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BRPOD123/birefnet-lite-1024-webgpu
birefnet-lite-1024-webgpu is a image segmentation model from BRPOD123. Use it for the image segmentation task on the model card, and read the license before you ship it in a product. It is set up for transformers.js. The card lists the license as mit.
Verbatim mirror of jiabins0303/birefnet-lite-1024-webgpu (MIT), pinned for the BRPOD print-on-demand decorator's client-side background removal. Fixed 1024x1024 input, graph patched to run fully on onnxruntime-web's W…
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From the Hugging Face model README
Verbatim mirror of jiabins0303/birefnet-lite-1024-webgpu (MIT), pinned for the
BRPOD print-on-demand decorator's client-side background removal. Fixed 1024x1024 input, graph patched to run fully on onnxruntime-web's WebGPU execution provider (needs an adapter with maxStorageBuffersPerShaderStage >= 8). Load with { device: 'webgpu', dtype: 'fp32', model_file_name: 'model_fp16' }; it does not run on the WASM backend. 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.
@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}
}