---
license: mit
library_name: onnx
tags:
  - onnx
  - image-segmentation
  - background-removal
  - birefnet
pipeline_tag: image-segmentation
---

# BiRefNet_lite — browser-tuned ONNX export (static 1024², opset 17)

Custom ONNX export of **BiRefNet_lite** (bilateral reference network for
dichotomous image segmentation, Swin-v1-tiny backbone) from the official
[ZhengPeng7/BiRefNet_lite](https://huggingface.co/ZhengPeng7/BiRefNet_lite)
weights, re-hosted for [AllPrivate](https://allprivate.app) — where every model
runs in the visitor's browser and nothing is uploaded.

## Why a custom export

The community export ([onnx-community/BiRefNet_lite-ONNX](https://huggingface.co/onnx-community/BiRefNet_lite-ONNX))
throws `OrtRun std::bad_alloc` in ONNX Runtime Web on every EP (fp32/fp16 ×
wasm/webgpu, tested 2026-07 on an M-series MacBook): it is a dynamic-shape
trace whose deformable convolutions decompose into GatherND/ScatterND/Clip
chains that fall back to CPU on the WebGPU EP and materialize im2col tensors of
hundreds of MB inside the 32-bit wasm heap.

This export replaces `DeformableConv2d.forward` with a numerically identical
per-kernel-tap **GridSample** decomposition (one bilinear GridSample + 1×1 Conv
per tap, accumulated — peak extra memory is one `[1,C,H,W]` tensor per tap) and
traces with a **static** input shape, so all shape dynamism constant-folds
away. It runs to completion on both the wasm and WebGPU execution providers of
ONNX Runtime Web (verified 1.26-dev).

## Provenance

- Source weights: [`model.safetensors`](https://huggingface.co/ZhengPeng7/BiRefNet_lite/blob/main/model.safetensors)
  at pinned revision [`7838f1c`](https://huggingface.co/ZhengPeng7/BiRefNet_lite/tree/7838f1c3472f827cd8ce13ab5ccc2ce48077360f)
  - sha256: `4417d89795250e698c3cb0ae8df15743810065f646f48a694fdfa7ca052d0815`
- Exported with `torch.onnx.export` (PyTorch 2.8.0, TorchScript exporter),
  opset 17, fp32, constant folding on, post-processed with onnxslim 0.1.94
  (constant-folds the Swin attention-mask construction; Gemm fusion disabled)
  plus a content-hash initializer dedupe (the backbone is traced twice for the
  multi-scale 'cat' input) — see [`export_birefnet_lite.py`](./export_birefnet_lite.py)
  for the exact reproducible script
- I/O: `input_image` `[1, 3, 1024, 1024]` (NCHW, RGB, float32, ImageNet
  normalization: `(x/255 - mean) / std`, mean `[0.485, 0.456, 0.406]`, std
  `[0.229, 0.224, 0.225]`) → `output_image` `[1, 1, 1024, 1024]` **logits**;
  apply sigmoid for the [0,1] matte
- Graph ops: `Add, BatchNormalization, Concat, Conv, Div, Erf, Gather,
  GlobalAveragePool, GridSample, LayerNormalization, MatMul, Mul, Pad, Relu,
  Reshape, Resize, Shape, Sigmoid, Slice, Softmax, Transpose, Unsqueeze` —
  no GatherND, no ScatterND, no Clip
- Validated against the unpatched PyTorch reference (torchvision
  `deform_conv2d`): GridSample decomposition max abs dev `3.1e-5` vs
  torchvision; full-model ONNX (onnxruntime CPU) max abs logits dev `1.0e-4`,
  max post-sigmoid dev `1.3e-9`
- File sha256: `50a57872cc739192446da2a934159f957c81af8b5a161dfda8e3daa51660ca67`

## License

MIT, inherited from [BiRefNet](https://github.com/ZhengPeng7/BiRefNet/blob/main/LICENSE)
(© Zheng Peng et al.). This repo only re-packages the officially released
weights in ONNX form.

## Citation

```bibtex
@article{BiRefNet,
  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}
}
```