README.md

metadata

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 / onnxruntime-web.

Used by the free Doetra Background Remover.

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

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.
• Redistributed under the same Apache-2.0 license.