data?<\/a><\/div><\/li><li class=\"fusion-li-item\"><span style=\"height:30.6px;width:30.6px;margin-right:12.6px;\" class=\"icon-wrapper circle-no\"><i class=\"fusion-li-icon fa fa-check\" style=\"color:#e63232;\"><\/i><\/span><div class=\"fusion-li-item-content\" style=\"margin-left:43.2px;\"><a 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#e63232;}#main .fusion-fullwidth.fusion-builder-row-3 .post .blog-shortcode-post-title a:hover {color: #e63232;}<\/style><\/p>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":2,"featured_media":19823,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[30],"tags":[59],"class_list":["post-19828","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog-de","tag-computer-vision-de"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Computer Vision: Training Your Model - Conclusion Intelligence<\/title>\n<meta name=\"description\" content=\"In this blog, we will show you how to check the performance of a model, along with how to quantify this performance.\" \/>\n<meta name=\"robots\" content=\"index, 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