Instructions to use BRPOD123/birefnet-lite-512 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers.js
How to use BRPOD123/birefnet-lite-512 with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('image-segmentation', 'BRPOD123/birefnet-lite-512');
BiRefNet-lite 512px ONNX (WASM tier)
Verbatim mirror of studioludens/birefnet-lite-512 (MIT), pinned for the
BRPOD print-on-demand decorator's client-side background removal. Fixed 512x512 input; onnx/model_fp16.onnx (94 MB) is what the decorator loads on the WASM backend, onnx/model.onnx (fp32, 183 MB) is kept as a fallback. Output is single-channel logits (transformers.js applies sigmoid).
Upstream weights: ZhengPeng7/BiRefNet_lite (MIT).
The ONNX export methodology and files come from the source repo above; nothing was retrained
or modified. Both config.json and preprocessor_config.json are byte-identical to the source.
Do not modify this repo in place. Publish changes as new commits and bump the pinned
revision in BRPOD's backgroundRemovalService.ts, which loads this repo by commit sha.
Citation
@article{zheng2024birefnet,
title={Bilateral Reference for High-Resolution Dichotomous Image Segmentation},
author={Zheng, Peng and Gao, Dehong and Fan, Deng-Ping and Liu, Li and Laaksonen, Jorma and Ouyang, Wanli and Sebe, Nicu},
journal={CAAI Artificial Intelligence Research},
year={2024}
}
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Model tree for BRPOD123/birefnet-lite-512
Base model
ZhengPeng7/BiRefNet_lite