A few years ago, removing a background from a photo required Photoshop, a steady hand on a selection tool, and considerable patience. Then tools like Remove.bg appeared — server-side AI that could do it in seconds. Now the same quality of AI runs entirely in your browser, with no upload required.
Here's how that works.
Remove.bg, Canva's background eraser, and similar tools work by sending your image to a server, running an AI model on their hardware, and returning the processed image. It's fast because they run powerful GPUs, but:
For personal photos, medical images, or anything commercially sensitive, sending files to a third-party server is a meaningful privacy concession.
Modern browsers can run machine learning models locally using two key technologies:
WebAssembly (WASM) — a low-level bytecode format that lets code written in C, C++, or Rust run in the browser at near-native speed. ML frameworks like ONNX Runtime and TensorFlow.js compile to WebAssembly, which is what makes running a neural network inside a browser tab possible.
ONNX Runtime Web — the Open Neural Network Exchange runtime, with a WebAssembly backend. It can load a trained neural network model (a .onnx file) and run inference on it client-side. No GPU required — it runs on the CPU, in the browser, in your tab.
The result: a neural network that would have required a server in 2020 now runs in the browser in 2025.
imgshrnk uses ISNet (Highly Accurate Dichotomous Image Segmentation), a state-of-the-art segmentation model developed by researchers at Dalian University of Technology. It was released in 2022 and quickly became one of the most accurate open-source background removal models available.
ISNet works by performing dichotomous image segmentation — it classifies every pixel in your image as either "foreground" (the subject you want to keep) or "background" (everything else). The output is a binary mask with soft edges, which is applied to the original image to produce a transparent PNG.
What makes ISNet good at this:
The trade-off is that ISNet is a large model (~20 MB for the full-precision version). imgshrnk offers three variants:
All three produce a clean result. The quantized version is worth using if you're on a slower connection or want faster processing.
When you click "Remove Background" on imgshrnk's BG Removal tool:
The entire process happens inside your browser tab. Nothing is sent to any server at any point.
Speed — Without a GPU, inference takes longer. A 1000×1000 pixel image takes 5–20 seconds depending on your CPU. A dedicated server with an NVIDIA A100 does the same in under a second. For single images, the wait is acceptable. For batch processing hundreds of images, a server-side tool is faster.
Edge cases — ISNet handles most subjects well, but struggles with:
Memory — Large images (5000×5000+) require significant RAM for inference. Most modern laptops handle this fine, but older devices may struggle.
The practical implication of on-device AI is that your images — product photos, ID documents, personal portraits, confidential materials — never leave your device. There's no request logged on a server, no file stored in a processing queue, no data retained for model training.
For most casual use cases, that's just a nice-to-have. For professionals handling client images, medical photos, or sensitive commercial assets, it's the difference between a tool you can actually use and one your security policy prohibits.
imgshrnk's Background Removal tool uses ISNet via ONNX Runtime Web, runs entirely in your browser, and produces a transparent PNG with clean edges — free, with no account and no upload limit.
The model loads once and is cached locally. After the first use, it runs instantly.