---
license: apache-2.0
library_name: onnx
pipeline_tag: image-segmentation
tags:
- background-removal
- onnx
- onnxruntime-web
- dichotomous-image-segmentation
---

# ISNet general-use (fp16 ONNX) for in-browser background removal

A half-precision (fp16) copy of the **ISNet general-use** model, used by techyCoderWorld's Image
Studio to remove image backgrounds entirely in the visitor's browser with ONNX Runtime Web
(WebGPU or WebAssembly). Images never leave the device.

## Source and licence

- Model: **ISNet** from *Highly Accurate Dichotomous Image Segmentation* (DIS), Xuebin Qin,
  Hang Dai, Xiaobin Hu, Deng-Ping Fan, Ling Shao, Luc Van Gool (ECCV 2022).
  Code and weights: https://github.com/xuebinqin/DIS, released under the **Apache License 2.0**.
- ONNX export used as the starting point: `isnet-general-use.onnx` distributed by the rembg
  project (https://github.com/danielgatis/rembg), SHA-256 `60920e99c45464f2ba57bee2ad08c919a52bbf852739e96947fbb4358c0d964a`.

## Changes made

- Weights and internal computation converted from float32 to float16 with
  `onnxconverter-common` 1.14.0 (`keep_io_types=True`), so inputs and outputs stay float32.
- No retraining or other changes. Output matches the float32 model to within fp16 precision.

## File

| File | SHA-256 | Size |
|---|---|---|
| `model_fp16.onnx` | `d27e782d3bec1c787c5b6c612a1aae8526373a721540d4046939e8a75d2db949` | 90662600 bytes |

## Input and output

- Input `input_image`: float32 `[1, 3, 1024, 1024]`, RGB, each pixel divided by the image's
  maximum value, then minus 0.5.
- Output: float32 `[1, 1, 1024, 1024]`; min-max normalise to get the foreground mask (0..1).

Licensed under the Apache License, Version 2.0: https://www.apache.org/licenses/LICENSE-2.0