---
license: apache-2.0
library_name: transformers.js
pipeline_tag: image-segmentation
tags:
  - background-removal
  - onnx
  - onnxruntime-web
  - webgpu
base_model: onnx-community/ormbg-ONNX
---

# ORMBG — compact web build (44 MB)

A compact build of **ORMBG** (Open Remove Background Model, by schirrmacher) for fast in-browser background removal
with [Transformers.js](https://huggingface.co/docs/transformers.js) / onnxruntime-web.

Used by the free [Doetra Background Remover](https://doetra.com).

## What changed vs. `onnx-community/ormbg-ONNX`

- Graph upgraded from opset 11 to opset 17.
- **int8 per-channel weights** (`DequantizeLinear`) for convolutions, fp16 for the remaining large tensors, **fp32
  compute** — 44 MB instead of 176 MB. Mask difference vs. fp32: mean 0.2/255, 99.93%+ identical after thresholding.
- Runs on WebGPU without `shader-f16` support.

## Usage

```js
import { AutoModel, AutoProcessor } from '@huggingface/transformers'
const model = await AutoModel.from_pretrained('Saimon8420/ormbg-web', { dtype: 'fp32', device: 'webgpu' })
const processor = await AutoProcessor.from_pretrained('Saimon8420/ormbg-web')
```

Input `pixel_values` `[1, 3, 1024, 1024]` in `[0, 1]`; output `alphas` `[1, 1, 1024, 1024]` in `[0, 1]`.

## Credits & license

- **ORMBG** by Maximilian Schirrmacher — https://github.com/schirrmacher/ormbg (Apache-2.0); ONNX by
  [onnx-community](https://huggingface.co/onnx-community/ormbg-ONNX).
- Redistributed under the same **Apache-2.0** license.