{"id":517330,"date":"2025-12-22T08:02:15","date_gmt":"2025-12-22T08:02:15","guid":{"rendered":"https:\/\/webkul.com\/blog\/?p=517330"},"modified":"2025-12-22T08:02:23","modified_gmt":"2025-12-22T08:02:23","slug":"browser-based-background-remover-using-onnx","status":"publish","type":"post","link":"https:\/\/webkul.com\/blog\/browser-based-background-remover-using-onnx\/","title":{"rendered":"Browser Based Background Remover using ONNX Runtime Web"},"content":{"rendered":"\n

What is ONNX and ONNX Runtime Web?<\/h2>\n\n\n\n

ONNX stands for Open Neural Network Exchange, a standardized, open format for representing machine learning models.<\/p>\n\n\n\n

Facebook and Microsoft introduced ONNX with the goal of solving interoperability issues across different ML models, making it easier to move models between frameworks and platforms.<\/p>\n\n\n\n

ONNX Runtime Web provides a high-performance JavaScript<\/a> inference engine that runs ONNX models directly within a web browser. <\/p>\n\n\n\n

It allows developers to run machine learning<\/a> models on the browser, consequently enabling privacy-preserving and low-latency applications.<\/p>\n\n\n\n

It uses WebAssembly (WASM) for its execution, enabling fast, portable, and secure inference directly in the browser.<\/p>\n\n\n\n

Background Removal: A quick rundown<\/strong><\/h2>\n\n\n\n

Background removal is a common computer vision task that separates the primary subject of an image\u2014such as a human, product, or object\u2014from its background.<\/p>\n\n\n\n

Image segmentation models process the image by classifying each pixel as either foreground or background, and then remove all the background pixels from the image.<\/p>\n\n\n\n

Traditionally, image background removal<\/a> models runs on the server because they require high computational power, but this approach often leads to higher server costs and privacy concerns.<\/p>\n\n\n\n

With the new approach where we utilize ONNX Runtime Web we can overcome these short Cummings providing a better interoperable solution. <\/p>\n\n\n\n

\"background_remover<\/figure>\n\n\n\n

Running these background removal models in the browser offers various advantages like privacy and inference cost. Thus, making it a crucial development.<\/p>\n\n\n\n

Browser Based Background Remover: Our Approach<\/strong><\/h3>\n\n\n\n

We have successfully built a browser-based background remover using ONNX Runtime Web as the inference engine.<\/p>\n\n\n\n

As a result, the solution runs a pre-trained background removal model entirely inside the user\u2019s browser using WebAssembly, eliminating the need for any server-side processing.<\/p>\n\n\n\n

Now with this, we can seamlessly utilize browser resources to load the background remover model. In fact, this provides a more scalable solution that works across browser without extra dependency.<\/p>\n\n\n\n

How our Background Remover Works<\/h3>\n\n\n\n

The background remover operates entirely inside the browser, following a simple yet efficient workflow that ensures performance, privacy, and flexibility.<\/p>\n\n\n\n

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  1. Model Download and Caching<\/strong>:\n