Instructions to use Ko033/isnet-general-use-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers.js
How to use Ko033/isnet-general-use-onnx with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('image-segmentation', 'Ko033/isnet-general-use-onnx');
isnet-general-use (ONNX, fp16 / q8)
ONNX builds of ISNet (general use) from xuebinqin/DIS (Apache-2.0), packaged for Transformers.js.
- Source ONNX export: the
isnet-general-use.onnxdistributed by the rembg project (MIT); original weights and architecture by Xuebin Qin et al. (DIS, Apache-2.0). - Modifications: the 11 auxiliary graph outputs (side stages d2-d6 and
intermediate feature taps) were removed, keeping only
output_image(stage-1 sigmoid probability map). fp16 and dynamic-int8 (q8) variants were generated with the official Transformers.js conversion scripts.
I/O
- Input
input_image: float32[1, 3, 1024, 1024], RGB, normalized asx / 255 - 0.5(mean 0.5, std 1.0). - Output
output_image: float32[1, 1, 1024, 1024], sigmoid probabilities. Min-max normalize per image for an alpha matte (as done by rembg).
Files
onnx/model.onnx- fp32 (aux outputs stripped)onnx/model_fp16.onnx- fp16onnx/model_quantized.onnx- dynamic int8 (q8)
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