{"id":7717,"date":"2023-01-10T14:40:49","date_gmt":"2023-01-10T19:40:49","guid":{"rendered":"https:\/\/blogs.sw.siemens.com\/tecnomatix\/?p=7717"},"modified":"2026-03-26T07:07:12","modified_gmt":"2026-03-26T11:07:12","slug":"synthai-synthetic-data-model-testing-and-evaluation-video","status":"publish","type":"post","link":"https:\/\/blogs.sw.siemens.com\/tecnomatix\/synthai-synthetic-data-model-testing-and-evaluation-video\/","title":{"rendered":"SynthAI Demo #3: Synthetic data model testing and evaluation [VIDEO]"},"content":{"rendered":"\n<p>As the use of artificial intelligence in industrial settings continues to grow, the need for effective methods of training and deploying machine learning models becomes increasingly important. Traditional approaches to data collection and labeling can be tedious, costly, and time-consuming. These challenges make it inherently difficult for engineers to keep up with the demands of rapidly evolving industries, such as the recent use case with a <a href=\"https:\/\/blogs.sw.siemens.com\/tecnomatix\/siemens-synthai-joins-forces-with-nvidia-to-turbo-charge-synthetic-data-generation\/\">leading electronics plant<\/a> where <a href=\"https:\/\/synth.ai.sws.siemens.com\/\" target=\"_blank\" rel=\"noopener\">SynthAI<\/a> helped to accelerate the set-up time for AI inspection models five times faster than before.<\/p>\n\n\n\n<p>By leveraging a self-service synthetic data generation platform like SynthAI, engineers can quickly and cost-effectively generate high-quality data to train and deploy reliable machine learning models. In our previous product demonstration, we showed you how SynthAI <a href=\"https:\/\/blogs.sw.siemens.com\/tecnomatix\/synthai-demo-2-combine-synthetic-data-with-real-images-to-annotate-and-improve-models-accuracy-video\/\">combines synthetic data with real images<\/a> to create a hybrid approach that allows for enhanced model accuracy in production environments.<\/p>\n\n\n\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button aligncenter is-style-primary-button\"><a class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/synth.ai.sws.siemens.com\/\" target=\"_blank\" rel=\"noreferrer noopener\">Explore SynthAI capabilities<\/a><\/div>\n<\/div>\n\n\n\n<p>Now, we invite you to join us for a demonstration on testing and deploying trained models using <a href=\"https:\/\/synth.ai.sws.siemens.com\/\" target=\"_blank\" rel=\"noopener\">SynthAI.<\/a> You\u2019ll learn how to iteratively improve the accuracy of your machine learning models with high-fidelity synthetic data, streamlining your modeling pipelines and helping you to stay ahead of the curve in the competitive world of industrial AI. Follow along to learn how to test and deploy a trained model so you can continue to enhance the accuracy of your ML models with quality synthetic data in the least amount of time.<\/p>\n\n\n\n<figure class=\"wp-block-embed aligncenter is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\n<iframe loading=\"lazy\" title=\"SynthAI Demo #3: Synthetic data model testing and evaluation\" width=\"640\" height=\"360\" src=\"https:\/\/www.youtube.com\/embed\/b_GXq_11GL0?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe>\n<\/div><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Download the trained model to a local folder:<\/strong><\/h2>\n\n\n\n<p>After the fine-tuning training session has been completed, you can download the trained model to a local folder and begin to test it by selecting \u201cDownload Trained Model.\u201d You can also download the Synthetic dataset by selecting the &#8220;Download Synthetic Data&#8221; button.&nbsp;<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Familiarize yourself with the \u201cTrained Model Package\u201d<\/strong><\/h2>\n\n\n\n<p>Open the Trained Model folder, which contains files, including:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Objection detection model<\/li>\n\n\n\n<li>Model configuration<\/li>\n\n\n\n<li>Statistics<\/li>\n<\/ul>\n\n\n\n<p>In this folder, you can find guides and files that will help you test the model, for example a README file, Inference Snippet python script, and an example image. For an initial test of the trained model, open the \u201cREADME\u201d file and follow the instructions. Finally, you can activate the virtual environment and run the code.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>View the Model&#8217;s results<\/strong><\/h2>\n\n\n\n<p>To evaluate the model&#8217;s results, open the image including the predictions and scores. Here, you can view the annotations that have been created over the image and the level of confidence associated with each prediction.&nbsp;&nbsp;<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Replace the &#8220;Sample Image&#8221; with a real image to test:<\/strong><\/h2>\n\n\n\n<p>Alternatively, you can replace the &#8220;Sample Image&#8221; with another image that represents a &#8220;real object&#8221; in a physical environment and run the test again. &nbsp;<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Model improvement:<\/strong><\/h2>\n\n\n\n<p>If you want to further improve the model\u2019s precision, conduct another fine-tuning session while adding more representing images of the object you are training the model on.&nbsp;Interested in learning how to use the fine-tuning tool? Watch the previous training that demonstrates <a href=\"https:\/\/blogs.sw.siemens.com\/tecnomatix\/synthai-demo-2-combine-synthetic-data-with-real-images-to-annotate-and-improve-models-accuracy-video\/\" target=\"_blank\" rel=\"noreferrer noopener\">how to use this capability<\/a>. <\/p>\n\n\n\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button aligncenter\"><a class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/synth.ai.sws.siemens.com\/\" target=\"_blank\" rel=\"noreferrer noopener\">Give SynthAI a try today!<\/a><\/div>\n<\/div>\n\n\n\n<p>By leveraging the power of SynthAI, engineers can overcome the limitations and expenses of acquiring real-world data and instead utilize high-quality synthetic data to train and optimize their machine learning models. The integration of AI in industrial use cases has the potential to revolutionize manufacturing operations by increasing efficiency, reducing costs, and improving product quality. <\/p>\n\n\n\n<p>Interested in learning more? Visit our <a href=\"https:\/\/community.sw.siemens.com\/s\/article\/SynthAI-Demo-3-Synthetic-data-model-testing-and-evaluation\" target=\"_blank\" rel=\"noreferrer noopener\">Tecnomatix Community<\/a> for an in-depth, step-by-step training of the product demonstration. Unlock the secret to revolutionizing your industrial operations with SynthAI &#8211; the cutting-edge technology that&#8217;s taking AI-powered computer vision systems to the next level. <\/p>\n","protected":false},"excerpt":{"rendered":"<p>Training a ML model is one task, but deploying the model to production can be another. In this product demonstration, watch the step-by-step guide on how SynthAI&#8217;s virtual environment can help accelerate the testing and deployment of a finely tuned AI model.<\/p>\n","protected":false},"author":85236,"featured_media":7733,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"spanish_translation":"","french_translation":"","german_translation":"","italian_translation":"","polish_translation":"","japanese_translation":"","chinese_translation":"","footnotes":""},"categories":[1],"tags":[531,225],"industry":[],"product":[],"coauthors":[6647],"class_list":["post-7717","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news","tag-digital-manufacturing","tag-tecnomatix"],"featured_image_url":"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/7\/2023\/01\/2023-01-10-09_30_10-synthAi_Model_Testing.mp4.png","_links":{"self":[{"href":"https:\/\/blogs.sw.siemens.com\/tecnomatix\/wp-json\/wp\/v2\/posts\/7717","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blogs.sw.siemens.com\/tecnomatix\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogs.sw.siemens.com\/tecnomatix\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/tecnomatix\/wp-json\/wp\/v2\/users\/85236"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/tecnomatix\/wp-json\/wp\/v2\/comments?post=7717"}],"version-history":[{"count":4,"href":"https:\/\/blogs.sw.siemens.com\/tecnomatix\/wp-json\/wp\/v2\/posts\/7717\/revisions"}],"predecessor-version":[{"id":7814,"href":"https:\/\/blogs.sw.siemens.com\/tecnomatix\/wp-json\/wp\/v2\/posts\/7717\/revisions\/7814"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/tecnomatix\/wp-json\/wp\/v2\/media\/7733"}],"wp:attachment":[{"href":"https:\/\/blogs.sw.siemens.com\/tecnomatix\/wp-json\/wp\/v2\/media?parent=7717"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/tecnomatix\/wp-json\/wp\/v2\/categories?post=7717"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/tecnomatix\/wp-json\/wp\/v2\/tags?post=7717"},{"taxonomy":"industry","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/tecnomatix\/wp-json\/wp\/v2\/industry?post=7717"},{"taxonomy":"product","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/tecnomatix\/wp-json\/wp\/v2\/product?post=7717"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/tecnomatix\/wp-json\/wp\/v2\/coauthors?post=7717"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}