Instructions to use Saimon8420/ormbg-web with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers.js
How to use Saimon8420/ormbg-web with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('image-segmentation', 'Saimon8420/ormbg-web');
ORMBG β 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 β 44 MB instead of 176 MB. Mask difference vs. fp32: mean 0.2/255, 99.93%+ identical after thresholding. - Runs on WebGPU without
shader-f16support.
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 β https://github.com/schirrmacher/ormbg (Apache-2.0); ONNX by onnx-community.
- Redistributed under the same Apache-2.0 license.
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